From 9a2c1bd74d54becf91b9fe1d8c0aca05a4b070ba Mon Sep 17 00:00:00 2001
From: frankaging <frankaging@users.noreply.github.com>
Date: Mon, 3 Feb 2025 19:07:56 +0000
Subject: [PATCH] deploy: b6e23100fdbbacbb1334b72da95cc6389adaea2f

---
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diff --git a/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.doctree b/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.doctree
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diff --git a/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.doctree b/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.doctree
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diff --git a/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.doctree b/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.doctree
new file mode 100644
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diff --git a/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.doctree b/.doctrees/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.doctree
new file mode 100644
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diff --git a/.doctrees/api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.doctree b/.doctrees/api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.doctree
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diff --git a/.doctrees/api/pyvene.models.configuration_intervenable_model.RepresentationConfig.doctree b/.doctrees/api/pyvene.models.configuration_intervenable_model.RepresentationConfig.doctree
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diff --git a/.doctrees/api/pyvene.models.intervenable_base.IntervenableModel.doctree b/.doctrees/api/pyvene.models.intervenable_base.IntervenableModel.doctree
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diff --git a/.doctrees/api/pyvene.models.modeling_utils.b_sd_to_bsd.doctree b/.doctrees/api/pyvene.models.modeling_utils.b_sd_to_bsd.doctree
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diff --git a/.doctrees/guides/ndif.doctree b/.doctrees/guides/ndif.doctree
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diff --git a/.doctrees/index.doctree b/.doctrees/index.doctree
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diff --git a/.doctrees/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.doctree b/.doctrees/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.doctree
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diff --git a/.doctrees/tutorials/advanced_tutorials/MQNLI.doctree b/.doctrees/tutorials/advanced_tutorials/MQNLI.doctree
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diff --git a/.doctrees/tutorials/advanced_tutorials/Probing_Gender.doctree b/.doctrees/tutorials/advanced_tutorials/Probing_Gender.doctree
new file mode 100644
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diff --git a/.doctrees/tutorials/basic_tutorials/Add_Activations_to_Streams.doctree b/.doctrees/tutorials/basic_tutorials/Add_Activations_to_Streams.doctree
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diff --git a/.doctrees/tutorials/basic_tutorials/Intervention_Training.doctree b/.doctrees/tutorials/basic_tutorials/Intervention_Training.doctree
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new file mode 100644
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.BaseModel.html">pyvene.models.intervenable_base.BaseModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.IntervenableModel.html">pyvene.models.intervenable_base.IntervenableModel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.IntervenableNdifModel.html">pyvene.models.intervenable_base.IntervenableNdifModel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.AdditionIntervention.html">pyvene.models.interventions.AdditionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.AutoencoderIntervention.html">pyvene.models.interventions.AutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.BasisAgnosticIntervention.html">pyvene.models.interventions.BasisAgnosticIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html">pyvene.models.interventions.BoundlessRotatedSpaceIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.ConstantSourceIntervention.html">pyvene.models.interventions.ConstantSourceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.DistributedRepresentationIntervention.html">pyvene.models.interventions.DistributedRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.Intervention.html">pyvene.models.interventions.Intervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.InterventionOutput.html">pyvene.models.interventions.InterventionOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html">pyvene.models.interventions.JumpReLUAutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.LocalistRepresentationIntervention.html">pyvene.models.interventions.LocalistRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html">pyvene.models.interventions.LowRankRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.NoiseIntervention.html">pyvene.models.interventions.NoiseIntervention</a></li>
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+  <h1>All modules for which code is available</h1>
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+<li><a href="pyvene/models/basic_utils.html">pyvene.models.basic_utils</a></li>
+<li><a href="pyvene/models/blip/modelings_blip.html">pyvene.models.blip.modelings_blip</a></li>
+<li><a href="pyvene/models/blip/modelings_blip_itm.html">pyvene.models.blip.modelings_blip_itm</a></li>
+<li><a href="pyvene/models/blip/modelings_intervenable_blip.html">pyvene.models.blip.modelings_intervenable_blip</a></li>
+<li><a href="pyvene/models/blip/modelings_intervenable_blip_itm.html">pyvene.models.blip.modelings_intervenable_blip_itm</a></li>
+<li><a href="pyvene/models/configuration_intervenable_model.html">pyvene.models.configuration_intervenable_model</a></li>
+<li><a href="pyvene/models/constants.html">pyvene.models.constants</a></li>
+<li><a href="pyvene/models/gemma/modelings_intervenable_gemma.html">pyvene.models.gemma.modelings_intervenable_gemma</a></li>
+<li><a href="pyvene/models/gemma2/modelings_intervenable_gemma2.html">pyvene.models.gemma2.modelings_intervenable_gemma2</a></li>
+<li><a href="pyvene/models/gpt2/modelings_intervenable_gpt2.html">pyvene.models.gpt2.modelings_intervenable_gpt2</a></li>
+<li><a href="pyvene/models/gpt_neo/modelings_intervenable_gpt_neo.html">pyvene.models.gpt_neo.modelings_intervenable_gpt_neo</a></li>
+<li><a href="pyvene/models/gpt_neox/modelings_intervenable_gpt_neox.html">pyvene.models.gpt_neox.modelings_intervenable_gpt_neox</a></li>
+<li><a href="pyvene/models/gru/modelings_gru.html">pyvene.models.gru.modelings_gru</a></li>
+<li><a href="pyvene/models/gru/modelings_intervenable_gru.html">pyvene.models.gru.modelings_intervenable_gru</a></li>
+<li><a href="pyvene/models/intervenable_base.html">pyvene.models.intervenable_base</a></li>
+<li><a href="pyvene/models/intervention_utils.html">pyvene.models.intervention_utils</a></li>
+<li><a href="pyvene/models/interventions.html">pyvene.models.interventions</a></li>
+<li><a href="pyvene/models/layers.html">pyvene.models.layers</a></li>
+<li><a href="pyvene/models/llama/modelings_intervenable_llama.html">pyvene.models.llama.modelings_intervenable_llama</a></li>
+<li><a href="pyvene/models/llava/modelings_intervenable_llava.html">pyvene.models.llava.modelings_intervenable_llava</a></li>
+<li><a href="pyvene/models/mistral/modellings_intervenable_mistral.html">pyvene.models.mistral.modellings_intervenable_mistral</a></li>
+<li><a href="pyvene/models/mlp/modelings_intervenable_mlp.html">pyvene.models.mlp.modelings_intervenable_mlp</a></li>
+<li><a href="pyvene/models/mlp/modelings_mlp.html">pyvene.models.mlp.modelings_mlp</a></li>
+<li><a href="pyvene/models/modeling_utils.html">pyvene.models.modeling_utils</a></li>
+<li><a href="pyvene/models/olmo/modelings_intervenable_olmo.html">pyvene.models.olmo.modelings_intervenable_olmo</a></li>
+<li><a href="pyvene/models/qwen2/modelings_intervenable_qwen2.html">pyvene.models.qwen2.modelings_intervenable_qwen2</a></li>
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+                  
+  <h1>Source code for pyvene.data_generators.causal_model</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">inspect</span>
+<span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+
+<div class="viewcode-block" id="CausalModel">
+<a class="viewcode-back" href="../../../api/pyvene.data_generators.causal_model.CausalModel.html#pyvene.data_generators.causal_model.CausalModel">[docs]</a>
+<span class="k">class</span> <span class="nc">CausalModel</span><span class="p">:</span>
+<div class="viewcode-block" id="CausalModel.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.data_generators.causal_model.CausalModel.html#pyvene.data_generators.causal_model.CausalModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">variables</span><span class="p">,</span>
+        <span class="n">values</span><span class="p">,</span>
+        <span class="n">parents</span><span class="p">,</span>
+        <span class="n">functions</span><span class="p">,</span>
+        <span class="n">timesteps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">equiv_classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">pos</span><span class="o">=</span><span class="p">{},</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">variables</span> <span class="o">=</span> <span class="n">variables</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">values</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">parents</span> <span class="o">=</span> <span class="n">parents</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">children</span> <span class="o">=</span> <span class="p">{</span><span class="n">var</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">}</span>
+        <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span>
+            <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">variable</span><span class="p">]:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">variable</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">functions</span> <span class="o">=</span> <span class="n">functions</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">start_variables</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span> <span class="o">=</span> <span class="n">timesteps</span>
+        <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span>
+            <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">children</span>
+            <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span>
+            <span class="k">if</span> <span class="n">timesteps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="n">timesteps</span>
+            <span class="k">for</span> <span class="n">variable2</span> <span class="ow">in</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">variable2</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">variable</span><span class="p">]:</span>
+                    <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="n">variable2</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">timesteps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">assert</span> <span class="n">timesteps</span><span class="p">[</span><span class="n">variable2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">timesteps</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">variable2</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="n">variable</span><span class="p">]:</span>
+                    <span class="k">assert</span> <span class="n">variable</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">[</span><span class="n">variable2</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">timesteps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">assert</span> <span class="n">timesteps</span><span class="p">[</span><span class="n">variable2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">timesteps</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">start_variables</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">variable</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents</span><span class="p">[</span><span class="n">var</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">variables</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">[</span><span class="n">child</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">parent</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span> <span class="o">=</span> <span class="n">timesteps</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">end_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_timesteps</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">[</span><span class="n">output</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">end_time</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">=</span> <span class="n">pos</span>
+        <span class="n">width</span> <span class="o">=</span> <span class="p">{</span><span class="n">_</span><span class="p">:</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">))}</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">pos</span><span class="p">:</span>
+                <span class="n">pos</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">[</span><span class="n">var</span><span class="p">]],</span> <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">[</span><span class="n">var</span><span class="p">])</span>
+                <span class="n">width</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">[</span><span class="n">var</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">equiv_classes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">equiv_classes</span> <span class="o">=</span> <span class="n">equiv_classes</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">equiv_classes</span> <span class="o">=</span> <span class="p">{}</span></div>
+
+    
+    <span class="k">def</span> <span class="nf">generate_equiv_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="ow">or</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">equiv_classes</span><span class="p">:</span>
+                <span class="k">continue</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">equiv_classes</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">val</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">var</span><span class="p">]}</span>
+            <span class="k">for</span> <span class="n">parent_values</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span>
+                <span class="o">*</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">par</span><span class="p">]</span> <span class="k">for</span> <span class="n">par</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">var</span><span class="p">]]</span>
+            <span class="p">):</span>
+                <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">var</span><span class="p">](</span><span class="o">*</span><span class="n">parent_values</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">equiv_classes</span><span class="p">[</span><span class="n">var</span><span class="p">][</span><span class="n">value</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="p">{</span><span class="n">par</span><span class="p">:</span> <span class="n">parent_values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">par</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">var</span><span class="p">])}</span>
+                <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">generate_timesteps</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">timesteps</span> <span class="o">=</span> <span class="p">{</span><span class="nb">input</span><span class="p">:</span> <span class="mi">0</span> <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">}</span>
+        <span class="n">step</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">change</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">while</span> <span class="n">change</span><span class="p">:</span>
+            <span class="n">change</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">copytimesteps</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">timesteps</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">timesteps</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">timesteps</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">==</span> <span class="n">step</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="n">parent</span><span class="p">]:</span>
+                        <span class="n">copytimesteps</span><span class="p">[</span><span class="n">child</span><span class="p">]</span> <span class="o">=</span> <span class="n">step</span>
+                        <span class="n">change</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">timesteps</span> <span class="o">=</span> <span class="n">copytimesteps</span>
+            <span class="n">step</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">timesteps</span>
+        <span class="c1"># return all timesteps and timestep of root</span>
+        <span class="k">return</span> <span class="n">timesteps</span><span class="p">,</span> <span class="n">step</span> <span class="o">-</span> <span class="mi">2</span>
+
+    <span class="k">def</span> <span class="nf">marginalize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">print_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">font</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+        <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>
+        <span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="p">(</span><span class="n">parent</span><span class="p">,</span> <span class="n">child</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span>
+                <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">child</span><span class="p">]</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+        <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pos</span><span class="p">,</span> <span class="n">font_size</span><span class="o">=</span><span class="n">font</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">find_live_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intervention</span><span class="p">):</span>
+        <span class="n">actual_setting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">intervention</span><span class="p">)</span>
+        <span class="n">paths</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="p">[[</span><span class="n">variable</span><span class="p">]</span> <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">]}</span>
+        <span class="n">step</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="n">paths</span><span class="p">[</span><span class="n">step</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">paths</span><span class="p">[</span><span class="n">step</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]:</span>
+                <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]:</span>
+                    <span class="n">actual_cause</span> <span class="o">=</span> <span class="kc">False</span>
+                    <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]:</span>
+                        <span class="n">newintervention</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">intervention</span><span class="p">)</span>
+                        <span class="n">newintervention</span><span class="p">[</span><span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">value</span>
+                        <span class="n">counterfactual_setting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">newintervention</span><span class="p">)</span>
+                        <span class="k">if</span> <span class="n">counterfactual_setting</span><span class="p">[</span><span class="n">child</span><span class="p">]</span> <span class="o">!=</span> <span class="n">actual_setting</span><span class="p">[</span><span class="n">child</span><span class="p">]:</span>
+                            <span class="n">actual_cause</span> <span class="o">=</span> <span class="kc">True</span>
+                    <span class="k">if</span> <span class="n">actual_cause</span><span class="p">:</span>
+                        <span class="n">paths</span><span class="p">[</span><span class="n">step</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="n">child</span><span class="p">])</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="n">step</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">break</span>
+            <span class="n">step</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">del</span> <span class="n">paths</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">paths</span>
+
+    <span class="k">def</span> <span class="nf">print_setting</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">total_setting</span><span class="p">,</span> <span class="n">font</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+        <span class="n">relabeler</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">var</span><span class="p">:</span> <span class="n">var</span> <span class="o">+</span> <span class="s2">&quot;: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">total_setting</span><span class="p">[</span><span class="n">var</span><span class="p">])</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span>
+        <span class="p">}</span>
+        <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span>
+        <span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="p">(</span><span class="n">parent</span><span class="p">,</span> <span class="n">child</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span>
+                <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">child</span><span class="p">]</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+        <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">relabel_nodes</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">relabeler</span><span class="p">)</span>
+        <span class="n">newpos</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span><span class="p">:</span>
+                <span class="n">newpos</span><span class="p">[</span><span class="n">relabeler</span><span class="p">[</span><span class="n">var</span><span class="p">]]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
+        <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">newpos</span><span class="p">,</span> <span class="n">font_size</span><span class="o">=</span><span class="n">font</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">run_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intervention</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">total_setting</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+        <span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">total_setting</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+        <span class="n">step</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">while</span> <span class="n">length</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">variable2</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">variable</span><span class="p">]:</span>
+                    <span class="k">if</span> <span class="n">variable2</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">total_setting</span><span class="p">:</span>
+                        <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">variable</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">:</span>
+                    <span class="n">total_setting</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="n">intervention</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">total_setting</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">variable</span><span class="p">](</span>
+                        <span class="o">*</span><span class="p">[</span><span class="n">total_setting</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">variable</span><span class="p">]]</span>
+                    <span class="p">)</span>
+            <span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">total_setting</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+        <span class="k">return</span> <span class="n">total_setting</span>
+
+    <span class="k">def</span> <span class="nf">run_interchange</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">source_interventions</span><span class="p">):</span>
+        <span class="n">interchange_intervention</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">source_interventions</span><span class="p">:</span>
+            <span class="n">setting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">source_interventions</span><span class="p">[</span><span class="n">var</span><span class="p">])</span>
+            <span class="n">interchange_intervention</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="n">setting</span><span class="p">[</span><span class="n">var</span><span class="p">]</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">interchange_intervention</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">add_variable</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">variable</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">children</span><span class="p">,</span> <span class="n">function</span><span class="p">,</span> <span class="n">timestep</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">timestep</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">timesteps</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="n">timestep</span>
+        <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">parents</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span>
+        <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">children</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">parents</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="n">parents</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">children</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="n">children</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="n">variable</span><span class="p">]</span> <span class="o">=</span> <span class="n">function</span>
+
+    <span class="k">def</span> <span class="nf">sample_intervention</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mandatory</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">intervention</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="ow">or</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">:</span>
+                    <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">intervention</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">var</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">intervention</span>
+
+    <span class="k">def</span> <span class="nf">sample_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mandatory</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="nb">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">var</span><span class="p">:</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">var</span><span class="p">],</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">}</span>
+        <span class="n">total</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">intervention</span><span class="o">=</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">while</span> <span class="n">mandatory</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">mandatory</span><span class="p">(</span><span class="n">total</span><span class="p">):</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">var</span><span class="p">:</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">var</span><span class="p">],</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">}</span>
+            <span class="n">total</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">intervention</span><span class="o">=</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">input</span>
+
+    <span class="k">def</span> <span class="nf">sample_input_tree_balanced</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_var</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_var_value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="n">output_var</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_equiv_classes</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">output_var</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">output_var</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">output_var_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">output_var_value</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">output_var</span><span class="p">])</span>
+
+
+        <span class="k">def</span> <span class="nf">create_input</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="p">{}):</span>
+            <span class="n">parent_values</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">equiv_classes</span><span class="p">[</span><span class="n">var</span><span class="p">][</span><span class="n">value</span><span class="p">])</span>
+            <span class="k">for</span> <span class="n">parent</span> <span class="ow">in</span> <span class="n">parent_values</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">:</span>
+                    <span class="nb">input</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">parent_values</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">create_input</span><span class="p">(</span><span class="n">parent</span><span class="p">,</span> <span class="n">parent_values</span><span class="p">[</span><span class="n">parent</span><span class="p">],</span> <span class="nb">input</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">input</span>
+
+        <span class="n">input_setting</span> <span class="o">=</span> <span class="n">create_input</span><span class="p">(</span><span class="n">output_var</span><span class="p">,</span> <span class="n">output_var_value</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">input_var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">input_var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">input_setting</span><span class="p">:</span>
+                <span class="n">input_setting</span><span class="p">[</span><span class="n">input_var</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">input_var</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">input_setting</span>
+
+    <span class="k">def</span> <span class="nf">get_path_maxlen_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengths</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">check_path</span><span class="p">(</span><span class="n">total_setting</span><span class="p">):</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">var</span><span class="p">:</span> <span class="n">total_setting</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">}</span>
+            <span class="n">paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">find_live_paths</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+            <span class="n">m</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">l</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">paths</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="n">l</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">lengths</span><span class="p">:</span>
+                <span class="k">return</span> <span class="kc">True</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">return</span> <span class="n">check_path</span>
+
+    <span class="k">def</span> <span class="nf">get_partial_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">partial_setting</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">compare</span><span class="p">(</span><span class="n">total_setting</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">partial_setting</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">total_setting</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">!=</span> <span class="n">partial_setting</span><span class="p">[</span><span class="n">var</span><span class="p">]:</span>
+                    <span class="k">return</span> <span class="kc">False</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="n">compare</span>
+
+    <span class="k">def</span> <span class="nf">get_specific_path_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">check_path</span><span class="p">(</span><span class="n">total_setting</span><span class="p">):</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">var</span><span class="p">:</span> <span class="n">total_setting</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">}</span>
+            <span class="n">paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">find_live_paths</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">paths</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">paths</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
+                    <span class="k">if</span> <span class="n">path</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">start</span> <span class="ow">and</span> <span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">end</span><span class="p">:</span>
+                        <span class="k">return</span> <span class="kc">True</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="k">return</span> <span class="n">check_path</span>
+
+    <span class="k">def</span> <span class="nf">input_to_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">setting</span><span class="p">):</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">:</span>
+            <span class="n">temp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">setting</span><span class="p">[</span><span class="nb">input</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">temp</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">temp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">temp</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
+            <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">output_to_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">setting</span><span class="p">):</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span><span class="p">:</span>
+            <span class="n">temp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">setting</span><span class="p">[</span><span class="n">output</span><span class="p">]))</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">temp</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">temp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">temp</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
+            <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">generate_factual_dataset</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">size</span><span class="p">,</span>
+        <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="nb">filter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">,</span>
+        <span class="n">input_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_tensors</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sampler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_input</span>
+        
+        <span class="k">if</span> <span class="n">input_function</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">input_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_to_tensor</span>
+        <span class="k">if</span> <span class="n">output_function</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">output_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_tensor</span>
+
+        <span class="n">examples</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">examples</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">size</span><span class="p">:</span>
+            <span class="n">example</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="n">sampler</span><span class="p">()</span>
+            <span class="k">if</span> <span class="nb">filter</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">filter</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">return_tensors</span><span class="p">:</span>
+                    <span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">input_function</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                    <span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">output_function</span><span class="p">(</span><span class="n">output</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span>
+                    <span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span>
+                <span class="n">examples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">example</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">examples</span>
+
+    <span class="k">def</span> <span class="nf">generate_counterfactual_dataset</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">size</span><span class="p">,</span>
+        <span class="n">intervention_id</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="p">,</span>
+        <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">intervention_sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="nb">filter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">,</span>
+        <span class="n">input_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_tensors</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">input_function</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">input_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_to_tensor</span>
+        <span class="k">if</span> <span class="n">output_function</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">output_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_tensor</span>
+
+        <span class="n">maxlength</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">var</span>
+                <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span>
+                <span class="k">if</span> <span class="n">var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="ow">and</span> <span class="n">var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sampler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_input</span>
+        <span class="k">if</span> <span class="n">intervention_sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">intervention_sampler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_intervention</span>
+        <span class="n">examples</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">examples</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">size</span><span class="p">:</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="n">intervention_sampler</span><span class="p">()</span>
+            <span class="k">if</span> <span class="nb">filter</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">filter</span><span class="p">(</span><span class="n">intervention</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">batch_size</span><span class="p">):</span>
+                    <span class="n">example</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+                    <span class="n">base</span> <span class="o">=</span> <span class="n">sampler</span><span class="p">()</span>
+                    <span class="n">sources</span> <span class="o">=</span> <span class="p">[]</span>
+                    <span class="n">source_dic</span> <span class="o">=</span> <span class="p">{}</span>
+                    <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">var</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">:</span>
+                            <span class="k">continue</span>
+                        <span class="c1"># sample input to match sampled intervention value</span>
+                        <span class="n">source</span> <span class="o">=</span> <span class="n">sampler</span><span class="p">(</span><span class="n">output_var</span><span class="o">=</span><span class="n">var</span><span class="p">,</span> <span class="n">output_var_value</span><span class="o">=</span><span class="n">intervention</span><span class="p">[</span><span class="n">var</span><span class="p">])</span>
+                        <span class="k">if</span> <span class="n">return_tensors</span><span class="p">:</span>
+                            <span class="n">sources</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_to_tensor</span><span class="p">(</span><span class="n">source</span><span class="p">))</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">sources</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+                        <span class="n">source_dic</span><span class="p">[</span><span class="n">var</span><span class="p">]</span> <span class="o">=</span> <span class="n">source</span>
+                    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">maxlength</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)):</span>
+                        <span class="k">if</span> <span class="n">return_tensors</span><span class="p">:</span>
+                            <span class="n">sources</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_to_tensor</span><span class="p">(</span><span class="n">base</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">sources</span><span class="o">.</span><span class="n">append</span><span class="p">({})</span>
+
+                    <span class="k">if</span> <span class="n">return_tensors</span><span class="p">:</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_tensor</span><span class="p">(</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">run_interchange</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">source_dic</span><span class="p">)</span>
+                        <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s2">&quot;base_labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_tensor</span><span class="p">(</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+                        <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_to_tensor</span><span class="p">(</span><span class="n">base</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+                            <span class="p">[</span><span class="n">intervention_id</span><span class="p">(</span><span class="n">intervention</span><span class="p">)]</span>
+                        <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_interchange</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">source_dic</span><span class="p">)</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s1">&#39;base_labels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">base</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s1">&#39;source_input_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sources</span>
+                        <span class="n">example</span><span class="p">[</span><span class="s1">&#39;intervention_id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">intervention_id</span><span class="p">(</span><span class="n">intervention</span><span class="p">)]</span>
+
+                    <span class="n">examples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">example</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">examples</span></div>
+
+
+
+<div class="viewcode-block" id="simple_example">
+<a class="viewcode-back" href="../../../api/pyvene.data_generators.causal_model.simple_example.html#pyvene.data_generators.causal_model.simple_example">[docs]</a>
+<span class="k">def</span> <span class="nf">simple_example</span><span class="p">():</span>
+    <span class="n">variables</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">]</span>
+    <span class="n">values</span> <span class="o">=</span> <span class="p">{</span><span class="n">variable</span><span class="p">:</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span> <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">}</span>
+    <span class="n">parents</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="p">[],</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">]}</span>
+
+    <span class="k">def</span> <span class="nf">A</span><span class="p">():</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">B</span><span class="p">():</span>
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">C</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">a</span> <span class="ow">and</span> <span class="n">b</span>
+
+    <span class="n">functions</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">}</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">CausalModel</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">functions</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">print_structure</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No intervention:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(),</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">print_setting</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">())</span>
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="s2">&quot;Intervention setting A and B to TRUE:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">({</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}),</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Timesteps:&quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">timesteps</span><span class="p">)</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">simple_example</span><span class="p">()</span>
+</pre></div>
+
+                </article>
+              
+
+              
+              
+              
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+  <h1>Source code for pyvene.models.backpack_gpt2.modelings_backpack_gpt2</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.utils.checkpoint</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+<span class="kn">from</span> <span class="nn">transformers.activations</span> <span class="kn">import</span> <span class="n">ACT2FN</span>
+<span class="kn">from</span> <span class="nn">transformers.pytorch_utils</span> <span class="kn">import</span> <span class="n">Conv1D</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ModelOutput</span><span class="p">,</span>
+    <span class="n">logging</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">transformers.models.gpt2.configuration_gpt2</span> <span class="kn">import</span> <span class="n">GPT2Config</span>
+<span class="kn">from</span> <span class="nn">transformers.models.gpt2.modeling_gpt2</span> <span class="kn">import</span> <span class="n">GPT2Model</span><span class="p">,</span> <span class="n">GPT2PreTrainedModel</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="BackpackGPT2Config">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackGPT2Config</span><span class="p">(</span><span class="n">GPT2Config</span><span class="p">):</span>
+<span class="w">  </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to</span>
+<span class="sd">    instantiate a Backpack GPT-2 model according to the specified arguments, defining the model architecture.</span>
+<span class="sd">    Configuration objects inherit from [`GPT2Config`] and can be used to control the model outputs. Read the</span>
+<span class="sd">    documentation from [`GPT2Config`] for more information.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        num_senses (`int`, *optional*, defaults to 16):</span>
+<span class="sd">            The number of sense vectors to define for each word.</span>
+<span class="sd">        sense_intermediate_scale (`int`, *optional*, defaults ot 4):</span>
+<span class="sd">            The hidden dimensionality of the sense vector network.</span>
+<span class="sd">    Example:</span>
+<span class="sd">    ```python</span>
+<span class="sd">    &gt;&gt;&gt; from transformers import BackpackGPT2Config, BackpackGPT2Model</span>
+<span class="sd">    &gt;&gt;&gt; # Initializing a GPT2 configuration</span>
+<span class="sd">    &gt;&gt;&gt; configuration = BackpackGPT2Config()</span>
+<span class="sd">    &gt;&gt;&gt; # Initializing a model (with random weights) from the configuration</span>
+<span class="sd">    &gt;&gt;&gt; model = BackpackGPT2Model(configuration)</span>
+<span class="sd">    &gt;&gt;&gt; # Accessing the model configuration</span>
+<span class="sd">    &gt;&gt;&gt; configuration = model.config</span>
+<span class="sd">  &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="BackpackGPT2Config.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__">[docs]</a>
+  <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+               <span class="n">vocab_size</span><span class="o">=</span><span class="mi">50264</span><span class="p">,</span>
+               <span class="n">num_senses</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+               <span class="n">sense_intermediate_scale</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+               <span class="n">n_positions</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
+               <span class="n">scale_attn_by_inverse_layer_idx</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+               <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+  <span class="p">):</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span> <span class="o">=</span> <span class="n">num_senses</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">sense_intermediate_scale</span> <span class="o">=</span> <span class="n">sense_intermediate_scale</span>
+    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">vocab_size</span><span class="o">=</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_positions</span><span class="o">=</span><span class="n">n_positions</span><span class="p">,</span>
+                     <span class="n">scale_attn_by_inverse_layer_idx</span><span class="o">=</span><span class="n">scale_attn_by_inverse_layer_idx</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+    
+
+<span class="c1">### Backpack-Specific</span>
+<div class="viewcode-block" id="BackpackGPT2PreTrainedModel">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackGPT2PreTrainedModel</span><span class="p">(</span><span class="n">GPT2PreTrainedModel</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained</span>
+<span class="sd">    models.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_keys_to_ignore_on_load_missing</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s2">&quot;attn.masked_bias&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;attn.bias&quot;</span><span class="p">]</span>
+
+    <span class="n">config_class</span> <span class="o">=</span> <span class="n">BackpackGPT2Config</span>
+    <span class="n">base_model_prefix</span> <span class="o">=</span> <span class="s2">&quot;backpack&quot;</span>
+    <span class="n">is_parallelizable</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">supports_gradient_checkpointing</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="n">_no_split_modules</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;GPT2Block&quot;</span><span class="p">,</span> <span class="s2">&quot;BackpackNoMixBlock&quot;</span><span class="p">]</span>
+
+<div class="viewcode-block" id="BackpackGPT2PreTrainedModel.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+
+<div class="viewcode-block" id="BackpackMLP">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackMLP</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+
+<div class="viewcode-block" id="BackpackMLP.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">intermediate_dim</span><span class="p">,</span> <span class="n">out_dim</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c_fc</span> <span class="o">=</span> <span class="n">Conv1D</span><span class="p">(</span><span class="n">intermediate_dim</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c_proj</span> <span class="o">=</span> <span class="n">Conv1D</span><span class="p">(</span><span class="n">out_dim</span><span class="p">,</span> <span class="n">intermediate_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">ACT2FN</span><span class="p">[</span><span class="n">config</span><span class="o">.</span><span class="n">activation_function</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">resid_pdrop</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BackpackMLP.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">:</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_fc</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">hidden_states</span></div>
+</div>
+
+
+<div class="viewcode-block" id="BackpackNoMixBlock">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackNoMixBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+
+<div class="viewcode-block" id="BackpackNoMixBlock.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ln_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">layer_norm_epsilon</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ln_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">layer_norm_epsilon</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span> <span class="o">=</span> <span class="n">BackpackMLP</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span> <span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resid_dropout1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">resid_pdrop</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resid_dropout2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">resid_pdrop</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BackpackNoMixBlock.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">,</span> <span class="n">residual</span><span class="p">):</span>
+        <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resid_dropout1</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> <span class="o">+</span> <span class="n">residual</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln_1</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
+        <span class="n">mlp_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resid_dropout2</span><span class="p">(</span><span class="n">mlp_out</span><span class="p">)</span> <span class="o">+</span> <span class="n">residual</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln_2</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">hidden_states</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="BackpackSenseNetwork">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackSenseNetwork</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<div class="viewcode-block" id="BackpackSenseNetwork.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">num_senses</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span> <span class="o">=</span> <span class="n">num_senses</span>
+        <span class="c1">#self.embeddings = embeddings</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_embd</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">n_embd</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">embd_pdrop</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">block</span> <span class="o">=</span> <span class="n">BackpackNoMixBlock</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ln</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">layer_norm_epsilon</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">final_mlp</span> <span class="o">=</span> <span class="n">BackpackMLP</span><span class="p">(</span>
+            <span class="n">embed_dim</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span>
+            <span class="n">intermediate_dim</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">sense_intermediate_scale</span><span class="o">*</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span>
+            <span class="n">out_dim</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="o">*</span><span class="n">config</span><span class="o">.</span><span class="n">num_senses</span><span class="p">,</span>
+            <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+            <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BackpackSenseNetwork.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_embeds</span><span class="p">):</span>
+        <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">input_embeds</span><span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">residual</span><span class="p">)</span>
+        <span class="n">senses</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_mlp</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="n">bs</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">nvd</span> <span class="o">=</span> <span class="n">senses</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">return</span> <span class="n">senses</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">bs</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_embd</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># (bs, nv, s, d)</span></div>
+</div>
+
+
+<div class="viewcode-block" id="BackpackWeightNetwork">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackWeightNetwork</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+
+<div class="viewcode-block" id="BackpackWeightNetwork.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_senses</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_embd</span> <span class="o">=</span> <span class="n">embed_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span> <span class="o">=</span> <span class="n">num_senses</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">embed_per_sense</span> <span class="o">=</span> <span class="n">embed_dim</span> <span class="o">//</span> <span class="n">num_senses</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c_attn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">embed_dim</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_senses</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_per_sense</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">softmax_scale</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="BackpackWeightNetwork.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoded</span><span class="p">):</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">encoded</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">encoded</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_attn</span><span class="p">(</span><span class="n">encoded</span><span class="p">)</span> <span class="c1"># (b, s, 2*d)</span>
+        <span class="n">encoded</span> <span class="o">=</span> <span class="n">encoded</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_per_sense</span><span class="p">)</span> <span class="c1">#(b, s, 2, nv, d//nv)</span>
+        <span class="n">batch_size</span><span class="p">,</span> <span class="n">seqlen</span> <span class="o">=</span> <span class="n">encoded</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">encoded</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># compute scores &amp; mask</span>
+        <span class="n">q</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">encoded</span><span class="o">.</span><span class="n">unbind</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">softmax_scale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">softmax_scale</span> <span class="ow">or</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">scores</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;bthd,bshd-&gt;bhts&#39;</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span> <span class="o">*</span> <span class="n">softmax_scale</span><span class="p">)</span>
+        <span class="n">causal_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">seqlen</span><span class="p">,</span> <span class="n">seqlen</span><span class="p">),</span> <span class="o">-</span><span class="mf">10000.0</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">scores</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">scores</span> <span class="o">=</span> <span class="n">scores</span> <span class="o">+</span> <span class="n">causal_mask</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">scores</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">q</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span></div>
+</div>
+
+    
+
+<div class="viewcode-block" id="BackpackGPT2BaseModelOutput">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">BackpackGPT2BaseModelOutput</span><span class="p">(</span><span class="n">ModelOutput</span><span class="p">):</span>
+    <span class="n">hidden_states</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">contextualization</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="BackpackGPT2Model">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackGPT2Model</span><span class="p">(</span><span class="n">BackpackGPT2PreTrainedModel</span><span class="p">):</span>
+    <span class="n">_keys_to_ignore_on_load_missing</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s2">&quot;.*attn.masked_bias&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;.*attn.bias&quot;</span><span class="p">]</span>
+
+<div class="viewcode-block" id="BackpackGPT2Model.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">n_embd</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">num_senses</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gpt2_model</span> <span class="o">=</span> <span class="n">GPT2Model</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sense_network</span> <span class="o">=</span> <span class="n">BackpackSenseNetwork</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpt2_model</span><span class="o">.</span><span class="n">wte</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpt2_model</span><span class="o">.</span><span class="n">wte</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">position_embeddings</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpt2_model</span><span class="o">.</span><span class="n">wpe</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sense_weight_net</span> <span class="o">=</span> <span class="n">BackpackWeightNetwork</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">)</span>
+        <span class="c1"># Model parallel</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_parallel</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">device_map</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gradient_checkpointing</span> <span class="o">=</span> <span class="kc">False</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_num_senses</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_senses</span>
+
+    <span class="k">def</span> <span class="nf">get_word_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span>
+
+    <span class="k">def</span> <span class="nf">get_sense_network</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sense_network</span>
+
+<div class="viewcode-block" id="BackpackGPT2Model.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">position_ids</span><span class="p">):</span>
+        <span class="c1"># Compute senses</span>
+        <span class="n">sense_input_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span>
+        <span class="n">senses</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sense_network</span><span class="p">(</span><span class="n">sense_input_embeds</span><span class="p">)</span> <span class="c1"># (bs, nv, s, d)</span>
+
+        <span class="c1"># Compute contextualization weights</span>
+        <span class="n">contextl_hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpt2_model</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">position_ids</span><span class="o">=</span><span class="n">position_ids</span><span class="p">)</span><span class="o">.</span><span class="n">last_hidden_state</span> <span class="c1"># (bs, s, d)</span>
+        <span class="n">contextualization</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sense_weight_net</span><span class="p">(</span><span class="n">contextl_hidden_states</span><span class="p">)</span> <span class="c1"># (bs, nv, s, s)</span>
+
+        <span class="c1"># Compute resulting outputs</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">contextualization</span> <span class="o">@</span> <span class="n">senses</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (bs, nv, s, d) -&gt; (bs, s, d)</span>
+        <span class="k">return</span> <span class="n">BackpackGPT2BaseModelOutput</span><span class="p">(</span>
+            <span class="n">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
+            <span class="n">contextualization</span><span class="o">=</span><span class="n">contextualization</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+    
+    <span class="k">def</span> <span class="nf">run_with_custom_contextualization</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">contextualization</span><span class="p">):</span>
+        <span class="c1"># Compute senses</span>
+        <span class="n">sense_input_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span>
+        <span class="n">senses</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sense_network</span><span class="p">(</span><span class="n">sense_input_embeds</span><span class="p">)</span> <span class="c1"># (bs, nv, s, d)</span>
+
+        <span class="c1"># Compute resulting outputs</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">contextualization</span> <span class="o">@</span> <span class="n">senses</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (bs, nv, s, d) -&gt; (bs, s, d)</span>
+        <span class="k">return</span> <span class="n">BackpackGPT2BaseModelOutput</span><span class="p">(</span>
+            <span class="n">hidden_states</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span>
+            <span class="n">contextualization</span><span class="o">=</span><span class="n">contextualization</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BackpackGPT2LMHeadModelOutput">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">BackpackGPT2LMHeadModelOutput</span><span class="p">(</span><span class="n">ModelOutput</span><span class="p">):</span>
+    <span class="n">logits</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">contextualization</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="BackpackGPT2LMHeadModel">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel">[docs]</a>
+<span class="k">class</span> <span class="nc">BackpackGPT2LMHeadModel</span><span class="p">(</span><span class="n">BackpackGPT2PreTrainedModel</span><span class="p">):</span>
+    <span class="n">_keys_to_ignore_on_load_missing</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s2">&quot;.*attn.masked_bias&quot;</span><span class="p">,</span> <span class="sa">r</span><span class="s2">&quot;.*attn.bias&quot;</span><span class="p">]</span>
+
+<div class="viewcode-block" id="BackpackGPT2LMHeadModel.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">backpack</span> <span class="o">=</span> <span class="n">BackpackGPT2Model</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="c1"># Model parallel</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_parallel</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">device_map</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">tie_weights</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="BackpackGPT2LMHeadModel.tie_weights">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights">[docs]</a>
+    <span class="k">def</span> <span class="nf">tie_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpack</span><span class="o">.</span><span class="n">word_embeddings</span><span class="o">.</span><span class="n">weight</span> <span class="c1"># also tied with the underlying underlying transf</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_lm_head</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span>
+
+<div class="viewcode-block" id="BackpackGPT2LMHeadModel.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">position_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpack</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">position_ids</span><span class="o">=</span><span class="n">position_ids</span><span class="p">)</span>
+        <span class="n">hidden_states</span><span class="p">,</span> <span class="n">contextualization</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">outputs</span><span class="o">.</span><span class="n">contextualization</span>
+        <span class="n">lm_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> <span class="c1"># (bs, s, V)</span>
+        <span class="k">return</span> <span class="n">BackpackGPT2LMHeadModelOutput</span><span class="p">(</span>
+            <span class="n">logits</span><span class="o">=</span><span class="n">lm_logits</span><span class="p">,</span>
+            <span class="n">contextualization</span><span class="o">=</span><span class="n">contextualization</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">run_with_custom_contextualization</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">contextualization</span><span class="p">):</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpack</span><span class="o">.</span><span class="n">run_with_custom_contextualization</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">contextualization</span><span class="p">)</span>
+        <span class="n">hidden_states</span><span class="p">,</span> <span class="n">contextualization</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">outputs</span><span class="o">.</span><span class="n">contextualization</span>
+        <span class="n">lm_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">BackpackGPT2LMHeadModelOutput</span><span class="p">(</span>
+        <span class="n">logits</span><span class="o">=</span><span class="n">lm_logits</span><span class="p">,</span>
+        <span class="n">contextualization</span><span class="o">=</span><span class="n">contextualization</span><span class="p">,</span>
+    <span class="p">)</span></div>
+
+            
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+  <h1>Source code for pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt2 base model&quot;&quot;&quot;</span>
+<span class="n">backpack_gpt2_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;sense_network_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;backpack.sense_network&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+<span class="p">}</span>
+
+
+<span class="n">backpack_gpt2_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;sense_network_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+<div class="viewcode-block" id="create_backpack_gpt2">
+<a class="viewcode-back" href="../../../../api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.html#pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2">[docs]</a>
+<span class="k">def</span> <span class="nf">create_backpack_gpt2</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;stanfordnlp/backpack-gpt2&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GPT2 model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="c1"># Load model directly</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+    <span class="kn">from</span> <span class="nn">pyvene.models.backpack_gpt2.modelings_backpack_gpt2</span> <span class="kn">import</span> <span class="n">BackpackGPT2LMHeadModel</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">,</span> <span class="n">trust_remote_code</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">BackpackGPT2LMHeadModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">trust_remote_code</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">model</span></div>
+
+
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+<li class="toctree-l4"><a class="reference internal" href="../../../api/pyvene.models.intervention_utils.broadcast_tensor_v1.html">pyvene.models.intervention_utils.broadcast_tensor_v1</a></li>
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+  <h1>Source code for pyvene.models.basic_utils</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Basic Utils</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">importlib</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<span class="n">lsm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">sm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="get_type_from_string">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.get_type_from_string.html#pyvene.models.basic_utils.get_type_from_string">[docs]</a>
+<span class="k">def</span> <span class="nf">get_type_from_string</span><span class="p">(</span><span class="n">type_str</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Help function to convert string to type&quot;&quot;&quot;</span>
+    <span class="c1"># Remove &lt;class &#39; and &#39;&gt; from the string</span>
+    <span class="n">type_str</span> <span class="o">=</span> <span class="n">type_str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&lt;class &#39;&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&#39;&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Split the string into module and class name</span>
+    <span class="n">module_name</span><span class="p">,</span> <span class="n">class_name</span> <span class="o">=</span> <span class="n">type_str</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Import the module</span>
+    <span class="n">module</span> <span class="o">=</span> <span class="n">importlib</span><span class="o">.</span><span class="n">import_module</span><span class="p">(</span><span class="n">module_name</span><span class="p">)</span>
+
+    <span class="c1"># Get the class</span>
+    <span class="bp">cls</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">class_name</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="bp">cls</span></div>
+
+
+
+<div class="viewcode-block" id="create_directory">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.create_directory.html#pyvene.models.basic_utils.create_directory">[docs]</a>
+<span class="k">def</span> <span class="nf">create_directory</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create directory if not exist&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Directory &#39;</span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s2">&#39; created successfully.&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Directory &#39;</span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s2">&#39; already exists.&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="embed_to_distrib">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.embed_to_distrib.html#pyvene.models.basic_utils.embed_to_distrib">[docs]</a>
+<span class="k">def</span> <span class="nf">embed_to_distrib</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">embed</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert an embedding to a distribution over the vocabulary&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="s2">&quot;gpt2&quot;</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">architectures</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span>
+            <span class="n">vocab</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">embed</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">wte</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
+            <span class="k">if</span> <span class="n">logits</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">vocab</span>
+            <span class="k">return</span> <span class="n">lsm</span><span class="p">(</span><span class="n">vocab</span><span class="p">)</span> <span class="k">if</span> <span class="n">log</span> <span class="k">else</span> <span class="n">sm</span><span class="p">(</span><span class="n">vocab</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="s2">&quot;llama&quot;</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">architectures</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">assert</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">&quot;Support for LLaMA is not here yet&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="set_seed">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.set_seed.html#pyvene.models.basic_utils.set_seed">[docs]</a>
+<span class="k">def</span> <span class="nf">set_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Set seed. Deprecate soon since it is in the huggingface library&quot;&quot;&quot;</span>
+    <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">manual_seed_all</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="sigmoid_boundary">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.sigmoid_boundary.html#pyvene.models.basic_utils.sigmoid_boundary">[docs]</a>
+<span class="k">def</span> <span class="nf">sigmoid_boundary</span><span class="p">(</span><span class="n">_input</span><span class="p">,</span> <span class="n">boundary_x</span><span class="p">,</span> <span class="n">boundary_y</span><span class="p">,</span> <span class="n">temperature</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate sigmoid mask&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">((</span><span class="n">_input</span> <span class="o">-</span> <span class="n">boundary_x</span><span class="p">)</span> <span class="o">/</span> <span class="n">temperature</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span>
+        <span class="p">(</span><span class="n">boundary_y</span> <span class="o">-</span> <span class="n">_input</span><span class="p">)</span> <span class="o">/</span> <span class="n">temperature</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="harmonic_sigmoid_boundary">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.harmonic_sigmoid_boundary.html#pyvene.models.basic_utils.harmonic_sigmoid_boundary">[docs]</a>
+<span class="k">def</span> <span class="nf">harmonic_sigmoid_boundary</span><span class="p">(</span><span class="n">_input</span><span class="p">,</span> <span class="n">boundary_x</span><span class="p">,</span> <span class="n">boundary_y</span><span class="p">,</span> <span class="n">temperature</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate harmonic sigmoid mask&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="p">(</span><span class="n">_input</span> <span class="o">&lt;=</span> <span class="n">boundary_x</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">((</span><span class="n">_input</span> <span class="o">-</span> <span class="n">boundary_x</span><span class="p">)</span> <span class="o">/</span> <span class="n">temperature</span><span class="p">)</span>
+        <span class="o">+</span> <span class="p">(</span><span class="n">_input</span> <span class="o">&gt;=</span> <span class="n">boundary_y</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">((</span><span class="n">boundary_y</span> <span class="o">-</span> <span class="n">_input</span><span class="p">)</span> <span class="o">/</span> <span class="n">temperature</span><span class="p">)</span>
+        <span class="o">+</span> <span class="p">((</span><span class="n">_input</span> <span class="o">&gt;</span> <span class="n">boundary_x</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">_input</span> <span class="o">&lt;</span> <span class="n">boundary_y</span><span class="p">))</span>
+        <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="mf">0.5</span>
+                <span class="o">*</span> <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">_input</span> <span class="o">-</span> <span class="n">boundary_x</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">_input</span> <span class="o">-</span> <span class="n">boundary_y</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+            <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="o">/</span> <span class="n">temperature</span>
+        <span class="p">)</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="count_parameters">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.count_parameters.html#pyvene.models.basic_utils.count_parameters">[docs]</a>
+<span class="k">def</span> <span class="nf">count_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Count parameters of a model that require gradients&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="random_permutation_matrix">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.random_permutation_matrix.html#pyvene.models.basic_utils.random_permutation_matrix">[docs]</a>
+<span class="k">def</span> <span class="nf">random_permutation_matrix</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate a random permutation matrix&quot;&quot;&quot;</span>
+    <span class="n">_p</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+    <span class="n">perm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+    <span class="n">_p</span> <span class="o">=</span> <span class="n">_p</span><span class="p">[</span><span class="n">perm</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">_p</span></div>
+
+
+
+<div class="viewcode-block" id="closeness_to_permutation_loss">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.closeness_to_permutation_loss.html#pyvene.models.basic_utils.closeness_to_permutation_loss">[docs]</a>
+<span class="k">def</span> <span class="nf">closeness_to_permutation_loss</span><span class="p">(</span><span class="n">rotation</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Measure how close a rotation m is close to a permutation m&quot;&quot;&quot;</span>
+    <span class="n">row_sum_diff</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rotation</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+    <span class="n">col_sum_diff</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rotation</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+    <span class="n">entry_diff</span> <span class="o">=</span> <span class="p">(</span><span class="n">rotation</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">rotation</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">row_sum_diff</span> <span class="o">+</span> <span class="n">col_sum_diff</span><span class="p">)</span> <span class="o">+</span> <span class="n">entry_diff</span>
+    <span class="k">return</span> <span class="n">loss</span></div>
+
+
+
+<div class="viewcode-block" id="format_token">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.format_token.html#pyvene.models.basic_utils.format_token">[docs]</a>
+<span class="k">def</span> <span class="nf">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">tok</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Format the token for some path patching experiment to show decoding diff&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">tok</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\\</span><span class="s2">n&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="top_vals">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.top_vals.html#pyvene.models.basic_utils.top_vals">[docs]</a>
+<span class="k">def</span> <span class="nf">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">return_results</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Pretty print the top n values of a distribution over the vocabulary&quot;&quot;&quot;</span>
+    <span class="n">top_values</span><span class="p">,</span> <span class="n">top_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">top_values</span><span class="p">):</span>
+        <span class="n">tok</span> <span class="o">=</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">top_indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+        <span class="n">ret</span> <span class="o">+=</span> <span class="p">[(</span><span class="n">tok</span><span class="p">,</span> <span class="n">top_values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())]</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_results</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tok</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">top_values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">return_results</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+        
+<div class="viewcode-block" id="get_list_depth">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.get_list_depth.html#pyvene.models.basic_utils.get_list_depth">[docs]</a>
+<span class="k">def</span> <span class="nf">get_list_depth</span><span class="p">(</span><span class="n">lst</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the max depth of the input list&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lst</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">1</span> <span class="o">+</span> <span class="nb">max</span><span class="p">((</span><span class="n">get_list_depth</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">lst</span><span class="p">),</span> <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="mi">0</span></div>
+
+
+
+<div class="viewcode-block" id="get_batch_size">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.get_batch_size.html#pyvene.models.basic_utils.get_batch_size">[docs]</a>
+<span class="k">def</span> <span class="nf">get_batch_size</span><span class="p">(</span><span class="n">model_input</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get batch size based on the input</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model_input</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">model_input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model_input</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">batch_size</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">break</span>
+    <span class="k">return</span> <span class="n">batch_size</span></div>
+
+
+
+<div class="viewcode-block" id="GET_LOC">
+<a class="viewcode-back" href="../../../api/pyvene.models.basic_utils.GET_LOC.html#pyvene.models.basic_utils.GET_LOC">[docs]</a>
+<span class="k">def</span> <span class="nf">GET_LOC</span><span class="p">(</span>
+    <span class="n">LOC</span><span class="p">,</span>
+    <span class="n">unit</span><span class="o">=</span><span class="s2">&quot;h.pos&quot;</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    From simple locale to nested one.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">unit</span> <span class="o">==</span> <span class="s2">&quot;h.pos&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">[</span> 
+                   <span class="p">[</span> 
+                       <span class="p">[</span> 
+                           <span class="p">[</span><span class="n">LOC</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> 
+                       <span class="p">]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> 
+                       <span class="p">[</span> 
+                           <span class="p">[</span><span class="n">LOC</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> 
+                       <span class="p">]</span> <span class="o">*</span> <span class="n">batch_size</span> 
+                   <span class="p">]</span> 
+               <span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">unit</span><span class="si">}</span><span class="s2"> is not supported.&quot;</span>
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+                  
+  <h1>Source code for pyvene.models.blip.modelings_blip</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BlipForQuestionAnswering</span><span class="p">,</span> <span class="n">BlipConfig</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">ModelOutput</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Dict</span>
+
+
+<div class="viewcode-block" id="BlipWrapper">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_blip.BlipWrapper.html#pyvene.models.blip.modelings_blip.BlipWrapper">[docs]</a>
+<span class="k">class</span> <span class="nc">BlipWrapper</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<div class="viewcode-block" id="BlipWrapper.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_blip.BlipWrapper.html#pyvene.models.blip.modelings_blip.BlipWrapper.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">BlipForQuestionAnswering</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">BlipWrapper</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_vis</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">vision_model</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">text_encoder</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_text_dec</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">text_decoder</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">decoder_pad_token_id</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decoder_pad_token_id</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">decoder_start_token_id</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decoder_start_token_id</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eos_token_id</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">sep_token_id</span><span class="p">,)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pad_token_id</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">pad_token_id</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_attentions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">output_attentions</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_return_dict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">use_return_dict</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_hidden_states</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">output_hidden_states</span></div>
+
+
+<div class="viewcode-block" id="BlipWrapper.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_blip.BlipWrapper.html#pyvene.models.blip.modelings_blip.BlipWrapper.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">,</span>
+        <span class="n">pixel_values</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">,</span>
+        <span class="n">attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_attentions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]:</span>
+        <span class="n">return_dict</span> <span class="o">=</span> <span class="n">return_dict</span> <span class="k">if</span> <span class="n">return_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_return_dict</span>
+        <span class="n">output_attentions</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">output_attentions</span>
+            <span class="k">if</span> <span class="n">output_attentions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_attentions</span>
+        <span class="p">)</span>
+        <span class="n">output_hidden_states</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">output_hidden_states</span>
+            <span class="k">if</span> <span class="n">output_hidden_states</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_hidden_states</span>
+        <span class="p">)</span>
+
+        <span class="n">vision_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_vis</span><span class="p">(</span>
+            <span class="n">pixel_values</span><span class="o">=</span><span class="n">pixel_values</span><span class="p">,</span>
+            <span class="n">output_attentions</span><span class="o">=</span><span class="n">output_attentions</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="o">=</span><span class="n">output_hidden_states</span><span class="p">,</span>
+            <span class="n">return_dict</span><span class="o">=</span><span class="n">return_dict</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">image_embeds</span> <span class="o">=</span> <span class="n">vision_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">image_attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">image_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+
+        <span class="n">input_ids</span> <span class="o">=</span> <span class="n">input_ids</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">question_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="p">(</span>
+            <span class="n">input_ids</span><span class="o">=</span><span class="n">input_ids</span><span class="p">,</span>
+            <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">,</span>
+            <span class="n">encoder_hidden_states</span><span class="o">=</span><span class="n">image_embeds</span><span class="p">,</span>
+            <span class="n">encoder_attention_mask</span><span class="o">=</span><span class="n">image_attention_mask</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">question_embeds_w</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">question_embeds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span> <span class="k">else</span> <span class="n">question_embeds</span><span class="o">.</span><span class="n">last_hidden_state</span>
+        <span class="p">)</span>
+
+        <span class="n">bos_ids</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">question_embeds_w</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span>
+            <span class="n">fill_value</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">decoder_start_token_id</span><span class="p">,</span>
+            <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="o">.</span><span class="n">device</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">answer_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_text_dec</span><span class="p">(</span>
+            <span class="n">input_ids</span><span class="o">=</span><span class="n">bos_ids</span><span class="p">,</span>
+            <span class="n">encoder_hidden_states</span><span class="o">=</span><span class="n">question_embeds_w</span><span class="p">,</span>
+            <span class="n">encoder_attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="s2">&quot;decoder_logits&quot;</span><span class="p">:</span> <span class="n">answer_output</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span>
+            <span class="s2">&quot;image_embeds&quot;</span><span class="p">:</span> <span class="n">image_embeds</span><span class="p">,</span>
+            <span class="s2">&quot;encoder_last_hidden_state&quot;</span><span class="p">:</span> <span class="n">question_embeds</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span>
+            <span class="s2">&quot;encoder_hidden_states&quot;</span><span class="p">:</span> <span class="n">question_embeds</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span>
+            <span class="s2">&quot;decoder_hidden_states&quot;</span><span class="p">:</span> <span class="n">answer_output</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span>
+        <span class="p">}</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+
+              
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+  <h1>Source code for pyvene.models.blip.modelings_blip_itm</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BlipConfig</span><span class="p">,</span> <span class="n">BlipForImageTextRetrieval</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">ModelOutput</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Dict</span>
+
+
+<div class="viewcode-block" id="BlipITMWrapper">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.html#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper">[docs]</a>
+<span class="k">class</span> <span class="nc">BlipITMWrapper</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<div class="viewcode-block" id="BlipITMWrapper.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.html#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">BlipForImageTextRetrieval</span><span class="p">,</span> <span class="n">use_itm_not_contrastive</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">BlipITMWrapper</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_vis</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">vision_model</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">text_encoder</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_vis_proj</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">vision_proj</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_text_proj</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">text_proj</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_itm</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">itm_head</span>
+        <span class="c1"># do I need to keep decoder_pad_token_id and decoder_start_token_id? might be a mistake in the HF implementation lol</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eos_token_id</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">sep_token_id</span><span class="p">,)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pad_token_id</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">pad_token_id</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_attentions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">output_attentions</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_return_dict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">use_return_dict</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_hidden_states</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">output_hidden_states</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_itm_head</span> <span class="o">=</span> <span class="n">use_itm_not_contrastive</span></div>
+
+
+<div class="viewcode-block" id="BlipITMWrapper.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.html#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">,</span>
+        <span class="n">pixel_values</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">,</span>
+        <span class="n">attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_attentions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]:</span>
+        <span class="n">return_dict</span> <span class="o">=</span> <span class="n">return_dict</span> <span class="k">if</span> <span class="n">return_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_return_dict</span>
+        <span class="n">output_attentions</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">output_attentions</span>
+            <span class="k">if</span> <span class="n">output_attentions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_attentions</span>
+        <span class="p">)</span>
+        <span class="n">output_hidden_states</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">output_hidden_states</span>
+            <span class="k">if</span> <span class="n">output_hidden_states</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_hidden_states</span>
+        <span class="p">)</span>
+
+        <span class="n">vision_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_vis</span><span class="p">(</span>
+            <span class="n">pixel_values</span><span class="o">=</span><span class="n">pixel_values</span><span class="p">,</span>
+            <span class="n">output_attentions</span><span class="o">=</span><span class="n">output_attentions</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="o">=</span><span class="n">output_hidden_states</span><span class="p">,</span>
+            <span class="n">return_dict</span><span class="o">=</span><span class="n">return_dict</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">image_embeds</span> <span class="o">=</span> <span class="n">vision_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">input_ids</span> <span class="o">=</span> <span class="n">input_ids</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_itm_head</span><span class="p">:</span>
+            <span class="n">image_attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
+                <span class="n">image_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span>
+            <span class="p">)</span>
+
+            <span class="n">caption_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="p">(</span>
+                <span class="n">input_ids</span><span class="o">=</span><span class="n">input_ids</span><span class="p">,</span>
+                <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">,</span>
+                <span class="n">encoder_hidden_states</span><span class="o">=</span><span class="n">image_embeds</span><span class="p">,</span>
+                <span class="n">encoder_attention_mask</span><span class="o">=</span><span class="n">image_attention_mask</span><span class="p">,</span>
+                <span class="n">output_hidden_states</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">caption_embeds</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">caption_embeds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span>
+                <span class="k">else</span> <span class="n">caption_embeds</span><span class="o">.</span><span class="n">last_hidden_state</span>
+            <span class="p">)</span>
+
+            <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_itm</span><span class="p">(</span><span class="n">caption_embeds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">caption_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_text_enc</span><span class="p">(</span>
+                <span class="n">input_ids</span><span class="o">=</span><span class="n">input_ids</span><span class="p">,</span>
+                <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">,</span>
+                <span class="n">return_dict</span><span class="o">=</span><span class="n">return_dict</span><span class="p">,</span>
+                <span class="n">output_hidden_states</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">caption_embeds</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">caption_embeds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span>
+                <span class="k">else</span> <span class="n">caption_embeds</span><span class="o">.</span><span class="n">last_hidden_state</span>
+            <span class="p">)</span>
+
+            <span class="n">image_feat</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">vision_proj</span><span class="p">(</span><span class="n">image_embeds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span>
+            <span class="p">)</span>
+            <span class="n">text_feat</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">text_proj</span><span class="p">(</span><span class="n">caption_embeds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span>
+            <span class="p">)</span>
+
+            <span class="n">output</span> <span class="o">=</span> <span class="n">image_feat</span> <span class="o">@</span> <span class="n">text_feat</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="s2">&quot;itm_score&quot;</span><span class="p">:</span> <span class="n">output</span><span class="p">,</span>
+            <span class="s2">&quot;image_embeds&quot;</span><span class="p">:</span> <span class="n">image_embeds</span><span class="p">,</span>
+            <span class="s2">&quot;encoder_last_hidden_state&quot;</span><span class="p">:</span> <span class="n">caption_embeds</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span>
+            <span class="s2">&quot;encoder_hidden_states&quot;</span><span class="p">:</span> <span class="n">caption_embeds</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span>
+        <span class="p">}</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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new file mode 100644
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+++ b/_modules/pyvene/models/blip/modelings_intervenable_blip.html
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+
+<!DOCTYPE html>
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+<html lang="en" data-content_root="../../../../" >
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+  <h1>Source code for pyvene.models.blip.modelings_intervenable_blip</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+<span class="sd">&quot;&quot;&quot;blip base model&quot;&quot;&quot;</span>
+<span class="n">blip_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="c1"># &#39;vis.block_input&#39;: (&quot;vision_model.encoder.layers[%s]&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.block_output&#39;: (&quot;vision_model.encoder.layers[%s]&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.mlp_activation&#39;: (&quot;vision_model.encoder.layers[%s].mlp.fc1&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.mlp_output&#39;: (&quot;vision_model.encoder.layers[%s].mlp&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.mlp_input&#39;: (&quot;vision_model.encoder.layers[%s].mlp&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.attention_value_output&#39;: (&quot;vision_model.encoder.layers[%s].self_attn.projection&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.attention_output&#39;: (&quot;vision_model.encoder.layers[%s].self_attn&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.attention_input&#39;: (&quot;vision_model.encoder.layers[%s].self_attn&quot;, CONST_INPUT_HOOK),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].intermediate.dense&quot;</span><span class="p">,</span>
+        <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].output&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].intermediate&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].attention.output.dense&quot;</span><span class="p">,</span>
+        <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].attention.output&quot;</span><span class="p">,</span>
+        <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].attention&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="c1"># &#39;block_input&#39;: (&quot;text_decoder.bert.encoder.layer[%s]&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;block_output&#39;: (&quot;text_decoder.bert.encoder.layer[%s]&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;mlp_activation&#39;: (&quot;text_decoder.bert.encoder.layer[%s].intermediate.dense&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;mlp_output&#39;: (&quot;text_decoder.bert.encoder.layer[%s].output&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;mlp_input&#39;: (&quot;text_decoder.bert.encoder.layer[%s].intermediate&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;attention_value_output&#39;: (&quot;text_decoder.bert.encoder.layer[%s].attention.output.dense&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;attention_output&#39;: (&quot;text_decoder.bert.encoder.layer[%s].attention.output&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;attention_input&#39;: (&quot;text_decoder.bert.encoder.layer[%s].attention&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;cross_attention_value_output&#39;: (&quot;text_decoder.bert.encoder.layer[%s].crossattention.output.dense&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;cross_attention_output&#39;: (&quot;text_decoder.bert.encoder.layer[%s].crossattention.output&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;cross_attention_input&#39;: (&quot;text_decoder.bert.encoder.layer[%s].crossattention&quot;, CONST_INPUT_HOOK),</span>
+<span class="p">}</span>
+
+
+<span class="n">blip_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="c1"># &#39;vis.block_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.block_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.mlp_activation&#39;: (&quot;projection_dim&quot;, ),</span>
+    <span class="c1"># &#39;vis.mlp_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.mlp_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.attention_value_output&#39;: (&quot;image_text_hidden_size/text_config.num_attention_heads&quot;, ),</span>
+    <span class="c1"># &#39;vis.attention_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.attention_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;lang.block_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;lang.block_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;lang.mlp_activation&#39;: (&quot;projection_dim&quot;, ),</span>
+    <span class="c1"># &#39;lang.mlp_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;lang.mlp_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;lang.attention_value_output&#39;: (&quot;image_text_hidden_size/text_config.num_attention_heads&quot;, ),</span>
+    <span class="c1"># &#39;lang.attention_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;lang.attention_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;projection_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;image_text_hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;cross_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;image_text_hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;cross_attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;cross_attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;blip model with wrapper&quot;&quot;&quot;</span>
+<span class="n">blip_wrapper_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">blip_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">blip_wrapper_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;text_encoder&quot;</span><span class="p">,</span> <span class="s2">&quot;model_text_enc&quot;</span><span class="p">),</span>
+        <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+    <span class="p">)</span>
+
+
+<span class="n">blip_wrapper_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">blip_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_blip">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_intervenable_blip.create_blip.html#pyvene.models.blip.modelings_intervenable_blip.create_blip">[docs]</a>
+<span class="k">def</span> <span class="nf">create_blip</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Salesforce/blip-vqa-base&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a BLIP VQA model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BlipConfig</span><span class="p">,</span> <span class="n">BlipProcessor</span><span class="p">,</span> <span class="n">BlipForQuestionAnswering</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">BlipConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">processor</span> <span class="o">=</span> <span class="n">BlipProcessor</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">blip</span> <span class="o">=</span> <span class="n">BlipForQuestionAnswering</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">processor</span><span class="p">,</span> <span class="n">blip</span></div>
+
+</pre></div>
+
+                </article>
+              
+
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diff --git a/_modules/pyvene/models/blip/modelings_intervenable_blip_itm.html b/_modules/pyvene/models/blip/modelings_intervenable_blip_itm.html
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
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+<ul class="nav bd-sidenav">
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+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
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+  <h1>Source code for pyvene.models.blip.modelings_intervenable_blip_itm</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+<span class="sd">&quot;&quot;&quot;blip ITM base model&quot;&quot;&quot;</span>
+<span class="n">blip_itm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="c1"># TODO: not sure why these are commented out in the BlipVQA implementation</span>
+    <span class="c1"># &#39;vis.block_input&#39;: (&quot;vision_model.encoder.layers[%s]&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.block_output&#39;: (&quot;vision_model.encoder.layers[%s]&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.mlp_activation&#39;: (&quot;vision_model.encoder.layers[%s].mlp.fc1&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.mlp_output&#39;: (&quot;vision_model.encoder.layers[%s].mlp&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.mlp_input&#39;: (&quot;vision_model.encoder.layers[%s].mlp&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.attention_value_output&#39;: (&quot;vision_model.encoder.layers[%s].self_attn.projection&quot;, CONST_INPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.attention_output&#39;: (&quot;vision_model.encoder.layers[%s].self_attn&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;vis.attention_input&#39;: (&quot;vision_model.encoder.layers[%s].self_attn&quot;, CONST_INPUT_HOOK),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].intermediate.dense&quot;</span><span class="p">,</span>
+        <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].output&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].intermediate&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].attention.output.dense&quot;</span><span class="p">,</span>
+        <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].attention.output&quot;</span><span class="p">,</span>
+        <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_encoder.encoder.layer[</span><span class="si">%s</span><span class="s2">].attention&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;itm_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;itm_head&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+<span class="p">}</span>
+
+
+<span class="n">blip_itm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="c1"># &#39;vis.block_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.block_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.mlp_activation&#39;: (&quot;projection_dim&quot;, ),</span>
+    <span class="c1"># &#39;vis.mlp_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.mlp_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.attention_value_output&#39;: (&quot;image_text_hidden_size/text_config.num_attention_heads&quot;, ),</span>
+    <span class="c1"># &#39;vis.attention_output&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;vis.attention_input&#39;: (&quot;image_text_hidden_size&quot;, ),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;projection_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;image_text_hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;cross_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;image_text_hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;cross_attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;cross_attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;itm_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;image_text_hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;itm_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,),</span> <span class="c1"># TODO: not sure how to specify this dim as it&#39;s not an attr in BlipConfig</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;blip model with wrapper&quot;&quot;&quot;</span>
+<span class="n">blip_itm_wrapper_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">blip_itm_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">blip_itm_wrapper_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;text_encoder&quot;</span><span class="p">,</span> <span class="s2">&quot;model_text_enc&quot;</span><span class="p">),</span> <span class="c1"># NOTE: don&#39;t fully understand why we do this</span>
+        <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+    <span class="p">)</span>
+
+
+<span class="n">blip_itm_wrapper_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">blip_itm_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_blip_itm">
+<a class="viewcode-back" href="../../../../api/pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm.html#pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm">[docs]</a>
+<span class="k">def</span> <span class="nf">create_blip_itm</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Salesforce/blip-itm-base-coco&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a BLIP ITM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BlipConfig</span><span class="p">,</span> <span class="n">BlipProcessor</span><span class="p">,</span> <span class="n">BlipForImageTextRetrieval</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">BlipConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">processor</span> <span class="o">=</span> <span class="n">BlipProcessor</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">blip</span> <span class="o">=</span> <span class="n">BlipForImageTextRetrieval</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">processor</span><span class="p">,</span> <span class="n">blip</span></div>
+
+</pre></div>
+
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+                  
+  <h1>Source code for pyvene.models.configuration_intervenable_model</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">warnings</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span><span class="p">,</span> <span class="n">namedtuple</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Mapping</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">PreTrainedTokenizer</span><span class="p">,</span> <span class="n">TensorType</span><span class="p">,</span> <span class="n">is_torch_available</span>
+<span class="kn">from</span> <span class="nn">transformers.configuration_utils</span> <span class="kn">import</span> <span class="n">PretrainedConfig</span>
+
+<span class="kn">from</span> <span class="nn">.interventions</span> <span class="kn">import</span> <span class="n">VanillaIntervention</span>
+
+
+<span class="n">RepresentationConfig</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span>
+    <span class="s2">&quot;RepresentationConfig&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;layer component unit &quot;</span>
+    <span class="s2">&quot;max_number_of_units &quot;</span>
+    <span class="s2">&quot;low_rank_dimension intervention_type intervention &quot;</span>
+    <span class="s2">&quot;subspace_partition group_key intervention_link_key moe_key &quot;</span>
+    <span class="s2">&quot;source_representation hidden_source_representation latent_dim&quot;</span><span class="p">,</span>
+    <span class="n">defaults</span><span class="o">=</span><span class="p">(</span>
+        <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;pos&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="IntervenableConfig">
+<a class="viewcode-back" href="../../../api/pyvene.models.configuration_intervenable_model.IntervenableConfig.html#pyvene.models.configuration_intervenable_model.IntervenableConfig">[docs]</a>
+<span class="k">class</span> <span class="nc">IntervenableConfig</span><span class="p">(</span><span class="n">PretrainedConfig</span><span class="p">):</span>
+<div class="viewcode-block" id="IntervenableConfig.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.configuration_intervenable_model.IntervenableConfig.html#pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span><span class="n">RepresentationConfig</span><span class="p">()],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+        <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;parallel&quot;</span><span class="p">,</span>
+        <span class="n">sorted_keys</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="c1"># deprecating</span>
+        <span class="c1"># hidden fields for backlog</span>
+        <span class="n">intervention_dimensions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">intervention_constant_sources</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">representations</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">representations</span> <span class="o">=</span> <span class="p">[</span><span class="n">representations</span><span class="p">]</span>
+
+        <span class="n">casted_representations</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">reprs</span> <span class="ow">in</span> <span class="n">representations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="n">RepresentationConfig</span><span class="p">):</span>
+                <span class="n">casted_representations</span> <span class="o">+=</span> <span class="p">[</span><span class="n">reprs</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">casted_representations</span> <span class="o">+=</span> <span class="p">[</span>
+                    <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">*</span><span class="n">reprs</span><span class="p">)]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="n">casted_representations</span> <span class="o">+=</span> <span class="p">[</span>
+                    <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">**</span><span class="n">reprs</span><span class="p">)]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">reprs</span><span class="si">}</span><span class="s2"> format in our representation list is not supported.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">representations</span> <span class="o">=</span> <span class="n">casted_representations</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">=</span> <span class="n">intervention_types</span>
+        <span class="c1"># the type inside reprs can overwrite</span>
+        <span class="n">overwrite</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="n">overwrite_intervention_types</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">reprs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">overwrite</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">reprs</span><span class="o">.</span><span class="n">intervention_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">reprs</span><span class="o">.</span><span class="n">intervention</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;intervention_type if used should be specified for all&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">reprs</span><span class="o">.</span><span class="n">intervention_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">overwrite</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="n">overwrite_intervention_types</span> <span class="o">+=</span> <span class="p">[</span><span class="n">reprs</span><span class="o">.</span><span class="n">intervention_type</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">reprs</span><span class="o">.</span><span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">overwrite</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="n">overwrite_intervention_types</span> <span class="o">+=</span> <span class="p">[</span><span class="nb">type</span><span class="p">(</span><span class="n">reprs</span><span class="o">.</span><span class="n">intervention</span><span class="p">)]</span>
+            <span class="k">if</span> <span class="n">reprs</span><span class="o">.</span><span class="n">intervention_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">reprs</span><span class="o">.</span><span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Only one of the field should be provided: intervention_type, intervention&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="kc">None</span> <span class="ow">in</span> <span class="n">overwrite_intervention_types</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;intervention_type if used should be specified for all&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">overwrite</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">=</span> <span class="n">overwrite_intervention_types</span>
+            
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="o">=</span> <span class="n">sorted_keys</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_dimensions</span> <span class="o">=</span> <span class="n">intervention_dimensions</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_constant_sources</span> <span class="o">=</span> <span class="n">intervention_constant_sources</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="n">model_type</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+    
+    <span class="k">def</span> <span class="nf">add_intervention</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">representations</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">representations</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">representations</span> <span class="o">=</span> <span class="p">[</span><span class="n">representations</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">reprs</span> <span class="ow">in</span> <span class="n">representations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="n">RepresentationConfig</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">representations</span> <span class="o">+=</span> <span class="p">[</span><span class="n">reprs</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">representations</span> <span class="o">+=</span> <span class="p">[</span>
+                    <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">*</span><span class="n">reprs</span><span class="p">)]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">representations</span> <span class="o">+=</span> <span class="p">[</span>
+                    <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">**</span><span class="n">reprs</span><span class="p">)]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">reprs</span><span class="si">}</span><span class="s2"> format in our representation list is not supported.&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">intervention_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;intervention_type should be provided.&quot;</span><span class="p">)</span>
+                
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">intervention_type</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">+=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">intervention_type</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">+=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">intervention</span><span class="p">]</span>
+            
+    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">representations</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">reprs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reprs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">reprs</span> <span class="o">=</span> <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">*</span><span class="n">reprs</span><span class="p">)</span>
+            <span class="n">new_d</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">reprs</span><span class="o">.</span><span class="n">_asdict</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">{</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">}</span> <span class="ow">and</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">v</span> <span class="o">!=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]:</span>
+                    <span class="n">new_d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;PLACEHOLDER&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">new_d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+            <span class="n">representations</span> <span class="o">+=</span> <span class="p">[</span><span class="n">new_d</span><span class="p">]</span>
+        <span class="n">_repr</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;model_type&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="p">),</span>
+            <span class="s2">&quot;representations&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">representations</span><span class="p">),</span>
+            <span class="s2">&quot;intervention_types&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">intervention_types</span>
+            <span class="p">),</span>
+            <span class="s2">&quot;mode&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span>
+            <span class="s2">&quot;sorted_keys&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">),</span>
+            <span class="s2">&quot;intervention_dimensions&quot;</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">intervention_dimensions</span><span class="p">),</span>
+        <span class="p">}</span>
+        <span class="n">_repr_string</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">_repr</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;IntervenableConfig</span><span class="se">\n</span><span class="si">{</span><span class="n">_repr_string</span><span class="si">}</span><span class="s2">&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">()</span></div>
+
+</pre></div>
+
+                </article>
+              
+
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diff --git a/_modules/pyvene/models/constants.html b/_modules/pyvene/models/constants.html
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+  <h1>Source code for pyvene.models.constants</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">CONST_INPUT_HOOK</span> <span class="o">=</span> <span class="s2">&quot;register_forward_pre_hook&quot;</span>
+<span class="n">CONST_OUTPUT_HOOK</span> <span class="o">=</span> <span class="s2">&quot;register_forward_hook&quot;</span>
+<span class="n">CONST_GRAD_HOOK</span> <span class="o">=</span> <span class="s2">&quot;register_hook&quot;</span>
+
+
+<span class="n">split_and_select</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">num_slice</span><span class="p">,</span> <span class="n">selct_index</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_slice</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[</span><span class="n">selct_index</span><span class="p">]</span>
+<div class="viewcode-block" id="split_heads">
+<a class="viewcode-back" href="../../../api/pyvene.models.constants.split_heads.html#pyvene.models.constants.split_heads">[docs]</a>
+<span class="k">def</span> <span class="nf">split_heads</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="n">attn_head_size</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Splits hidden_size dim into attn_head_size and num_heads.&quot;&quot;&quot;</span>
+    <span class="n">new_shape</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">num_heads</span><span class="p">,</span> <span class="n">attn_head_size</span><span class="p">)</span>
+    <span class="n">tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># (batch, head, seq_length, head_features)</span></div>
+
+
+<span class="n">split_half</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">selct_index</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[</span><span class="n">selct_index</span><span class="p">]</span>
+<span class="n">split_three</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">selct_index</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[</span><span class="n">selct_index</span><span class="p">]</span>
+<span class="n">split_head_and_permute</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">num_head</span><span class="p">:</span> <span class="n">split_heads</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_head</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="n">num_head</span><span class="p">)</span>
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/pyvene_101.html">Introduction to pyvene</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+  <h1>Source code for pyvene.models.gemma.modelings_intervenable_gemma</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="n">gemma_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">gemma_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hhead_dim&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;gemma model with LM head&quot;&quot;&quot;</span>
+<span class="n">gemma_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gemma_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gemma_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">gemma_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gemma_type_to_dimension_mapping</span>
+
+
+<span class="sd">&quot;&quot;&quot;gemma model with classifier head&quot;&quot;&quot;</span>
+<span class="n">gemma_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gemma_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gemma_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">gemma_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gemma_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_gemma">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gemma.modelings_intervenable_gemma.create_gemma.html#pyvene.models.gemma.modelings_intervenable_gemma.create_gemma">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gemma</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;google/gemma-2b-it&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a Gemma Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GemmaForCausalLM</span><span class="p">,</span> <span class="n">GemmaTokenizer</span><span class="p">,</span> <span class="n">GemmaConfig</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">GemmaConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">GemmaTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">gemma</span> <span class="o">=</span> <span class="n">GemmaForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span>
+        <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+        <span class="n">torch_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>  <span class="c1"># save memory</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gemma</span></div>
+
+</pre></div>
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+  <h1>Source code for pyvene.models.gemma2.modelings_intervenable_gemma2</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="n">gemma2_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">gemma2_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;gemma2 model with LM head&quot;&quot;&quot;</span>
+<span class="n">gemma2_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gemma2_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gemma2_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">gemma2_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gemma2_type_to_dimension_mapping</span>
+
+
+<span class="sd">&quot;&quot;&quot;gemma2 model with classifier head&quot;&quot;&quot;</span>
+<span class="n">gemma2_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gemma2_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gemma2_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">gemma2_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gemma2_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_gemma2">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.html#pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gemma2</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;google/gemma2-2b&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoConfig</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">gemma</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span>
+        <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+        <span class="n">torch_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gemma</span></div>
+
+</pre></div>
+
+                </article>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
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+
+
+
+
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+  <h1>Source code for pyvene.models.gpt2.modelings_intervenable_gpt2</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt2 base model&quot;&quot;&quot;</span>
+<span class="n">gpt2_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].mlp.act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_weight&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.attn_dropout&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.resid_dropout&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span> 
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.c_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">gpt2_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_head&quot;</span><span class="p">,</span> <span class="p">),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;n_inner&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;n_embd*4&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd/n_head&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_weight&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;max_position_embeddings&quot;</span><span class="p">,</span> <span class="p">),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd/n_head&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd/n_head&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;n_embd/n_head&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt2 model with LM head&quot;&quot;&quot;</span>
+<span class="n">gpt2_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gpt2_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gpt2_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;transformer.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+<span class="n">gpt2_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gpt2_type_to_dimension_mapping</span>
+
+<span class="sd">&quot;&quot;&quot;gpt2 model with classifier head&quot;&quot;&quot;</span>
+<span class="n">gpt2_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gpt2_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gpt2_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;transformer.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+<span class="n">gpt2_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gpt2_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_gpt2">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gpt2</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;gpt2&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GPT2 model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GPT2Model</span><span class="p">,</span> <span class="n">GPT2Tokenizer</span><span class="p">,</span> <span class="n">GPT2Config</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">GPT2Config</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="s1">&#39;_attn_implementation&#39;</span><span class="p">):</span>
+        <span class="n">config</span><span class="o">.</span><span class="n">_attn_implementation</span> <span class="o">=</span> <span class="s2">&quot;eager&quot;</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">GPT2Tokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">gpt</span> <span class="o">=</span> <span class="n">GPT2Model</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span></div>
+
+
+
+<div class="viewcode-block" id="create_gpt2_lm">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gpt2_lm</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;gpt2&quot;</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GPT2 LM, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GPT2LMHeadModel</span><span class="p">,</span> <span class="n">GPT2Tokenizer</span><span class="p">,</span> <span class="n">GPT2Config</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">GPT2Tokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">config</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">GPT2Config</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="s1">&#39;_attn_implementation&#39;</span><span class="p">):</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">_attn_implementation</span> <span class="o">=</span> <span class="s2">&quot;eager&quot;</span>
+        <span class="n">gpt</span> <span class="o">=</span> <span class="n">GPT2LMHeadModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="s1">&#39;_attn_implementation&#39;</span><span class="p">):</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">_attn_implementation</span> <span class="o">=</span> <span class="s2">&quot;eager&quot;</span>
+        <span class="n">gpt</span> <span class="o">=</span> <span class="n">GPT2LMHeadModel</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span></div>
+
+
+<div class="viewcode-block" id="create_gpt2_classifier">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gpt2_classifier</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;gpt2&quot;</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GPT2ForSequenceClassification, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GPT2LMForSequenceClassification</span><span class="p">,</span> <span class="n">GPT2Tokenizer</span><span class="p">,</span> <span class="n">GPT2Config</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">GPT2Tokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">config</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">GPT2Config</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="s1">&#39;_attn_implementation&#39;</span><span class="p">):</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">_attn_implementation</span> <span class="o">=</span> <span class="s2">&quot;eager&quot;</span>
+        <span class="n">gpt</span> <span class="o">=</span> <span class="n">GPT2LMForSequenceClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="s1">&#39;_attn_implementation&#39;</span><span class="p">):</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">_attn_implementation</span> <span class="o">=</span> <span class="s2">&quot;eager&quot;</span>
+        <span class="n">gpt</span> <span class="o">=</span> <span class="n">GPT2LMForSequenceClassification</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span></div>
+
+</pre></div>
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+  <h1>Source code for pyvene.models.gpt_neo.modelings_intervenable_gpt_neo</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt_neo base model&quot;&quot;&quot;</span>
+<span class="n">gpt_neo_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].mlp.act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.out_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.out_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">gpt_neo_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="s2">&quot;num_heads&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;intermediate_size&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;hidden_size*4&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_heads&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt_neo model with LM head&quot;&quot;&quot;</span>
+<span class="n">gpt_neo_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gpt_neo_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gpt_neo_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;transformer.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">gpt_neo_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gpt_neo_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_gpt_neo">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo.html#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gpt_neo</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;roneneldan/TinyStories-33M&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GPT2 model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GPTNeoForCausalLM</span><span class="p">,</span> <span class="n">GPT2Tokenizer</span><span class="p">,</span> <span class="n">GPTNeoConfig</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">GPTNeoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">GPT2Tokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;EleutherAI/gpt-neo-125M&quot;</span><span class="p">)</span>  <span class="c1"># not sure</span>
+    <span class="n">gpt_neo</span> <span class="o">=</span> <span class="n">GPTNeoForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt_neo</span></div>
+
+</pre></div>
+
+                </article>
+              
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+
+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+  <h1>Source code for pyvene.models.gpt_neox.modelings_intervenable_gpt_neox</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt_neox base model&quot;&quot;&quot;</span>
+<span class="n">gpt_neox_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].attention.dense&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].attention.dense&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].attention&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].attention&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="c1"># &#39;query_output&#39;: (&quot;layers[%s].attention.query_key_value&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;key_output&#39;: (&quot;layers[%s].attention.query_key_value&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;value_output&#39;: (&quot;layers[%s].attention.query_key_value&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;head_query_output&#39;: (&quot;layers[%s].attention.query_key_value&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;head_key_output&#39;: (&quot;layers[%s].attention.query_key_value&quot;, CONST_OUTPUT_HOOK),</span>
+    <span class="c1"># &#39;head_value_output&#39;: (&quot;layers[%s].attention.query_key_value&quot;, CONST_OUTPUT_HOOK),</span>
+<span class="p">}</span>
+
+
+<span class="n">gpt_neox_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span>
+        <span class="s2">&quot;intermediate_size&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;hidden_size*4&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="c1"># &#39;query_output&#39;: (&quot;hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;key_output&#39;: (&quot;hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;value_output&#39;: (&quot;hidden_size&quot;, ),</span>
+    <span class="c1"># &#39;head_query_output&#39;: (&quot;hidden_size/num_attention_heads&quot;, ),</span>
+    <span class="c1"># &#39;head_key_output&#39;: (&quot;hidden_size/num_attention_heads&quot;, ),</span>
+    <span class="c1"># &#39;head_value_output&#39;: (&quot;hidden_size/num_attention_heads&quot;, ),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;gpt_neox model with LM head&quot;&quot;&quot;</span>
+<span class="n">gpt_neox_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gpt_neox_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gpt_neox_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;gpt_neox.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">gpt_neox_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gpt_neox_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_gpt_neox">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox.html#pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gpt_neox</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;EleutherAI/pythia-70m&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GPT2 model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GPTNeoXForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">GPTNeoXConfig</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">GPTNeoXConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">gpt_neox</span> <span class="o">=</span> <span class="n">GPTNeoXForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt_neox</span></div>
+
+</pre></div>
+
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+                  
+  <h1>Source code for pyvene.models.gru.modelings_gru</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">Variable</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">ModelOutput</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span><span class="p">,</span> <span class="n">CrossEntropyLoss</span><span class="p">,</span> <span class="n">MSELoss</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">PretrainedConfig</span><span class="p">,</span> <span class="n">PreTrainedModel</span>
+<span class="kn">from</span> <span class="nn">transformers.activations</span> <span class="kn">import</span> <span class="n">ACT2FN</span>
+<span class="kn">from</span> <span class="nn">transformers.modeling_outputs</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ModelOutput</span><span class="p">,</span>
+    <span class="n">SequenceClassifierOutput</span><span class="p">,</span>
+    <span class="n">CausalLMOutput</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="GRUConfig">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUConfig.html#pyvene.models.gru.modelings_gru.GRUConfig">[docs]</a>
+<span class="k">class</span> <span class="nc">GRUConfig</span><span class="p">(</span><span class="n">PretrainedConfig</span><span class="p">):</span>
+    <span class="n">model_type</span> <span class="o">=</span> <span class="s2">&quot;gru&quot;</span>
+
+<div class="viewcode-block" id="GRUConfig.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUConfig.html#pyvene.models.gru.modelings_gru.GRUConfig.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">include_emb</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">vocab_size</span><span class="o">=</span><span class="mi">50_257</span><span class="p">,</span>
+        <span class="n">max_position_embeddings</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
+        <span class="n">n_layer</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">h_dim</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
+        <span class="n">n_labels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">include_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">pdrop</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
+        <span class="n">problem_type</span><span class="o">=</span><span class="s2">&quot;single_label_classification&quot;</span><span class="p">,</span>
+        <span class="n">initializer_range</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">include_emb</span> <span class="o">=</span> <span class="n">include_emb</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">=</span> <span class="n">vocab_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_position_embeddings</span> <span class="o">=</span> <span class="n">max_position_embeddings</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">=</span> <span class="n">n_layer</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span> <span class="o">=</span> <span class="n">h_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">include_bias</span> <span class="o">=</span> <span class="n">include_bias</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pdrop</span> <span class="o">=</span> <span class="n">pdrop</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_labels</span> <span class="o">=</span> <span class="n">n_labels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="n">problem_type</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initializer_range</span> <span class="o">=</span> <span class="n">initializer_range</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="GRUCell">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUCell.html#pyvene.models.gru.modelings_gru.GRUCell">[docs]</a>
+<span class="k">class</span> <span class="nc">GRUCell</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<div class="viewcode-block" id="GRUCell.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUCell.html#pyvene.models.gru.modelings_gru.GRUCell.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GRUCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">h_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">include_bias</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">include_bias</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">x2h</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">include_bias</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h2h</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">include_bias</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">reset_act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update_act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">new_act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">std</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
+            <span class="n">w</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">std</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>
+
+<div class="viewcode-block" id="GRUCell.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUCell.html#pyvene.models.gru.modelings_gru.GRUCell.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">current_states</span><span class="p">,</span> <span class="n">hidden_states</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">hidden_states</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span>
+                <span class="n">current_states</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="n">current_states</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="n">x_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x2h</span><span class="p">(</span><span class="n">current_states</span><span class="p">)</span>
+        <span class="n">h_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">h2h</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+
+        <span class="n">x_reset</span><span class="p">,</span> <span class="n">x_upd</span><span class="p">,</span> <span class="n">x_new</span> <span class="o">=</span> <span class="n">x_t</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">h_reset</span><span class="p">,</span> <span class="n">h_upd</span><span class="p">,</span> <span class="n">h_new</span> <span class="o">=</span> <span class="n">h_t</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">reset_gate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_act</span><span class="p">(</span><span class="n">x_reset</span> <span class="o">+</span> <span class="n">h_reset</span><span class="p">)</span>
+        <span class="n">update_gate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_act</span><span class="p">(</span><span class="n">x_upd</span> <span class="o">+</span> <span class="n">h_upd</span><span class="p">)</span>
+        <span class="n">new_gate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_act</span><span class="p">(</span><span class="n">x_new</span> <span class="o">+</span> <span class="p">(</span><span class="n">reset_gate</span> <span class="o">*</span> <span class="n">h_new</span><span class="p">))</span>
+
+        <span class="n">hy</span> <span class="o">=</span> <span class="n">update_gate</span> <span class="o">*</span> <span class="n">hidden_states</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">update_gate</span><span class="p">)</span> <span class="o">*</span> <span class="n">new_gate</span>
+
+        <span class="k">return</span> <span class="n">hy</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="GRUModelOutput">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUModelOutput.html#pyvene.models.gru.modelings_gru.GRUModelOutput">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">GRUModelOutput</span><span class="p">(</span><span class="n">ModelOutput</span><span class="p">):</span>
+    <span class="n">last_hidden_state</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+
+<div class="viewcode-block" id="GRUPreTrainedModel">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel">[docs]</a>
+<span class="k">class</span> <span class="nc">GRUPreTrainedModel</span><span class="p">(</span><span class="n">PreTrainedModel</span><span class="p">):</span>
+<div class="viewcode-block" id="GRUPreTrainedModel.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_init_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialize the weights.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">):</span>
+            <span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">initializer_range</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">module</span><span class="o">.</span><span class="n">padding_idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">module</span><span class="o">.</span><span class="n">padding_idx</span><span class="p">]</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="GRUModel">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel">[docs]</a>
+<span class="k">class</span> <span class="nc">GRUModel</span><span class="p">(</span><span class="n">GRUPreTrainedModel</span><span class="p">):</span>
+<div class="viewcode-block" id="GRUModel.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">h_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">include_bias</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">include_bias</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">n_layer</span>
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">include_emb</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wte</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wpe</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">max_position_embeddings</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">cells</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">GRUCell</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span><span class="p">)]</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Initialize weights and apply final processing</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">post_init</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="GRUModel.get_input_embeddings">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_input_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">wte</span></div>
+
+
+<div class="viewcode-block" id="GRUModel.set_input_embeddings">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_input_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_embeddings</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">wte</span> <span class="o">=</span> <span class="n">new_embeddings</span></div>
+
+
+<div class="viewcode-block" id="GRUModel.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">position_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">inputs_embeds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">inputs_embeds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">inputs_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wte</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">position_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">position_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wpe</span><span class="p">(</span><span class="n">position_ids</span><span class="p">)</span>
+            <span class="n">inputs_embeds</span> <span class="o">+=</span> <span class="n">position_embeds</span>
+
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">max_seq_len</span> <span class="o">=</span> <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">hidden_states</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">h0</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
+                <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">device</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">h0</span> <span class="o">=</span> <span class="n">hidden_states</span>
+        <span class="n">all_layer_hidden_states</span> <span class="o">=</span> <span class="p">[</span><span class="n">h0</span><span class="p">[</span><span class="n">layer</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span><span class="p">)]</span>
+
+        <span class="n">all_hidden_states</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_seq_len</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">layer</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">current_layer_hidden_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="n">layer</span><span class="p">](</span>
+                        <span class="n">inputs_embeds</span><span class="p">[:,</span> <span class="n">t</span><span class="p">,</span> <span class="p">:],</span> <span class="n">all_layer_hidden_states</span><span class="p">[</span><span class="n">layer</span><span class="p">]</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">current_layer_hidden_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="n">layer</span><span class="p">](</span>
+                        <span class="n">all_layer_hidden_states</span><span class="p">[</span><span class="n">layer</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span>
+                        <span class="n">all_layer_hidden_states</span><span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="n">all_layer_hidden_states</span><span class="p">[</span><span class="n">layer</span><span class="p">]</span> <span class="o">=</span> <span class="n">current_layer_hidden_state</span>
+
+            <span class="n">all_hidden_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">current_layer_hidden_state</span><span class="p">)</span>
+
+        <span class="n">all_hidden_states</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">all_hidden_states</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span>
+                <span class="n">v</span>
+                <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="p">[</span><span class="n">all_hidden_states</span><span class="p">,</span> <span class="n">current_layer_hidden_state</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">GRUModelOutput</span><span class="p">(</span>
+            <span class="n">hidden_states</span><span class="o">=</span><span class="n">all_hidden_states</span><span class="p">,</span>
+            <span class="n">last_hidden_state</span><span class="o">=</span><span class="n">current_layer_hidden_state</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="GRUForClassification">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUForClassification.html#pyvene.models.gru.modelings_gru.GRUForClassification">[docs]</a>
+<span class="k">class</span> <span class="nc">GRUForClassification</span><span class="p">(</span><span class="n">GRUPreTrainedModel</span><span class="p">):</span>
+<div class="viewcode-block" id="GRUForClassification.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUForClassification.html#pyvene.models.gru.modelings_gru.GRUForClassification.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_labels</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">n_labels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gru</span> <span class="o">=</span> <span class="n">GRUModel</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">score</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_labels</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GRUForClassification.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRUForClassification.html#pyvene.models.gru.modelings_gru.GRUForClassification.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">position_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">inputs_embeds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">gru_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gru</span><span class="p">(</span>
+            <span class="n">input_ids</span><span class="p">,</span>
+            <span class="n">position_ids</span><span class="p">,</span>
+            <span class="n">inputs_embeds</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="p">,</span>
+            <span class="n">return_dict</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">gru_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">input_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">batch_size</span><span class="p">,</span> <span class="n">sequence_length</span> <span class="o">=</span> <span class="n">input_ids</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">batch_size</span><span class="p">,</span> <span class="n">sequence_length</span> <span class="o">=</span> <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">attention_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">input_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">sequence_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">sequence_lengths</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">inputs_embeds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">inputs_embeds</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+                    <span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="o">.</span><span class="n">int</span><span class="p">()</span>
+                    <span class="o">-</span> <span class="mi">1</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">sequence_lengths</span> <span class="o">=</span> <span class="n">attention_mask</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span>
+
+        <span class="n">pooled_hidden_states</span> <span class="o">=</span> <span class="n">hidden_states</span><span class="p">[</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">hidden_states</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="n">sequence_lengths</span>
+        <span class="p">]</span>
+        <span class="n">pooled_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">pooled_hidden_states</span><span class="p">)</span>
+
+        <span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_labels</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="s2">&quot;regression&quot;</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_labels</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span>
+                    <span class="n">labels</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">long</span> <span class="ow">or</span> <span class="n">labels</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int</span>
+                <span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="s2">&quot;single_label_classification&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="s2">&quot;multi_label_classification&quot;</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">==</span> <span class="s2">&quot;regression&quot;</span><span class="p">:</span>
+                <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">MSELoss</span><span class="p">()</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_labels</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">pooled_logits</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">labels</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">pooled_logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">==</span> <span class="s2">&quot;single_label_classification&quot;</span><span class="p">:</span>
+                <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+                <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span>
+                    <span class="n">pooled_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_labels</span><span class="p">),</span> <span class="n">labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">==</span> <span class="s2">&quot;multi_label_classification&quot;</span><span class="p">:</span>
+                <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
+                <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">pooled_logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">pooled_logits</span><span class="p">,)</span> <span class="o">+</span> <span class="n">gru_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+            <span class="k">return</span> <span class="p">((</span><span class="n">loss</span><span class="p">,)</span> <span class="o">+</span> <span class="n">output</span><span class="p">)</span> <span class="k">if</span> <span class="n">loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">output</span>
+
+        <span class="k">return</span> <span class="n">SequenceClassifierOutput</span><span class="p">(</span>
+            <span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span>
+            <span class="n">logits</span><span class="o">=</span><span class="n">pooled_logits</span><span class="p">,</span>
+            <span class="n">hidden_states</span><span class="o">=</span><span class="n">gru_outputs</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="GRULMHeadModel">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel">[docs]</a>
+<span class="k">class</span> <span class="nc">GRULMHeadModel</span><span class="p">(</span><span class="n">GRUPreTrainedModel</span><span class="p">):</span>
+    <span class="n">_tied_weights_keys</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;lm_head.weight&quot;</span><span class="p">]</span>
+
+<div class="viewcode-block" id="GRULMHeadModel.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_labels</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">n_labels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gru</span> <span class="o">=</span> <span class="n">GRUModel</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GRULMHeadModel.get_output_embeddings">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_output_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span></div>
+
+
+    <span class="k">def</span> <span class="nf">set_output_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_embeddings</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span> <span class="o">=</span> <span class="n">new_embeddings</span>
+
+<div class="viewcode-block" id="GRULMHeadModel.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">position_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">inputs_embeds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">gru_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gru</span><span class="p">(</span>
+            <span class="n">input_ids</span><span class="p">,</span>
+            <span class="n">position_ids</span><span class="p">,</span>
+            <span class="n">inputs_embeds</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="p">,</span>
+            <span class="n">return_dict</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">gru_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">lm_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lm_head</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+
+        <span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># move labels to correct device to enable model parallelism</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">lm_logits</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+            <span class="c1"># Shift so that tokens &lt; n predict n</span>
+            <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">lm_logits</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+            <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+            <span class="c1"># Flatten the tokens</span>
+            <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span>
+                <span class="n">shift_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">shift_logits</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)),</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">lm_logits</span><span class="p">,)</span> <span class="o">+</span> <span class="n">gru_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+            <span class="k">return</span> <span class="p">((</span><span class="n">loss</span><span class="p">,)</span> <span class="o">+</span> <span class="n">output</span><span class="p">)</span> <span class="k">if</span> <span class="n">loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">output</span>
+
+        <span class="k">return</span> <span class="n">CausalLMOutput</span><span class="p">(</span>
+            <span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span>
+            <span class="n">logits</span><span class="o">=</span><span class="n">lm_logits</span><span class="p">,</span>
+            <span class="n">hidden_states</span><span class="o">=</span><span class="n">gru_outputs</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span>
+        <span class="p">)</span></div>
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+  <h1>Source code for pyvene.models.gru.modelings_intervenable_gru</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="sd">&quot;&quot;&quot;gru base model&quot;&quot;&quot;</span>
+<span class="n">gru_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;cell_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;reset_gate_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].reset_act&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;update_gate_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].update_act&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;new_gate_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].new_act&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;reset_gate_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].reset_act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;update_gate_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].update_act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;new_gate_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].new_act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].x2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].h2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;reset_x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].x2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
+    <span class="s2">&quot;update_x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].x2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="s2">&quot;new_x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].x2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
+    <span class="s2">&quot;reset_h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].h2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
+    <span class="s2">&quot;update_h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].h2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="s2">&quot;new_h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">].h2h&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_three</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
+    <span class="s2">&quot;cell_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;cells[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+<span class="p">}</span>
+
+
+<span class="n">gru_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;cell_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;reset_gate_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;update_gate_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;new_gate_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;reset_gate_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;update_gate_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;new_gate_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim*3&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim*3&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;reset_x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;update_x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;new_x2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;reset_h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;update_h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;new_h2h_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;cell_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;mlp model with classification head&quot;&quot;&quot;</span>
+<span class="n">gru_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gru_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gru_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;gru.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+<span class="n">gru_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gru_type_to_dimension_mapping</span>
+
+
+<span class="sd">&quot;&quot;&quot;mlp model with lm head&quot;&quot;&quot;</span>
+<span class="n">gru_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gru_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">gru_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;gru.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="n">gru_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">gru_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_gru">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_intervenable_gru.create_gru.html#pyvene.models.gru.modelings_intervenable_gru.create_gru">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gru</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GRU model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+    <span class="kn">from</span> <span class="nn">models.gru.modelings_gru</span> <span class="kn">import</span> <span class="n">GRUModel</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">tokenizer_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">tokenizer_name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">mlp</span> <span class="o">=</span> <span class="n">GRUModel</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">mlp</span></div>
+
+
+
+<div class="viewcode-block" id="create_gru_lm">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_intervenable_gru.create_gru_lm.html#pyvene.models.gru.modelings_intervenable_gru.create_gru_lm">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gru_lm</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GRU model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+    <span class="kn">from</span> <span class="nn">models.gru.modelings_gru</span> <span class="kn">import</span> <span class="n">GRULMHeadModel</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">tokenizer_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">tokenizer_name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">mlp</span> <span class="o">=</span> <span class="n">GRULMHeadModel</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">mlp</span></div>
+
+
+
+<div class="viewcode-block" id="create_gru_classifier">
+<a class="viewcode-back" href="../../../../api/pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier.html#pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier">[docs]</a>
+<span class="k">def</span> <span class="nf">create_gru_classifier</span><span class="p">(</span>
+    <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a GRU model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+    <span class="kn">from</span> <span class="nn">pyvene.models.gru.modelings_gru</span> <span class="kn">import</span> <span class="n">GRUForClassification</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">tokenizer_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">tokenizer_name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">mlp</span> <span class="o">=</span> <span class="n">GRUForClassification</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">mlp</span></div>
+
+</pre></div>
+
+                </article>
+              
+
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+              
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diff --git a/_modules/pyvene/models/intervenable_base.html b/_modules/pyvene/models/intervenable_base.html
new file mode 100644
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+  <h1>Source code for pyvene.models.intervenable_base</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">logging</span><span class="o">,</span> <span class="nn">torch</span><span class="o">,</span> <span class="nn">types</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
+
+<span class="kn">from</span> <span class="nn">.constants</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.basic_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.modeling_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.intervention_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.interventions</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.configuration_intervenable_model</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">.interventions</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">TrainableIntervention</span><span class="p">,</span>
+    <span class="n">SkipIntervention</span><span class="p">,</span>
+    <span class="n">CollectIntervention</span><span class="p">,</span>
+    <span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">InterventionOutput</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">get_linear_schedule_with_warmup</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">ModelOutput</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span><span class="p">,</span> <span class="n">trange</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">nnsight</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;nnsight is not detected. Please install via &#39;pip install nnsight&#39; for nnsight backend.&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="IntervenableModelOutput">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModelOutput.html#pyvene.models.intervenable_base.IntervenableModelOutput">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">IntervenableModelOutput</span><span class="p">(</span><span class="n">ModelOutput</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Output of the IntervenableModel, including original outputs, intervened outputs, and collected activations.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">original_outputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">intervened_outputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">collected_activations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+
+<div class="viewcode-block" id="BaseModel">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel">[docs]</a>
+<span class="k">class</span> <span class="nc">BaseModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Base model class for sharing static vars and methods.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="BaseModel.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+                <span class="n">representations</span> <span class="o">=</span> <span class="n">config</span>
+            <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">mode</span>
+        <span class="n">intervention_type</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">intervention_types</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span> <span class="o">=</span> <span class="n">is_stateless</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">))</span> <span class="c1"># backfill</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;use_fast&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;use_fast&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">as_adaptor</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;as_adaptor&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;as_adaptor&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="kc">False</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_has_grad</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Detected use_fast=True means the intervention location &quot;</span>
+                <span class="s2">&quot;will be static within a batch.</span><span class="se">\n\n</span><span class="s2">In case multiple &quot;</span>
+                <span class="s2">&quot;location tags are passed only the first one will &quot;</span>
+                <span class="s2">&quot;be considered&quot;</span>
+            <span class="p">)</span>
+        <span class="c1"># each representation can get a different intervention type</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">intervention_type</span><span class="p">)</span> <span class="o">==</span> <span class="nb">list</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervention_type</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
+                <span class="n">config</span><span class="o">.</span><span class="n">representations</span>
+            <span class="p">)</span>
+
+        <span class="c1">###</span>
+        <span class="c1"># We instantiate intervention_layers at locations.</span>
+        <span class="c1"># Note that the layer name mentioned in the config is</span>
+        <span class="c1"># abstract. Not the actual module name of the model.</span>
+        <span class="c1">#</span>
+        <span class="c1"># This script will automatically convert abstract</span>
+        <span class="c1"># name into module name if the model type is supported.</span>
+        <span class="c1">#</span>
+        <span class="c1"># To support a new model type, you need to provide a</span>
+        <span class="c1"># mapping between supported abstract type and module name.</span>
+        <span class="c1">###</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">representations</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleDict</span><span class="p">({})</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_hooks</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_key_collision_counter</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="c1"># Flags and counters below are for interventions in the model.generate</span>
+        <span class="c1"># call. We can intervene on the prompt tokens only, on each generated</span>
+        <span class="c1"># token, or on a combination of both.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervene_on_prompt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_key_getter_call_counter</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_key_setter_call_counter</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_pointers</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># hooks are stateful internally, meaning that it&#39;s aware of how many times</span>
+        <span class="c1"># it is called during the execution.</span>
+        <span class="c1"># TODO: this could be merged with call counter above later.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># We want to associate interventions with a group to do group-wise interventions.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">_any_group_key</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="n">_original_key_order</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">representation</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
+            <span class="n">config</span><span class="o">.</span><span class="n">representations</span>
+        <span class="p">):</span>
+            <span class="n">_key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_representation_key</span><span class="p">(</span><span class="n">representation</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Intervention key: </span><span class="si">{</span><span class="n">_key</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">representation</span><span class="o">.</span><span class="n">intervention</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">intervention</span> <span class="o">=</span> <span class="n">representation</span><span class="o">.</span><span class="n">intervention</span>
+                <span class="n">intervention</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">intervention_function</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">intervention_type</span>
+                    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">intervention_type</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">list</span>
+                    <span class="k">else</span> <span class="n">intervention_type</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="p">)</span>
+                <span class="n">all_metadata</span> <span class="o">=</span> <span class="n">representation</span><span class="o">.</span><span class="n">_asdict</span><span class="p">()</span>
+                <span class="n">component_dim</span> <span class="o">=</span> <span class="n">get_dimension_by_component</span><span class="p">(</span>
+                    <span class="n">get_internal_model_type</span><span class="p">(</span><span class="n">model</span><span class="p">),</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="p">,</span> 
+                    <span class="n">representation</span><span class="o">.</span><span class="n">component</span>
+                <span class="p">)</span>
+                <span class="k">if</span> <span class="n">component_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">component_dim</span> <span class="o">*=</span> <span class="nb">int</span><span class="p">(</span><span class="n">representation</span><span class="o">.</span><span class="n">max_number_of_units</span><span class="p">)</span>
+                <span class="n">all_metadata</span><span class="p">[</span><span class="s2">&quot;embed_dim&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">component_dim</span>
+                <span class="n">all_metadata</span><span class="p">[</span><span class="s2">&quot;use_fast&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span>
+                <span class="n">intervention</span> <span class="o">=</span> <span class="n">intervention_function</span><span class="p">(</span>
+                    <span class="o">**</span><span class="n">all_metadata</span>
+                <span class="p">)</span>
+                
+            <span class="k">if</span> <span class="n">representation</span><span class="o">.</span><span class="n">intervention_link_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_pointers</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span>
+                    <span class="n">_key</span>
+                <span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;link#</span><span class="si">{</span><span class="n">representation</span><span class="o">.</span><span class="n">intervention_link_key</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="n">intervention</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_pointers</span><span class="p">[</span>
+                    <span class="n">representation</span><span class="o">.</span><span class="n">intervention_link_key</span>
+                <span class="p">]</span>
+            <span class="k">elif</span> <span class="n">representation</span><span class="o">.</span><span class="n">intervention_link_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_pointers</span><span class="p">[</span>
+                    <span class="n">representation</span><span class="o">.</span><span class="n">intervention_link_key</span>
+                <span class="p">]</span> <span class="o">=</span> <span class="n">intervention</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span>
+                    <span class="n">_key</span>
+                <span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;link#</span><span class="si">{</span><span class="n">representation</span><span class="o">.</span><span class="n">intervention_link_key</span><span class="si">}</span><span class="s2">&quot;</span>
+                    
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                <span class="n">intervention</span><span class="p">,</span>
+                <span class="n">CollectIntervention</span>
+            <span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span> <span class="o">=</span> <span class="kc">True</span>
+            
+            <span class="n">module_hook</span> <span class="o">=</span> <span class="n">get_module_hook</span><span class="p">(</span>
+                <span class="n">model</span><span class="p">,</span> <span class="n">representation</span><span class="p">,</span> <span class="n">backend</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">representation</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">types</span><span class="o">.</span><span class="n">FunctionType</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">LambdaIntervention</span><span class="p">(</span><span class="n">intervention</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">intervention</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">intervention_hooks</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">module_hook</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_key_getter_call_counter</span><span class="p">[</span>
+                <span class="n">_key</span>
+            <span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># we memo how many the hook is called,</span>
+            <span class="c1"># usually, it&#39;s a one time call per</span>
+            <span class="c1"># hook unless model generates.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_key_setter_call_counter</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">InterventionState</span><span class="p">(</span><span class="n">_key</span><span class="p">)</span>
+            <span class="n">_original_key_order</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_key</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">representation</span><span class="o">.</span><span class="n">group_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">_any_group_key</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;The key is provided in the config. &quot;</span>
+                <span class="s2">&quot;Assuming this is loaded from a pretrained module.&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="ow">or</span> <span class="s2">&quot;intervenables_sort_fn&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span>
+        <span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="o">=</span> <span class="n">_original_key_order</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># the key order is independent of group, it is used to read out intervention locations.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;intervenables_sort_fn&quot;</span><span class="p">](</span>
+                <span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span>
+            <span class="p">)</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        We later use _intervention_group to run actual interventions.</span>
+<span class="sd">        The order the group by group; and there should not be dependency</span>
+<span class="sd">        between groups.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">_any_group_key</span><span class="p">:</span>
+            <span class="c1"># In case they are grouped, we would expect the execution order is given</span>
+            <span class="c1"># by the source inputs.</span>
+            <span class="n">_validate_group_keys</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">:</span>
+                <span class="n">representation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="n">_key</span><span class="p">]</span>
+                <span class="k">assert</span> <span class="n">representation</span><span class="o">.</span><span class="n">group_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                <span class="k">if</span> <span class="n">representation</span><span class="o">.</span><span class="n">group_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">[</span><span class="n">representation</span><span class="o">.</span><span class="n">group_key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_key</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">[</span><span class="n">representation</span><span class="o">.</span><span class="n">group_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">_key</span><span class="p">]</span>
+                <span class="n">_validate_group_keys</span> <span class="o">+=</span> <span class="p">[</span><span class="n">representation</span><span class="o">.</span><span class="n">group_key</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">_validate_group_keys</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">_validate_group_keys</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">_validate_group_keys</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;This is not a valid group key order: </span><span class="si">{</span><span class="n">_validate_group_keys</span><span class="si">}</span><span class="s2">&quot;</span> 
+                    <span class="p">)</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;Must be ascending order. &quot;</span>
+                        <span class="s2">&quot;Interventions would be performed in order within group as well&quot;</span>
+                    <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># assign each key to an unique group based on topological order</span>
+            <span class="n">_group_key_inc</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">[</span><span class="n">_group_key_inc</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">_key</span><span class="p">]</span>
+                <span class="n">_group_key_inc</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="c1"># sort group key with ascending order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
+
+        <span class="c1"># cached swap-in activations</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="c1"># cached swapped activations (hot)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">full_intervention_outputs</span> <span class="o">=</span> <span class="p">[]</span>
+        
+        <span class="c1"># temp fields should not be accessed outside</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Activations in the future list is ALWAYS causally before</span>
+<span class="sd">        the vanilla activation list. This field becomes crucial</span>
+<span class="sd">        if we intervene at the same place multiple times.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_config</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="n">get_internal_model_type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trainable_model_parameters</span> <span class="o">=</span> <span class="p">{}</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Print out basic info about this intervenable instance</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">attr_dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;model_type&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="p">,</span>
+            <span class="s2">&quot;intervention_types&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_types</span><span class="p">,</span>
+            <span class="s2">&quot;alignabls&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">,</span>
+            <span class="s2">&quot;mode&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span>
+        <span class="p">}</span>
+        <span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">attr_dict</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_representation_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">representation</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Provide unique key for each intervention</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="n">representation</span><span class="o">.</span><span class="n">layer</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="n">representation</span><span class="o">.</span><span class="n">component</span>
+        <span class="n">u</span> <span class="o">=</span> <span class="n">representation</span><span class="o">.</span><span class="n">unit</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="n">representation</span><span class="o">.</span><span class="n">max_number_of_units</span>
+        <span class="n">_u</span> <span class="o">=</span> <span class="n">u</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span> <span class="c1"># this will need internal functions to be changed as well.</span>
+        <span class="k">if</span> <span class="s2">&quot;.&quot;</span> <span class="ow">in</span> <span class="n">c</span><span class="p">:</span>
+            <span class="n">_c</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span>
+            <span class="c1"># string access for sure</span>
+            <span class="n">key_proposal</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;comp_</span><span class="si">{</span><span class="n">_c</span><span class="si">}</span><span class="s2">_unit_</span><span class="si">{</span><span class="n">_u</span><span class="si">}</span><span class="s2">_nunit_</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">key_proposal</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;layer_</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">_comp_</span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2">_unit_</span><span class="si">{</span><span class="n">_u</span><span class="si">}</span><span class="s2">_nunit_</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="k">if</span> <span class="n">key_proposal</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_key_collision_counter</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_key_collision_counter</span><span class="p">[</span><span class="n">key_proposal</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_key_collision_counter</span><span class="p">[</span><span class="n">key_proposal</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">key_proposal</span><span class="si">}</span><span class="s2">#</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_key_collision_counter</span><span class="p">[</span><span class="n">key_proposal</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+    
+<div class="viewcode-block" id="BaseModel.get_trainable_parameters">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_trainable_parameters">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_trainable_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return trainable params as key value pairs</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret_params</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">ret_params</span> <span class="o">+=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">parameters</span><span class="p">()]</span>
+        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
+                <span class="n">ret_params</span> <span class="o">+=</span> <span class="p">[</span><span class="n">p</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">ret_params</span></div>
+
+    
+<div class="viewcode-block" id="BaseModel.named_parameters">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.named_parameters">[docs]</a>
+    <span class="k">def</span> <span class="nf">named_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The above, but for HuggingFace.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ret_params</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">ret_params</span> <span class="o">+=</span> <span class="p">[(</span><span class="n">k</span> <span class="o">+</span> <span class="s1">&#39;.&#39;</span> <span class="o">+</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()]</span>
+        <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
+                <span class="n">ret_params</span> <span class="o">+=</span> <span class="p">[(</span><span class="s1">&#39;model.&#39;</span> <span class="o">+</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">)]</span>
+        <span class="k">return</span> <span class="n">ret_params</span></div>
+
+    
+<div class="viewcode-block" id="BaseModel.get_cached_activations">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_cached_activations">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_cached_activations</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the cached activations with keys</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.get_cached_hot_activations">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_cached_hot_activations</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return the cached hot activations with linked keys</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.set_temperature">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.set_temperature">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">temp</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set temperature if needed</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">)</span> <span class="ow">or</span> \
+                <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">SigmoidMaskIntervention</span><span class="p">):</span>
+                <span class="n">v</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.enable_model_gradients">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.enable_model_gradients">[docs]</a>
+    <span class="k">def</span> <span class="nf">enable_model_gradients</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Enable gradient in the model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Unfreeze all model weights</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
+            <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span> 
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_has_grad</span> <span class="o">=</span> <span class="kc">True</span></div>
+
+                
+<div class="viewcode-block" id="BaseModel.disable_model_gradients">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.disable_model_gradients">[docs]</a>
+    <span class="k">def</span> <span class="nf">disable_model_gradients</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Disable gradient in the model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Freeze all model weights</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
+            <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_has_grad</span> <span class="o">=</span> <span class="kc">False</span></div>
+
+            
+<div class="viewcode-block" id="BaseModel.disable_intervention_gradients">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients">[docs]</a>
+    <span class="k">def</span> <span class="nf">disable_intervention_gradients</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Disable gradient in the trainable intervention</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Freeze all intervention weights</span>
+        <span class="k">pass</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.set_device">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.set_device">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">set_model</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set device of interventions and the model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">set_model</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.get_device">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_device">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Get device of interventions and the model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">device</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.count_parameters">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.count_parameters">[docs]</a>
+    <span class="k">def</span> <span class="nf">count_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">include_model</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set device of interventions and the model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">_linked_key_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([])</span>
+        <span class="n">total_parameters</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="ow">in</span> <span class="n">_linked_key_set</span><span class="p">:</span>
+                        <span class="n">_linked_key_set</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+                        <span class="n">total_parameters</span> <span class="o">+=</span> <span class="n">count_parameters</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">total_parameters</span> <span class="o">+=</span> <span class="n">count_parameters</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">include_model</span><span class="p">:</span>
+            <span class="n">total_parameters</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span>
+                <span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">total_parameters</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.set_zero_grad">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.set_zero_grad">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set device of interventions and the model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">v</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="BaseModel.zero_grad">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.zero_grad">[docs]</a>
+    <span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The above, but for HuggingFace.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">v</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_input_validation</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+        <span class="n">activations_sources</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fail fast input validation&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;parallel&quot;</span> <span class="ow">and</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="s2">&quot;sources-&gt;base&quot;</span> <span class="ow">in</span> <span class="n">unit_locations</span> <span class="ow">or</span> <span class="s2">&quot;base&quot;</span> <span class="ow">in</span> <span class="n">unit_locations</span>
+        <span class="k">elif</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;serial&quot;</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="s2">&quot;sources-&gt;base&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">unit_locations</span>
+        
+        <span class="c1"># sources may contain None, but length should match</span>
+        <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">sources</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Source length </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span><span class="si">}</span><span class="s2"> is not &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;equal to intervention length </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">activations_sources</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Source activations length </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">activations_sources</span><span class="p">)</span><span class="si">}</span><span class="s2"> is not &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;equal to intervention length </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="p">)</span>
+
+        <span class="c1"># if it is stateful models, the passed in activations need to have states</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span> <span class="ow">and</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">activations_sources</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="p">(</span>
+                    <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span>
+                    <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">)</span>
+                    <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span> <span class="o">!=</span> <span class="kc">True</span>
+                <span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;Stateful models need nested activations. See our documentions.&quot;</span>
+                    <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_gather_intervention_output</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">representations_key</span><span class="p">,</span> <span class="n">unit_locations</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Gather intervening activations from the output based on indices</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">representations_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span>
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span><span class="n">representations_key</span><span class="p">]</span>
+            <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span>
+        <span class="p">):</span>
+            <span class="c1"># hot gather</span>
+            <span class="c1"># clone is needed here by acting as a different module</span>
+            <span class="c1"># to avoid gradient conflict.</span>
+            <span class="c1">#</span>
+            <span class="c1"># enable the following line when an error is hit</span>
+            <span class="c1"># torch.autograd.set_detect_anomaly(True)</span>
+            <span class="n">selected_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span><span class="p">[</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span><span class="n">representations_key</span><span class="p">]</span>
+            <span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># data structure casting</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+                <span class="n">original_output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="n">original_output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">original_output</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+            <span class="c1"># for non-sequence models, there is no concept of</span>
+            <span class="c1"># unit location anyway.</span>
+            <span class="k">if</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">original_output</span>
+            <span class="c1"># gather subcomponent</span>
+            <span class="n">original_output</span> <span class="o">=</span> <span class="n">output_to_subcomponent</span><span class="p">(</span>
+                <span class="n">original_output</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span>
+                    <span class="n">representations_key</span>
+                <span class="p">]</span><span class="o">.</span><span class="n">component</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">model_config</span><span class="p">,</span>
+            <span class="p">)</span>
+
+            <span class="c1"># gather based on intervention locations</span>
+            <span class="n">selected_output</span> <span class="o">=</span> <span class="n">gather_neurons</span><span class="p">(</span>
+                <span class="n">original_output</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span>
+                    <span class="n">representations_key</span>
+                <span class="p">]</span><span class="o">.</span><span class="n">unit</span><span class="p">,</span>
+                <span class="n">unit_locations</span><span class="p">,</span>
+                <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_device</span><span class="p">()</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">selected_output</span>
+
+    <span class="k">def</span> <span class="nf">_scatter_intervention_output</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">output</span><span class="p">,</span>
+        <span class="n">intervened_representation</span><span class="p">,</span>
+        <span class="n">representations_key</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Scatter in the intervened activations in the output</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># data structure casting</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+            <span class="n">original_output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="n">original_output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">original_output</span> <span class="o">=</span> <span class="n">output</span>
+        <span class="c1"># for non-sequence-based models, we simply replace</span>
+        <span class="c1"># all the activations.</span>
+        <span class="k">if</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">original_output</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">intervened_representation</span><span class="p">[:]</span>
+            <span class="k">return</span> <span class="n">original_output</span>
+        
+        <span class="n">component</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span>
+            <span class="n">representations_key</span>
+        <span class="p">]</span><span class="o">.</span><span class="n">component</span>
+        <span class="n">unit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span>
+            <span class="n">representations_key</span>
+        <span class="p">]</span><span class="o">.</span><span class="n">unit</span>
+        
+        <span class="c1"># scatter in-place</span>
+        <span class="n">_</span> <span class="o">=</span> <span class="n">scatter_neurons</span><span class="p">(</span>
+            <span class="n">original_output</span><span class="p">,</span>
+            <span class="n">intervened_representation</span><span class="p">,</span>
+            <span class="n">component</span><span class="p">,</span>
+            <span class="n">unit</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_config</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+            <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_device</span><span class="p">()</span>
+        <span class="p">)</span>
+        
+        <span class="k">return</span> <span class="n">original_output</span>
+    
+    <span class="k">def</span> <span class="nf">_broadcast_unit_locations</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="p">,</span>
+        <span class="n">unit_locations</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># this means, we don&#39;t filter based on location at all.</span>
+            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">))}</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;parallel&quot;</span><span class="p">:</span>
+            <span class="n">_unit_locations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unit_locations</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="c1"># special broadcast for base-only interventions</span>
+                <span class="n">is_base_only</span> <span class="o">=</span> <span class="kc">False</span>
+                <span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="s2">&quot;base&quot;</span><span class="p">:</span>
+                    <span class="n">is_base_only</span> <span class="o">=</span> <span class="kc">True</span>
+                    <span class="n">k</span> <span class="o">=</span> <span class="s2">&quot;sources-&gt;base&quot;</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">is_base_only</span><span class="p">:</span>
+                        <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[[</span><span class="n">v</span><span class="p">]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                            <span class="p">[[[</span><span class="n">v</span><span class="p">]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> 
+                            <span class="p">[[[</span><span class="n">v</span><span class="p">]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+                        <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> 
+                        <span class="p">[[[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+                    <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">))</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">([[[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> <span class="kc">None</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="n">get_list_depth</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># [0,1,2,3] -&gt; [[[0,1,2,3]]], ...</span>
+                    <span class="k">if</span> <span class="n">is_base_only</span><span class="p">:</span>
+                        <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="n">v</span><span class="p">]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                            <span class="p">[[</span><span class="n">v</span><span class="p">]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> 
+                            <span class="p">[[</span><span class="n">v</span><span class="p">]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+                        <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">is_base_only</span><span class="p">:</span>
+                        <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;serial&quot;</span><span class="p">:</span>
+            <span class="n">_unit_locations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unit_locations</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="n">v</span><span class="p">]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> 
+                        <span class="p">[[[</span><span class="n">v</span><span class="p">]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+                    <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> 
+                        <span class="p">[[[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+                    <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">))</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">([[[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> <span class="kc">None</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="n">get_list_depth</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># [0,1,2,3] -&gt; [[[0,1,2,3]]], ...</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="p">[[</span><span class="n">v</span><span class="p">]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">),</span> 
+                        <span class="p">[[</span><span class="n">v</span><span class="p">]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+                    <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_unit_locations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The mode </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="si">}</span><span class="s2"> is not supported.&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">_unit_locations</span>
+    
+    <span class="k">def</span> <span class="nf">_broadcast_source_representations</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">source_representations</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Broadcast simple inputs to a dict&quot;&quot;&quot;</span>
+        <span class="n">_source_representations</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_representations</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">or</span> <span class="n">source_representations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># pass to broadcast for advance usage</span>
+            <span class="n">_source_representations</span> <span class="o">=</span> <span class="n">source_representations</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_representations</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">):</span>
+                <span class="n">_source_representations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">source_representations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_representations</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">:</span>
+                <span class="n">_source_representations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">source_representations</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Accept input type for source_representations is [Dict, List, torch.Tensor]&quot;</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="n">_source_representations</span>
+            
+    <span class="k">def</span> <span class="nf">_broadcast_sources</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">sources</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Broadcast simple inputs to a dict&quot;&quot;&quot;</span>
+        <span class="n">_sources</span> <span class="o">=</span> <span class="n">sources</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+                <span class="n">_sources</span> <span class="o">+=</span> <span class="p">[</span><span class="n">sources</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">_sources</span> <span class="o">=</span> <span class="n">sources</span>
+        <span class="k">return</span> <span class="n">_sources</span>
+    
+    <span class="k">def</span> <span class="nf">_broadcast_subspaces</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="p">,</span>
+        <span class="n">subspaces</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Broadcast simple subspaces input&quot;&quot;&quot;</span>
+        <span class="n">_subspaces</span> <span class="o">=</span> <span class="n">subspaces</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subspaces</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+            <span class="n">_subspaces</span> <span class="o">=</span> <span class="p">[[[</span><span class="n">subspaces</span><span class="p">]]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+            
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subspaces</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subspaces</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
+            <span class="n">_subspaces</span> <span class="o">=</span> <span class="p">[[</span><span class="n">subspaces</span><span class="p">]</span><span class="o">*</span><span class="n">batch_size</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># TODO: subspaces is easier to add more broadcast majic.</span>
+            <span class="k">pass</span>
+        <span class="k">return</span> <span class="n">_subspaces</span>
+    
+<div class="viewcode-block" id="BaseModel.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Please Implement this method&quot;</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Please Implement this method&quot;</span><span class="p">)</span></div>
+
+        
+
+<div class="viewcode-block" id="IntervenableNdifModel">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel">[docs]</a>
+<span class="k">class</span> <span class="nc">IntervenableNdifModel</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Intervenable model via ndif backend.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">BACKEND</span> <span class="o">=</span> <span class="s2">&quot;ndif&quot;</span>
+    
+<div class="viewcode-block" id="IntervenableNdifModel.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="s2">&quot;ndif&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="c1"># this is not used for now.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">remote</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;remote&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;remote&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="kc">False</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;We currently have very limited intervention support for ndif backend.&quot;</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">save_directory</span><span class="p">,</span> <span class="n">save_to_hf_hub</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">hf_repo_name</span><span class="o">=</span><span class="s2">&quot;my-awesome-model&quot;</span>
+    <span class="p">):</span>
+        <span class="k">pass</span>
+
+<div class="viewcode-block" id="IntervenableNdifModel.load">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel.load">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">load_directory</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">local_directory</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">from_huggingface_hub</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Load interventions from disk or hub</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_cleanup_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skip_activation_gc</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Clean up all old in memo states of interventions</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">skip_activation_gc</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">def</span> <span class="nf">_tidy_stateful_activations</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">_need_tidify</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_reconcile_stateful_cached_activations</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">key</span><span class="p">,</span>
+        <span class="n">intervening_activations</span><span class="p">,</span>
+        <span class="n">intervening_unit_locations</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Based on the key, we consolidate activations based on key&#39;s state&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">None</span>
+
+        <span class="n">cached_activations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+            <span class="c1"># nothing to reconcile if stateless</span>
+            <span class="k">return</span> <span class="n">cached_activations</span>
+
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Activation reconcile is not implemented for ndif backend&quot;</span><span class="p">)</span>
+    
+    <span class="k">def</span> <span class="nf">_intervention_getter</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">keys</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a list of getter handlers that will fetch activations</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">handlers</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">key_i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">keys</span><span class="p">):</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="p">(</span><span class="n">module_hook</span><span class="p">,</span> <span class="n">hook_type</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_hooks</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Generation is not implemented for ndif backend&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">hook_type</span> <span class="o">==</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">:</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">module_hook</span><span class="o">.</span><span class="n">input</span>
+            <span class="k">elif</span> <span class="n">hook_type</span> <span class="o">==</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">:</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">module_hook</span><span class="o">.</span><span class="n">output</span>
+
+            <span class="c1"># TODO: this could be faulty by assuming the types.</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">dtype</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">SkipIntervention</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Skip intervention is not implemented for ndif backend&quot;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">selected_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gather_intervention_output</span><span class="p">(</span>
+                    <span class="n">output</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                <span class="p">)</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+                    <span class="c1"># WARNING: might be worth to check the below assertion at runtime,</span>
+                    <span class="c1"># but commenting it out for now just to avoid confusion.</span>
+                    <span class="c1"># assert key not in self.activations</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">selected_output</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Stateful models are not supported for ndif backend&quot;</span><span class="p">)</span>
+
+                <span class="c1"># set version for stateful models</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">inc_getter_version</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_intervention_setter</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">keys</span><span class="p">,</span>
+        <span class="n">unit_locations_base</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">HandlerList</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a list of setter tracer that will set activations</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_tidy_stateful_activations</span><span class="p">()</span>
+        
+        <span class="k">for</span> <span class="n">key_i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">keys</span><span class="p">):</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="p">(</span><span class="n">module_hook</span><span class="p">,</span> <span class="n">hook_type</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_hooks</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+                    <span class="mi">0</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">unit_locations_base</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="p">]</span>  <span class="c1"># batch_size</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Generation is not implemented for ndif backend&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">hook_type</span> <span class="o">==</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">:</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">module_hook</span><span class="o">.</span><span class="n">input</span>
+            <span class="k">elif</span> <span class="n">hook_type</span> <span class="o">==</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">:</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">module_hook</span><span class="o">.</span><span class="n">output</span>
+
+            <span class="c1"># TODO: this could be faulty by assuming the types.</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">dtype</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">selected_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gather_intervention_output</span><span class="p">(</span>
+                <span class="n">output</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+            <span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Stateful models are not supported for ndif backend&quot;</span><span class="p">)</span>
+
+            <span class="c1"># intervention in-place</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                <span class="n">intervention</span><span class="p">,</span>
+                <span class="n">CollectIntervention</span>
+            <span class="p">):</span>
+                <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                    <span class="n">selected_output</span><span class="p">,</span>
+                    <span class="kc">None</span><span class="p">,</span>
+                    <span class="n">intervention</span><span class="p">,</span>
+                    <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="c1"># fail if this is not a fresh collect</span>
+                <span class="k">assert</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">intervened_representation</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
+                <span class="c1"># no-op to the output</span>
+                
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">LambdaIntervention</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">intervention</span><span class="o">.</span><span class="n">is_source_constant</span><span class="p">:</span>
+                        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                            <span class="n">selected_output</span><span class="p">,</span>
+                            <span class="kc">None</span><span class="p">,</span>
+                            <span class="n">intervention</span><span class="p">,</span>
+                            <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                        <span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                            <span class="n">selected_output</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_reconcile_stateful_cached_activations</span><span class="p">(</span>
+                                <span class="n">key</span><span class="p">,</span>
+                                <span class="n">selected_output</span><span class="p">,</span>
+                                <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">],</span>
+                            <span class="p">),</span>
+                            <span class="n">intervention</span><span class="p">,</span>
+                            <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                        <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># highly unlikely it&#39;s a primitive intervention type</span>
+                    <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                        <span class="n">selected_output</span><span class="p">,</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_reconcile_stateful_cached_activations</span><span class="p">(</span>
+                            <span class="n">key</span><span class="p">,</span>
+                            <span class="n">selected_output</span><span class="p">,</span>
+                            <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">],</span>
+                        <span class="p">),</span>
+                        <span class="n">intervention</span><span class="p">,</span>
+                        <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="k">if</span> <span class="n">intervened_representation</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">return</span>
+
+                <span class="c1"># setter can produce hot activations for shared subspace interventions if linked</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span><span class="p">[</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                    <span class="p">]</span> <span class="o">=</span> <span class="n">intervened_representation</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+                
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+                    <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scatter_intervention_output</span><span class="p">(</span>
+                        <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">intervened_representation</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scatter_intervention_output</span><span class="p">(</span>
+                        <span class="n">output</span><span class="p">,</span> <span class="n">intervened_representation</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                    <span class="p">)</span>
+                        
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">inc_setter_version</span><span class="p">()</span>
+    
+    <span class="k">def</span> <span class="nf">_sync_forward_with_parallel_intervention</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+        <span class="n">activations_sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="c1"># torch.autograd.set_detect_anomaly(True)</span>
+        <span class="n">all_set_handlers</span> <span class="o">=</span> <span class="n">HandlerList</span><span class="p">([])</span>
+        <span class="n">unit_locations_sources</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">unit_locations_base</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># for each source, we hook in getters to cache activations</span>
+        <span class="c1"># at each aligning representations</span>
+        <span class="k">if</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">group_id</span><span class="p">,</span> <span class="n">keys</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">sources</span><span class="p">[</span><span class="n">group_id</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">continue</span>  <span class="c1"># smart jump for advance usage only</span>
+
+                <span class="c1"># meta tracer to get activations for all components</span>
+                <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">sources</span><span class="p">[</span><span class="n">group_id</span><span class="p">])</span> <span class="k">as</span> <span class="n">tracer</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_getter</span><span class="p">(</span>
+                            <span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                            <span class="p">[</span>
+                                <span class="n">unit_locations_sources</span><span class="p">[</span>
+                                    <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                                <span class="p">]</span>
+                            <span class="p">],</span>
+                        <span class="p">)</span>
+                <span class="c1"># upon exist, all activations should be saved</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># simply patch in the ones passed in</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="o">=</span> <span class="n">activations_sources</span>
+            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">passed_in_key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="n">passed_in_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span>
+        
+        <span class="c1"># in parallel mode with ndif backend, we don&#39;t need to wait </span>
+        <span class="c1"># for the intervention hook, we synchronously do the interventions.</span>
+        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">as</span> <span class="n">tracer</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">group_id</span><span class="p">,</span> <span class="n">keys</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+                    <span class="c1"># skip in case smart jump</span>
+                    <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="ow">or</span> \
+                        <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">LambdaIntervention</span><span class="p">)</span> <span class="ow">or</span> \
+                        <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">is_source_constant</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_setter</span><span class="p">(</span>
+                            <span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                            <span class="p">[</span>
+                                <span class="n">unit_locations_base</span><span class="p">[</span>
+                                    <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                                <span class="p">]</span>
+                            <span class="p">],</span>
+                            <span class="c1"># assume same group targeting the same subspace</span>
+                            <span class="p">[</span>
+                                <span class="n">subspaces</span><span class="p">[</span>
+                                    <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                                <span class="p">]</span>
+                            <span class="p">]</span>
+                            <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                            <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                        <span class="p">)</span>
+            <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
+        
+        <span class="k">return</span> <span class="n">counterfactual_outputs</span>
+
+    <span class="k">def</span> <span class="nf">_sync_forward_with_serial_intervention</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+        <span class="n">activations_sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Please Implement serial intervention support for ndif&quot;</span><span class="p">)</span>
+    
+<div class="viewcode-block" id="IntervenableNdifModel.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">source_representations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_original_output</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">use_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">activations_sources</span> <span class="o">=</span> <span class="n">source_representations</span>
+        <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sources</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">sources</span><span class="p">]</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cleanup_states</span><span class="p">()</span>
+
+        <span class="c1"># if no source input or intervention, we return base</span>
+        <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span> \
+            <span class="ow">and</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># ndif backend call</span>
+            <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">base</span><span class="p">)</span> <span class="k">as</span> <span class="n">tracer</span><span class="p">:</span>
+                <span class="n">base_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
+            <span class="k">return</span> <span class="n">base_outputs</span><span class="p">,</span> <span class="kc">None</span>
+        <span class="c1"># broadcast</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_unit_locations</span><span class="p">(</span><span class="n">get_batch_size</span><span class="p">(</span><span class="n">base</span><span class="p">),</span> <span class="n">unit_locations</span><span class="p">)</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span> <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">sources</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_sources</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span>
+        <span class="n">activations_sources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_source_representations</span><span class="p">(</span><span class="n">activations_sources</span><span class="p">)</span>
+        <span class="n">subspaces</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_subspaces</span><span class="p">(</span><span class="n">get_batch_size</span><span class="p">(</span><span class="n">base</span><span class="p">),</span> <span class="n">subspaces</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_validation</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="p">,</span>
+            <span class="n">activations_sources</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+        <span class="p">)</span>
+        
+        <span class="n">base_outputs</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">output_original_output</span><span class="p">:</span>
+            <span class="c1"># returning un-intervened output with gradients with ndif backend call</span>
+            <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">base</span><span class="p">)</span> <span class="k">as</span> <span class="n">tracer</span><span class="p">:</span>
+                <span class="n">base_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
+
+        <span class="c1"># intervene the model based on ndif APIs</span>
+        <span class="k">try</span><span class="p">:</span>
+
+            <span class="c1"># run intervened forward</span>
+            <span class="n">model_kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># for training</span>
+                <span class="n">model_kwargs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">labels</span>
+            <span class="k">if</span> <span class="n">use_cache</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">&#39;use_cache&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">to_dict</span><span class="p">():</span> <span class="c1"># for transformer models</span>
+                <span class="n">model_kwargs</span><span class="p">[</span><span class="s2">&quot;use_cache&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">use_cache</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;parallel&quot;</span><span class="p">:</span>
+                <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sync_forward_with_parallel_intervention</span><span class="p">(</span>
+                    <span class="n">base</span><span class="p">,</span>
+                    <span class="n">sources</span><span class="p">,</span>
+                    <span class="n">unit_locations</span><span class="p">,</span>
+                    <span class="n">activations_sources</span><span class="p">,</span>
+                    <span class="n">subspaces</span><span class="p">,</span>
+                    <span class="o">**</span><span class="n">model_kwargs</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;serial&quot;</span><span class="p">:</span>
+                <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sync_forward_with_serial_intervention</span><span class="p">(</span>
+                    <span class="n">base</span><span class="p">,</span>
+                    <span class="n">sources</span><span class="p">,</span>
+                    <span class="n">unit_locations</span><span class="p">,</span>
+                    <span class="n">activations_sources</span><span class="p">,</span>
+                    <span class="n">subspaces</span><span class="p">,</span>
+                    <span class="o">**</span><span class="n">model_kwargs</span><span class="p">,</span>
+                <span class="p">)</span>
+            
+            <span class="n">collected_activations</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="n">CollectIntervention</span>
+                    <span class="p">):</span>
+                        <span class="n">collected_activations</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="n">e</span>
+        <span class="k">finally</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cleanup_states</span><span class="p">(</span>
+                <span class="n">skip_activation_gc</span> <span class="o">=</span> \
+                    <span class="p">(</span><span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> \
+                    <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span>
+            <span class="p">)</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">return_dict</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">IntervenableModelOutput</span><span class="p">(</span>
+                    <span class="n">original_outputs</span><span class="o">=</span><span class="n">base_outputs</span><span class="p">,</span>
+                    <span class="n">intervened_outputs</span><span class="o">=</span><span class="n">counterfactual_outputs</span><span class="p">,</span>
+                    <span class="n">collected_activations</span><span class="o">=</span><span class="n">collected_activations</span>
+                <span class="p">)</span>
+            
+            <span class="k">return</span> <span class="p">(</span><span class="n">base_outputs</span><span class="p">,</span> <span class="n">collected_activations</span><span class="p">),</span> <span class="n">counterfactual_outputs</span>
+        
+        <span class="k">if</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">IntervenableModelOutput</span><span class="p">(</span>
+                <span class="n">original_outputs</span><span class="o">=</span><span class="n">base_outputs</span><span class="p">,</span>
+                <span class="n">intervened_outputs</span><span class="o">=</span><span class="n">counterfactual_outputs</span><span class="p">,</span>
+                <span class="n">collected_activations</span><span class="o">=</span><span class="kc">None</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">base_outputs</span><span class="p">,</span> <span class="n">counterfactual_outputs</span></div>
+
+
+    <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">source_representations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">intervene_on_prompt</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_original_output</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Please Implement this method&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="IntervenableModel">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel">[docs]</a>
+<span class="k">class</span> <span class="nc">IntervenableModel</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Intervenable model via pyvene native backend (hook-based).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">BACKEND</span> <span class="o">=</span> <span class="s2">&quot;native&quot;</span>
+    
+<div class="viewcode-block" id="IntervenableModel.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="s2">&quot;native&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_reset_hook_count</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Reset the hook count before any generate call</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_key_getter_call_counter</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_key_getter_call_counter</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_key_setter_call_counter</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_key_setter_call_counter</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_remove_forward_hooks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Clean up all the remaining hooks before any call</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">remove_forward_hooks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_cleanup_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skip_activation_gc</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Clean up all old in memo states of interventions</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_remove_forward_hooks</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_hook_count</span><span class="p">()</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">skip_activation_gc</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span> <span class="o">=</span> <span class="p">{}</span>
+    
+<div class="viewcode-block" id="IntervenableModel.save">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.save">[docs]</a>
+    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">save_directory</span><span class="p">,</span> <span class="n">save_to_hf_hub</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">hf_repo_name</span><span class="o">=</span><span class="s2">&quot;my-awesome-model&quot;</span><span class="p">,</span>
+        <span class="n">include_model</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Save interventions to disk or hub</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">save_to_hf_hub</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">huggingface_hub</span> <span class="kn">import</span> <span class="n">HfApi</span>
+
+            <span class="n">api</span> <span class="o">=</span> <span class="n">HfApi</span><span class="p">()</span>
+
+        <span class="n">create_directory</span><span class="p">(</span><span class="n">save_directory</span><span class="p">)</span>
+
+        <span class="n">saving_config</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">)</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">sorted_keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span>
+            <span class="n">saving_config</span><span class="o">.</span><span class="n">model_type</span>
+        <span class="p">)</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_dimensions</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_constant_sources</span> <span class="o">=</span> <span class="p">[]</span>
+        
+        <span class="c1"># handle constant source reprs if passed in.</span>
+        <span class="n">serialized_representations</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">reprs</span> <span class="ow">in</span> <span class="n">saving_config</span><span class="o">.</span><span class="n">representations</span><span class="p">:</span>
+            <span class="n">serialized_reprs</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">reprs</span><span class="o">.</span><span class="n">_asdict</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="s2">&quot;hidden_source_representation&quot;</span><span class="p">:</span>
+                    <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="s2">&quot;source_representation&quot;</span><span class="p">:</span>
+                    <span class="c1"># hidden flag only set here</span>
+                    <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="n">serialized_reprs</span><span class="p">[</span><span class="s2">&quot;hidden_source_representation&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+                    <span class="n">serialized_reprs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">elif</span> <span class="n">k</span> <span class="o">==</span> <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span>
+                    <span class="n">serialized_reprs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">elif</span> <span class="n">k</span> <span class="o">==</span> <span class="s2">&quot;intervention&quot;</span><span class="p">:</span>
+                    <span class="n">serialized_reprs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">serialized_reprs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+            <span class="n">serialized_representations</span> <span class="o">+=</span> <span class="p">[</span>
+                <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">**</span><span class="n">serialized_reprs</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">representations</span> <span class="o">=</span> \
+            <span class="n">serialized_representations</span>
+        
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="n">v</span>
+            <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">+=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">intervention</span><span class="p">))]</span>
+            <span class="n">binary_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;intkey_</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">.bin&quot;</span>
+            <span class="c1"># save intervention binary file</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">)</span> <span class="ow">or</span> \
+                <span class="n">intervention</span><span class="o">.</span><span class="n">source_representation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># logging.info(f&quot;Saving trainable intervention to {binary_filename}.&quot;)</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
+                    <span class="n">intervention</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="n">binary_filename</span><span class="p">),</span>
+                <span class="p">)</span>
+                <span class="k">if</span> <span class="n">save_to_hf_hub</span><span class="p">:</span>
+                    <span class="c1"># push to huggingface hub</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">api</span><span class="o">.</span><span class="n">create_repo</span><span class="p">(</span><span class="n">hf_repo_name</span><span class="p">)</span>
+                    <span class="k">except</span><span class="p">:</span>
+                        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                            <span class="sa">f</span><span class="s2">&quot;Uploading: </span><span class="si">{</span><span class="n">binary_filename</span><span class="si">}</span><span class="s2">, but skipping creating the repo since &quot;</span>
+                            <span class="sa">f</span><span class="s2">&quot;either </span><span class="si">{</span><span class="n">hf_repo_name</span><span class="si">}</span><span class="s2"> exists or having authentication error.&quot;</span>
+                        <span class="p">)</span>
+                    <span class="n">api</span><span class="o">.</span><span class="n">upload_file</span><span class="p">(</span>
+                        <span class="n">path_or_fileobj</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="n">binary_filename</span><span class="p">),</span>
+                        <span class="n">path_in_repo</span><span class="o">=</span><span class="n">binary_filename</span><span class="p">,</span>
+                        <span class="n">repo_id</span><span class="o">=</span><span class="n">hf_repo_name</span><span class="p">,</span>
+                        <span class="n">repo_type</span><span class="o">=</span><span class="s2">&quot;model&quot;</span><span class="p">,</span>
+                    <span class="p">)</span>
+            <span class="k">if</span> <span class="n">intervention</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_dimensions</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_dimensions</span> <span class="o">+=</span> <span class="p">[</span><span class="n">intervention</span><span class="o">.</span><span class="n">interchange_dim</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
+            <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_constant_sources</span> <span class="o">+=</span> <span class="p">[</span><span class="n">intervention</span><span class="o">.</span><span class="n">is_source_constant</span><span class="p">]</span>
+
+        <span class="c1"># save model&#39;s trainable parameters as well</span>
+        <span class="k">if</span> <span class="n">include_model</span><span class="p">:</span>
+            <span class="n">model_state_dict</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">model_binary_filename</span> <span class="o">=</span> <span class="s2">&quot;pytorch_model.bin&quot;</span>
+            <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
+                    <span class="n">model_state_dict</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">p</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="n">model_binary_filename</span><span class="p">))</span>
+            
+        <span class="c1"># save metadata config</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">save_directory</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">save_to_hf_hub</span><span class="p">:</span>
+            <span class="c1"># push to huggingface hub</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">api</span><span class="o">.</span><span class="n">create_repo</span><span class="p">(</span><span class="n">hf_repo_name</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Uploading the config, Skipping creating the repo since &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;either </span><span class="si">{</span><span class="n">hf_repo_name</span><span class="si">}</span><span class="s2"> exists or having authentication error.&quot;</span>
+                <span class="p">)</span>
+            <span class="n">api</span><span class="o">.</span><span class="n">upload_file</span><span class="p">(</span>
+                <span class="n">path_or_fileobj</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="s2">&quot;config.json&quot;</span><span class="p">),</span>
+                <span class="n">path_in_repo</span><span class="o">=</span><span class="s2">&quot;config.json&quot;</span><span class="p">,</span>
+                <span class="n">repo_id</span><span class="o">=</span><span class="n">hf_repo_name</span><span class="p">,</span>
+                <span class="n">repo_type</span><span class="o">=</span><span class="s2">&quot;model&quot;</span><span class="p">,</span>
+            <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="IntervenableModel.load">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.load">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span>
+        <span class="n">load_directory</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">local_directory</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">from_huggingface_hub</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">include_model</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Load interventions from disk or hub</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">load_directory</span><span class="p">)</span> <span class="ow">or</span> <span class="n">from_huggingface_hub</span><span class="p">:</span>
+            <span class="n">from_huggingface_hub</span> <span class="o">=</span> <span class="kc">True</span>
+            
+            <span class="kn">from</span> <span class="nn">huggingface_hub</span> <span class="kn">import</span> <span class="n">snapshot_download</span>
+            <span class="n">load_directory</span> <span class="o">=</span> <span class="n">snapshot_download</span><span class="p">(</span>
+                <span class="n">repo_id</span><span class="o">=</span><span class="n">load_directory</span><span class="p">,</span>
+                <span class="n">local_dir</span><span class="o">=</span><span class="n">local_directory</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="c1"># load config</span>
+        <span class="n">saving_config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">load_directory</span><span class="p">)</span>
+        <span class="n">casted_intervention_types</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">type_str</span> <span class="ow">in</span> <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_types</span><span class="p">:</span>
+            <span class="n">casted_intervention_types</span> <span class="o">+=</span> <span class="p">[</span><span class="n">get_type_from_string</span><span class="p">(</span><span class="n">type_str</span><span class="p">)]</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_types</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">casted_intervention_types</span>
+        <span class="p">)</span>
+        <span class="n">casted_representations</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="p">(</span>
+            <span class="n">representation_opts</span>
+        <span class="p">)</span> <span class="ow">in</span> <span class="n">saving_config</span><span class="o">.</span><span class="n">representations</span><span class="p">:</span>
+            <span class="n">casted_representations</span> <span class="o">+=</span> <span class="p">[</span>
+                <span class="n">RepresentationConfig</span><span class="p">(</span><span class="o">*</span><span class="n">representation_opts</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="n">saving_config</span><span class="o">.</span><span class="n">representations</span> <span class="o">=</span> <span class="n">casted_representations</span>
+        <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">saving_config</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+
+        <span class="c1"># load binary files</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="n">v</span>
+            <span class="n">binary_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;intkey_</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">.bin&quot;</span>
+            <span class="n">intervention</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> \
+                <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_constant_sources</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_dimensions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># Infer interchange dimension from component name to be compatible with old versions</span>
+                <span class="n">component_name</span> <span class="o">=</span> <span class="n">saving_config</span><span class="o">.</span><span class="n">representations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">component</span>
+                <span class="k">if</span> <span class="n">component_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;head_&quot;</span><span class="p">):</span>
+                    <span class="n">dim</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">//</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_attention_heads</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">dim</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span>
+
+            <span class="n">intervention</span><span class="o">.</span><span class="n">set_interchange_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">saving_config</span><span class="o">.</span><span class="n">intervention_constant_sources</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">and</span> \
+                <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">ZeroIntervention</span><span class="p">)</span> <span class="ow">and</span> \
+                <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">SourcelessIntervention</span><span class="p">):</span>
+                <span class="c1"># logging.warn(f&quot;Loading trainable intervention from {binary_filename}.&quot;)</span>
+                <span class="n">saved_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">load_directory</span><span class="p">,</span> <span class="n">binary_filename</span><span class="p">))</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">intervention</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span>
+                        <span class="s1">&#39;source_representation&#39;</span><span class="p">,</span> <span class="n">saved_state_dict</span><span class="p">[</span><span class="s1">&#39;source_representation&#39;</span><span class="p">]</span>
+                    <span class="p">)</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="n">intervention</span><span class="o">.</span><span class="n">source_representation</span> <span class="o">=</span> <span class="n">saved_state_dict</span><span class="p">[</span><span class="s1">&#39;source_representation&#39;</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">saved_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">load_directory</span><span class="p">,</span> <span class="n">binary_filename</span><span class="p">))</span>
+                <span class="n">intervention</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">saved_state_dict</span><span class="p">)</span>
+
+        <span class="c1"># load model&#39;s trainable parameters as well</span>
+        <span class="k">if</span> <span class="n">include_model</span><span class="p">:</span>
+            <span class="n">model_binary_filename</span> <span class="o">=</span> <span class="s2">&quot;pytorch_model.bin&quot;</span>
+            <span class="n">saved_model_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">load_directory</span><span class="p">,</span> <span class="n">model_binary_filename</span><span class="p">))</span>
+            <span class="n">intervenable</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">saved_model_state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">intervenable</span></div>
+
+
+<div class="viewcode-block" id="IntervenableModel.save_intervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.save_intervention">[docs]</a>
+    <span class="k">def</span> <span class="nf">save_intervention</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">save_directory</span><span class="p">,</span> <span class="n">include_model</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instead of saving the metadata with artifacts, it only saves artifacts such as</span>
+<span class="sd">        trainable weights. This is not a static method, and returns nothing.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">create_directory</span><span class="p">(</span><span class="n">save_directory</span><span class="p">)</span>
+        
+        <span class="c1"># save binary files</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="n">v</span>
+            <span class="n">binary_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;intkey_</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">.bin&quot;</span>
+            <span class="c1"># save intervention binary file</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="n">binary_filename</span><span class="p">))</span>
+
+        <span class="c1"># save model&#39;s trainable parameters as well</span>
+        <span class="k">if</span> <span class="n">include_model</span><span class="p">:</span>
+            <span class="n">model_state_dict</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">model_binary_filename</span> <span class="o">=</span> <span class="s2">&quot;pytorch_model.bin&quot;</span>
+            <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
+                    <span class="n">model_state_dict</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">p</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="n">model_binary_filename</span><span class="p">))</span></div>
+
+    
+<div class="viewcode-block" id="IntervenableModel.load_intervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.load_intervention">[docs]</a>
+    <span class="k">def</span> <span class="nf">load_intervention</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">load_directory</span><span class="p">,</span> <span class="n">include_model</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Instead of creating an new object, this function loads existing weights onto</span>
+<span class="sd">        the current object. This is not a static method, and returns nothing.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># load binary files</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="n">v</span>
+            <span class="n">binary_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;intkey_</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">.bin&quot;</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">TrainableIntervention</span><span class="p">):</span>
+                <span class="n">saved_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">load_directory</span><span class="p">,</span> <span class="n">binary_filename</span><span class="p">))</span>
+                <span class="n">intervention</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">saved_state_dict</span><span class="p">)</span>
+
+        <span class="c1"># load model&#39;s trainable parameters as well</span>
+        <span class="k">if</span> <span class="n">include_model</span><span class="p">:</span>
+            <span class="n">model_binary_filename</span> <span class="o">=</span> <span class="s2">&quot;pytorch_model.bin&quot;</span>
+            <span class="n">saved_model_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">load_directory</span><span class="p">,</span> <span class="n">model_binary_filename</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">saved_model_state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_intervention_getter</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">keys</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">HandlerList</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a list of getter handlers that will fetch activations</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">handlers</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">key_i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">keys</span><span class="p">):</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="n">module_hook</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_hooks</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+            <span class="k">def</span> <span class="nf">hook_callback</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span><span class="p">:</span>
+                    <span class="k">pass</span>
+                    <span class="c1"># for getter, there is no restriction.</span>
+                    <span class="c1"># is_prompt = self._key_getter_call_counter[key] == 0</span>
+                    <span class="c1"># if not self._intervene_on_prompt or is_prompt:</span>
+                    <span class="c1">#     self._key_getter_call_counter[key] += 1</span>
+                    <span class="c1"># if self._intervene_on_prompt ^ is_prompt:</span>
+                    <span class="c1">#     return  # no-op</span>
+                <span class="k">if</span> <span class="n">output</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># kwargs based calls</span>
+                        <span class="c1"># PR: https://github.com/frankaging/align-transformers/issues/11</span>
+                        <span class="c1"># We cannot assume the dict only contain one element</span>
+                        <span class="n">output</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">output</span> <span class="o">=</span> <span class="n">args</span>
+
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">SkipIntervention</span><span class="p">):</span>
+                    <span class="n">selected_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gather_intervention_output</span><span class="p">(</span>
+                        <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>  <span class="c1"># this is actually the input to the module</span>
+                        <span class="n">key</span><span class="p">,</span>
+                        <span class="n">unit_locations</span><span class="p">[</span><span class="n">key_i</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">selected_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gather_intervention_output</span><span class="p">(</span>
+                        <span class="n">output</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                    <span class="p">)</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+                    <span class="c1"># WARNING: might be worth to check the below assertion at runtime,</span>
+                    <span class="c1"># but commenting it out for now just to avoid confusion.</span>
+                    <span class="c1"># assert key not in self.activations</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">selected_output</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">state_select_flag</span> <span class="o">=</span> <span class="p">[]</span>
+                    <span class="k">for</span> <span class="n">unit_location</span> <span class="ow">in</span> <span class="n">unit_locations</span><span class="p">[</span><span class="n">key_i</span><span class="p">]:</span>
+                        <span class="k">if</span> <span class="p">(</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">getter_version</span><span class="p">()</span>
+                            <span class="ow">in</span> <span class="n">unit_location</span>
+                        <span class="p">):</span>
+                            <span class="n">state_select_flag</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">True</span><span class="p">]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">state_select_flag</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span>
+                    <span class="c1"># for stateful model (e.g., gru), we save extra activations and metadata to do</span>
+                    <span class="c1"># stateful interventions.</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                        <span class="p">(</span><span class="n">selected_output</span><span class="p">,</span> <span class="n">state_select_flag</span><span class="p">)</span>
+                    <span class="p">)</span>
+                <span class="c1"># set version for stateful models</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">inc_getter_version</span><span class="p">()</span>
+
+            <span class="n">handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">module_hook</span><span class="p">(</span><span class="n">hook_callback</span><span class="p">,</span> <span class="n">with_kwargs</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">HandlerList</span><span class="p">(</span><span class="n">handlers</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_tidy_stateful_activations</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">_need_tidify</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">_need_tidify</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">break</span>
+        <span class="k">if</span> <span class="n">_need_tidify</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_tidify_activations</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
+                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)):</span>
+                    <span class="n">activations_at_t</span> <span class="o">=</span> <span class="n">v</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># a batched tensor</span>
+                    <span class="n">states_at_t</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">bool</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">activations_at_t</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+                    <span class="p">)</span>  <span class="c1"># a batched bools</span>
+                    <span class="n">selected_activations</span> <span class="o">=</span> <span class="n">activations_at_t</span><span class="p">[</span><span class="n">states_at_t</span><span class="p">]</span>
+                    <span class="n">selected_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">states_at_t</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">selected_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">selected_indices</span> <span class="o">=</span> <span class="n">selected_indices</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">activation</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+                        <span class="n">selected_indices</span><span class="p">,</span> <span class="n">selected_activations</span>
+                    <span class="p">):</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_tidify_activations</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tidify_activations</span>
+
+    <span class="k">def</span> <span class="nf">_reconcile_stateful_cached_activations</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">key</span><span class="p">,</span>
+        <span class="n">intervening_activations</span><span class="p">,</span>
+        <span class="n">intervening_unit_locations</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Based on the key, we consolidate activations based on key&#39;s state&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">None</span>
+
+        <span class="n">cached_activations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+            <span class="c1"># nothing to reconcile if stateless</span>
+            <span class="k">return</span> <span class="n">cached_activations</span>
+
+        <span class="n">state_select_flag</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">unit_location</span> <span class="ow">in</span> <span class="n">intervening_unit_locations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">setter_version</span><span class="p">()</span> <span class="ow">in</span> <span class="n">unit_location</span><span class="p">:</span>
+                <span class="n">state_select_flag</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">True</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">state_select_flag</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span>
+        <span class="n">state_select_flag</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">state_select_flag</span><span class="p">)</span><span class="o">.</span><span class="n">bool</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervening_activations</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">selected_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">state_select_flag</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">selected_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">selected_indices</span> <span class="o">=</span> <span class="n">selected_indices</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="c1"># fill activations with proposed only source activations</span>
+        <span class="n">reconciled_activations</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">select_version</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="p">):</span>
+            <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">selected_indices</span><span class="p">:</span>
+                <span class="n">reconciled_activations</span> <span class="o">+=</span> <span class="p">[</span><span class="n">cached_activations</span><span class="p">[</span><span class="n">index</span><span class="p">][</span><span class="n">select_version</span><span class="p">]]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># WARNING: put a dummy tensor, super danger here but let&#39;s trust the code for now.</span>
+                <span class="n">reconciled_activations</span> <span class="o">+=</span> <span class="p">[</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">cached_activations</span><span class="p">[</span><span class="n">index</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+                <span class="p">]</span>
+        <span class="c1"># increment pointer for those we are actually intervening</span>
+        <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">selected_indices</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">index</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="c1"># for non-intervening ones, we copy again from base</span>
+        <span class="n">reconciled_activations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">reconciled_activations</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># batched</span>
+        <span class="c1"># reconciled_activations[~state_select_flag] = intervening_activations[~state_select_flag]</span>
+
+        <span class="k">return</span> <span class="n">reconciled_activations</span>
+
+    <span class="k">def</span> <span class="nf">_intervention_setter</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">keys</span><span class="p">,</span>
+        <span class="n">unit_locations_base</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">HandlerList</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a list of setter handlers that will set activations</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_tidy_stateful_activations</span><span class="p">()</span>
+        
+        <span class="n">handlers</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">key_i</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">keys</span><span class="p">):</span>
+            <span class="n">intervention</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="n">module_hook</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_hooks</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_batched_setter_activation_select</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+                    <span class="mi">0</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">unit_locations_base</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="p">]</span>  <span class="c1"># batch_size</span>
+
+            <span class="k">def</span> <span class="nf">hook_callback</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+                <span class="c1"># if it is None, we use it as adaptor.</span>
+                <span class="k">if</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span><span class="p">:</span>
+                    <span class="n">is_prompt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_key_setter_call_counter</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervene_on_prompt</span> <span class="ow">or</span> <span class="n">is_prompt</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">_key_setter_call_counter</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervene_on_prompt</span> <span class="o">^</span> <span class="n">is_prompt</span><span class="p">:</span>
+                        <span class="k">return</span>  <span class="c1"># no-op</span>
+                <span class="k">if</span> <span class="n">output</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># kwargs based calls</span>
+                        <span class="c1"># PR: https://github.com/frankaging/align-transformers/issues/11</span>
+                        <span class="c1"># We cannot assume the dict only contain one element</span>
+                        <span class="n">output</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">output</span> <span class="o">=</span> <span class="n">args</span>
+                        
+                <span class="n">selected_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gather_intervention_output</span><span class="p">(</span>
+                    <span class="n">output</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                <span class="p">)</span>
+                <span class="c1"># TODO: need to figure out why clone is needed</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+                    <span class="n">selected_output</span> <span class="o">=</span> <span class="n">selected_output</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+                
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                    <span class="n">intervention</span><span class="p">,</span>
+                    <span class="n">CollectIntervention</span>
+                <span class="p">):</span>
+                    <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                        <span class="n">selected_output</span><span class="p">,</span>
+                        <span class="kc">None</span><span class="p">,</span>
+                        <span class="n">intervention</span><span class="p">,</span>
+                        <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="c1"># fail if this is not a fresh collect</span>
+                    <span class="k">assert</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span>
+                    
+                    <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">intervened_representation</span>
+                    <span class="c1"># no-op to the output</span>
+                    
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">LambdaIntervention</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="n">intervention</span><span class="o">.</span><span class="n">is_source_constant</span><span class="p">:</span>
+                            <span class="n">raw_intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                                <span class="n">selected_output</span><span class="p">,</span>
+                                <span class="kc">None</span><span class="p">,</span>
+                                <span class="n">intervention</span><span class="p">,</span>
+                                <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                            <span class="p">)</span>
+                            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">raw_intervened_representation</span><span class="p">,</span> <span class="n">InterventionOutput</span><span class="p">):</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">full_intervention_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">raw_intervened_representation</span><span class="p">)</span>
+                                <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">raw_intervened_representation</span><span class="o">.</span><span class="n">output</span>
+                            <span class="k">else</span><span class="p">:</span>
+                                <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">raw_intervened_representation</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                                <span class="n">selected_output</span><span class="p">,</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">_reconcile_stateful_cached_activations</span><span class="p">(</span>
+                                    <span class="n">key</span><span class="p">,</span>
+                                    <span class="n">selected_output</span><span class="p">,</span>
+                                    <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">],</span>
+                                <span class="p">),</span>
+                                <span class="n">intervention</span><span class="p">,</span>
+                                <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                            <span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="c1"># highly unlikely it&#39;s a primitive intervention type</span>
+                        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">do_intervention</span><span class="p">(</span>
+                            <span class="n">selected_output</span><span class="p">,</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_reconcile_stateful_cached_activations</span><span class="p">(</span>
+                                <span class="n">key</span><span class="p">,</span>
+                                <span class="n">selected_output</span><span class="p">,</span>
+                                <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">],</span>
+                            <span class="p">),</span>
+                            <span class="n">intervention</span><span class="p">,</span>
+                            <span class="n">subspaces</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span> <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                        <span class="p">)</span>
+                    <span class="k">if</span> <span class="n">intervened_representation</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">return</span>
+
+                    <span class="c1"># setter can produce hot activations for shared subspace interventions if linked</span>
+                    <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">hot_activations</span><span class="p">[</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_reverse_link</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                        <span class="p">]</span> <span class="o">=</span> <span class="n">intervened_representation</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+                    
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+                        <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scatter_intervention_output</span><span class="p">(</span>
+                            <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">intervened_representation</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                        <span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scatter_intervention_output</span><span class="p">(</span>
+                            <span class="n">output</span><span class="p">,</span> <span class="n">intervened_representation</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">unit_locations_base</span><span class="p">[</span><span class="n">key_i</span><span class="p">]</span>
+                        <span class="p">)</span>
+                            
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">inc_setter_version</span><span class="p">()</span>
+
+            <span class="n">handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">module_hook</span><span class="p">(</span><span class="n">hook_callback</span><span class="p">,</span> <span class="n">with_kwargs</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">HandlerList</span><span class="p">(</span><span class="n">handlers</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_output_validation</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Safe guarding the execution by checking memory states&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_stateless</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">v</span><span class="o">.</span><span class="n">getter_version</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">v</span><span class="o">.</span><span class="n">setter_version</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;For stateless model, each getter and setter &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;should be called only once: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_state</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_flatten_input_dict_as_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_dict</span><span class="p">):</span>
+        <span class="c1"># we also accept grouped sources, will batch them and provide partition info.</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_dict</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_dict</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span>
+            <span class="n">flatten_input_dict</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">input_dict</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">flatten_input_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_dict</span><span class="p">)):</span>
+                <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">input_dict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="n">flatten_input_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">flatten_input_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="c1"># flatten as one single batch</span>
+                <span class="n">flatten_input_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">flatten_input_dict</span> <span class="o">=</span> <span class="n">input_dict</span>
+        <span class="k">return</span> <span class="n">flatten_input_dict</span>
+
+    <span class="k">def</span> <span class="nf">_get_partition_size</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_dict</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_dict</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_dict</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span>
+            <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_dict</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">1</span>
+
+    <span class="k">def</span> <span class="nf">_wait_for_forward_with_parallel_intervention</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+        <span class="n">activations_sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="c1"># torch.autograd.set_detect_anomaly(True)</span>
+        <span class="n">all_set_handlers</span> <span class="o">=</span> <span class="n">HandlerList</span><span class="p">([])</span>
+        <span class="n">unit_locations_sources</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">unit_locations_base</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># for each source, we hook in getters to cache activations</span>
+        <span class="c1"># at each aligning representations</span>
+        <span class="k">if</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">group_id</span><span class="p">,</span> <span class="n">keys</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">sources</span><span class="p">[</span><span class="n">group_id</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">continue</span>  <span class="c1"># smart jump for advance usage only</span>
+                <span class="n">group_get_handlers</span> <span class="o">=</span> <span class="n">HandlerList</span><span class="p">([])</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+                    <span class="n">get_handlers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_getter</span><span class="p">(</span>
+                        <span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="p">[</span>
+                            <span class="n">unit_locations_sources</span><span class="p">[</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                            <span class="p">]</span>
+                        <span class="p">],</span>
+                    <span class="p">)</span>
+                    <span class="n">group_get_handlers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">get_handlers</span><span class="p">)</span>
+                <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">sources</span><span class="p">[</span><span class="n">group_id</span><span class="p">])</span>
+                <span class="n">group_get_handlers</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># simply patch in the ones passed in</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="o">=</span> <span class="n">activations_sources</span>
+            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">passed_in_key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="n">passed_in_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span>
+        
+        <span class="c1"># in parallel mode, we swap cached activations all into</span>
+        <span class="c1"># base at once</span>
+        <span class="k">for</span> <span class="n">group_id</span><span class="p">,</span> <span class="n">keys</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+                <span class="c1"># skip in case smart jump</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="ow">or</span> \
+                    <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">LambdaIntervention</span><span class="p">)</span> <span class="ow">or</span> \
+                    <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">is_source_constant</span><span class="p">:</span>
+                    <span class="n">set_handlers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_setter</span><span class="p">(</span>
+                        <span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="p">[</span>
+                            <span class="n">unit_locations_base</span><span class="p">[</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                            <span class="p">]</span>
+                        <span class="p">],</span>
+                        <span class="c1"># assume same group targeting the same subspace</span>
+                        <span class="p">[</span>
+                            <span class="n">subspaces</span><span class="p">[</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                            <span class="p">]</span>
+                        <span class="p">]</span>
+                        <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                        <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="c1"># for setters, we don&#39;t remove them.</span>
+                    <span class="n">all_set_handlers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">set_handlers</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">all_set_handlers</span>
+
+    <span class="k">def</span> <span class="nf">_wait_for_forward_with_serial_intervention</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">,</span>
+        <span class="n">activations_sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">all_set_handlers</span> <span class="o">=</span> <span class="n">HandlerList</span><span class="p">([])</span>
+        <span class="k">for</span> <span class="n">group_id</span><span class="p">,</span> <span class="n">keys</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">sources</span><span class="p">[</span><span class="n">group_id</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">continue</span>  <span class="c1"># smart jump for advance usage only</span>
+            <span class="k">for</span> <span class="n">key_id</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">keys</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">group_id</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">unit_locations_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;source_</span><span class="si">{</span><span class="n">group_id</span><span class="si">}</span><span class="s2">-&gt;source_</span><span class="si">{</span><span class="n">group_id</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">unit_locations_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;source_</span><span class="si">{</span><span class="n">group_id</span><span class="si">}</span><span class="s2">-&gt;base&quot;</span>
+                <span class="n">unit_locations_source</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="n">unit_locations_key</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span>
+                    <span class="n">key_id</span>
+                <span class="p">]</span>
+                <span class="k">if</span> <span class="n">unit_locations_source</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">continue</span>  <span class="c1"># smart jump for advance usage only</span>
+
+                <span class="n">unit_locations_base</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="n">unit_locations_key</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span>
+                    <span class="n">key_id</span>
+                <span class="p">]</span>
+                <span class="k">if</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="c1"># get activation from source_i</span>
+                    <span class="n">get_handlers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_getter</span><span class="p">(</span>
+                        <span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="p">[</span><span class="n">unit_locations_source</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">activations_sources</span><span class="p">[</span>
+                        <span class="n">key</span>
+                    <span class="p">]</span>
+            <span class="c1"># call once per group. each intervention is by its own group by default</span>
+            <span class="k">if</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="c1"># this is when previous setter and THEN the getter get called</span>
+                <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">sources</span><span class="p">[</span><span class="n">group_id</span><span class="p">])</span>
+                <span class="n">get_handlers</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+                <span class="c1"># remove existing setters after getting the curr intervened reprs</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_set_handlers</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">all_set_handlers</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+                    <span class="n">all_set_handlers</span> <span class="o">=</span> <span class="n">HandlerList</span><span class="p">([])</span>
+
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
+                <span class="c1"># skip in case smart jump</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span> <span class="ow">or</span> \
+                    <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">LambdaIntervention</span><span class="p">)</span> <span class="ow">or</span> \
+                    <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">is_source_constant</span><span class="p">:</span>
+                    <span class="c1"># set with intervened activation to source_i+1</span>
+                    <span class="n">set_handlers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intervention_setter</span><span class="p">(</span>
+                        <span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="p">[</span><span class="n">unit_locations_base</span><span class="p">],</span>
+                        <span class="c1"># assume the order</span>
+                        <span class="p">[</span>
+                            <span class="n">subspaces</span><span class="p">[</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                            <span class="p">]</span>
+                        <span class="p">]</span>
+                        <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                        <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="c1"># for setters, we don&#39;t remove them.</span>
+                    <span class="n">all_set_handlers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">set_handlers</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">all_set_handlers</span>
+
+<div class="viewcode-block" id="IntervenableModel.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">source_representations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_original_output</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">use_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Main forward function that serves a wrapper to</span>
+<span class="sd">        actual model forward calls. It will use forward</span>
+<span class="sd">        hooks to do interventions.</span>
+
+<span class="sd">        In essense, sources will lead to getter hooks to</span>
+<span class="sd">        get activations. We will use these activations to</span>
+<span class="sd">        intervene on our base example.</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        base:                The base example.</span>
+<span class="sd">        sources:             A list of source examples.</span>
+<span class="sd">        unit_locations:      The intervention locations.</span>
+<span class="sd">        activations_sources: A list of representations.</span>
+<span class="sd">        subspace:            Subspace interventions.</span>
+
+<span class="sd">        Return:</span>
+<span class="sd">        base_output: the non-intervened output of the base</span>
+<span class="sd">        input.</span>
+<span class="sd">        counterfactual_outputs: the intervened output of the</span>
+<span class="sd">        base input.</span>
+
+<span class="sd">        Notes:</span>
+
+<span class="sd">        1) unit_locations</span>
+<span class="sd">        unit_locations is a dict where keys are tied with</span>
+<span class="sd">        example pairs involved in one intervention as,</span>
+<span class="sd">        {</span>
+<span class="sd">            &quot;sources-&gt;base&quot; : List[]</span>
+<span class="sd">        }</span>
+
+<span class="sd">        the shape can be</span>
+
+<span class="sd">        2 * num_intervention * bs * num_max_unit</span>
+
+<span class="sd">        OR</span>
+
+<span class="sd">        2 * num_intervention * num_intervention_level * bs * num_max_unit</span>
+
+<span class="sd">        if we intervene on h.pos which is a nested intervention location.</span>
+
+<span class="sd">        2) subspaces</span>
+<span class="sd">        subspaces is a list of indices indicating which subspace will</span>
+<span class="sd">        this intervention target given an example in the batch.</span>
+
+<span class="sd">        An intervention could be initialized with subspace parition as,</span>
+<span class="sd">        [[... subspace_1 ...], [... subspace_2 ...], [rest]]</span>
+
+<span class="sd">        An intervention may be targeting a specific partition.</span>
+
+<span class="sd">        This input field should look like something like,</span>
+<span class="sd">        [</span>
+<span class="sd">            [[subspace indices], [subspace indices]], &lt;- for the first intervention</span>
+<span class="sd">            None,                                     &lt;- for the second intervention</span>
+<span class="sd">            [[subspace indices], [subspace indices]]</span>
+<span class="sd">        ]</span>
+
+<span class="sd">        Only setter (where do_intervention is called) needs this field.</span>
+
+<span class="sd">        *We assume base and source targetting the same subspace for now.</span>
+<span class="sd">        *We assume only a single space is targeted for now (although 2d list is provided).</span>
+
+<span class="sd">        Since we now support group-based intervention, the number of sources</span>
+<span class="sd">        should be equal to the total number of groups.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># TODO: forgive me now, i will change this later.</span>
+        <span class="n">activations_sources</span> <span class="o">=</span> <span class="n">source_representations</span>
+        <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sources</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">sources</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">full_intervention_outputs</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cleanup_states</span><span class="p">()</span>
+
+        <span class="c1"># if no source input or intervention, we return base</span>
+        <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="kc">None</span> \
+            <span class="ow">and</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">base</span><span class="p">),</span> <span class="kc">None</span>
+        <span class="c1"># broadcast</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_unit_locations</span><span class="p">(</span><span class="n">get_batch_size</span><span class="p">(</span><span class="n">base</span><span class="p">),</span> <span class="n">unit_locations</span><span class="p">)</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span> <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">sources</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_sources</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span>
+        <span class="n">activations_sources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_source_representations</span><span class="p">(</span><span class="n">activations_sources</span><span class="p">)</span>
+        <span class="n">subspaces</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_subspaces</span><span class="p">(</span><span class="n">get_batch_size</span><span class="p">(</span><span class="n">base</span><span class="p">),</span> <span class="n">subspaces</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_validation</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="p">,</span>
+            <span class="n">activations_sources</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+        <span class="p">)</span>
+        
+        <span class="n">base_outputs</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">output_original_output</span><span class="p">:</span>
+            <span class="c1"># returning un-intervened output with gradients</span>
+            <span class="n">base_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">base</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="c1"># intervene</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;parallel&quot;</span><span class="p">:</span>
+                <span class="n">set_handlers_to_remove</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_wait_for_forward_with_parallel_intervention</span><span class="p">(</span>
+                        <span class="n">sources</span><span class="p">,</span>
+                        <span class="n">unit_locations</span><span class="p">,</span>
+                        <span class="n">activations_sources</span><span class="p">,</span>
+                        <span class="n">subspaces</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;serial&quot;</span><span class="p">:</span>
+                <span class="n">set_handlers_to_remove</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_wait_for_forward_with_serial_intervention</span><span class="p">(</span>
+                        <span class="n">sources</span><span class="p">,</span>
+                        <span class="n">unit_locations</span><span class="p">,</span>
+                        <span class="n">activations_sources</span><span class="p">,</span>
+                        <span class="n">subspaces</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+
+            <span class="c1"># run intervened forward</span>
+            <span class="n">model_kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># for training</span>
+                <span class="n">model_kwargs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">labels</span>
+            <span class="k">if</span> <span class="n">use_cache</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">&#39;use_cache&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">to_dict</span><span class="p">():</span> <span class="c1"># for transformer models</span>
+                <span class="n">model_kwargs</span><span class="p">[</span><span class="s2">&quot;use_cache&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">use_cache</span>
+
+            <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">base</span><span class="p">,</span> <span class="o">**</span><span class="n">model_kwargs</span><span class="p">)</span>
+
+            <span class="n">set_handlers_to_remove</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_output_validation</span><span class="p">()</span>
+            
+            <span class="n">collected_activations</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="n">CollectIntervention</span>
+                    <span class="p">):</span>
+                        <span class="n">collected_activations</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="n">e</span>
+        <span class="k">finally</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cleanup_states</span><span class="p">(</span>
+                <span class="n">skip_activation_gc</span> <span class="o">=</span> \
+                    <span class="p">(</span><span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> \
+                    <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span>
+            <span class="p">)</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">return_dict</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">IntervenableModelOutput</span><span class="p">(</span>
+                    <span class="n">original_outputs</span><span class="o">=</span><span class="n">base_outputs</span><span class="p">,</span>
+                    <span class="n">intervened_outputs</span><span class="o">=</span><span class="n">counterfactual_outputs</span><span class="p">,</span>
+                    <span class="n">collected_activations</span><span class="o">=</span><span class="n">collected_activations</span>
+                <span class="p">)</span>
+            
+            <span class="k">return</span> <span class="p">(</span><span class="n">base_outputs</span><span class="p">,</span> <span class="n">collected_activations</span><span class="p">),</span> <span class="n">counterfactual_outputs</span>
+        
+        <span class="k">if</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">IntervenableModelOutput</span><span class="p">(</span>
+                <span class="n">original_outputs</span><span class="o">=</span><span class="n">base_outputs</span><span class="p">,</span>
+                <span class="n">intervened_outputs</span><span class="o">=</span><span class="n">counterfactual_outputs</span><span class="p">,</span>
+                <span class="n">collected_activations</span><span class="o">=</span><span class="kc">None</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">base_outputs</span><span class="p">,</span> <span class="n">counterfactual_outputs</span></div>
+
+
+<div class="viewcode-block" id="IntervenableModel.generate">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.generate">[docs]</a>
+    <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">source_representations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">intervene_on_prompt</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_original_output</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Intervenable generation function that serves a</span>
+<span class="sd">        wrapper to regular model generate calls.</span>
+
+<span class="sd">        Currently, we support basic interventions **in the</span>
+<span class="sd">        prompt only**. We will support generation interventions</span>
+<span class="sd">        in the next release.</span>
+
+<span class="sd">        TODO: Unroll sources and intervene in the generation step.</span>
+
+<span class="sd">        Parameters:</span>
+<span class="sd">        base:                The base example.</span>
+<span class="sd">        sources:             A list of source examples.</span>
+<span class="sd">        unit_locations:      The intervention locations of</span>
+<span class="sd">                             base.</span>
+<span class="sd">        activations_sources: A list of representations.</span>
+<span class="sd">        intervene_on_prompt: Whether only intervene on prompt.</span>
+<span class="sd">        **kwargs:            All other generation parameters.</span>
+
+<span class="sd">        Return:</span>
+<span class="sd">        base_output: the non-intervened output of the base</span>
+<span class="sd">        input.</span>
+<span class="sd">        counterfactual_outputs: the intervened output of the</span>
+<span class="sd">        base input.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># TODO: forgive me now, i will change this later.</span>
+        <span class="n">activations_sources</span> <span class="o">=</span> <span class="n">source_representations</span>
+        <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sources</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">sources</span><span class="p">]</span>
+            
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cleanup_states</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_intervene_on_prompt</span> <span class="o">=</span> <span class="n">intervene_on_prompt</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span> <span class="o">=</span> <span class="kc">True</span>
+        
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">intervene_on_prompt</span> <span class="ow">and</span> <span class="n">unit_locations</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># that means, we intervene on every generated tokens!</span>
+            <span class="n">unit_locations</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
+        
+        <span class="c1"># broadcast</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_unit_locations</span><span class="p">(</span><span class="n">get_batch_size</span><span class="p">(</span><span class="n">base</span><span class="p">),</span> <span class="n">unit_locations</span><span class="p">)</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_intervention_group</span><span class="p">)</span> <span class="k">if</span> <span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">sources</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_sources</span><span class="p">(</span><span class="n">sources</span><span class="p">)</span>
+        <span class="n">activations_sources</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_source_representations</span><span class="p">(</span><span class="n">activations_sources</span><span class="p">)</span>
+        <span class="n">subspaces</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_subspaces</span><span class="p">(</span><span class="n">get_batch_size</span><span class="p">(</span><span class="n">base</span><span class="p">),</span> <span class="n">subspaces</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_validation</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="p">,</span>
+            <span class="n">activations_sources</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+        <span class="p">)</span>
+        
+        <span class="n">base_outputs</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">output_original_output</span><span class="p">:</span>
+            <span class="c1"># returning un-intervened output</span>
+            <span class="n">base_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="o">**</span><span class="n">base</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">set_handlers_to_remove</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="c1"># intervene</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;parallel&quot;</span><span class="p">:</span>
+                <span class="n">set_handlers_to_remove</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_wait_for_forward_with_parallel_intervention</span><span class="p">(</span>
+                        <span class="n">sources</span><span class="p">,</span>
+                        <span class="n">unit_locations</span><span class="p">,</span>
+                        <span class="n">activations_sources</span><span class="p">,</span>
+                        <span class="n">subspaces</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;serial&quot;</span><span class="p">:</span>
+                <span class="n">set_handlers_to_remove</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_wait_for_forward_with_serial_intervention</span><span class="p">(</span>
+                        <span class="n">sources</span><span class="p">,</span>
+                        <span class="n">unit_locations</span><span class="p">,</span>
+                        <span class="n">activations_sources</span><span class="p">,</span>
+                        <span class="n">subspaces</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+            
+            <span class="c1"># run intervened generate</span>
+            <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
+                <span class="o">**</span><span class="n">base</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+            <span class="p">)</span>
+            
+            <span class="n">collected_activations</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted_keys</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="n">key</span><span class="p">],</span>
+                        <span class="n">CollectIntervention</span>
+                    <span class="p">):</span>
+                        <span class="n">collected_activations</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="n">e</span>
+        <span class="k">finally</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">set_handlers_to_remove</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">set_handlers_to_remove</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_is_generation</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cleanup_states</span><span class="p">(</span>
+                <span class="n">skip_activation_gc</span> <span class="o">=</span> \
+                    <span class="p">(</span><span class="n">sources</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">activations_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> \
+                    <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span>
+            <span class="p">)</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_collect_activations</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">base_outputs</span><span class="p">,</span> <span class="n">collected_activations</span><span class="p">),</span> <span class="n">counterfactual_outputs</span>
+        
+        <span class="k">return</span> <span class="n">base_outputs</span><span class="p">,</span> <span class="n">counterfactual_outputs</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_batch_process_unit_location</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Convert original data batch according</span>
+<span class="sd">        to the intervenable settings.</span>
+
+<span class="sd">        The function respects inputs in the following</span>
+<span class="sd">        data format.</span>
+
+
+<span class="sd">        Each location list in the raw input as,</span>
+
+<span class="sd">        [[i, j, ...], [m, n, ...], ...] batched</span>
+<span class="sd">        where i, j are the unit index, the outter</span>
+<span class="sd">        list is for the batch</span>
+
+
+<span class="sd">        Possible fields in the input:</span>
+
+<span class="sd">        inputs[&quot;source_0-&gt;base.0.pos&quot;] -&gt; batched</span>
+<span class="sd">        inputs[&quot;source_0-&gt;base.1.pos&quot;] -&gt; batched</span>
+<span class="sd">        AND</span>
+<span class="sd">        inputs[&quot;source_0-&gt;source_1.0.pos&quot;] -&gt; batched</span>
+<span class="sd">        inputs[&quot;source_0-&gt;source_1.1.pos&quot;] -&gt; batched</span>
+<span class="sd">        ...</span>
+
+<span class="sd">        multiple source locations are included in case</span>
+<span class="sd">        there are multiple sources.</span>
+
+<span class="sd">        We also need to consider whether we are doing</span>
+<span class="sd">        parallel or serial interventions.</span>
+
+<span class="sd">        We also need to consider the granularity. In case</span>
+<span class="sd">        we are intervening h.pos, which is a specific location</span>
+<span class="sd">        in a specific head:</span>
+
+<span class="sd">        inputs[&quot;source_0-&gt;base.0.pos&quot;] -&gt; batched</span>
+<span class="sd">        inputs[&quot;source_0-&gt;source_1.0.h&quot;] -&gt; batched</span>
+
+<span class="sd">        inputs[&quot;source_0-&gt;base.0.pos&quot;] -&gt; batched</span>
+<span class="sd">        inputs[&quot;source_0-&gt;source_1.0.pos&quot;] -&gt; batched</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">batched_location_dict</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">_source_ind</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="s2">&quot;-&gt;&quot;</span> <span class="ow">in</span> <span class="n">k</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">sub_k</span> <span class="ow">in</span> <span class="n">k</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;-&gt;&quot;</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="s2">&quot;source&quot;</span> <span class="ow">in</span> <span class="n">sub_k</span><span class="p">:</span>
+                        <span class="n">_source_ind</span> <span class="o">+=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">sub_k</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])]</span>
+        <span class="n">_max_source_ind</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">_source_ind</span><span class="p">)</span>
+
+        <span class="c1"># we assume source_0 -&gt; source_1, ..., source_last -&gt; base</span>
+        <span class="c1"># each pair uses an intervention</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;parallel&quot;</span><span class="p">:</span>
+            <span class="c1"># all source into base at once but may engage different locations</span>
+            <span class="n">_curr_source_ind</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">_parallel_aggr_left</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">_parallel_aggr_right</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">rep</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">_curr_source_ind_inc</span> <span class="o">=</span> <span class="n">_curr_source_ind</span> <span class="o">+</span> <span class="mi">1</span>
+                <span class="n">_prefix</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;source_</span><span class="si">{</span><span class="n">_curr_source_ind</span><span class="si">}</span><span class="s2">-&gt;base&quot;</span>
+                <span class="n">_prefix_left</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix</span><span class="si">}</span><span class="s2">.0&quot;</span>
+                <span class="n">_prefix_right</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix</span><span class="si">}</span><span class="s2">.1&quot;</span>
+                <span class="n">_sub_loc_aggr_left</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 3d</span>
+                <span class="n">_sub_loc_aggr_right</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 3d</span>
+                <span class="k">for</span> <span class="n">sub_loc</span> <span class="ow">in</span> <span class="n">rep</span><span class="o">.</span><span class="n">unit</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">):</span>
+                    <span class="n">_sub_loc_aggr_left</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix_left</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">sub_loc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]]</span>
+                    <span class="n">_sub_loc_aggr_right</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix_right</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">sub_loc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]]</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rep</span><span class="o">.</span><span class="n">unit</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">_sub_loc_aggr_left</span> <span class="o">=</span> <span class="n">_sub_loc_aggr_left</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">_sub_loc_aggr_right</span> <span class="o">=</span> <span class="n">_sub_loc_aggr_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">_parallel_aggr_left</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_sub_loc_aggr_left</span><span class="p">]</span>  <span class="c1"># 3D or 4D</span>
+                <span class="n">_parallel_aggr_right</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_sub_loc_aggr_right</span><span class="p">]</span>  <span class="c1"># 3D or 4D</span>
+                <span class="n">_curr_source_ind</span> <span class="o">+=</span> <span class="mi">1</span>
+
+            <span class="n">batched_location_dict</span><span class="p">[</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">_parallel_aggr_left</span><span class="p">,</span>
+                <span class="n">_parallel_aggr_right</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># source into another source and finally to the base engaging different locations</span>
+            <span class="n">_curr_source_ind</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">rep</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">_curr_source_ind_inc</span> <span class="o">=</span> <span class="n">_curr_source_ind</span> <span class="o">+</span> <span class="mi">1</span>
+                <span class="n">_prefix</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;source_</span><span class="si">{</span><span class="n">_curr_source_ind</span><span class="si">}</span><span class="s2">-&gt;base&quot;</span>
+                    <span class="k">if</span> <span class="n">_curr_source_ind</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">representations</span><span class="p">)</span>
+                    <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;source_</span><span class="si">{</span><span class="n">_curr_source_ind</span><span class="si">}</span><span class="s2">-&gt;source</span><span class="si">{</span><span class="n">_curr_source_ind_inc</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+                <span class="n">_prefix_left</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix</span><span class="si">}</span><span class="s2">.0&quot;</span>
+                <span class="n">_prefix_right</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix</span><span class="si">}</span><span class="s2">.1&quot;</span>
+                <span class="n">_sub_loc_aggr_left</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 3d</span>
+                <span class="n">_sub_loc_aggr_right</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 3d</span>
+                <span class="k">for</span> <span class="n">sub_loc</span> <span class="ow">in</span> <span class="n">rep</span><span class="o">.</span><span class="n">unit</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">):</span>
+                    <span class="n">_sub_loc_aggr_left</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix_left</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">sub_loc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]]</span>
+                    <span class="n">_sub_loc_aggr_right</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">_prefix_right</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">sub_loc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]]</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rep</span><span class="o">.</span><span class="n">unit</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">_sub_loc_aggr_left</span> <span class="o">=</span> <span class="n">_sub_loc_aggr_left</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">_sub_loc_aggr_right</span> <span class="o">=</span> <span class="n">_sub_loc_aggr_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">_curr_source_ind</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">batched_location_dict</span><span class="p">[</span><span class="n">_prefix</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="p">[</span><span class="n">_sub_loc_aggr_left</span><span class="p">],</span>  <span class="c1"># 3D or 4D</span>
+                    <span class="p">[</span><span class="n">_sub_loc_aggr_right</span><span class="p">],</span>  <span class="c1"># 3D or 4D</span>
+                <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">batched_location_dict</span>
+    
+<div class="viewcode-block" id="IntervenableModel.train">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.train">[docs]</a>
+    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">)</span></div>
+
+    
+<div class="viewcode-block" id="IntervenableModel.eval">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.eval">[docs]</a>
+    <span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="IntervenableModel.train_alignment">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.train_alignment">[docs]</a>
+    <span class="k">def</span> <span class="nf">train_alignment</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">train_dataloader</span><span class="p">,</span>
+        <span class="n">compute_loss</span><span class="p">,</span>
+        <span class="n">compute_metrics</span><span class="p">,</span>
+        <span class="n">inputs_collator</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The method find alignment.</span>
+
+<span class="sd">        a.k.a. training the intervention</span>
+
+<span class="sd">        Notes:</span>
+<span class="sd">        1) we use Adam, and linear lr scheduling.</span>
+<span class="sd">        2) you can pass in lr or using default 1e-3</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># preprocess basic kwargs</span>
+        <span class="n">lr</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;lr&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;lr&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="mf">1e-3</span>
+        <span class="n">epochs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;epochs&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;epochs&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="mi">10</span>
+        <span class="n">warm_up_steps</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;warm_up_steps&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;warm_up_steps&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="mf">0.1</span>
+        <span class="n">gradient_accumulation_steps</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;gradient_accumulation_steps&quot;</span><span class="p">]</span>
+            <span class="k">if</span> <span class="s2">&quot;gradient_accumulation_steps&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span>
+            <span class="k">else</span> <span class="mi">1</span>
+        <span class="p">)</span>
+
+        <span class="c1"># some deeper kwargs</span>
+        <span class="n">t_total</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span><span class="p">)</span>
+        <span class="n">warm_up_steps</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">t_total</span>
+        <span class="n">target_total_step</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span>
+        <span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_trainable_parameters</span><span class="p">()}]</span>
+        <span class="n">optimizer</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;optimizer&quot;</span><span class="p">]</span>
+            <span class="k">if</span> <span class="s2">&quot;optimizer&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span>
+            <span class="k">else</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">scheduler</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;scheduler&quot;</span><span class="p">]</span>
+            <span class="k">if</span> <span class="s2">&quot;scheduler&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span>
+            <span class="k">else</span> <span class="n">get_linear_schedule_with_warmup</span><span class="p">(</span>
+                <span class="n">optimizer</span><span class="p">,</span> <span class="n">num_warmup_steps</span><span class="o">=</span><span class="n">warm_up_steps</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="o">=</span><span class="n">t_total</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="c1"># in case we need additional temp scheduling</span>
+        <span class="n">temperature_start</span> <span class="o">=</span> <span class="mf">50.0</span>
+        <span class="n">temperature_end</span> <span class="o">=</span> <span class="mf">0.1</span>
+        <span class="n">temperature_schedule</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">temperature_start</span><span class="p">,</span> <span class="n">temperature_end</span><span class="p">,</span> <span class="n">target_total_step</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span>
+        <span class="p">)</span>
+
+        <span class="c1"># train main loop</span>
+        <span class="n">remove_forward_hooks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>  <span class="c1"># train enables drop-off but no grads</span>
+        <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">epochs</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Epoch&quot;</span><span class="p">)</span>
+        <span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">epoch_iterator</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">inputs_collator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs_collator</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+                <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">unit_location_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_process_unit_location</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span>
+                    <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                    <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+                    <span class="n">unit_location_dict</span><span class="p">,</span>
+                    <span class="n">subspaces</span><span class="o">=</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;subspaces&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;subspaces&quot;</span> <span class="ow">in</span> <span class="n">inputs</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+                    <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">],</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+                <span class="p">)</span>
+
+                <span class="c1"># loss and backprop</span>
+                <span class="n">loss</span> <span class="o">=</span> <span class="n">compute_loss</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+                <span class="n">loss_str</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">2</span><span class="p">)</span>
+                <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="n">loss_str</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">eval_metrics</span><span class="p">})</span>
+
+                <span class="k">if</span> <span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">gradient_accumulation_steps</span>
+                <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+                <span class="k">if</span> <span class="n">total_step</span> <span class="o">%</span> <span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">total_step</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                        <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">set_zero_grad</span><span class="p">()</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+                <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span></div>
+
+
+<div class="viewcode-block" id="IntervenableModel.eval_alignment">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.eval_alignment">[docs]</a>
+    <span class="k">def</span> <span class="nf">eval_alignment</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">eval_dataloader</span><span class="p">,</span>
+        <span class="n">compute_metrics</span><span class="p">,</span>
+        <span class="n">inputs_collator</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The method evaluate alignment.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">all_metrics</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">all_num_examples</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span>
+        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+            <span class="k">for</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">eval_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Evaluating&quot;</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">inputs_collator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs_collator</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+                <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">unit_location_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_process_unit_location</span><span class="p">(</span>
+                    <span class="n">inputs</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span>
+                    <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                    <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+                    <span class="n">unit_location_dict</span><span class="p">,</span>
+                    <span class="n">subspaces</span><span class="o">=</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;subspaces&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;subspaces&quot;</span> <span class="ow">in</span> <span class="n">inputs</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+                    <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">],</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+                <span class="p">)</span>
+                <span class="n">all_metrics</span> <span class="o">+=</span> <span class="p">[</span><span class="n">eval_metrics</span><span class="p">]</span>
+                <span class="n">all_num_examples</span> <span class="o">+=</span> <span class="p">[</span><span class="n">b_s</span><span class="p">]</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">weighted_average</span><span class="p">(</span><span class="n">all_metrics</span><span class="p">,</span> <span class="n">all_num_examples</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">result</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="build_intervenable_model">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervenable_base.build_intervenable_model.html#pyvene.models.intervenable_base.build_intervenable_model">[docs]</a>
+<span class="k">def</span> <span class="nf">build_intervenable_model</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Factory design pattern for different types of intervenable models.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">nnsight</span><span class="o">.</span><span class="n">LanguageModel</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">IntervenableNdifModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+</pre></div>
+
+                </article>
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+                  
+  <h1>Source code for pyvene.models.intervention_utils</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">json</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<div class="viewcode-block" id="InterventionState">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervention_utils.InterventionState.html#pyvene.models.intervention_utils.InterventionState">[docs]</a>
+<span class="k">class</span> <span class="nc">InterventionState</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<div class="viewcode-block" id="InterventionState.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervention_utils.InterventionState.html#pyvene.models.intervention_utils.InterventionState.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">key</span> <span class="o">=</span> <span class="n">key</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">inc_getter_version</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;getter_version&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="k">def</span> <span class="nf">inc_setter_version</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;setter_version&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="k">def</span> <span class="nf">getter_version</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;getter_version&quot;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">setter_version</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;setter_version&quot;</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">get_states</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span>
+
+    <span class="k">def</span> <span class="nf">set_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span> <span class="o">=</span> <span class="n">state_dict</span>
+
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;key&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">key</span><span class="p">,</span>
+            <span class="s2">&quot;getter_version&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+            <span class="s2">&quot;setter_version&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="broadcast_tensor_v1">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervention_utils.broadcast_tensor_v1.html#pyvene.models.intervention_utils.broadcast_tensor_v1">[docs]</a>
+<span class="k">def</span> <span class="nf">broadcast_tensor_v1</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">target_shape</span><span class="p">):</span>
+    <span class="c1"># Ensure the last dimension of target_shape matches x&#39;s size</span>
+    <span class="k">if</span> <span class="n">target_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The last dimension of target_shape must match the size of x&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Create a shape for reshaping x that is compatible with target_shape</span>
+    <span class="n">reshape_shape</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">target_shape</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+
+    <span class="c1"># Reshape x and then broadcast it</span>
+    <span class="n">x_reshaped</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">reshape_shape</span><span class="p">)</span>
+    <span class="n">broadcasted_x</span> <span class="o">=</span> <span class="n">x_reshaped</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">*</span><span class="n">target_shape</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">broadcasted_x</span></div>
+
+    
+<div class="viewcode-block" id="broadcast_tensor_v2">
+<a class="viewcode-back" href="../../../api/pyvene.models.intervention_utils.broadcast_tensor_v2.html#pyvene.models.intervention_utils.broadcast_tensor_v2">[docs]</a>
+<span class="k">def</span> <span class="nf">broadcast_tensor_v2</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">target_shape</span><span class="p">):</span>
+    <span class="c1"># Ensure that target_shape has at least one dimension</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">target_shape</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Target shape must have at least one dimension&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Extract the first n-1 dimensions from the target shape</span>
+    <span class="n">target_dims_except_last</span> <span class="o">=</span> <span class="n">target_shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="c1"># Broadcast the input tensor x to match the target_dims_except_last and keep its last dimension</span>
+    <span class="n">broadcasted_x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">*</span><span class="n">target_dims_except_last</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">broadcasted_x</span></div>
+
+
+<span class="k">def</span> <span class="nf">_can_cast_tensor</span><span class="p">(</span>
+    <span class="n">subspaces</span>
+<span class="p">):</span>
+    <span class="n">tensorfiable</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">tensorfiable</span> <span class="o">=</span> <span class="kc">False</span>
+        
+    <span class="k">return</span> <span class="n">tensorfiable</span>
+
+<span class="k">def</span> <span class="nf">_can_use_fast</span><span class="p">(</span>
+    <span class="n">subspaces</span>
+<span class="p">):</span>
+    <span class="n">tensorfiable</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">row_same_val</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">subspaces</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)</span>
+        <span class="n">row_same_val</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">subspaces</span> <span class="o">==</span> <span class="n">subspaces</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="n">tensorfiable</span> <span class="o">=</span> <span class="kc">False</span>
+        
+    <span class="k">return</span> <span class="n">row_same_val</span> <span class="ow">and</span> <span class="n">tensorfiable</span>
+
+<span class="k">def</span> <span class="nf">_do_intervention_by_swap</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span>
+    <span class="n">source</span><span class="p">,</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+    <span class="n">interchange_dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">subspace_partition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">use_fast</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The basic do function that guards interventions&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;collect&quot;</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">source</span> <span class="ow">is</span> <span class="kc">None</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># auto broadcast</span>
+        <span class="k">if</span> <span class="n">base</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">source</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">source</span> <span class="o">=</span> <span class="n">broadcast_tensor_v1</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;source with shape </span><span class="si">{</span><span class="n">source</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> cannot be broadcasted &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;into base with shape </span><span class="si">{</span><span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="p">)</span>
+    <span class="c1"># if subspace is none, then we are doing swap based on interchange_dim</span>
+    <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;interchange&quot;</span><span class="p">:</span>
+            <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">source</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;add&quot;</span><span class="p">:</span>
+            <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">+=</span> <span class="n">source</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;subtract&quot;</span><span class="p">:</span>
+            <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">-=</span> <span class="n">source</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;collect&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="c1"># return without side-effect</span>
+        <span class="k">return</span> <span class="n">base</span>
+
+    <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">use_fast</span> <span class="ow">or</span> <span class="n">_can_use_fast</span><span class="p">(</span><span class="n">subspaces</span><span class="p">):</span>
+        <span class="c1"># its tensor, and each row the same</span>
+        <span class="k">if</span> <span class="n">subspace_partition</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="n">subspaces</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">subspace</span> <span class="ow">in</span> <span class="n">subspaces</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">sel_subspace_indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                    <span class="n">subspace_partition</span><span class="p">[</span><span class="n">subspace</span><span class="p">]</span>
+                <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">_can_cast_tensor</span><span class="p">(</span><span class="n">subspaces</span><span class="p">):</span>
+        <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">example_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)):</span>
+            <span class="c1"># render subspace as column indices</span>
+            <span class="k">if</span> <span class="n">subspace_partition</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">sel_subspace_indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subspaces</span><span class="p">[</span><span class="n">example_i</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">_sel_subspace_indices</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">subspace</span> <span class="ow">in</span> <span class="n">subspaces</span><span class="p">[</span><span class="n">example_i</span><span class="p">]:</span>
+                    <span class="n">_sel_subspace_indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                        <span class="n">subspace_partition</span><span class="p">[</span><span class="n">subspace</span><span class="p">]</span>
+                    <span class="p">)</span>
+                <span class="n">sel_subspace_indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_sel_subspace_indices</span><span class="p">)</span>
+    
+    <span class="c1"># _can_use_fast or _can_cast_tensor will prepare the sel_subspace_indices</span>
+    <span class="k">if</span> <span class="n">sel_subspace_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">pad_idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;interchange&quot;</span><span class="p">:</span>
+            <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">source</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;add&quot;</span><span class="p">:</span>
+            <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="o">+=</span> <span class="n">source</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;subtract&quot;</span><span class="p">:</span>
+            <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="o">-=</span> <span class="n">source</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;collect&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">pad_idx</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="c1"># return without side-effect</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">collect_base</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">example_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)):</span>
+            <span class="c1"># render subspace as column indices</span>
+            <span class="k">if</span> <span class="n">subspace_partition</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="n">subspaces</span><span class="p">[</span><span class="n">example_i</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">subspace</span> <span class="ow">in</span> <span class="n">subspaces</span><span class="p">[</span><span class="n">example_i</span><span class="p">]:</span>
+                    <span class="n">sel_subspace_indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                        <span class="n">subspace_partition</span><span class="p">[</span><span class="n">subspace</span><span class="p">]</span>
+                    <span class="p">)</span>
+            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;interchange&quot;</span><span class="p">:</span>
+                <span class="n">base</span><span class="p">[</span><span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">source</span><span class="p">[</span>
+                    <span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span>
+                <span class="p">]</span>
+            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;add&quot;</span><span class="p">:</span>
+                <span class="n">base</span><span class="p">[</span><span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="o">+=</span> <span class="n">source</span><span class="p">[</span>
+                    <span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span>
+                <span class="p">]</span>
+            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;subtract&quot;</span><span class="p">:</span>
+                <span class="n">base</span><span class="p">[</span><span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span> <span class="o">-=</span> <span class="n">source</span><span class="p">[</span>
+                    <span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span>
+                <span class="p">]</span>
+            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;collect&quot;</span><span class="p">:</span>
+                <span class="n">collect_base</span> <span class="o">+=</span> <span class="p">[</span><span class="n">base</span><span class="p">[</span><span class="n">example_i</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]]</span>
+        <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;collect&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">collect_base</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># return without side-effect</span>
+
+    <span class="k">return</span> <span class="n">base</span>
+</pre></div>
+
+                </article>
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new file mode 100644
index 00000000..842eb650
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@@ -0,0 +1,1358 @@
+
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+                  
+  <h1>Source code for pyvene.models.interventions</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Any</span>
+
+<span class="kn">from</span> <span class="nn">.layers</span> <span class="kn">import</span> <span class="n">RotateLayer</span><span class="p">,</span> <span class="n">LowRankRotateLayer</span><span class="p">,</span> <span class="n">SubspaceLowRankRotateLayer</span><span class="p">,</span> <span class="n">AutoencoderLayer</span>
+<span class="kn">from</span> <span class="nn">.basic_utils</span> <span class="kn">import</span> <span class="n">sigmoid_boundary</span>
+<span class="kn">from</span> <span class="nn">.intervention_utils</span> <span class="kn">import</span> <span class="n">_can_use_fast</span><span class="p">,</span> <span class="n">_do_intervention_by_swap</span>
+
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+<span class="kn">from</span> <span class="nn">transformers.activations</span> <span class="kn">import</span> <span class="n">ACT2FN</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">ModelOutput</span>
+
+
+<div class="viewcode-block" id="InterventionOutput">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.InterventionOutput.html#pyvene.models.interventions.InterventionOutput">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">InterventionOutput</span><span class="p">(</span><span class="n">ModelOutput</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Output of the IntervenableModel, including original outputs, intervened outputs, and collected activations.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">output</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">latent</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+
+<div class="viewcode-block" id="Intervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention">[docs]</a>
+<span class="k">class</span> <span class="nc">Intervention</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention the original representations.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Intervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">keep_last_dim</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;keep_last_dim&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;keep_last_dim&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;use_fast&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;use_fast&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;subspace_partition&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;subspace_partition&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="kc">None</span>
+        <span class="p">)</span>
+        <span class="c1"># we turn the partition into list indices</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">expanded_subspace_partition</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">subspace</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">subspace</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subspace</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">):</span>
+                    <span class="n">expanded_subspace_partition</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">subspace</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">subspace</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># it could be discrete indices.</span>
+                    <span class="n">expanded_subspace_partition</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subspace</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span> <span class="o">=</span> <span class="n">expanded_subspace_partition</span>
+            
+        <span class="k">if</span> <span class="s2">&quot;embed_dim&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;embed_dim&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;embed_dim&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;embed_dim&quot;</span><span class="p">]))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;interchange_dim&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;embed_dim&quot;</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="o">=</span> <span class="kc">None</span>
+            
+        <span class="k">if</span> <span class="s2">&quot;source_representation&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;source_representation&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;source_representation&#39;</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;source_representation&quot;</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;hidden_source_representation&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> \
+                <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;hidden_source_representation&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+                
+    <span class="k">def</span> <span class="nf">set_source_representation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_representation</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;source_representation&#39;</span><span class="p">,</span> <span class="n">source_representation</span><span class="p">)</span>
+                
+    <span class="k">def</span> <span class="nf">set_interchange_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interchange_dim</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">interchange_dim</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+            <span class="c1"># Convert integer or list into torch.Tensor.</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">interchange_dim</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="o">=</span> <span class="n">interchange_dim</span>
+            
+<div class="viewcode-block" id="Intervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention.forward">[docs]</a>
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="LocalistRepresentationIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">LocalistRepresentationIntervention</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Localist representation.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="LocalistRepresentationIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_repr_distributed</span> <span class="o">=</span> <span class="kc">False</span></div>
+</div>
+
+        
+        
+<div class="viewcode-block" id="DistributedRepresentationIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">DistributedRepresentationIntervention</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Distributed representation.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="DistributedRepresentationIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_repr_distributed</span> <span class="o">=</span> <span class="kc">True</span></div>
+</div>
+
+        
+        
+<div class="viewcode-block" id="TrainableIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">TrainableIntervention</span><span class="p">(</span><span class="n">Intervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention the original representations.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="TrainableIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">False</span></div>
+
+        
+    <span class="k">def</span> <span class="nf">tie_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">linked_intervention</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+
+
+<div class="viewcode-block" id="ConstantSourceIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">ConstantSourceIntervention</span><span class="p">(</span><span class="n">Intervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Constant source.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="ConstantSourceIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">True</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SourcelessIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SourcelessIntervention.html#pyvene.models.interventions.SourcelessIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">SourcelessIntervention</span><span class="p">(</span><span class="n">Intervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;No source.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SourcelessIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SourcelessIntervention.html#pyvene.models.interventions.SourcelessIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_source_constant</span> <span class="o">=</span> <span class="kc">True</span></div>
+</div>
+
+    
+    
+<div class="viewcode-block" id="BasisAgnosticIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">BasisAgnosticIntervention</span><span class="p">(</span><span class="n">Intervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention that will modify its basis in a uncontrolled manner.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="BasisAgnosticIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">basis_agnostic</span> <span class="o">=</span> <span class="kc">True</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SharedWeightsTrainableIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html#pyvene.models.interventions.SharedWeightsTrainableIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">SharedWeightsTrainableIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention the original representations.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SharedWeightsTrainableIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html#pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shared_weights</span> <span class="o">=</span> <span class="kc">True</span></div>
+</div>
+
+        
+        
+<div class="viewcode-block" id="ZeroIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">ZeroIntervention</span><span class="p">(</span><span class="n">ConstantSourceIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Zero-out activations.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="ZeroIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+        
+<div class="viewcode-block" id="ZeroIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">base</span><span class="p">),</span>
+            <span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;ZeroIntervention()&quot;</span></div>
+
+        
+        
+<div class="viewcode-block" id="CollectIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">CollectIntervention</span><span class="p">(</span><span class="n">ConstantSourceIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Collect activations.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="CollectIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+        
+<div class="viewcode-block" id="CollectIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">source</span><span class="p">,</span>
+            <span class="s2">&quot;collect&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;CollectIntervention()&quot;</span></div>
+
+        
+        
+<div class="viewcode-block" id="SkipIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">SkipIntervention</span><span class="p">(</span><span class="n">BasisAgnosticIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Skip the current intervening layer&#39;s computation in the hook function.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SkipIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+        
+<div class="viewcode-block" id="SkipIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="c1"># source here is the base example input to the hook</span>
+        <span class="k">return</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">source</span><span class="p">,</span>
+            <span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;SkipIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="VanillaIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">VanillaIntervention</span><span class="p">(</span><span class="n">Intervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention the original representations.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="VanillaIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="VanillaIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> 
+        <span class="k">return</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">source</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span><span class="p">,</span>
+            <span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;VanillaIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="AdditionIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">AdditionIntervention</span><span class="p">(</span><span class="n">BasisAgnosticIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention the original representations with activation addition.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="AdditionIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="AdditionIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">source</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span><span class="p">,</span>
+            <span class="s2">&quot;add&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;AdditionIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="SubtractionIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">SubtractionIntervention</span><span class="p">(</span><span class="n">BasisAgnosticIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention the original representations with activation subtraction.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SubtractionIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SubtractionIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        
+        <span class="k">return</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">source</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">source_representation</span><span class="p">,</span>
+            <span class="s2">&quot;subtract&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;SubtractionIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="RotatedSpaceIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">RotatedSpaceIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">,</span> <span class="n">DistributedRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the rotated space.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="RotatedSpaceIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">RotateLayer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parametrizations</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">(</span><span class="n">rotate_layer</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="RotatedSpaceIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">rotated_source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="c1"># interchange</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">rotated_base</span><span class="p">,</span>
+            <span class="n">rotated_source</span><span class="p">,</span>
+            <span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># inverse base</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">rotated_base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;RotatedSpaceIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="BoundlessRotatedSpaceIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">BoundlessRotatedSpaceIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">,</span> <span class="n">DistributedRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the rotated space with boundary mask.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="BoundlessRotatedSpaceIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">RotateLayer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parametrizations</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">(</span><span class="n">rotate_layer</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_boundaries</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">0.5</span><span class="p">]),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">50.0</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_population</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_boundary_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_boundaries</span>
+
+    <span class="k">def</span> <span class="nf">get_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span>
+
+    <span class="k">def</span> <span class="nf">set_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">temp</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">temp</span>
+
+    <span class="k">def</span> <span class="nf">set_intervention_boundaries</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intervention_boundaries</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">intervention_boundaries</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">intervention_boundaries</span><span class="p">]),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">)</span>
+        
+<div class="viewcode-block" id="BoundlessRotatedSpaceIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">rotated_source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="c1"># get boundary</span>
+        <span class="n">intervention_boundaries</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">,</span> <span class="mf">1e-3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">boundary_mask</span> <span class="o">=</span> <span class="n">sigmoid_boundary</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">intervention_population</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+            <span class="mf">0.0</span><span class="p">,</span>
+            <span class="n">intervention_boundaries</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">),</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">boundary_mask</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">base</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">boundary_mask</span>
+        <span class="p">)</span>
+        <span class="n">boundary_mask</span> <span class="o">=</span> <span class="n">boundary_mask</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rotated_base</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="c1"># interchange</span>
+        <span class="n">rotated_output</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="mf">1.0</span> <span class="o">-</span> <span class="n">boundary_mask</span>
+        <span class="p">)</span> <span class="o">*</span> <span class="n">rotated_base</span> <span class="o">+</span> <span class="n">boundary_mask</span> <span class="o">*</span> <span class="n">rotated_source</span>
+        <span class="c1"># inverse output</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">rotated_output</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;BoundlessRotatedSpaceIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="SigmoidMaskRotatedSpaceIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">SigmoidMaskRotatedSpaceIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">,</span> <span class="n">DistributedRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the rotated space with boundary mask.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SigmoidMaskRotatedSpaceIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">RotateLayer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parametrizations</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">(</span><span class="n">rotate_layer</span><span class="p">)</span>
+        <span class="c1"># boundary masks are initialized to close to 1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">masks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">100.0</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">50.0</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_boundary_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">intervention_boundaries</span>
+
+    <span class="k">def</span> <span class="nf">get_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span>
+
+    <span class="k">def</span> <span class="nf">set_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">temp</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">temp</span>
+
+<div class="viewcode-block" id="SigmoidMaskRotatedSpaceIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">rotated_source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="c1"># get boundary mask between 0 and 1 from sigmoid</span>
+        <span class="n">boundary_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">masks</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="p">)</span>
+
+        <span class="n">boundary_mask</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">base</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">boundary_mask</span>
+        <span class="p">)</span>
+        <span class="n">boundary_mask</span> <span class="o">=</span> <span class="n">boundary_mask</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rotated_base</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="c1"># interchange</span>
+        <span class="n">rotated_output</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="mf">1.0</span> <span class="o">-</span> <span class="n">boundary_mask</span>
+        <span class="p">)</span> <span class="o">*</span> <span class="n">rotated_base</span> <span class="o">+</span> <span class="n">boundary_mask</span> <span class="o">*</span> <span class="n">rotated_source</span>
+        <span class="c1"># inverse output</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">rotated_output</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;SigmoidMaskRotatedSpaceIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="SigmoidMaskIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">SigmoidMaskIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the original basis with binary mask.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SigmoidMaskIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">0.01</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">get_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span>
+
+    <span class="k">def</span> <span class="nf">set_temperature</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">temp</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">temp</span>
+
+<div class="viewcode-block" id="SigmoidMaskIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="c1"># get boundary mask between 0 and 1 from sigmoid</span>
+        <span class="n">mask_sigmoid</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">temperature</span><span class="p">))</span> 
+        
+        <span class="c1"># interchange</span>
+        <span class="n">intervened_output</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="mf">1.0</span> <span class="o">-</span> <span class="n">mask_sigmoid</span>
+        <span class="p">)</span> <span class="o">*</span> <span class="n">base</span> <span class="o">+</span> <span class="n">mask_sigmoid</span> <span class="o">*</span> <span class="n">source</span>
+
+        <span class="k">return</span> <span class="n">intervened_output</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;SigmoidMaskIntervention()&quot;</span></div>
+
+    
+
+<div class="viewcode-block" id="LowRankRotatedSpaceIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">LowRankRotatedSpaceIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">,</span> <span class="n">DistributedRepresentationIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the rotated space.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="LowRankRotatedSpaceIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">LowRankRotateLayer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parametrizations</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">(</span><span class="n">rotate_layer</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="LowRankRotatedSpaceIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">rotated_source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span> <span class="ow">or</span> <span class="n">_can_use_fast</span><span class="p">(</span><span class="n">subspaces</span><span class="p">):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="n">subspaces</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="p">[]</span>
+                    <span class="k">for</span> <span class="n">subspace</span> <span class="ow">in</span> <span class="n">subspaces</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                        <span class="n">sel_subspace_indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">[</span><span class="n">subspace</span><span class="p">]</span>
+                        <span class="p">)</span>
+                <span class="n">diff</span> <span class="o">=</span> <span class="n">rotated_source</span> <span class="o">-</span> <span class="n">rotated_base</span>
+                <span class="k">assert</span> <span class="n">rotated_base</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)</span>
+                <span class="n">batched_subspace</span> <span class="o">=</span> <span class="n">diff</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+                <span class="n">batched_weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">base</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">batched_subspace</span><span class="p">,</span> <span class="n">batched_weights</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">1</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">diff</span> <span class="o">=</span> <span class="n">rotated_source</span> <span class="o">-</span> <span class="n">rotated_base</span>
+                <span class="k">assert</span> <span class="n">rotated_base</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)</span>
+                <span class="n">batched_subspace</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">batched_weights</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">example_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">subspaces</span><span class="p">)):</span>
+                    <span class="c1"># render subspace as column indices</span>
+                    <span class="n">sel_subspace_indices</span> <span class="o">=</span> <span class="p">[]</span>
+                    <span class="k">for</span> <span class="n">subspace</span> <span class="ow">in</span> <span class="n">subspaces</span><span class="p">[</span><span class="n">example_i</span><span class="p">]:</span>
+                        <span class="n">sel_subspace_indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">[</span><span class="n">subspace</span><span class="p">]</span>
+                        <span class="p">)</span>
+
+                    <span class="n">LHS</span> <span class="o">=</span> <span class="n">diff</span><span class="p">[</span><span class="n">example_i</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                    <span class="n">RHS</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">sel_subspace_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+                    <span class="n">batched_subspace</span> <span class="o">+=</span> <span class="p">[</span><span class="n">LHS</span><span class="p">]</span>
+                    <span class="n">batched_weights</span> <span class="o">+=</span> <span class="p">[</span><span class="n">RHS</span><span class="p">]</span>
+                <span class="n">batched_subspace</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">batched_subspace</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                <span class="n">batched_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">batched_weights</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                <span class="n">output</span> <span class="o">=</span> <span class="n">base</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">batched_subspace</span><span class="p">,</span> <span class="n">batched_weights</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">1</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">=</span> <span class="n">base</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span>
+                <span class="p">(</span><span class="n">rotated_source</span> <span class="o">-</span> <span class="n">rotated_base</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;LowRankRotatedSpaceIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="PCARotatedSpaceIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">PCARotatedSpaceIntervention</span><span class="p">(</span><span class="n">BasisAgnosticIntervention</span><span class="p">,</span> <span class="n">DistributedRepresentationIntervention</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the pca space.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="PCARotatedSpaceIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">pca</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;pca&quot;</span><span class="p">]</span>
+        <span class="n">pca_mean</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;pca_mean&quot;</span><span class="p">]</span>
+        <span class="n">pca_std</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;pca_std&quot;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pca_components</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pca_mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">pca_mean</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pca_std</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">pca_std</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="kc">False</span></div>
+
+
+<div class="viewcode-block" id="PCARotatedSpaceIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">base_norm</span> <span class="o">=</span> <span class="p">(</span><span class="n">base</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_mean</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_std</span>
+        <span class="n">source_norm</span> <span class="o">=</span> <span class="p">(</span><span class="n">source</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_mean</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_std</span>
+
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">base_norm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_components</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>  <span class="c1"># B * D_R</span>
+        <span class="n">rotated_source</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">source_norm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_components</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+        <span class="c1"># interchange</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">rotated_base</span><span class="p">,</span>
+            <span class="n">rotated_source</span><span class="p">,</span>
+            <span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># inverse base</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">rotated_base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_components</span><span class="p">)</span>  <span class="c1"># B * D</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">output</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_std</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca_mean</span>
+        <span class="k">return</span> <span class="n">output</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;PCARotatedSpaceIntervention()&quot;</span></div>
+
+
+<div class="viewcode-block" id="NoiseIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">NoiseIntervention</span><span class="p">(</span><span class="n">ConstantSourceIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Noise intervention&quot;&quot;&quot;</span>
+    
+<div class="viewcode-block" id="NoiseIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">rs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">prng</span> <span class="o">=</span> <span class="k">lambda</span> <span class="o">*</span><span class="n">shape</span><span class="p">:</span> <span class="n">rs</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">noise_level</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;noise_leve&quot;</span><span class="p">]</span> \
+            <span class="k">if</span> <span class="s2">&quot;noise_leve&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="mf">0.13462981581687927</span> 
+        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;noise&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span>
+            <span class="n">prng</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">)))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;noise_level&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">noise_level</span><span class="p">))</span></div>
+
+        
+<div class="viewcode-block" id="NoiseIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span>
+        <span class="k">return</span> <span class="n">base</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;NoiseIntervention()&quot;</span></div>
+
+ 
+
+<div class="viewcode-block" id="AutoencoderIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">AutoencoderIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervene in the latent space of an autoencoder.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="AutoencoderIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s2">&quot;latent_dim&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+          <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Missing latent_dim in kwargs.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s2">&quot;embed_dim&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+          <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;embed_dim&quot;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span> <span class="o">=</span> <span class="n">AutoencoderLayer</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;latent_dim&quot;</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="AutoencoderIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">base_dtype</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">dtype</span>
+        <span class="n">base</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encoder</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">base_latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">source_latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="n">base_latent</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">source_latent</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span>
+        <span class="n">inv_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">base_latent</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">inv_output</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base_dtype</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;AutoencoderIntervention()&quot;</span></div>
+
+
+
+<div class="viewcode-block" id="JumpReLUAutoencoderIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">JumpReLUAutoencoderIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Interchange intervention on JumpReLU SAE&#39;s latent subspaces&quot;&quot;&quot;</span>
+<div class="viewcode-block" id="JumpReLUAutoencoderIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="c1"># Note that we initialise these to zeros because we&#39;re loading in pre-trained weights.</span>
+        <span class="c1"># If you want to train your own SAEs then we recommend using blah</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">keep_last_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">W_enc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">W_dec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b_enc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b_dec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_acts</span><span class="p">):</span>
+        <span class="n">pre_acts</span> <span class="o">=</span> <span class="n">input_acts</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">W_enc</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_enc</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">pre_acts</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span>
+        <span class="n">acts</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">pre_acts</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">acts</span>
+
+    <span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acts</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">acts</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">W_dec</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_dec</span>
+
+<div class="viewcode-block" id="JumpReLUAutoencoderIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="c1"># generate latents for base and source runs.</span>
+        <span class="n">base_latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">source_latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="c1"># intervention.</span>
+        <span class="n">intervened_latent</span> <span class="o">=</span> <span class="n">_do_intervention_by_swap</span><span class="p">(</span>
+            <span class="n">base_latent</span><span class="p">,</span>
+            <span class="n">source_latent</span><span class="p">,</span>
+            <span class="s2">&quot;interchange&quot;</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">,</span>
+            <span class="n">subspaces</span><span class="p">,</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="n">use_fast</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_fast</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># decode intervened latent.</span>
+        <span class="n">recon</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">intervened_latent</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">recon</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;JumpReLUAutoencoderIntervention()&quot;</span></div>
+
+
+</pre></div>
+
+                </article>
+              
+
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+              
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diff --git a/_modules/pyvene/models/layers.html b/_modules/pyvene/models/layers.html
new file mode 100644
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+
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+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
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+
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+                  
+  <h1>Source code for pyvene.models.layers</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+
+
+<div class="viewcode-block" id="InverseRotateLayer">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer">[docs]</a>
+<span class="k">class</span> <span class="nc">InverseRotateLayer</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The inverse of a given `LinearLayer` module.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="InverseRotateLayer.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lin_layer</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lin_layer</span> <span class="o">=</span> <span class="n">lin_layer</span></div>
+
+
+<div class="viewcode-block" id="InverseRotateLayer.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lin_layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">output</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="RotateLayer">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer">[docs]</a>
+<span class="k">class</span> <span class="nc">RotateLayer</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A linear transformation with orthogonal initialization.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="RotateLayer.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">init_orth</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+        <span class="c1"># we don&#39;t need init if the saved checkpoint has a nice</span>
+        <span class="c1"># starting point already.</span>
+        <span class="c1"># you can also study this if you want, but it is our focus.</span>
+        <span class="k">if</span> <span class="n">init_orth</span><span class="p">:</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">orthogonal_</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="RotateLayer.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="LowRankRotateLayer">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer">[docs]</a>
+<span class="k">class</span> <span class="nc">LowRankRotateLayer</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A linear transformation with orthogonal initialization.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="LowRankRotateLayer.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">init_orth</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="c1"># n &gt; m</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">init_orth</span><span class="p">:</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">orthogonal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="LowRankRotateLayer.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="SubspaceLowRankRotateLayer">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer">[docs]</a>
+<span class="k">class</span> <span class="nc">SubspaceLowRankRotateLayer</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A linear transformation with orthogonal initialization with subspace.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SubspaceLowRankRotateLayer.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">init_orth</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="c1"># n &gt; m</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">init_orth</span><span class="p">:</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">orthogonal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SubspaceLowRankRotateLayer.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">l</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">[:,</span> <span class="n">l</span><span class="p">:</span><span class="n">r</span><span class="p">])</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="AutoencoderLayerBase">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.AutoencoderLayerBase.html#pyvene.models.layers.AutoencoderLayerBase">[docs]</a>
+<span class="k">class</span> <span class="nc">AutoencoderLayerBase</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;An abstract base class that defines an interface of an autoencoder.&quot;&quot;&quot;</span>
+
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="o">...</span>
+
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">latent</span><span class="p">):</span>
+        <span class="o">...</span></div>
+
+
+
+<div class="viewcode-block" id="AutoencoderLayer">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer">[docs]</a>
+<span class="k">class</span> <span class="nc">AutoencoderLayer</span><span class="p">(</span><span class="n">AutoencoderLayerBase</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;An autoencoder with a single-layer encoder and single-layer decoder.&quot;&quot;&quot;</span>
+<div class="viewcode-block" id="AutoencoderLayer.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_dim</span><span class="p">,</span> <span class="n">latent_dim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_dim</span> <span class="o">=</span> <span class="n">input_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">latent_dim</span> <span class="o">=</span> <span class="n">latent_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+              <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">input_dim</span><span class="p">,</span> <span class="n">latent_dim</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+              <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">())</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+              <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">latent_dim</span><span class="p">,</span> <span class="n">input_dim</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">bias</span>
+        <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">latent</span>
+
+    <span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">latent</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span>
+
+<div class="viewcode-block" id="AutoencoderLayer.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">return_latent</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">base_type</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">dtype</span>
+        <span class="n">base</span> <span class="o">=</span> <span class="n">base</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">base_reconstruct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_latent</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">base_reconstruct</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">base_type</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;latent&#39;</span><span class="p">:</span> <span class="n">latent</span><span class="p">,</span> <span class="s1">&#39;output&#39;</span><span class="p">:</span> <span class="n">base_reconstruct</span><span class="p">}</span></div>
+</div>
+
+</pre></div>
+
+                </article>
+              
+
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new file mode 100644
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+</ul>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/pyvene_101.html">Introduction to pyvene</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
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+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
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+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">API</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../api/core.html"><code class="docutils literal notranslate"><span class="pre">pyvene</span></code>: Core API</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+  <h1>Source code for pyvene.models.llama.modelings_intervenable_llama</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="n">llama_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">llama_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;llama model with LM head&quot;&quot;&quot;</span>
+<span class="n">llama_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">llama_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">llama_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">llama_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">llama_type_to_dimension_mapping</span>
+
+
+<span class="sd">&quot;&quot;&quot;llama model with classifier head&quot;&quot;&quot;</span>
+<span class="n">llama_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">llama_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">llama_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">llama_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">llama_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_llama">
+<a class="viewcode-back" href="../../../../api/pyvene.models.llama.modelings_intervenable_llama.create_llama.html#pyvene.models.llama.modelings_intervenable_llama.create_llama">[docs]</a>
+<span class="k">def</span> <span class="nf">create_llama</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;sharpbai/alpaca-7b-merged&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a LLaMA Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">LlamaForCausalLM</span><span class="p">,</span> <span class="n">LlamaTokenizer</span><span class="p">,</span> <span class="n">LlamaConfig</span>
+    <span class="k">if</span> <span class="n">config</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">LlamaConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+        <span class="n">llama</span> <span class="o">=</span> <span class="n">LlamaForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+            <span class="n">name</span><span class="p">,</span>
+            <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+            <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+            <span class="n">torch_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>  <span class="c1"># save memory</span>
+        <span class="p">)</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">LlamaTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">llama</span> <span class="o">=</span> <span class="n">LlamaForCausalLM</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">LlamaTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">llama</span></div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for pyvene.models.llava.modelings_intervenable_llava</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+<span class="n">llava_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;language_model.model.layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">llava_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.num_key_value_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;text_config.hidden_size/text_config.num_attention_heads&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;llava model with LM head&quot;&quot;&quot;</span>
+<span class="n">llava_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">llava_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">llava_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">llava_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">llava_type_to_dimension_mapping</span>
+
+
+<span class="sd">&quot;&quot;&quot;llava model with classifier head&quot;&quot;&quot;</span>
+<span class="n">llava_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">llava_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">llava_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">llava_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">llava_type_to_dimension_mapping</span>
+
+
+
+
+<div class="viewcode-block" id="create_llava">
+<a class="viewcode-back" href="../../../../api/pyvene.models.llava.modelings_intervenable_llava.create_llava.html#pyvene.models.llava.modelings_intervenable_llava.create_llava">[docs]</a>
+<span class="k">def</span> <span class="nf">create_llava</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;llava-hf/llava-1.5-7b-hf&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a llava Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">LlavaForConditionalGeneration</span><span class="p">,</span> <span class="n">LlavaConfig</span><span class="p">,</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoProcessor</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">LlavaConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">use_fast</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">llava</span> <span class="o">=</span> <span class="n">LlavaForConditionalGeneration</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span>
+        <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+        <span class="n">torch_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">image_processor</span> <span class="o">=</span> <span class="n">AutoProcessor</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">llava</span><span class="p">,</span> <span class="n">image_processor</span></div>
+
+
+</pre></div>
+
+                </article>
+              
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
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+<ul class="nav bd-sidenav">
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+
+
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+  <h1>Source code for pyvene.models.mistral.modellings_intervenable_mistral</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="n">mistral_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">mistral_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;mistral model with LM head&quot;&quot;&quot;</span>
+<span class="n">mistral_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">mistral_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">mistral_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">mistral_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">mistral_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_mistral">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mistral.modellings_intervenable_mistral.create_mistral.html#pyvene.models.mistral.modellings_intervenable_mistral.create_mistral">[docs]</a>
+<span class="k">def</span> <span class="nf">create_mistral</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;mistralai/Mistral-7B-v0.1&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a Mistral Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoConfig</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">mistral</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span>
+        <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+        <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span>  <span class="c1"># save memory</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">mistral</span></div>
+
+</pre></div>
+
+                </article>
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+
+
+
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+  <h1>Source code for pyvene.models.mlp.modelings_intervenable_mlp</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="sd">&quot;&quot;&quot;mlp base model&quot;&quot;&quot;</span>
+<span class="n">mlp_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h[</span><span class="si">%s</span><span class="s2">].act&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+<span class="p">}</span>
+
+
+<span class="n">mlp_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;h_dim&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;mlp model with classification head&quot;&quot;&quot;</span>
+<span class="n">mlp_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">mlp_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">mlp_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mlp.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="n">mlp_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">mlp_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_mlp_classifier">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.html#pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier">[docs]</a>
+<span class="k">def</span> <span class="nf">create_mlp_classifier</span><span class="p">(</span>
+    <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a MLP model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+    <span class="kn">from</span> <span class="nn">pyvene.models.mlp.modelings_mlp</span> <span class="kn">import</span> <span class="n">MLPForClassification</span>
+
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">tokenizer_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">tokenizer_name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPForClassification</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">mlp</span></div>
+
+</pre></div>
+
+                </article>
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+  <h1>Source code for pyvene.models.mlp.modelings_mlp</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span><span class="p">,</span> <span class="n">CrossEntropyLoss</span><span class="p">,</span> <span class="n">MSELoss</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">PretrainedConfig</span><span class="p">,</span> <span class="n">PreTrainedModel</span>
+<span class="kn">from</span> <span class="nn">transformers.activations</span> <span class="kn">import</span> <span class="n">ACT2FN</span>
+<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">ModelOutput</span>
+<span class="kn">from</span> <span class="nn">transformers.modeling_outputs</span> <span class="kn">import</span> <span class="n">SequenceClassifierOutput</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+
+<div class="viewcode-block" id="MLPConfig">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPConfig.html#pyvene.models.mlp.modelings_mlp.MLPConfig">[docs]</a>
+<span class="k">class</span> <span class="nc">MLPConfig</span><span class="p">(</span><span class="n">PretrainedConfig</span><span class="p">):</span>
+    <span class="n">model_type</span> <span class="o">=</span> <span class="s2">&quot;mlp&quot;</span>
+
+<div class="viewcode-block" id="MLPConfig.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPConfig.html#pyvene.models.mlp.modelings_mlp.MLPConfig.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">include_emb</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">vocab_size</span><span class="o">=</span><span class="mi">50_257</span><span class="p">,</span>
+        <span class="n">max_position_embeddings</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
+        <span class="n">n_layer</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">h_dim</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
+        <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">activation_function</span><span class="o">=</span><span class="s2">&quot;gelu&quot;</span><span class="p">,</span>
+        <span class="n">pdrop</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
+        <span class="n">problem_type</span><span class="o">=</span><span class="s2">&quot;single_label_classification&quot;</span><span class="p">,</span>
+        <span class="n">include_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">squeeze_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">include_emb</span> <span class="o">=</span> <span class="n">include_emb</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span> <span class="o">=</span> <span class="n">vocab_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_position_embeddings</span> <span class="o">=</span> <span class="n">max_position_embeddings</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">=</span> <span class="n">n_layer</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span> <span class="o">=</span> <span class="n">h_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">activation_function</span> <span class="o">=</span> <span class="n">activation_function</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pdrop</span> <span class="o">=</span> <span class="n">pdrop</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="n">problem_type</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">include_bias</span> <span class="o">=</span> <span class="n">include_bias</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">squeeze_output</span> <span class="o">=</span> <span class="n">squeeze_output</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+
+<div class="viewcode-block" id="MLPModelOutput">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPModelOutput.html#pyvene.models.mlp.modelings_mlp.MLPModelOutput">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">MLPModelOutput</span><span class="p">(</span><span class="n">ModelOutput</span><span class="p">):</span>
+    <span class="n">last_hidden_state</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+
+<div class="viewcode-block" id="MLPBlock">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPBlock.html#pyvene.models.mlp.modelings_mlp.MLPBlock">[docs]</a>
+<span class="k">class</span> <span class="nc">MLPBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<div class="viewcode-block" id="MLPBlock.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPBlock.html#pyvene.models.mlp.modelings_mlp.MLPBlock.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ff1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">include_bias</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">ACT2FN</span><span class="p">[</span><span class="n">config</span><span class="o">.</span><span class="n">activation_function</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">pdrop</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="MLPBlock.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPBlock.html#pyvene.models.mlp.modelings_mlp.MLPBlock.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_states</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ff1</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)))</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="MLPModel">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPModel.html#pyvene.models.mlp.modelings_mlp.MLPModel">[docs]</a>
+<span class="k">class</span> <span class="nc">MLPModel</span><span class="p">(</span><span class="n">PreTrainedModel</span><span class="p">):</span>
+<div class="viewcode-block" id="MLPModel.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPModel.html#pyvene.models.mlp.modelings_mlp.MLPModel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">h_dim</span>
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">include_emb</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wte</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wpe</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">max_position_embeddings</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">pdrop</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">h</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">([</span><span class="n">MLPBlock</span><span class="p">(</span><span class="n">config</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">)])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">post_init</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="MLPModel.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPModel.html#pyvene.models.mlp.modelings_mlp.MLPModel.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">position_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">inputs_embeds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">inputs_embeds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">inputs_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wte</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">inputs_embeds</span>
+        <span class="k">if</span> <span class="n">position_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">position_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">wpe</span><span class="p">(</span><span class="n">position_ids</span><span class="p">)</span>
+            <span class="n">hidden_states</span> <span class="o">+=</span> <span class="n">position_embeds</span>
+
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="n">all_hidden_states</span> <span class="o">=</span> <span class="p">()</span> <span class="k">if</span> <span class="n">output_hidden_states</span> <span class="k">else</span> <span class="kc">None</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">output_hidden_states</span><span class="p">:</span>
+                <span class="n">all_hidden_states</span> <span class="o">=</span> <span class="n">all_hidden_states</span> <span class="o">+</span> <span class="p">(</span><span class="n">hidden_states</span><span class="p">,)</span>
+            <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">block</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">output_hidden_states</span><span class="p">:</span>
+            <span class="n">all_hidden_states</span> <span class="o">=</span> <span class="n">all_hidden_states</span> <span class="o">+</span> <span class="p">(</span><span class="n">hidden_states</span><span class="p">,)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="p">[</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">all_hidden_states</span><span class="p">]</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">MLPModelOutput</span><span class="p">(</span>
+            <span class="n">last_hidden_state</span><span class="o">=</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">hidden_states</span><span class="o">=</span><span class="n">all_hidden_states</span>
+        <span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="MLPForClassification">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPForClassification.html#pyvene.models.mlp.modelings_mlp.MLPForClassification">[docs]</a>
+<span class="k">class</span> <span class="nc">MLPForClassification</span><span class="p">(</span><span class="n">PreTrainedModel</span><span class="p">):</span>
+<div class="viewcode-block" id="MLPForClassification.__init__">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPForClassification.html#pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">num_classes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">squeeze_output</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">squeeze_output</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPModel</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">score</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">include_bias</span><span class="p">)</span>
+
+        <span class="c1"># Initialize weights and apply final processing</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">post_init</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="MLPForClassification.forward">
+<a class="viewcode-back" href="../../../../api/pyvene.models.mlp.modelings_mlp.MLPForClassification.html#pyvene.models.mlp.modelings_mlp.MLPForClassification.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">input_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">position_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">inputs_embeds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">return_dict</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">mlp_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span><span class="p">(</span>
+            <span class="n">input_ids</span><span class="p">,</span>
+            <span class="n">position_ids</span><span class="p">,</span>
+            <span class="n">inputs_embeds</span><span class="p">,</span>
+            <span class="n">output_hidden_states</span><span class="p">,</span>
+            <span class="n">return_dict</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">hidden_states</span> <span class="o">=</span> <span class="n">mlp_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">pooled_logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">squeeze_output</span><span class="p">:</span>
+            <span class="n">pooled_logits</span> <span class="o">=</span> <span class="n">pooled_logits</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="s2">&quot;regression&quot;</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="p">(</span>
+                    <span class="n">labels</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">long</span> <span class="ow">or</span> <span class="n">labels</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int</span>
+                <span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="s2">&quot;single_label_classification&quot;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">=</span> <span class="s2">&quot;multi_label_classification&quot;</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">==</span> <span class="s2">&quot;regression&quot;</span><span class="p">:</span>
+                <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">MSELoss</span><span class="p">()</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">pooled_logits</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">labels</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">pooled_logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">==</span> <span class="s2">&quot;single_label_classification&quot;</span><span class="p">:</span>
+                <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+                <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span>
+                    <span class="n">pooled_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">),</span> <span class="n">labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">problem_type</span> <span class="o">==</span> <span class="s2">&quot;multi_label_classification&quot;</span><span class="p">:</span>
+                <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
+                <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">pooled_logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_dict</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">pooled_logits</span><span class="p">,)</span> <span class="o">+</span> <span class="n">mlp_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+            <span class="k">return</span> <span class="p">((</span><span class="n">loss</span><span class="p">,)</span> <span class="o">+</span> <span class="n">output</span><span class="p">)</span> <span class="k">if</span> <span class="n">loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">output</span>
+
+        <span class="k">return</span> <span class="n">SequenceClassifierOutput</span><span class="p">(</span>
+            <span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span>
+            <span class="n">logits</span><span class="o">=</span><span class="n">pooled_logits</span><span class="p">,</span>
+            <span class="n">hidden_states</span><span class="o">=</span><span class="n">mlp_outputs</span><span class="o">.</span><span class="n">hidden_states</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+                </article>
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+                <article class="bd-article">
+                  
+  <h1>Source code for pyvene.models.modeling_utils</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">random</span><span class="o">,</span> <span class="nn">torch</span><span class="o">,</span> <span class="nn">types</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">.intervenable_modelcard</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.interventions</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<div class="viewcode-block" id="LambdaIntervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention">[docs]</a>
+<span class="k">class</span> <span class="nc">LambdaIntervention</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    A generic wrapper to turn any Python callable (e.g. a lambda) </span>
+<span class="sd">    into an nn.Module. This does *not* automatically turn external </span>
+<span class="sd">    Tensors into parameters or buffers—it&#39;s just a functional wrapper.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+<div class="viewcode-block" id="LambdaIntervention.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">func</span>  <span class="c1"># store the lambda or any callable</span></div>
+
+
+<div class="viewcode-block" id="LambdaIntervention.forward">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="c1"># Simply call the stored function</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="get_internal_model_type">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.get_internal_model_type.html#pyvene.models.modeling_utils.get_internal_model_type">[docs]</a>
+<span class="k">def</span> <span class="nf">get_internal_model_type</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the model type.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="is_stateless">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.is_stateless.html#pyvene.models.modeling_utils.is_stateless">[docs]</a>
+<span class="k">def</span> <span class="nf">is_stateless</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Determine if the model is stateful (e.g., rnn) or stateless (e.g.,</span>
+<span class="sd">    transformer)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">is_gru</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">False</span>
+    <span class="k">return</span> <span class="kc">True</span></div>
+
+
+
+<div class="viewcode-block" id="is_gru">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.is_gru.html#pyvene.models.modeling_utils.is_gru">[docs]</a>
+<span class="k">def</span> <span class="nf">is_gru</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Determine if this is a transformer model.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">==</span> <span class="n">GRUModel</span>
+        <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">==</span> <span class="n">GRULMHeadModel</span>
+        <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">==</span> <span class="n">GRUForClassification</span>
+    <span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">return</span> <span class="kc">False</span></div>
+
+
+
+<div class="viewcode-block" id="is_mlp">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.is_mlp.html#pyvene.models.modeling_utils.is_mlp">[docs]</a>
+<span class="k">def</span> <span class="nf">is_mlp</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Determine if this is a mlp model.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">==</span> <span class="n">MLPModel</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">==</span> <span class="n">MLPForClassification</span><span class="p">:</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">return</span> <span class="kc">False</span></div>
+
+
+
+<div class="viewcode-block" id="is_transformer">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.is_transformer.html#pyvene.models.modeling_utils.is_transformer">[docs]</a>
+<span class="k">def</span> <span class="nf">is_transformer</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Determine if this is a transformer model.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">is_gru</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">is_mlp</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">return</span> <span class="kc">False</span></div>
+
+
+
+<div class="viewcode-block" id="print_forward_hooks">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.print_forward_hooks.html#pyvene.models.modeling_utils.print_forward_hooks">[docs]</a>
+<span class="k">def</span> <span class="nf">print_forward_hooks</span><span class="p">(</span><span class="n">main_module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to print forward hooks of a module and its sub-modules.&quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">submodule</span> <span class="ow">in</span> <span class="n">main_module</span><span class="o">.</span><span class="n">named_modules</span><span class="p">():</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">submodule</span><span class="p">,</span> <span class="s2">&quot;_forward_hooks&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Module: </span><span class="si">{</span><span class="n">name</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">&#39;Main Module&#39;</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">hook_id</span><span class="p">,</span> <span class="n">hook</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;  ID: </span><span class="si">{</span><span class="n">hook_id</span><span class="si">}</span><span class="s2">, Hook: </span><span class="si">{</span><span class="n">hook</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">submodule</span><span class="p">,</span> <span class="s2">&quot;_forward_pre_hooks&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Module: </span><span class="si">{</span><span class="n">name</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">name</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">&#39;Main Module&#39;</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">hook_id</span><span class="p">,</span> <span class="n">hook</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;  ID: </span><span class="si">{</span><span class="n">hook_id</span><span class="si">}</span><span class="s2">, Hook: </span><span class="si">{</span><span class="n">hook</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="remove_forward_hooks">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.remove_forward_hooks.html#pyvene.models.modeling_utils.remove_forward_hooks">[docs]</a>
+<span class="k">def</span> <span class="nf">remove_forward_hooks</span><span class="p">(</span><span class="n">main_module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to remove all forward and pre-forward hooks from a module and</span>
+
+<span class="sd">    its sub-modules.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Remove forward hooks</span>
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">submodule</span> <span class="ow">in</span> <span class="n">main_module</span><span class="o">.</span><span class="n">named_modules</span><span class="p">():</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">submodule</span><span class="p">,</span> <span class="s2">&quot;_forward_hooks&quot;</span><span class="p">):</span>
+            <span class="n">hooks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">submodule</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>  <span class="c1"># Get a list of hook IDs</span>
+            <span class="k">for</span> <span class="n">hook_id</span> <span class="ow">in</span> <span class="n">hooks</span><span class="p">:</span>
+                <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">hook_id</span><span class="p">)</span>
+
+        <span class="c1"># Remove pre-forward hooks</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">submodule</span><span class="p">,</span> <span class="s2">&quot;_forward_pre_hooks&quot;</span><span class="p">):</span>
+            <span class="n">pre_hooks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
+                <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+            <span class="p">)</span>  <span class="c1"># Get a list of pre-hook IDs</span>
+            <span class="k">for</span> <span class="n">pre_hook_id</span> <span class="ow">in</span> <span class="n">pre_hooks</span><span class="p">:</span>
+                <span class="n">submodule</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">pre_hook_id</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="getattr_for_torch_module">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.getattr_for_torch_module.html#pyvene.models.modeling_utils.getattr_for_torch_module">[docs]</a>
+<span class="k">def</span> <span class="nf">getattr_for_torch_module</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">parameter_name</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Recursively fetch the model based on the name.&quot;&quot;&quot;</span>
+    <span class="n">current_module</span> <span class="o">=</span> <span class="n">model</span>
+    <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">parameter_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">&quot;[&quot;</span> <span class="ow">in</span> <span class="n">param</span><span class="p">:</span>
+            <span class="n">current_module</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">current_module</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])[</span>
+                <span class="nb">int</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;]&quot;</span><span class="p">))</span>
+            <span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">current_module</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">current_module</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">current_module</span></div>
+
+
+
+<div class="viewcode-block" id="get_dimension_by_component">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.get_dimension_by_component.html#pyvene.models.modeling_utils.get_dimension_by_component">[docs]</a>
+<span class="k">def</span> <span class="nf">get_dimension_by_component</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">model_config</span><span class="p">,</span> <span class="n">component</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Based on the representation, get the aligning dimension size.&quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">component</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">type_to_dimension_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="n">dimension_proposals</span> <span class="o">=</span> <span class="n">type_to_dimension_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">][</span><span class="n">component</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">proposal</span> <span class="ow">in</span> <span class="n">dimension_proposals</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">proposal</span><span class="o">.</span><span class="n">isnumeric</span><span class="p">():</span>
+            <span class="n">dimension</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">proposal</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="s2">&quot;*&quot;</span> <span class="ow">in</span> <span class="n">proposal</span><span class="p">:</span>
+            <span class="c1"># often constant multiplier with MLP</span>
+            <span class="n">dimension</span> <span class="o">=</span> <span class="n">getattr_for_torch_module</span><span class="p">(</span>
+                <span class="n">model_config</span><span class="p">,</span> <span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;*&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="p">)</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;*&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="s2">&quot;/&quot;</span> <span class="ow">in</span> <span class="n">proposal</span><span class="p">:</span>
+            <span class="c1"># often split by head number</span>
+            <span class="k">if</span> <span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">isnumeric</span><span class="p">():</span>
+                <span class="n">numr</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">numr</span> <span class="o">=</span> <span class="n">getattr_for_torch_module</span><span class="p">(</span><span class="n">model_config</span><span class="p">,</span> <span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">isnumeric</span><span class="p">():</span>
+                <span class="n">denr</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">denr</span> <span class="o">=</span> <span class="n">getattr_for_torch_module</span><span class="p">(</span><span class="n">model_config</span><span class="p">,</span> <span class="n">proposal</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">dimension</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numr</span> <span class="o">/</span> <span class="n">denr</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">dimension</span> <span class="o">=</span> <span class="n">getattr_for_torch_module</span><span class="p">(</span><span class="n">model_config</span><span class="p">,</span> <span class="n">proposal</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">dimension</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">dimension</span>
+
+    <span class="k">assert</span> <span class="kc">False</span></div>
+
+
+
+<div class="viewcode-block" id="get_module_hook">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.get_module_hook.html#pyvene.models.modeling_utils.get_module_hook">[docs]</a>
+<span class="k">def</span> <span class="nf">get_module_hook</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">representation</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;native&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Render the intervening module with a hook.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="p">(</span>
+        <span class="n">get_internal_model_type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="ow">in</span> <span class="n">type_to_module_mapping</span> <span class="ow">and</span>
+        <span class="n">representation</span><span class="o">.</span><span class="n">component</span>
+        <span class="ow">in</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">get_internal_model_type</span><span class="p">(</span><span class="n">model</span><span class="p">)]</span>
+    <span class="p">):</span>
+        <span class="n">type_info</span> <span class="o">=</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">get_internal_model_type</span><span class="p">(</span><span class="n">model</span><span class="p">)][</span>
+            <span class="n">representation</span><span class="o">.</span><span class="n">component</span>
+        <span class="p">]</span>
+        <span class="n">parameter_name</span> <span class="o">=</span> <span class="n">type_info</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">hook_type</span> <span class="o">=</span> <span class="n">type_info</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="ow">in</span> <span class="n">parameter_name</span> <span class="ow">and</span> <span class="n">representation</span><span class="o">.</span><span class="n">moe_key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># we assume it is for the layer.</span>
+            <span class="n">parameter_name</span> <span class="o">=</span> <span class="n">parameter_name</span> <span class="o">%</span> <span class="p">(</span><span class="n">representation</span><span class="o">.</span><span class="n">layer</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="ow">in</span> <span class="n">parameter_name</span> <span class="ow">and</span> <span class="n">representation</span><span class="o">.</span><span class="n">moe_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">parameter_name</span> <span class="o">=</span> <span class="n">parameter_name</span> <span class="o">%</span> <span class="p">(</span>
+                <span class="nb">int</span><span class="p">(</span><span class="n">representation</span><span class="o">.</span><span class="n">layer</span><span class="p">),</span>
+                <span class="nb">int</span><span class="p">(</span><span class="n">representation</span><span class="o">.</span><span class="n">moe_key</span><span class="p">),</span>
+            <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">parameter_name</span> <span class="o">=</span> <span class="s2">&quot;.&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">representation</span><span class="o">.</span><span class="n">component</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">representation</span><span class="o">.</span><span class="n">component</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;input&quot;</span><span class="p">:</span>
+            <span class="n">hook_type</span> <span class="o">=</span> <span class="n">CONST_INPUT_HOOK</span>
+        <span class="k">elif</span> <span class="n">representation</span><span class="o">.</span><span class="n">component</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;output&quot;</span><span class="p">:</span>
+            <span class="n">hook_type</span> <span class="o">=</span> <span class="n">CONST_OUTPUT_HOOK</span>
+
+    <span class="n">module</span> <span class="o">=</span> <span class="n">getattr_for_torch_module</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">parameter_name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;native&quot;</span><span class="p">:</span>
+        <span class="n">module_hook</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">hook_type</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;ndif&quot;</span><span class="p">:</span>
+        <span class="c1"># we assume the input v.s. output is handled outside</span>
+        <span class="n">module_hook</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">module_hook</span><span class="p">,</span> <span class="n">hook_type</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">module_hook</span></div>
+
+
+
+<div class="viewcode-block" id="HandlerList">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.HandlerList.html#pyvene.models.modeling_utils.HandlerList">[docs]</a>
+<span class="k">class</span> <span class="nc">HandlerList</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;General class to set hooks and set off hooks.&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="HandlerList.__init__">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.HandlerList.html#pyvene.models.modeling_utils.HandlerList.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">handlers</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span> <span class="o">=</span> <span class="n">handlers</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handlers</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span><span class="p">:</span>
+            <span class="n">handler</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">extend</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_handlers</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new_handlers</span><span class="o">.</span><span class="n">handlers</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+
+<div class="viewcode-block" id="bsd_to_b_sd">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.bsd_to_b_sd.html#pyvene.models.modeling_utils.bsd_to_b_sd">[docs]</a>
+<span class="k">def</span> <span class="nf">bsd_to_b_sd</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a tensor of shape (b, s, d) to (b, s*d).&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">tensor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">tensor</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span> <span class="o">*</span> <span class="n">d</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="b_sd_to_bsd">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.b_sd_to_bsd.html#pyvene.models.modeling_utils.b_sd_to_bsd">[docs]</a>
+<span class="k">def</span> <span class="nf">b_sd_to_bsd</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a tensor of shape (b, s*d) back to (b, s, d).&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">tensor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">tensor</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">sd</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="n">sd</span> <span class="o">//</span> <span class="n">s</span>
+    <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="bhsd_to_bs_hd">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.bhsd_to_bs_hd.html#pyvene.models.modeling_utils.bhsd_to_bs_hd">[docs]</a>
+<span class="k">def</span> <span class="nf">bhsd_to_bs_hd</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a tensor of shape (b, h, s, d) to (b, s, h*d).&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">tensor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">tensor</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">h</span> <span class="o">*</span> <span class="n">d</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="bs_hd_to_bhsd">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.bs_hd_to_bhsd.html#pyvene.models.modeling_utils.bs_hd_to_bhsd">[docs]</a>
+<span class="k">def</span> <span class="nf">bs_hd_to_bhsd</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a tensor of shape (b, s, h*d) back to (b, h, s, d).&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">tensor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">tensor</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">hd</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span>
+
+    <span class="n">d</span> <span class="o">=</span> <span class="n">hd</span> <span class="o">//</span> <span class="n">h</span>
+
+    <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="output_to_subcomponent">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.output_to_subcomponent.html#pyvene.models.modeling_utils.output_to_subcomponent">[docs]</a>
+<span class="k">def</span> <span class="nf">output_to_subcomponent</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">component</span><span class="p">,</span> <span class="n">model_type</span><span class="p">,</span> <span class="n">model_config</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Split the raw output to subcomponents if specified in the config.</span>
+
+<span class="sd">    :param output: the original output from the model component.</span>
+<span class="sd">    :param component: types of model component, such as</span>
+<span class="sd">    &quot;block_output&quot; and &quot;query_output&quot; or it can be direct referece, such as</span>
+<span class="sd">    &quot;h[0].mlp.act&quot; which we will not splice into any subcomponent.</span>
+<span class="sd">    :param model_type: Hugging Face Model Type</span>
+<span class="sd">    :param model_config: Hugging Face Model Config</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">subcomponent</span> <span class="o">=</span> <span class="n">output</span>
+    <span class="k">if</span> <span class="n">model_type</span> <span class="ow">in</span> <span class="n">type_to_module_mapping</span> <span class="ow">and</span> \
+        <span class="n">component</span> <span class="ow">in</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">]:</span>
+        <span class="n">split_last_dim_by</span> <span class="o">=</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">][</span><span class="n">component</span><span class="p">][</span><span class="mi">2</span><span class="p">:]</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">split_last_dim_by</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">split_last_dim_by</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unsupported </span><span class="si">{</span><span class="n">split_last_dim_by</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">split_fn</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">split_last_dim_by</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="n">param</span> <span class="o">=</span> <span class="n">get_dimension_by_component</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">model_config</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span>
+            <span class="n">subcomponent</span> <span class="o">=</span> <span class="n">split_fn</span><span class="p">(</span><span class="n">subcomponent</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">subcomponent</span></div>
+
+
+
+<div class="viewcode-block" id="gather_neurons">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.gather_neurons.html#pyvene.models.modeling_utils.gather_neurons">[docs]</a>
+<span class="k">def</span> <span class="nf">gather_neurons</span><span class="p">(</span><span class="n">tensor_input</span><span class="p">,</span> <span class="n">unit</span><span class="p">,</span> <span class="n">unit_locations_as_list</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Gather intervening neurons.</span>
+
+<span class="sd">    :param tensor_input: tensors of shape (batch_size, sequence_length, ...) if</span>
+<span class="sd">    `unit` is &quot;pos&quot; or &quot;h&quot;, tensors of shape (batch_size, num_heads,</span>
+<span class="sd">    sequence_length, ...) if `unit` is &quot;h.pos&quot;</span>
+<span class="sd">    :param unit: the intervention units to gather. Units could be &quot;h&quot; - head</span>
+<span class="sd">    number, &quot;pos&quot; - position in the sequence, or &quot;dim&quot; - a particular dimension in</span>
+<span class="sd">    the embedding space. If intervening multiple units, they are ordered and</span>
+<span class="sd">    separated by `.`. Currently only support &quot;pos&quot;, &quot;h&quot;, and &quot;h.pos&quot; units.</span>
+<span class="sd">    :param unit_locations_as_list: tuple of lists of lists of positions to gather</span>
+<span class="sd">    in tensor_input, according to the unit.</span>
+<span class="sd">    :return the gathered tensor as tensor_output</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">unit</span> <span class="ow">in</span> <span class="p">{</span><span class="s2">&quot;t&quot;</span><span class="p">}:</span>
+        <span class="k">return</span> <span class="n">tensor_input</span>
+
+    <span class="k">if</span> <span class="s2">&quot;.&quot;</span> <span class="ow">in</span> <span class="n">unit</span><span class="p">:</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">unit_locations_as_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> 
+                         <span class="n">device</span><span class="o">=</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span><span class="p">),</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">unit_locations_as_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> 
+                         <span class="n">device</span><span class="o">=</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="c1"># we assume unit_locations is a tuple</span>
+        <span class="n">head_unit_locations</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">pos_unit_locations</span> <span class="o">=</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="n">head_tensor_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span>
+            <span class="n">tensor_input</span><span class="p">,</span>
+            <span class="mi">1</span><span class="p">,</span>
+            <span class="n">head_unit_locations</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
+                <span class="o">*</span><span class="n">head_unit_locations</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span>
+        <span class="p">)</span>  <span class="c1"># b, h, s, d</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">head_tensor_output</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pos_tensor_input</span> <span class="o">=</span> <span class="n">bhsd_to_bs_hd</span><span class="p">(</span><span class="n">head_tensor_output</span><span class="p">)</span>
+        <span class="n">pos_tensor_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span>
+            <span class="n">pos_tensor_input</span><span class="p">,</span>
+            <span class="mi">1</span><span class="p">,</span>
+            <span class="n">pos_unit_locations</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
+                <span class="o">*</span><span class="n">pos_unit_locations</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pos_tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">pos_tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span>
+        <span class="p">)</span>  <span class="c1"># b, num_unit (pos), num_unit (h)*d</span>
+        <span class="n">tensor_output</span> <span class="o">=</span> <span class="n">bs_hd_to_bhsd</span><span class="p">(</span><span class="n">pos_tensor_output</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">tensor_output</span>  <span class="c1"># b, num_unit (h), num_unit (pos), d</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+            <span class="n">unit_locations_as_list</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span>
+        <span class="p">)</span>
+
+        <span class="n">tensor_output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span>
+            <span class="n">tensor_input</span><span class="p">,</span>
+            <span class="mi">1</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
+                <span class="o">*</span><span class="n">unit_locations</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">tensor_output</span></div>
+
+
+
+<div class="viewcode-block" id="scatter_neurons">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.scatter_neurons.html#pyvene.models.modeling_utils.scatter_neurons">[docs]</a>
+<span class="k">def</span> <span class="nf">scatter_neurons</span><span class="p">(</span>
+    <span class="n">tensor_input</span><span class="p">,</span>
+    <span class="n">replacing_tensor_input</span><span class="p">,</span>
+    <span class="n">component</span><span class="p">,</span>
+    <span class="n">unit</span><span class="p">,</span>
+    <span class="n">unit_locations_as_list</span><span class="p">,</span>
+    <span class="n">model_type</span><span class="p">,</span>
+    <span class="n">model_config</span><span class="p">,</span>
+    <span class="n">use_fast</span><span class="p">,</span>
+    <span class="n">device</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Replace selected neurons in `tensor_input` by `replacing_tensor_input`.</span>
+
+<span class="sd">    :param tensor_input: tensors of shape (batch_size, sequence_length, ...) if</span>
+<span class="sd">    `unit` is &quot;pos&quot; or &quot;h&quot;, tensors of shape (batch_size, num_heads,</span>
+<span class="sd">    sequence_length, ...) if `unit` is &quot;h.pos&quot;</span>
+<span class="sd">    :param replacing_tensor_input: tensors of shape (batch_size, sequence_length,</span>
+<span class="sd">    ...) if `unit` is &quot;pos&quot; or</span>
+<span class="sd">    &quot;h&quot;, tensors of shape (batch_size, num_heads, sequence_length, ...) if</span>
+<span class="sd">    `unit` is &quot;h.pos&quot;.</span>
+<span class="sd">    :param component: types of intervention representations, such as</span>
+<span class="sd">    &quot;block_output&quot; and &quot;query_output&quot;</span>
+<span class="sd">    :param unit: the intervention units to gather. Units could be &quot;h&quot; - head</span>
+<span class="sd">    number, &quot;pos&quot; - position in the sequence, or &quot;dim&quot; - a particular dimension in</span>
+<span class="sd">    the embedding space. If intervening multiple units, they are ordered and</span>
+<span class="sd">    separated by `.`. Currently only support &quot;pos&quot;, &quot;h&quot;, and &quot;h.pos&quot; units.</span>
+<span class="sd">    :param unit_locations_as_list: tuple of lists of lists of positions to gather</span>
+<span class="sd">    in tensor_input, according to the unit.</span>
+<span class="sd">    :param model_type: Hugging Face Model Type</span>
+<span class="sd">    :param model_config: Hugging Face Model Config</span>
+<span class="sd">    :param use_fast: whether to use fast path (TODO: fast path condition)</span>
+<span class="sd">    :return the in-place modified tensor_input</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="s2">&quot;.&quot;</span> <span class="ow">in</span> <span class="n">unit</span><span class="p">:</span>
+        <span class="c1"># extra dimension for multi-level intervention</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">unit_locations_as_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> 
+                         <span class="n">device</span><span class="o">=</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span><span class="p">),</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">unit_locations_as_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> 
+                         <span class="n">device</span><span class="o">=</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span><span class="p">),</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">unit_locations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+            <span class="n">unit_locations_as_list</span><span class="p">,</span> 
+            <span class="n">device</span><span class="o">=</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span>
+        <span class="p">)</span>
+
+    <span class="c1"># if tensor is splitted, we need to get the start and end indices</span>
+    <span class="n">meta_component</span> <span class="o">=</span> <span class="n">output_to_subcomponent</span><span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+        <span class="n">component</span><span class="p">,</span>
+        <span class="n">model_type</span><span class="p">,</span>
+        <span class="n">model_config</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">start_index</span><span class="p">,</span> <span class="n">end_index</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">meta_component</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
+        <span class="n">meta_component</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">last_dim</span> <span class="o">=</span> <span class="n">meta_component</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">_batch_idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">tensor_input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># in case it is time step, there is no sequence-related index</span>
+    <span class="k">if</span> <span class="n">unit</span> <span class="ow">in</span> <span class="p">{</span><span class="s2">&quot;t&quot;</span><span class="p">}:</span>
+        <span class="c1"># time series models, e.g., gru</span>
+        <span class="n">tensor_input</span><span class="p">[</span><span class="n">_batch_idx</span><span class="p">,</span> <span class="n">start_index</span><span class="p">:</span><span class="n">end_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span>
+        <span class="k">return</span> <span class="n">tensor_input</span>
+    <span class="k">elif</span> <span class="n">unit</span> <span class="ow">in</span> <span class="p">{</span><span class="s2">&quot;pos&quot;</span><span class="p">}:</span>
+        <span class="k">if</span> <span class="n">use_fast</span><span class="p">:</span>
+            <span class="c1"># maybe this is all redundant, but maybe faster slightly?</span>
+            <span class="n">tensor_input</span><span class="p">[</span>
+                <span class="n">_batch_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">start_index</span><span class="p">:</span><span class="n">end_index</span>
+            <span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tensor_input</span><span class="p">[</span>
+                <span class="n">_batch_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">,</span> <span class="n">start_index</span><span class="p">:</span><span class="n">end_index</span>
+            <span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span>
+        <span class="k">return</span> <span class="n">tensor_input</span>
+    <span class="k">elif</span> <span class="n">unit</span> <span class="ow">in</span> <span class="p">{</span><span class="s2">&quot;h&quot;</span><span class="p">,</span> <span class="s2">&quot;h.pos&quot;</span><span class="p">}:</span>
+        <span class="c1"># head-based scattering is only special for transformer-based model</span>
+        <span class="c1"># replacing_tensor_input: b_s, num_h, s, h_dim -&gt; b_s, s, num_h*h_dim</span>
+        <span class="n">old_shape</span> <span class="o">=</span> <span class="n">tensor_input</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>  <span class="c1"># b_s, s, -1*num_h*d</span>
+        <span class="n">new_shape</span> <span class="o">=</span> <span class="n">tensor_input</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span>
+            <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+            <span class="n">meta_component</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">last_dim</span><span class="p">,</span>
+        <span class="p">)</span>  <span class="c1"># b_s, s, -1, num_h, d</span>
+        <span class="c1"># get whether split by QKV</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">component</span> <span class="ow">in</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">]</span>
+            <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">][</span><span class="n">component</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">2</span>
+            <span class="ow">and</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">][</span><span class="n">component</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">split_three</span>
+        <span class="p">):</span>
+            <span class="n">_slice_idx</span> <span class="o">=</span> <span class="n">type_to_module_mapping</span><span class="p">[</span><span class="n">model_type</span><span class="p">][</span><span class="n">component</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">_slice_idx</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">tensor_permute</span> <span class="o">=</span> <span class="n">tensor_input</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>  <span class="c1"># b_s, s, -1, num_h, d</span>
+        <span class="n">tensor_permute</span> <span class="o">=</span> <span class="n">tensor_permute</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>  <span class="c1"># b_s, num_h, -1, s, d</span>
+        <span class="k">if</span> <span class="s2">&quot;.&quot;</span> <span class="ow">in</span> <span class="n">unit</span><span class="p">:</span>
+            <span class="c1"># cannot advance indexing on two columns, thus a single for loop is unavoidable.</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">unit_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">tensor_permute</span><span class="p">[</span>
+                    <span class="n">_batch_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span> <span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">_slice_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tensor_permute</span><span class="p">[</span>
+                <span class="n">_batch_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">,</span> <span class="n">_slice_idx</span>
+            <span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span>
+        <span class="c1"># permute back and reshape</span>
+        <span class="n">tensor_output</span> <span class="o">=</span> <span class="n">tensor_permute</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>  <span class="c1"># b_s, s, -1, num_h, d</span>
+        <span class="n">tensor_output</span> <span class="o">=</span> <span class="n">tensor_output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">old_shape</span><span class="p">)</span>  <span class="c1"># b_s, s, -1*num_h*d</span>
+        <span class="k">return</span> <span class="n">tensor_output</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="s2">&quot;.&quot;</span> <span class="ow">in</span> <span class="n">unit</span><span class="p">:</span>
+            <span class="c1"># cannot advance indexing on two columns, thus a single for loop is unavoidable.</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">unit_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="n">tensor_input</span><span class="p">[</span>
+                    <span class="n">_batch_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span> <span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">unit_locations</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tensor_input</span><span class="p">[</span><span class="n">_batch_idx</span><span class="p">,</span> <span class="n">unit_locations</span><span class="p">]</span> <span class="o">=</span> <span class="n">replacing_tensor_input</span>
+        <span class="k">return</span> <span class="n">tensor_input</span>
+    <span class="k">assert</span> <span class="kc">False</span></div>
+
+
+
+<div class="viewcode-block" id="do_intervention">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.do_intervention.html#pyvene.models.modeling_utils.do_intervention">[docs]</a>
+<span class="k">def</span> <span class="nf">do_intervention</span><span class="p">(</span>
+    <span class="n">base_representation</span><span class="p">,</span> <span class="n">source_representation</span><span class="p">,</span> <span class="n">intervention</span><span class="p">,</span> <span class="n">subspaces</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Do the actual intervention.&quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">LambdaIntervention</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">subspaces</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">intervention</span><span class="p">(</span><span class="n">base_representation</span><span class="p">,</span> <span class="n">source_representation</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">intervention</span><span class="p">(</span><span class="n">base_representation</span><span class="p">,</span> <span class="n">source_representation</span><span class="p">,</span> <span class="n">subspaces</span><span class="p">)</span>
+
+    <span class="n">num_unit</span> <span class="o">=</span> <span class="n">base_representation</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="c1"># flatten</span>
+    <span class="n">original_base_shape</span> <span class="o">=</span> <span class="n">base_representation</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">original_base_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">or</span> <span class="p">(</span>
+        <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">)</span>
+    <span class="p">)</span> <span class="ow">or</span> <span class="n">intervention</span><span class="o">.</span><span class="n">keep_last_dim</span><span class="p">:</span>
+        <span class="c1"># no pos dimension, e.g., gru, or opt-out concate last two dims</span>
+        <span class="n">base_representation_f</span> <span class="o">=</span> <span class="n">base_representation</span>
+        <span class="n">source_representation_f</span> <span class="o">=</span> <span class="n">source_representation</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">original_base_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="c1"># b, num_unit (pos), d -&gt; b, num_unit*d</span>
+        <span class="n">base_representation_f</span> <span class="o">=</span> <span class="n">bsd_to_b_sd</span><span class="p">(</span><span class="n">base_representation</span><span class="p">)</span>
+        <span class="n">source_representation_f</span> <span class="o">=</span> <span class="n">bsd_to_b_sd</span><span class="p">(</span><span class="n">source_representation</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">original_base_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="c1"># b, num_unit (h), s, d -&gt; b, s, num_unit*d</span>
+        <span class="n">base_representation_f</span> <span class="o">=</span> <span class="n">bhsd_to_bs_hd</span><span class="p">(</span><span class="n">base_representation</span><span class="p">)</span>
+        <span class="n">source_representation_f</span> <span class="o">=</span> <span class="n">bhsd_to_bs_hd</span><span class="p">(</span><span class="n">source_representation</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="kc">False</span>  <span class="c1"># what&#39;s going on?</span>
+
+    <span class="n">intervention_output</span> <span class="o">=</span> <span class="n">intervention</span><span class="p">(</span>
+        <span class="n">base_representation_f</span><span class="p">,</span> <span class="n">source_representation_f</span><span class="p">,</span> <span class="n">subspaces</span>
+    <span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention_output</span><span class="p">,</span> <span class="n">InterventionOutput</span><span class="p">):</span>
+        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">intervention_output</span><span class="o">.</span><span class="n">output</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">intervention_output</span>
+
+    <span class="n">post_d</span> <span class="o">=</span> <span class="n">intervened_representation</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="c1"># unflatten</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">original_base_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span>
+        <span class="n">intervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span>
+    <span class="p">)</span> <span class="ow">or</span> <span class="n">intervention</span><span class="o">.</span><span class="n">keep_last_dim</span><span class="p">:</span>
+        <span class="c1"># no pos dimension, e.g., gru or opt-out concate last two dims</span>
+        <span class="k">pass</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">original_base_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">b_sd_to_bsd</span><span class="p">(</span><span class="n">intervened_representation</span><span class="p">,</span> <span class="n">num_unit</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">original_base_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">intervened_representation</span> <span class="o">=</span> <span class="n">bs_hd_to_bhsd</span><span class="p">(</span><span class="n">intervened_representation</span><span class="p">,</span> <span class="n">num_unit</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="kc">False</span>  <span class="c1"># what&#39;s going on?</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intervention_output</span><span class="p">,</span> <span class="n">InterventionOutput</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">intervened_representation</span>
+
+    <span class="n">intervention_output</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">intervened_representation</span>
+    <span class="k">return</span> <span class="n">intervention_output</span></div>
+
+
+
+<div class="viewcode-block" id="simple_output_to_subcomponent">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.simple_output_to_subcomponent.html#pyvene.models.modeling_utils.simple_output_to_subcomponent">[docs]</a>
+<span class="k">def</span> <span class="nf">simple_output_to_subcomponent</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">representation_type</span><span class="p">,</span> <span class="n">model_config</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is an oversimplied version for demo.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">output</span></div>
+
+
+
+<div class="viewcode-block" id="simple_scatter_intervention_output">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.simple_scatter_intervention_output.html#pyvene.models.modeling_utils.simple_scatter_intervention_output">[docs]</a>
+<span class="k">def</span> <span class="nf">simple_scatter_intervention_output</span><span class="p">(</span>
+    <span class="n">original_output</span><span class="p">,</span>
+    <span class="n">intervened_representation</span><span class="p">,</span>
+    <span class="n">representation_type</span><span class="p">,</span>
+    <span class="n">unit</span><span class="p">,</span>
+    <span class="n">unit_locations</span><span class="p">,</span>
+    <span class="n">model_config</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This is an oversimplied version for demo.&quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">batch_i</span><span class="p">,</span> <span class="n">locations</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unit_locations</span><span class="p">):</span>
+        <span class="n">original_output</span><span class="p">[</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">locations</span><span class="p">]</span> <span class="o">=</span> <span class="n">intervened_representation</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]</span></div>
+
+
+
+<div class="viewcode-block" id="weighted_average">
+<a class="viewcode-back" href="../../../api/pyvene.models.modeling_utils.weighted_average.html#pyvene.models.modeling_utils.weighted_average">[docs]</a>
+<span class="k">def</span> <span class="nf">weighted_average</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">values</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The length of values and weights must be the same.&quot;</span><span class="p">)</span>
+
+    <span class="n">total</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">v</span> <span class="o">*</span> <span class="n">w</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">weights</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">total</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span></div>
+
+</pre></div>
+
+                </article>
+              
+
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+              
+              
+              
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+                  
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diff --git a/_modules/pyvene/models/olmo/modelings_intervenable_olmo.html b/_modules/pyvene/models/olmo/modelings_intervenable_olmo.html
new file mode 100644
index 00000000..54bef128
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@@ -0,0 +1,623 @@
+
+<!DOCTYPE html>
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+</ul>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/pyvene_101.html">Introduction to pyvene</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+</ul>
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+<ul class="nav bd-sidenav">
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+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">API</span></p>
+<ul class="nav bd-sidenav">
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+  <h1>Source code for pyvene.models.olmo.modelings_intervenable_olmo</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoConfig</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+
+<span class="n">olmo_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+
+<span class="n">olmo_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size/num_attention_heads&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+
+<span class="sd">&quot;&quot;&quot;olmo model with LM head&quot;&quot;&quot;</span>
+<span class="n">olmo_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">olmo_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">olmo_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">olmo_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">olmo_type_to_dimension_mapping</span>
+
+
+<span class="sd">&quot;&quot;&quot;olmo model with classifier head&quot;&quot;&quot;</span>
+<span class="n">olmo_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">olmo_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">olmo_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+
+<span class="n">olmo_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">olmo_type_to_dimension_mapping</span>
+
+
+<div class="viewcode-block" id="create_olmo">
+<a class="viewcode-back" href="../../../../api/pyvene.models.olmo.modelings_intervenable_olmo.create_olmo.html#pyvene.models.olmo.modelings_intervenable_olmo.create_olmo">[docs]</a>
+<span class="k">def</span> <span class="nf">create_olmo</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;allenai/OLMo-7B-0424-hf&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">revision</span><span class="o">=</span><span class="s1">&#39;main&#39;</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a OLMo Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">config</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+        <span class="n">olmo</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+            <span class="n">name</span><span class="p">,</span>
+            <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+            <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+            <span class="n">torch_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">olmo</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span> <span class="n">revision</span><span class="o">=</span><span class="n">revision</span><span class="p">)</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">olmo</span></div>
+
+</pre></div>
+
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+  <h1>Source code for pyvene.models.qwen2.modelings_intervenable_qwen2</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Each modeling file in this library is a mapping between</span>
+<span class="sd">abstract naming of intervention anchor points and actual</span>
+<span class="sd">model module defined in the huggingface library.</span>
+<span class="sd">We also want to let the intervention library know how to</span>
+<span class="sd">config the dimensions of intervention based on model config</span>
+<span class="sd">defined in the huggingface library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">..constants</span> <span class="kn">import</span> <span class="o">*</span>
+
+<span class="n">qwen2_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">]&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp.act_fn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].mlp&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.o_proj&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn&quot;</span><span class="p">,</span> <span class="n">CONST_INPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.q_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.k_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;layers[</span><span class="si">%s</span><span class="s2">].self_attn.v_proj&quot;</span><span class="p">,</span> <span class="n">CONST_OUTPUT_HOOK</span><span class="p">,</span> <span class="p">(</span><span class="n">split_head_and_permute</span><span class="p">,</span> <span class="s2">&quot;n_kv_head&quot;</span><span class="p">)),</span>
+<span class="p">}</span>
+
+<span class="n">qwen2_type_to_dimension_mapping</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;n_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_attention_heads&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;n_kv_head&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;num_key_value_heads&quot;</span><span class="p">,),</span> 
+    <span class="s2">&quot;block_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;intermediate_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;hidden_size&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_query_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_key_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;head_dim&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+<span class="sd">&quot;&quot;&quot;qwen2 model with LM head&quot;&quot;&quot;</span>
+<span class="n">qwen2_lm_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">qwen2_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">qwen2_lm_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+<span class="n">qwen2_lm_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">qwen2_type_to_dimension_mapping</span>
+
+<span class="sd">&quot;&quot;&quot;qwen2 model with classifier head&quot;&quot;&quot;</span>
+<span class="n">qwen2_classifier_type_to_module_mapping</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">qwen2_type_to_module_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">qwen2_classifier_type_to_module_mapping</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model.</span><span class="si">{</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+<span class="n">qwen2_classifier_type_to_dimension_mapping</span> <span class="o">=</span> <span class="n">qwen2_type_to_dimension_mapping</span>
+
+<div class="viewcode-block" id="create_qwen2">
+<a class="viewcode-back" href="../../../../api/pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2.html#pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2">[docs]</a>
+<span class="k">def</span> <span class="nf">create_qwen2</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Qwen/Qwen2-7B-beta&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a Causal LM model, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoConfig</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+    
+    <span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">name</span><span class="p">,</span>
+        <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+        <span class="n">torch_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;loaded model&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">model</span></div>
+
+</pre></div>
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+</div>
+                </footer>
+              
+            </div>
+            
+            
+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
+<div class="bd-footer-content__inner container">
+  
+  <div class="footer-item">
+    
+<p class="component-author">
+By Stanford NLP
+</p>
+
+  </div>
+  
+  <div class="footer-item">
+    
+
+  <p class="copyright">
+    
+      © Copyright 2024, Stanford NLP.
+      <br/>
+    
+  </p>
+
+  </div>
+  
+  <div class="footer-item">
+    
+  </div>
+  
+  <div class="footer-item">
+    
+  </div>
+  
+</div>
+          </footer>
+        
+
+      </main>
+    </div>
+  </div>
+  
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script defer src="../../../../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf"></script>
+<script defer src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf"></script>
+
+  <footer class="bd-footer">
+  </footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/_sources/api/core.rst b/_sources/api/core.rst
new file mode 100644
index 00000000..2b4ab631
--- /dev/null
+++ b/_sources/api/core.rst
@@ -0,0 +1,14 @@
+``pyvene``: Core API
+=========================================
+
+.. automodule:: pyvene
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.data_generators
+   pyvene.models
\ No newline at end of file
diff --git a/_sources/api/pyvene.data_generators.causal_model.CausalModel.rst b/_sources/api/pyvene.data_generators.causal_model.CausalModel.rst
new file mode 100644
index 00000000..c3e71533
--- /dev/null
+++ b/_sources/api/pyvene.data_generators.causal_model.CausalModel.rst
@@ -0,0 +1,43 @@
+pyvene.data\_generators.causal\_model.CausalModel
+=================================================
+
+.. currentmodule:: pyvene.data_generators.causal_model
+
+.. autoclass:: CausalModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~CausalModel.__init__
+      ~CausalModel.add_variable
+      ~CausalModel.find_live_paths
+      ~CausalModel.generate_counterfactual_dataset
+      ~CausalModel.generate_equiv_classes
+      ~CausalModel.generate_factual_dataset
+      ~CausalModel.generate_timesteps
+      ~CausalModel.get_partial_filter
+      ~CausalModel.get_path_maxlen_filter
+      ~CausalModel.get_specific_path_filter
+      ~CausalModel.input_to_tensor
+      ~CausalModel.marginalize
+      ~CausalModel.output_to_tensor
+      ~CausalModel.print_setting
+      ~CausalModel.print_structure
+      ~CausalModel.run_forward
+      ~CausalModel.run_interchange
+      ~CausalModel.sample_input
+      ~CausalModel.sample_input_tree_balanced
+      ~CausalModel.sample_intervention
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.data_generators.causal_model.rst b/_sources/api/pyvene.data_generators.causal_model.rst
new file mode 100644
index 00000000..259e6b11
--- /dev/null
+++ b/_sources/api/pyvene.data_generators.causal_model.rst
@@ -0,0 +1,38 @@
+pyvene.data\_generators.causal\_model
+=====================================
+
+.. automodule:: pyvene.data_generators.causal_model
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      simple_example
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      CausalModel
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.data_generators.causal_model.simple_example.rst b/_sources/api/pyvene.data_generators.causal_model.simple_example.rst
new file mode 100644
index 00000000..3db41971
--- /dev/null
+++ b/_sources/api/pyvene.data_generators.causal_model.simple_example.rst
@@ -0,0 +1,6 @@
+pyvene.data\_generators.causal\_model.simple\_example
+=====================================================
+
+.. currentmodule:: pyvene.data_generators.causal_model
+
+.. autofunction:: simple_example
\ No newline at end of file
diff --git a/_sources/api/pyvene.data_generators.rst b/_sources/api/pyvene.data_generators.rst
new file mode 100644
index 00000000..5d7fc864
--- /dev/null
+++ b/_sources/api/pyvene.data_generators.rst
@@ -0,0 +1,32 @@
+pyvene.data\_generators
+=======================
+
+.. automodule:: pyvene.data_generators
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.data_generators.causal_model
+
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.rst
new file mode 100644
index 00000000..6b3e5e50
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.rst
@@ -0,0 +1,44 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackGPT2BaseModelOutput
+==================================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackGPT2BaseModelOutput
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackGPT2BaseModelOutput.__init__
+      ~BackpackGPT2BaseModelOutput.clear
+      ~BackpackGPT2BaseModelOutput.copy
+      ~BackpackGPT2BaseModelOutput.fromkeys
+      ~BackpackGPT2BaseModelOutput.get
+      ~BackpackGPT2BaseModelOutput.items
+      ~BackpackGPT2BaseModelOutput.keys
+      ~BackpackGPT2BaseModelOutput.move_to_end
+      ~BackpackGPT2BaseModelOutput.pop
+      ~BackpackGPT2BaseModelOutput.popitem
+      ~BackpackGPT2BaseModelOutput.setdefault
+      ~BackpackGPT2BaseModelOutput.to_tuple
+      ~BackpackGPT2BaseModelOutput.update
+      ~BackpackGPT2BaseModelOutput.values
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackGPT2BaseModelOutput.contextualization
+      ~BackpackGPT2BaseModelOutput.hidden_states
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.rst
new file mode 100644
index 00000000..f331c9c7
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.rst
@@ -0,0 +1,54 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackGPT2Config
+=========================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackGPT2Config
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackGPT2Config.__init__
+      ~BackpackGPT2Config.dict_torch_dtype_to_str
+      ~BackpackGPT2Config.from_dict
+      ~BackpackGPT2Config.from_json_file
+      ~BackpackGPT2Config.from_pretrained
+      ~BackpackGPT2Config.get_config_dict
+      ~BackpackGPT2Config.get_text_config
+      ~BackpackGPT2Config.push_to_hub
+      ~BackpackGPT2Config.register_for_auto_class
+      ~BackpackGPT2Config.save_pretrained
+      ~BackpackGPT2Config.to_dict
+      ~BackpackGPT2Config.to_diff_dict
+      ~BackpackGPT2Config.to_json_file
+      ~BackpackGPT2Config.to_json_string
+      ~BackpackGPT2Config.update
+      ~BackpackGPT2Config.update_from_string
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackGPT2Config.attribute_map
+      ~BackpackGPT2Config.base_config_key
+      ~BackpackGPT2Config.base_model_tp_plan
+      ~BackpackGPT2Config.is_composition
+      ~BackpackGPT2Config.keys_to_ignore_at_inference
+      ~BackpackGPT2Config.model_type
+      ~BackpackGPT2Config.name_or_path
+      ~BackpackGPT2Config.num_labels
+      ~BackpackGPT2Config.sub_configs
+      ~BackpackGPT2Config.use_return_dict
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.rst
new file mode 100644
index 00000000..2c6473c7
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.rst
@@ -0,0 +1,149 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackGPT2LMHeadModel
+==============================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackGPT2LMHeadModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackGPT2LMHeadModel.__init__
+      ~BackpackGPT2LMHeadModel.active_adapter
+      ~BackpackGPT2LMHeadModel.active_adapters
+      ~BackpackGPT2LMHeadModel.add_adapter
+      ~BackpackGPT2LMHeadModel.add_memory_hooks
+      ~BackpackGPT2LMHeadModel.add_model_tags
+      ~BackpackGPT2LMHeadModel.add_module
+      ~BackpackGPT2LMHeadModel.apply
+      ~BackpackGPT2LMHeadModel.bfloat16
+      ~BackpackGPT2LMHeadModel.buffers
+      ~BackpackGPT2LMHeadModel.can_generate
+      ~BackpackGPT2LMHeadModel.children
+      ~BackpackGPT2LMHeadModel.compile
+      ~BackpackGPT2LMHeadModel.compute_transition_scores
+      ~BackpackGPT2LMHeadModel.cpu
+      ~BackpackGPT2LMHeadModel.create_extended_attention_mask_for_decoder
+      ~BackpackGPT2LMHeadModel.cuda
+      ~BackpackGPT2LMHeadModel.delete_adapter
+      ~BackpackGPT2LMHeadModel.dequantize
+      ~BackpackGPT2LMHeadModel.disable_adapters
+      ~BackpackGPT2LMHeadModel.disable_input_require_grads
+      ~BackpackGPT2LMHeadModel.double
+      ~BackpackGPT2LMHeadModel.enable_adapters
+      ~BackpackGPT2LMHeadModel.enable_input_require_grads
+      ~BackpackGPT2LMHeadModel.estimate_tokens
+      ~BackpackGPT2LMHeadModel.eval
+      ~BackpackGPT2LMHeadModel.extra_repr
+      ~BackpackGPT2LMHeadModel.float
+      ~BackpackGPT2LMHeadModel.floating_point_ops
+      ~BackpackGPT2LMHeadModel.forward
+      ~BackpackGPT2LMHeadModel.from_pretrained
+      ~BackpackGPT2LMHeadModel.generate
+      ~BackpackGPT2LMHeadModel.get_adapter_state_dict
+      ~BackpackGPT2LMHeadModel.get_buffer
+      ~BackpackGPT2LMHeadModel.get_compiled_call
+      ~BackpackGPT2LMHeadModel.get_extended_attention_mask
+      ~BackpackGPT2LMHeadModel.get_extra_state
+      ~BackpackGPT2LMHeadModel.get_head_mask
+      ~BackpackGPT2LMHeadModel.get_input_embeddings
+      ~BackpackGPT2LMHeadModel.get_lm_head
+      ~BackpackGPT2LMHeadModel.get_memory_footprint
+      ~BackpackGPT2LMHeadModel.get_output_embeddings
+      ~BackpackGPT2LMHeadModel.get_parameter
+      ~BackpackGPT2LMHeadModel.get_position_embeddings
+      ~BackpackGPT2LMHeadModel.get_submodule
+      ~BackpackGPT2LMHeadModel.gradient_checkpointing_disable
+      ~BackpackGPT2LMHeadModel.gradient_checkpointing_enable
+      ~BackpackGPT2LMHeadModel.half
+      ~BackpackGPT2LMHeadModel.heal_tokens
+      ~BackpackGPT2LMHeadModel.init_weights
+      ~BackpackGPT2LMHeadModel.invert_attention_mask
+      ~BackpackGPT2LMHeadModel.ipu
+      ~BackpackGPT2LMHeadModel.load_adapter
+      ~BackpackGPT2LMHeadModel.load_state_dict
+      ~BackpackGPT2LMHeadModel.load_tf_weights
+      ~BackpackGPT2LMHeadModel.modules
+      ~BackpackGPT2LMHeadModel.mtia
+      ~BackpackGPT2LMHeadModel.named_buffers
+      ~BackpackGPT2LMHeadModel.named_children
+      ~BackpackGPT2LMHeadModel.named_modules
+      ~BackpackGPT2LMHeadModel.named_parameters
+      ~BackpackGPT2LMHeadModel.num_parameters
+      ~BackpackGPT2LMHeadModel.parameters
+      ~BackpackGPT2LMHeadModel.post_init
+      ~BackpackGPT2LMHeadModel.prepare_inputs_for_generation
+      ~BackpackGPT2LMHeadModel.prune_heads
+      ~BackpackGPT2LMHeadModel.push_to_hub
+      ~BackpackGPT2LMHeadModel.register_backward_hook
+      ~BackpackGPT2LMHeadModel.register_buffer
+      ~BackpackGPT2LMHeadModel.register_for_auto_class
+      ~BackpackGPT2LMHeadModel.register_forward_hook
+      ~BackpackGPT2LMHeadModel.register_forward_pre_hook
+      ~BackpackGPT2LMHeadModel.register_full_backward_hook
+      ~BackpackGPT2LMHeadModel.register_full_backward_pre_hook
+      ~BackpackGPT2LMHeadModel.register_load_state_dict_post_hook
+      ~BackpackGPT2LMHeadModel.register_load_state_dict_pre_hook
+      ~BackpackGPT2LMHeadModel.register_module
+      ~BackpackGPT2LMHeadModel.register_parameter
+      ~BackpackGPT2LMHeadModel.register_state_dict_post_hook
+      ~BackpackGPT2LMHeadModel.register_state_dict_pre_hook
+      ~BackpackGPT2LMHeadModel.requires_grad_
+      ~BackpackGPT2LMHeadModel.reset_memory_hooks_state
+      ~BackpackGPT2LMHeadModel.resize_position_embeddings
+      ~BackpackGPT2LMHeadModel.resize_token_embeddings
+      ~BackpackGPT2LMHeadModel.retrieve_modules_from_names
+      ~BackpackGPT2LMHeadModel.reverse_bettertransformer
+      ~BackpackGPT2LMHeadModel.run_with_custom_contextualization
+      ~BackpackGPT2LMHeadModel.save_pretrained
+      ~BackpackGPT2LMHeadModel.set_adapter
+      ~BackpackGPT2LMHeadModel.set_extra_state
+      ~BackpackGPT2LMHeadModel.set_input_embeddings
+      ~BackpackGPT2LMHeadModel.set_submodule
+      ~BackpackGPT2LMHeadModel.share_memory
+      ~BackpackGPT2LMHeadModel.state_dict
+      ~BackpackGPT2LMHeadModel.tensor_parallel
+      ~BackpackGPT2LMHeadModel.tie_weights
+      ~BackpackGPT2LMHeadModel.to
+      ~BackpackGPT2LMHeadModel.to_bettertransformer
+      ~BackpackGPT2LMHeadModel.to_empty
+      ~BackpackGPT2LMHeadModel.train
+      ~BackpackGPT2LMHeadModel.type
+      ~BackpackGPT2LMHeadModel.warn_if_padding_and_no_attention_mask
+      ~BackpackGPT2LMHeadModel.xpu
+      ~BackpackGPT2LMHeadModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackGPT2LMHeadModel.T_destination
+      ~BackpackGPT2LMHeadModel.base_model
+      ~BackpackGPT2LMHeadModel.base_model_prefix
+      ~BackpackGPT2LMHeadModel.call_super_init
+      ~BackpackGPT2LMHeadModel.device
+      ~BackpackGPT2LMHeadModel.dtype
+      ~BackpackGPT2LMHeadModel.dummy_inputs
+      ~BackpackGPT2LMHeadModel.dump_patches
+      ~BackpackGPT2LMHeadModel.framework
+      ~BackpackGPT2LMHeadModel.is_gradient_checkpointing
+      ~BackpackGPT2LMHeadModel.is_parallelizable
+      ~BackpackGPT2LMHeadModel.loss_function
+      ~BackpackGPT2LMHeadModel.main_input_name
+      ~BackpackGPT2LMHeadModel.model_tags
+      ~BackpackGPT2LMHeadModel.supports_gradient_checkpointing
+      ~BackpackGPT2LMHeadModel.supports_tp_plan
+      ~BackpackGPT2LMHeadModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.rst
new file mode 100644
index 00000000..28be2980
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.rst
@@ -0,0 +1,44 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackGPT2LMHeadModelOutput
+====================================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackGPT2LMHeadModelOutput
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackGPT2LMHeadModelOutput.__init__
+      ~BackpackGPT2LMHeadModelOutput.clear
+      ~BackpackGPT2LMHeadModelOutput.copy
+      ~BackpackGPT2LMHeadModelOutput.fromkeys
+      ~BackpackGPT2LMHeadModelOutput.get
+      ~BackpackGPT2LMHeadModelOutput.items
+      ~BackpackGPT2LMHeadModelOutput.keys
+      ~BackpackGPT2LMHeadModelOutput.move_to_end
+      ~BackpackGPT2LMHeadModelOutput.pop
+      ~BackpackGPT2LMHeadModelOutput.popitem
+      ~BackpackGPT2LMHeadModelOutput.setdefault
+      ~BackpackGPT2LMHeadModelOutput.to_tuple
+      ~BackpackGPT2LMHeadModelOutput.update
+      ~BackpackGPT2LMHeadModelOutput.values
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackGPT2LMHeadModelOutput.contextualization
+      ~BackpackGPT2LMHeadModelOutput.logits
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.rst
new file mode 100644
index 00000000..0c34d427
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.rst
@@ -0,0 +1,151 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackGPT2Model
+========================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackGPT2Model
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackGPT2Model.__init__
+      ~BackpackGPT2Model.active_adapter
+      ~BackpackGPT2Model.active_adapters
+      ~BackpackGPT2Model.add_adapter
+      ~BackpackGPT2Model.add_memory_hooks
+      ~BackpackGPT2Model.add_model_tags
+      ~BackpackGPT2Model.add_module
+      ~BackpackGPT2Model.apply
+      ~BackpackGPT2Model.bfloat16
+      ~BackpackGPT2Model.buffers
+      ~BackpackGPT2Model.can_generate
+      ~BackpackGPT2Model.children
+      ~BackpackGPT2Model.compile
+      ~BackpackGPT2Model.compute_transition_scores
+      ~BackpackGPT2Model.cpu
+      ~BackpackGPT2Model.create_extended_attention_mask_for_decoder
+      ~BackpackGPT2Model.cuda
+      ~BackpackGPT2Model.delete_adapter
+      ~BackpackGPT2Model.dequantize
+      ~BackpackGPT2Model.disable_adapters
+      ~BackpackGPT2Model.disable_input_require_grads
+      ~BackpackGPT2Model.double
+      ~BackpackGPT2Model.enable_adapters
+      ~BackpackGPT2Model.enable_input_require_grads
+      ~BackpackGPT2Model.estimate_tokens
+      ~BackpackGPT2Model.eval
+      ~BackpackGPT2Model.extra_repr
+      ~BackpackGPT2Model.float
+      ~BackpackGPT2Model.floating_point_ops
+      ~BackpackGPT2Model.forward
+      ~BackpackGPT2Model.from_pretrained
+      ~BackpackGPT2Model.generate
+      ~BackpackGPT2Model.get_adapter_state_dict
+      ~BackpackGPT2Model.get_buffer
+      ~BackpackGPT2Model.get_compiled_call
+      ~BackpackGPT2Model.get_extended_attention_mask
+      ~BackpackGPT2Model.get_extra_state
+      ~BackpackGPT2Model.get_head_mask
+      ~BackpackGPT2Model.get_input_embeddings
+      ~BackpackGPT2Model.get_memory_footprint
+      ~BackpackGPT2Model.get_num_senses
+      ~BackpackGPT2Model.get_output_embeddings
+      ~BackpackGPT2Model.get_parameter
+      ~BackpackGPT2Model.get_position_embeddings
+      ~BackpackGPT2Model.get_sense_network
+      ~BackpackGPT2Model.get_submodule
+      ~BackpackGPT2Model.get_word_embeddings
+      ~BackpackGPT2Model.gradient_checkpointing_disable
+      ~BackpackGPT2Model.gradient_checkpointing_enable
+      ~BackpackGPT2Model.half
+      ~BackpackGPT2Model.heal_tokens
+      ~BackpackGPT2Model.init_weights
+      ~BackpackGPT2Model.invert_attention_mask
+      ~BackpackGPT2Model.ipu
+      ~BackpackGPT2Model.load_adapter
+      ~BackpackGPT2Model.load_state_dict
+      ~BackpackGPT2Model.load_tf_weights
+      ~BackpackGPT2Model.modules
+      ~BackpackGPT2Model.mtia
+      ~BackpackGPT2Model.named_buffers
+      ~BackpackGPT2Model.named_children
+      ~BackpackGPT2Model.named_modules
+      ~BackpackGPT2Model.named_parameters
+      ~BackpackGPT2Model.num_parameters
+      ~BackpackGPT2Model.parameters
+      ~BackpackGPT2Model.post_init
+      ~BackpackGPT2Model.prepare_inputs_for_generation
+      ~BackpackGPT2Model.prune_heads
+      ~BackpackGPT2Model.push_to_hub
+      ~BackpackGPT2Model.register_backward_hook
+      ~BackpackGPT2Model.register_buffer
+      ~BackpackGPT2Model.register_for_auto_class
+      ~BackpackGPT2Model.register_forward_hook
+      ~BackpackGPT2Model.register_forward_pre_hook
+      ~BackpackGPT2Model.register_full_backward_hook
+      ~BackpackGPT2Model.register_full_backward_pre_hook
+      ~BackpackGPT2Model.register_load_state_dict_post_hook
+      ~BackpackGPT2Model.register_load_state_dict_pre_hook
+      ~BackpackGPT2Model.register_module
+      ~BackpackGPT2Model.register_parameter
+      ~BackpackGPT2Model.register_state_dict_post_hook
+      ~BackpackGPT2Model.register_state_dict_pre_hook
+      ~BackpackGPT2Model.requires_grad_
+      ~BackpackGPT2Model.reset_memory_hooks_state
+      ~BackpackGPT2Model.resize_position_embeddings
+      ~BackpackGPT2Model.resize_token_embeddings
+      ~BackpackGPT2Model.retrieve_modules_from_names
+      ~BackpackGPT2Model.reverse_bettertransformer
+      ~BackpackGPT2Model.run_with_custom_contextualization
+      ~BackpackGPT2Model.save_pretrained
+      ~BackpackGPT2Model.set_adapter
+      ~BackpackGPT2Model.set_extra_state
+      ~BackpackGPT2Model.set_input_embeddings
+      ~BackpackGPT2Model.set_submodule
+      ~BackpackGPT2Model.share_memory
+      ~BackpackGPT2Model.state_dict
+      ~BackpackGPT2Model.tensor_parallel
+      ~BackpackGPT2Model.tie_weights
+      ~BackpackGPT2Model.to
+      ~BackpackGPT2Model.to_bettertransformer
+      ~BackpackGPT2Model.to_empty
+      ~BackpackGPT2Model.train
+      ~BackpackGPT2Model.type
+      ~BackpackGPT2Model.warn_if_padding_and_no_attention_mask
+      ~BackpackGPT2Model.xpu
+      ~BackpackGPT2Model.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackGPT2Model.T_destination
+      ~BackpackGPT2Model.base_model
+      ~BackpackGPT2Model.base_model_prefix
+      ~BackpackGPT2Model.call_super_init
+      ~BackpackGPT2Model.device
+      ~BackpackGPT2Model.dtype
+      ~BackpackGPT2Model.dummy_inputs
+      ~BackpackGPT2Model.dump_patches
+      ~BackpackGPT2Model.framework
+      ~BackpackGPT2Model.is_gradient_checkpointing
+      ~BackpackGPT2Model.is_parallelizable
+      ~BackpackGPT2Model.loss_function
+      ~BackpackGPT2Model.main_input_name
+      ~BackpackGPT2Model.model_tags
+      ~BackpackGPT2Model.supports_gradient_checkpointing
+      ~BackpackGPT2Model.supports_tp_plan
+      ~BackpackGPT2Model.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.rst
new file mode 100644
index 00000000..f6cbb2b5
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.rst
@@ -0,0 +1,147 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackGPT2PreTrainedModel
+==================================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackGPT2PreTrainedModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackGPT2PreTrainedModel.__init__
+      ~BackpackGPT2PreTrainedModel.active_adapter
+      ~BackpackGPT2PreTrainedModel.active_adapters
+      ~BackpackGPT2PreTrainedModel.add_adapter
+      ~BackpackGPT2PreTrainedModel.add_memory_hooks
+      ~BackpackGPT2PreTrainedModel.add_model_tags
+      ~BackpackGPT2PreTrainedModel.add_module
+      ~BackpackGPT2PreTrainedModel.apply
+      ~BackpackGPT2PreTrainedModel.bfloat16
+      ~BackpackGPT2PreTrainedModel.buffers
+      ~BackpackGPT2PreTrainedModel.can_generate
+      ~BackpackGPT2PreTrainedModel.children
+      ~BackpackGPT2PreTrainedModel.compile
+      ~BackpackGPT2PreTrainedModel.compute_transition_scores
+      ~BackpackGPT2PreTrainedModel.cpu
+      ~BackpackGPT2PreTrainedModel.create_extended_attention_mask_for_decoder
+      ~BackpackGPT2PreTrainedModel.cuda
+      ~BackpackGPT2PreTrainedModel.delete_adapter
+      ~BackpackGPT2PreTrainedModel.dequantize
+      ~BackpackGPT2PreTrainedModel.disable_adapters
+      ~BackpackGPT2PreTrainedModel.disable_input_require_grads
+      ~BackpackGPT2PreTrainedModel.double
+      ~BackpackGPT2PreTrainedModel.enable_adapters
+      ~BackpackGPT2PreTrainedModel.enable_input_require_grads
+      ~BackpackGPT2PreTrainedModel.estimate_tokens
+      ~BackpackGPT2PreTrainedModel.eval
+      ~BackpackGPT2PreTrainedModel.extra_repr
+      ~BackpackGPT2PreTrainedModel.float
+      ~BackpackGPT2PreTrainedModel.floating_point_ops
+      ~BackpackGPT2PreTrainedModel.forward
+      ~BackpackGPT2PreTrainedModel.from_pretrained
+      ~BackpackGPT2PreTrainedModel.generate
+      ~BackpackGPT2PreTrainedModel.get_adapter_state_dict
+      ~BackpackGPT2PreTrainedModel.get_buffer
+      ~BackpackGPT2PreTrainedModel.get_compiled_call
+      ~BackpackGPT2PreTrainedModel.get_extended_attention_mask
+      ~BackpackGPT2PreTrainedModel.get_extra_state
+      ~BackpackGPT2PreTrainedModel.get_head_mask
+      ~BackpackGPT2PreTrainedModel.get_input_embeddings
+      ~BackpackGPT2PreTrainedModel.get_memory_footprint
+      ~BackpackGPT2PreTrainedModel.get_output_embeddings
+      ~BackpackGPT2PreTrainedModel.get_parameter
+      ~BackpackGPT2PreTrainedModel.get_position_embeddings
+      ~BackpackGPT2PreTrainedModel.get_submodule
+      ~BackpackGPT2PreTrainedModel.gradient_checkpointing_disable
+      ~BackpackGPT2PreTrainedModel.gradient_checkpointing_enable
+      ~BackpackGPT2PreTrainedModel.half
+      ~BackpackGPT2PreTrainedModel.heal_tokens
+      ~BackpackGPT2PreTrainedModel.init_weights
+      ~BackpackGPT2PreTrainedModel.invert_attention_mask
+      ~BackpackGPT2PreTrainedModel.ipu
+      ~BackpackGPT2PreTrainedModel.load_adapter
+      ~BackpackGPT2PreTrainedModel.load_state_dict
+      ~BackpackGPT2PreTrainedModel.load_tf_weights
+      ~BackpackGPT2PreTrainedModel.modules
+      ~BackpackGPT2PreTrainedModel.mtia
+      ~BackpackGPT2PreTrainedModel.named_buffers
+      ~BackpackGPT2PreTrainedModel.named_children
+      ~BackpackGPT2PreTrainedModel.named_modules
+      ~BackpackGPT2PreTrainedModel.named_parameters
+      ~BackpackGPT2PreTrainedModel.num_parameters
+      ~BackpackGPT2PreTrainedModel.parameters
+      ~BackpackGPT2PreTrainedModel.post_init
+      ~BackpackGPT2PreTrainedModel.prepare_inputs_for_generation
+      ~BackpackGPT2PreTrainedModel.prune_heads
+      ~BackpackGPT2PreTrainedModel.push_to_hub
+      ~BackpackGPT2PreTrainedModel.register_backward_hook
+      ~BackpackGPT2PreTrainedModel.register_buffer
+      ~BackpackGPT2PreTrainedModel.register_for_auto_class
+      ~BackpackGPT2PreTrainedModel.register_forward_hook
+      ~BackpackGPT2PreTrainedModel.register_forward_pre_hook
+      ~BackpackGPT2PreTrainedModel.register_full_backward_hook
+      ~BackpackGPT2PreTrainedModel.register_full_backward_pre_hook
+      ~BackpackGPT2PreTrainedModel.register_load_state_dict_post_hook
+      ~BackpackGPT2PreTrainedModel.register_load_state_dict_pre_hook
+      ~BackpackGPT2PreTrainedModel.register_module
+      ~BackpackGPT2PreTrainedModel.register_parameter
+      ~BackpackGPT2PreTrainedModel.register_state_dict_post_hook
+      ~BackpackGPT2PreTrainedModel.register_state_dict_pre_hook
+      ~BackpackGPT2PreTrainedModel.requires_grad_
+      ~BackpackGPT2PreTrainedModel.reset_memory_hooks_state
+      ~BackpackGPT2PreTrainedModel.resize_position_embeddings
+      ~BackpackGPT2PreTrainedModel.resize_token_embeddings
+      ~BackpackGPT2PreTrainedModel.retrieve_modules_from_names
+      ~BackpackGPT2PreTrainedModel.reverse_bettertransformer
+      ~BackpackGPT2PreTrainedModel.save_pretrained
+      ~BackpackGPT2PreTrainedModel.set_adapter
+      ~BackpackGPT2PreTrainedModel.set_extra_state
+      ~BackpackGPT2PreTrainedModel.set_input_embeddings
+      ~BackpackGPT2PreTrainedModel.set_submodule
+      ~BackpackGPT2PreTrainedModel.share_memory
+      ~BackpackGPT2PreTrainedModel.state_dict
+      ~BackpackGPT2PreTrainedModel.tensor_parallel
+      ~BackpackGPT2PreTrainedModel.tie_weights
+      ~BackpackGPT2PreTrainedModel.to
+      ~BackpackGPT2PreTrainedModel.to_bettertransformer
+      ~BackpackGPT2PreTrainedModel.to_empty
+      ~BackpackGPT2PreTrainedModel.train
+      ~BackpackGPT2PreTrainedModel.type
+      ~BackpackGPT2PreTrainedModel.warn_if_padding_and_no_attention_mask
+      ~BackpackGPT2PreTrainedModel.xpu
+      ~BackpackGPT2PreTrainedModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackGPT2PreTrainedModel.T_destination
+      ~BackpackGPT2PreTrainedModel.base_model
+      ~BackpackGPT2PreTrainedModel.base_model_prefix
+      ~BackpackGPT2PreTrainedModel.call_super_init
+      ~BackpackGPT2PreTrainedModel.device
+      ~BackpackGPT2PreTrainedModel.dtype
+      ~BackpackGPT2PreTrainedModel.dummy_inputs
+      ~BackpackGPT2PreTrainedModel.dump_patches
+      ~BackpackGPT2PreTrainedModel.framework
+      ~BackpackGPT2PreTrainedModel.is_gradient_checkpointing
+      ~BackpackGPT2PreTrainedModel.is_parallelizable
+      ~BackpackGPT2PreTrainedModel.loss_function
+      ~BackpackGPT2PreTrainedModel.main_input_name
+      ~BackpackGPT2PreTrainedModel.model_tags
+      ~BackpackGPT2PreTrainedModel.supports_gradient_checkpointing
+      ~BackpackGPT2PreTrainedModel.supports_tp_plan
+      ~BackpackGPT2PreTrainedModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.rst
new file mode 100644
index 00000000..39046433
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.rst
@@ -0,0 +1,83 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackMLP
+==================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackMLP
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackMLP.__init__
+      ~BackpackMLP.add_module
+      ~BackpackMLP.apply
+      ~BackpackMLP.bfloat16
+      ~BackpackMLP.buffers
+      ~BackpackMLP.children
+      ~BackpackMLP.compile
+      ~BackpackMLP.cpu
+      ~BackpackMLP.cuda
+      ~BackpackMLP.double
+      ~BackpackMLP.eval
+      ~BackpackMLP.extra_repr
+      ~BackpackMLP.float
+      ~BackpackMLP.forward
+      ~BackpackMLP.get_buffer
+      ~BackpackMLP.get_extra_state
+      ~BackpackMLP.get_parameter
+      ~BackpackMLP.get_submodule
+      ~BackpackMLP.half
+      ~BackpackMLP.ipu
+      ~BackpackMLP.load_state_dict
+      ~BackpackMLP.modules
+      ~BackpackMLP.mtia
+      ~BackpackMLP.named_buffers
+      ~BackpackMLP.named_children
+      ~BackpackMLP.named_modules
+      ~BackpackMLP.named_parameters
+      ~BackpackMLP.parameters
+      ~BackpackMLP.register_backward_hook
+      ~BackpackMLP.register_buffer
+      ~BackpackMLP.register_forward_hook
+      ~BackpackMLP.register_forward_pre_hook
+      ~BackpackMLP.register_full_backward_hook
+      ~BackpackMLP.register_full_backward_pre_hook
+      ~BackpackMLP.register_load_state_dict_post_hook
+      ~BackpackMLP.register_load_state_dict_pre_hook
+      ~BackpackMLP.register_module
+      ~BackpackMLP.register_parameter
+      ~BackpackMLP.register_state_dict_post_hook
+      ~BackpackMLP.register_state_dict_pre_hook
+      ~BackpackMLP.requires_grad_
+      ~BackpackMLP.set_extra_state
+      ~BackpackMLP.set_submodule
+      ~BackpackMLP.share_memory
+      ~BackpackMLP.state_dict
+      ~BackpackMLP.to
+      ~BackpackMLP.to_empty
+      ~BackpackMLP.train
+      ~BackpackMLP.type
+      ~BackpackMLP.xpu
+      ~BackpackMLP.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackMLP.T_destination
+      ~BackpackMLP.call_super_init
+      ~BackpackMLP.dump_patches
+      ~BackpackMLP.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.rst
new file mode 100644
index 00000000..75adb181
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.rst
@@ -0,0 +1,83 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackNoMixBlock
+=========================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackNoMixBlock
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackNoMixBlock.__init__
+      ~BackpackNoMixBlock.add_module
+      ~BackpackNoMixBlock.apply
+      ~BackpackNoMixBlock.bfloat16
+      ~BackpackNoMixBlock.buffers
+      ~BackpackNoMixBlock.children
+      ~BackpackNoMixBlock.compile
+      ~BackpackNoMixBlock.cpu
+      ~BackpackNoMixBlock.cuda
+      ~BackpackNoMixBlock.double
+      ~BackpackNoMixBlock.eval
+      ~BackpackNoMixBlock.extra_repr
+      ~BackpackNoMixBlock.float
+      ~BackpackNoMixBlock.forward
+      ~BackpackNoMixBlock.get_buffer
+      ~BackpackNoMixBlock.get_extra_state
+      ~BackpackNoMixBlock.get_parameter
+      ~BackpackNoMixBlock.get_submodule
+      ~BackpackNoMixBlock.half
+      ~BackpackNoMixBlock.ipu
+      ~BackpackNoMixBlock.load_state_dict
+      ~BackpackNoMixBlock.modules
+      ~BackpackNoMixBlock.mtia
+      ~BackpackNoMixBlock.named_buffers
+      ~BackpackNoMixBlock.named_children
+      ~BackpackNoMixBlock.named_modules
+      ~BackpackNoMixBlock.named_parameters
+      ~BackpackNoMixBlock.parameters
+      ~BackpackNoMixBlock.register_backward_hook
+      ~BackpackNoMixBlock.register_buffer
+      ~BackpackNoMixBlock.register_forward_hook
+      ~BackpackNoMixBlock.register_forward_pre_hook
+      ~BackpackNoMixBlock.register_full_backward_hook
+      ~BackpackNoMixBlock.register_full_backward_pre_hook
+      ~BackpackNoMixBlock.register_load_state_dict_post_hook
+      ~BackpackNoMixBlock.register_load_state_dict_pre_hook
+      ~BackpackNoMixBlock.register_module
+      ~BackpackNoMixBlock.register_parameter
+      ~BackpackNoMixBlock.register_state_dict_post_hook
+      ~BackpackNoMixBlock.register_state_dict_pre_hook
+      ~BackpackNoMixBlock.requires_grad_
+      ~BackpackNoMixBlock.set_extra_state
+      ~BackpackNoMixBlock.set_submodule
+      ~BackpackNoMixBlock.share_memory
+      ~BackpackNoMixBlock.state_dict
+      ~BackpackNoMixBlock.to
+      ~BackpackNoMixBlock.to_empty
+      ~BackpackNoMixBlock.train
+      ~BackpackNoMixBlock.type
+      ~BackpackNoMixBlock.xpu
+      ~BackpackNoMixBlock.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackNoMixBlock.T_destination
+      ~BackpackNoMixBlock.call_super_init
+      ~BackpackNoMixBlock.dump_patches
+      ~BackpackNoMixBlock.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.rst
new file mode 100644
index 00000000..6900ca38
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.rst
@@ -0,0 +1,83 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackSenseNetwork
+===========================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackSenseNetwork
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackSenseNetwork.__init__
+      ~BackpackSenseNetwork.add_module
+      ~BackpackSenseNetwork.apply
+      ~BackpackSenseNetwork.bfloat16
+      ~BackpackSenseNetwork.buffers
+      ~BackpackSenseNetwork.children
+      ~BackpackSenseNetwork.compile
+      ~BackpackSenseNetwork.cpu
+      ~BackpackSenseNetwork.cuda
+      ~BackpackSenseNetwork.double
+      ~BackpackSenseNetwork.eval
+      ~BackpackSenseNetwork.extra_repr
+      ~BackpackSenseNetwork.float
+      ~BackpackSenseNetwork.forward
+      ~BackpackSenseNetwork.get_buffer
+      ~BackpackSenseNetwork.get_extra_state
+      ~BackpackSenseNetwork.get_parameter
+      ~BackpackSenseNetwork.get_submodule
+      ~BackpackSenseNetwork.half
+      ~BackpackSenseNetwork.ipu
+      ~BackpackSenseNetwork.load_state_dict
+      ~BackpackSenseNetwork.modules
+      ~BackpackSenseNetwork.mtia
+      ~BackpackSenseNetwork.named_buffers
+      ~BackpackSenseNetwork.named_children
+      ~BackpackSenseNetwork.named_modules
+      ~BackpackSenseNetwork.named_parameters
+      ~BackpackSenseNetwork.parameters
+      ~BackpackSenseNetwork.register_backward_hook
+      ~BackpackSenseNetwork.register_buffer
+      ~BackpackSenseNetwork.register_forward_hook
+      ~BackpackSenseNetwork.register_forward_pre_hook
+      ~BackpackSenseNetwork.register_full_backward_hook
+      ~BackpackSenseNetwork.register_full_backward_pre_hook
+      ~BackpackSenseNetwork.register_load_state_dict_post_hook
+      ~BackpackSenseNetwork.register_load_state_dict_pre_hook
+      ~BackpackSenseNetwork.register_module
+      ~BackpackSenseNetwork.register_parameter
+      ~BackpackSenseNetwork.register_state_dict_post_hook
+      ~BackpackSenseNetwork.register_state_dict_pre_hook
+      ~BackpackSenseNetwork.requires_grad_
+      ~BackpackSenseNetwork.set_extra_state
+      ~BackpackSenseNetwork.set_submodule
+      ~BackpackSenseNetwork.share_memory
+      ~BackpackSenseNetwork.state_dict
+      ~BackpackSenseNetwork.to
+      ~BackpackSenseNetwork.to_empty
+      ~BackpackSenseNetwork.train
+      ~BackpackSenseNetwork.type
+      ~BackpackSenseNetwork.xpu
+      ~BackpackSenseNetwork.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackSenseNetwork.T_destination
+      ~BackpackSenseNetwork.call_super_init
+      ~BackpackSenseNetwork.dump_patches
+      ~BackpackSenseNetwork.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.rst
new file mode 100644
index 00000000..2c0398bf
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.rst
@@ -0,0 +1,83 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2.BackpackWeightNetwork
+============================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+.. autoclass:: BackpackWeightNetwork
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BackpackWeightNetwork.__init__
+      ~BackpackWeightNetwork.add_module
+      ~BackpackWeightNetwork.apply
+      ~BackpackWeightNetwork.bfloat16
+      ~BackpackWeightNetwork.buffers
+      ~BackpackWeightNetwork.children
+      ~BackpackWeightNetwork.compile
+      ~BackpackWeightNetwork.cpu
+      ~BackpackWeightNetwork.cuda
+      ~BackpackWeightNetwork.double
+      ~BackpackWeightNetwork.eval
+      ~BackpackWeightNetwork.extra_repr
+      ~BackpackWeightNetwork.float
+      ~BackpackWeightNetwork.forward
+      ~BackpackWeightNetwork.get_buffer
+      ~BackpackWeightNetwork.get_extra_state
+      ~BackpackWeightNetwork.get_parameter
+      ~BackpackWeightNetwork.get_submodule
+      ~BackpackWeightNetwork.half
+      ~BackpackWeightNetwork.ipu
+      ~BackpackWeightNetwork.load_state_dict
+      ~BackpackWeightNetwork.modules
+      ~BackpackWeightNetwork.mtia
+      ~BackpackWeightNetwork.named_buffers
+      ~BackpackWeightNetwork.named_children
+      ~BackpackWeightNetwork.named_modules
+      ~BackpackWeightNetwork.named_parameters
+      ~BackpackWeightNetwork.parameters
+      ~BackpackWeightNetwork.register_backward_hook
+      ~BackpackWeightNetwork.register_buffer
+      ~BackpackWeightNetwork.register_forward_hook
+      ~BackpackWeightNetwork.register_forward_pre_hook
+      ~BackpackWeightNetwork.register_full_backward_hook
+      ~BackpackWeightNetwork.register_full_backward_pre_hook
+      ~BackpackWeightNetwork.register_load_state_dict_post_hook
+      ~BackpackWeightNetwork.register_load_state_dict_pre_hook
+      ~BackpackWeightNetwork.register_module
+      ~BackpackWeightNetwork.register_parameter
+      ~BackpackWeightNetwork.register_state_dict_post_hook
+      ~BackpackWeightNetwork.register_state_dict_pre_hook
+      ~BackpackWeightNetwork.requires_grad_
+      ~BackpackWeightNetwork.set_extra_state
+      ~BackpackWeightNetwork.set_submodule
+      ~BackpackWeightNetwork.share_memory
+      ~BackpackWeightNetwork.state_dict
+      ~BackpackWeightNetwork.to
+      ~BackpackWeightNetwork.to_empty
+      ~BackpackWeightNetwork.train
+      ~BackpackWeightNetwork.type
+      ~BackpackWeightNetwork.xpu
+      ~BackpackWeightNetwork.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BackpackWeightNetwork.T_destination
+      ~BackpackWeightNetwork.call_super_init
+      ~BackpackWeightNetwork.dump_patches
+      ~BackpackWeightNetwork.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.rst
new file mode 100644
index 00000000..2262daf3
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.rst
@@ -0,0 +1,40 @@
+pyvene.models.backpack\_gpt2.modelings\_backpack\_gpt2
+======================================================
+
+.. automodule:: pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      BackpackGPT2BaseModelOutput
+      BackpackGPT2Config
+      BackpackGPT2LMHeadModel
+      BackpackGPT2LMHeadModelOutput
+      BackpackGPT2Model
+      BackpackGPT2PreTrainedModel
+      BackpackMLP
+      BackpackNoMixBlock
+      BackpackSenseNetwork
+      BackpackWeightNetwork
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.rst
new file mode 100644
index 00000000..06dedebd
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.rst
@@ -0,0 +1,6 @@
+pyvene.models.backpack\_gpt2.modelings\_intervenable\_backpack\_gpt2.create\_backpack\_gpt2
+===========================================================================================
+
+.. currentmodule:: pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2
+
+.. autofunction:: create_backpack_gpt2
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.rst b/_sources/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.rst
new file mode 100644
index 00000000..1470fc28
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.rst
@@ -0,0 +1,30 @@
+pyvene.models.backpack\_gpt2.modelings\_intervenable\_backpack\_gpt2
+====================================================================
+
+.. automodule:: pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_backpack_gpt2
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.backpack_gpt2.rst b/_sources/api/pyvene.models.backpack_gpt2.rst
new file mode 100644
index 00000000..2d3597d6
--- /dev/null
+++ b/_sources/api/pyvene.models.backpack_gpt2.rst
@@ -0,0 +1,33 @@
+pyvene.models.backpack\_gpt2
+============================
+
+.. automodule:: pyvene.models.backpack_gpt2
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+   pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2
+
diff --git a/_sources/api/pyvene.models.basic_utils.GET_LOC.rst b/_sources/api/pyvene.models.basic_utils.GET_LOC.rst
new file mode 100644
index 00000000..38001ddc
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.GET_LOC.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.GET\_LOC
+===================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: GET_LOC
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.closeness_to_permutation_loss.rst b/_sources/api/pyvene.models.basic_utils.closeness_to_permutation_loss.rst
new file mode 100644
index 00000000..3dc29beb
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.closeness_to_permutation_loss.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.closeness\_to\_permutation\_loss
+===========================================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: closeness_to_permutation_loss
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.count_parameters.rst b/_sources/api/pyvene.models.basic_utils.count_parameters.rst
new file mode 100644
index 00000000..55570529
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.count_parameters.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.count\_parameters
+============================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: count_parameters
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.create_directory.rst b/_sources/api/pyvene.models.basic_utils.create_directory.rst
new file mode 100644
index 00000000..dc44a953
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.create_directory.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.create\_directory
+============================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: create_directory
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.embed_to_distrib.rst b/_sources/api/pyvene.models.basic_utils.embed_to_distrib.rst
new file mode 100644
index 00000000..02e8d6f1
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.embed_to_distrib.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.embed\_to\_distrib
+=============================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: embed_to_distrib
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.format_token.rst b/_sources/api/pyvene.models.basic_utils.format_token.rst
new file mode 100644
index 00000000..89d5fe0a
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.format_token.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.format\_token
+========================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: format_token
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.get_batch_size.rst b/_sources/api/pyvene.models.basic_utils.get_batch_size.rst
new file mode 100644
index 00000000..668f8a9d
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.get_batch_size.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.get\_batch\_size
+===========================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: get_batch_size
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.get_list_depth.rst b/_sources/api/pyvene.models.basic_utils.get_list_depth.rst
new file mode 100644
index 00000000..d3b17b69
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.get_list_depth.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.get\_list\_depth
+===========================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: get_list_depth
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.get_type_from_string.rst b/_sources/api/pyvene.models.basic_utils.get_type_from_string.rst
new file mode 100644
index 00000000..42426ce6
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.get_type_from_string.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.get\_type\_from\_string
+==================================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: get_type_from_string
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.harmonic_sigmoid_boundary.rst b/_sources/api/pyvene.models.basic_utils.harmonic_sigmoid_boundary.rst
new file mode 100644
index 00000000..35b7f40f
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.harmonic_sigmoid_boundary.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.harmonic\_sigmoid\_boundary
+======================================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: harmonic_sigmoid_boundary
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.random_permutation_matrix.rst b/_sources/api/pyvene.models.basic_utils.random_permutation_matrix.rst
new file mode 100644
index 00000000..6c35fa54
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.random_permutation_matrix.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.random\_permutation\_matrix
+======================================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: random_permutation_matrix
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.rst b/_sources/api/pyvene.models.basic_utils.rst
new file mode 100644
index 00000000..3654a642
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.rst
@@ -0,0 +1,43 @@
+pyvene.models.basic\_utils
+==========================
+
+.. automodule:: pyvene.models.basic_utils
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      GET_LOC
+      closeness_to_permutation_loss
+      count_parameters
+      create_directory
+      embed_to_distrib
+      format_token
+      get_batch_size
+      get_list_depth
+      get_type_from_string
+      harmonic_sigmoid_boundary
+      random_permutation_matrix
+      set_seed
+      sigmoid_boundary
+      top_vals
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.basic_utils.set_seed.rst b/_sources/api/pyvene.models.basic_utils.set_seed.rst
new file mode 100644
index 00000000..72e8184f
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.set_seed.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.set\_seed
+====================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: set_seed
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.sigmoid_boundary.rst b/_sources/api/pyvene.models.basic_utils.sigmoid_boundary.rst
new file mode 100644
index 00000000..6bc2c5c4
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.sigmoid_boundary.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.sigmoid\_boundary
+============================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: sigmoid_boundary
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.basic_utils.top_vals.rst b/_sources/api/pyvene.models.basic_utils.top_vals.rst
new file mode 100644
index 00000000..ad5adcb8
--- /dev/null
+++ b/_sources/api/pyvene.models.basic_utils.top_vals.rst
@@ -0,0 +1,6 @@
+pyvene.models.basic\_utils.top\_vals
+====================================
+
+.. currentmodule:: pyvene.models.basic_utils
+
+.. autofunction:: top_vals
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_blip.BlipWrapper.rst b/_sources/api/pyvene.models.blip.modelings_blip.BlipWrapper.rst
new file mode 100644
index 00000000..78276bfb
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_blip.BlipWrapper.rst
@@ -0,0 +1,83 @@
+pyvene.models.blip.modelings\_blip.BlipWrapper
+==============================================
+
+.. currentmodule:: pyvene.models.blip.modelings_blip
+
+.. autoclass:: BlipWrapper
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BlipWrapper.__init__
+      ~BlipWrapper.add_module
+      ~BlipWrapper.apply
+      ~BlipWrapper.bfloat16
+      ~BlipWrapper.buffers
+      ~BlipWrapper.children
+      ~BlipWrapper.compile
+      ~BlipWrapper.cpu
+      ~BlipWrapper.cuda
+      ~BlipWrapper.double
+      ~BlipWrapper.eval
+      ~BlipWrapper.extra_repr
+      ~BlipWrapper.float
+      ~BlipWrapper.forward
+      ~BlipWrapper.get_buffer
+      ~BlipWrapper.get_extra_state
+      ~BlipWrapper.get_parameter
+      ~BlipWrapper.get_submodule
+      ~BlipWrapper.half
+      ~BlipWrapper.ipu
+      ~BlipWrapper.load_state_dict
+      ~BlipWrapper.modules
+      ~BlipWrapper.mtia
+      ~BlipWrapper.named_buffers
+      ~BlipWrapper.named_children
+      ~BlipWrapper.named_modules
+      ~BlipWrapper.named_parameters
+      ~BlipWrapper.parameters
+      ~BlipWrapper.register_backward_hook
+      ~BlipWrapper.register_buffer
+      ~BlipWrapper.register_forward_hook
+      ~BlipWrapper.register_forward_pre_hook
+      ~BlipWrapper.register_full_backward_hook
+      ~BlipWrapper.register_full_backward_pre_hook
+      ~BlipWrapper.register_load_state_dict_post_hook
+      ~BlipWrapper.register_load_state_dict_pre_hook
+      ~BlipWrapper.register_module
+      ~BlipWrapper.register_parameter
+      ~BlipWrapper.register_state_dict_post_hook
+      ~BlipWrapper.register_state_dict_pre_hook
+      ~BlipWrapper.requires_grad_
+      ~BlipWrapper.set_extra_state
+      ~BlipWrapper.set_submodule
+      ~BlipWrapper.share_memory
+      ~BlipWrapper.state_dict
+      ~BlipWrapper.to
+      ~BlipWrapper.to_empty
+      ~BlipWrapper.train
+      ~BlipWrapper.type
+      ~BlipWrapper.xpu
+      ~BlipWrapper.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BlipWrapper.T_destination
+      ~BlipWrapper.call_super_init
+      ~BlipWrapper.dump_patches
+      ~BlipWrapper.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_blip.rst b/_sources/api/pyvene.models.blip.modelings_blip.rst
new file mode 100644
index 00000000..d479b32a
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_blip.rst
@@ -0,0 +1,31 @@
+pyvene.models.blip.modelings\_blip
+==================================
+
+.. automodule:: pyvene.models.blip.modelings_blip
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      BlipWrapper
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.rst b/_sources/api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.rst
new file mode 100644
index 00000000..6c67e195
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.rst
@@ -0,0 +1,83 @@
+pyvene.models.blip.modelings\_blip\_itm.BlipITMWrapper
+======================================================
+
+.. currentmodule:: pyvene.models.blip.modelings_blip_itm
+
+.. autoclass:: BlipITMWrapper
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BlipITMWrapper.__init__
+      ~BlipITMWrapper.add_module
+      ~BlipITMWrapper.apply
+      ~BlipITMWrapper.bfloat16
+      ~BlipITMWrapper.buffers
+      ~BlipITMWrapper.children
+      ~BlipITMWrapper.compile
+      ~BlipITMWrapper.cpu
+      ~BlipITMWrapper.cuda
+      ~BlipITMWrapper.double
+      ~BlipITMWrapper.eval
+      ~BlipITMWrapper.extra_repr
+      ~BlipITMWrapper.float
+      ~BlipITMWrapper.forward
+      ~BlipITMWrapper.get_buffer
+      ~BlipITMWrapper.get_extra_state
+      ~BlipITMWrapper.get_parameter
+      ~BlipITMWrapper.get_submodule
+      ~BlipITMWrapper.half
+      ~BlipITMWrapper.ipu
+      ~BlipITMWrapper.load_state_dict
+      ~BlipITMWrapper.modules
+      ~BlipITMWrapper.mtia
+      ~BlipITMWrapper.named_buffers
+      ~BlipITMWrapper.named_children
+      ~BlipITMWrapper.named_modules
+      ~BlipITMWrapper.named_parameters
+      ~BlipITMWrapper.parameters
+      ~BlipITMWrapper.register_backward_hook
+      ~BlipITMWrapper.register_buffer
+      ~BlipITMWrapper.register_forward_hook
+      ~BlipITMWrapper.register_forward_pre_hook
+      ~BlipITMWrapper.register_full_backward_hook
+      ~BlipITMWrapper.register_full_backward_pre_hook
+      ~BlipITMWrapper.register_load_state_dict_post_hook
+      ~BlipITMWrapper.register_load_state_dict_pre_hook
+      ~BlipITMWrapper.register_module
+      ~BlipITMWrapper.register_parameter
+      ~BlipITMWrapper.register_state_dict_post_hook
+      ~BlipITMWrapper.register_state_dict_pre_hook
+      ~BlipITMWrapper.requires_grad_
+      ~BlipITMWrapper.set_extra_state
+      ~BlipITMWrapper.set_submodule
+      ~BlipITMWrapper.share_memory
+      ~BlipITMWrapper.state_dict
+      ~BlipITMWrapper.to
+      ~BlipITMWrapper.to_empty
+      ~BlipITMWrapper.train
+      ~BlipITMWrapper.type
+      ~BlipITMWrapper.xpu
+      ~BlipITMWrapper.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BlipITMWrapper.T_destination
+      ~BlipITMWrapper.call_super_init
+      ~BlipITMWrapper.dump_patches
+      ~BlipITMWrapper.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_blip_itm.rst b/_sources/api/pyvene.models.blip.modelings_blip_itm.rst
new file mode 100644
index 00000000..c5f5d5b3
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_blip_itm.rst
@@ -0,0 +1,31 @@
+pyvene.models.blip.modelings\_blip\_itm
+=======================================
+
+.. automodule:: pyvene.models.blip.modelings_blip_itm
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      BlipITMWrapper
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..e0ecba3e
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.blip.modelings\_intervenable\_blip.blip\_type\_to\_dimension\_mapping
+===================================================================================
+
+.. currentmodule:: pyvene.models.blip.modelings_intervenable_blip
+
+.. autodata:: blip_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_intervenable_blip.create_blip.rst b/_sources/api/pyvene.models.blip.modelings_intervenable_blip.create_blip.rst
new file mode 100644
index 00000000..97077303
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_intervenable_blip.create_blip.rst
@@ -0,0 +1,6 @@
+pyvene.models.blip.modelings\_intervenable\_blip.create\_blip
+=============================================================
+
+.. currentmodule:: pyvene.models.blip.modelings_intervenable_blip
+
+.. autofunction:: create_blip
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_intervenable_blip.rst b/_sources/api/pyvene.models.blip.modelings_intervenable_blip.rst
new file mode 100644
index 00000000..f2cea346
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_intervenable_blip.rst
@@ -0,0 +1,37 @@
+pyvene.models.blip.modelings\_intervenable\_blip
+================================================
+
+.. automodule:: pyvene.models.blip.modelings_intervenable_blip
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      blip_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_blip
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..c49eced7
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.blip.modelings\_intervenable\_blip\_itm.blip\_itm\_type\_to\_dimension\_mapping
+=============================================================================================
+
+.. currentmodule:: pyvene.models.blip.modelings_intervenable_blip_itm
+
+.. autodata:: blip_itm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm.rst b/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm.rst
new file mode 100644
index 00000000..09eaaa77
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm.rst
@@ -0,0 +1,6 @@
+pyvene.models.blip.modelings\_intervenable\_blip\_itm.create\_blip\_itm
+=======================================================================
+
+.. currentmodule:: pyvene.models.blip.modelings_intervenable_blip_itm
+
+.. autofunction:: create_blip_itm
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.rst b/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.rst
new file mode 100644
index 00000000..738bcc4f
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.modelings_intervenable_blip_itm.rst
@@ -0,0 +1,37 @@
+pyvene.models.blip.modelings\_intervenable\_blip\_itm
+=====================================================
+
+.. automodule:: pyvene.models.blip.modelings_intervenable_blip_itm
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      blip_itm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_blip_itm
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.blip.rst b/_sources/api/pyvene.models.blip.rst
new file mode 100644
index 00000000..025d3866
--- /dev/null
+++ b/_sources/api/pyvene.models.blip.rst
@@ -0,0 +1,35 @@
+pyvene.models.blip
+==================
+
+.. automodule:: pyvene.models.blip
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.blip.modelings_blip
+   pyvene.models.blip.modelings_blip_itm
+   pyvene.models.blip.modelings_intervenable_blip
+   pyvene.models.blip.modelings_intervenable_blip_itm
+
diff --git a/_sources/api/pyvene.models.configuration_intervenable_model.IntervenableConfig.rst b/_sources/api/pyvene.models.configuration_intervenable_model.IntervenableConfig.rst
new file mode 100644
index 00000000..54fa05e4
--- /dev/null
+++ b/_sources/api/pyvene.models.configuration_intervenable_model.IntervenableConfig.rst
@@ -0,0 +1,54 @@
+pyvene.models.configuration\_intervenable\_model.IntervenableConfig
+===================================================================
+
+.. currentmodule:: pyvene.models.configuration_intervenable_model
+
+.. autoclass:: IntervenableConfig
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~IntervenableConfig.__init__
+      ~IntervenableConfig.add_intervention
+      ~IntervenableConfig.dict_torch_dtype_to_str
+      ~IntervenableConfig.from_dict
+      ~IntervenableConfig.from_json_file
+      ~IntervenableConfig.from_pretrained
+      ~IntervenableConfig.get_config_dict
+      ~IntervenableConfig.get_text_config
+      ~IntervenableConfig.push_to_hub
+      ~IntervenableConfig.register_for_auto_class
+      ~IntervenableConfig.save_pretrained
+      ~IntervenableConfig.to_dict
+      ~IntervenableConfig.to_diff_dict
+      ~IntervenableConfig.to_json_file
+      ~IntervenableConfig.to_json_string
+      ~IntervenableConfig.update
+      ~IntervenableConfig.update_from_string
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~IntervenableConfig.attribute_map
+      ~IntervenableConfig.base_config_key
+      ~IntervenableConfig.base_model_tp_plan
+      ~IntervenableConfig.is_composition
+      ~IntervenableConfig.model_type
+      ~IntervenableConfig.name_or_path
+      ~IntervenableConfig.num_labels
+      ~IntervenableConfig.sub_configs
+      ~IntervenableConfig.use_return_dict
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.configuration_intervenable_model.RepresentationConfig.rst b/_sources/api/pyvene.models.configuration_intervenable_model.RepresentationConfig.rst
new file mode 100644
index 00000000..d1cdc46a
--- /dev/null
+++ b/_sources/api/pyvene.models.configuration_intervenable_model.RepresentationConfig.rst
@@ -0,0 +1,45 @@
+pyvene.models.configuration\_intervenable\_model.RepresentationConfig
+=====================================================================
+
+.. currentmodule:: pyvene.models.configuration_intervenable_model
+
+.. autoclass:: RepresentationConfig
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~RepresentationConfig.__init__
+      ~RepresentationConfig.count
+      ~RepresentationConfig.index
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~RepresentationConfig.component
+      ~RepresentationConfig.group_key
+      ~RepresentationConfig.hidden_source_representation
+      ~RepresentationConfig.intervention
+      ~RepresentationConfig.intervention_link_key
+      ~RepresentationConfig.intervention_type
+      ~RepresentationConfig.latent_dim
+      ~RepresentationConfig.layer
+      ~RepresentationConfig.low_rank_dimension
+      ~RepresentationConfig.max_number_of_units
+      ~RepresentationConfig.moe_key
+      ~RepresentationConfig.source_representation
+      ~RepresentationConfig.subspace_partition
+      ~RepresentationConfig.unit
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.configuration_intervenable_model.rst b/_sources/api/pyvene.models.configuration_intervenable_model.rst
new file mode 100644
index 00000000..92fe9436
--- /dev/null
+++ b/_sources/api/pyvene.models.configuration_intervenable_model.rst
@@ -0,0 +1,32 @@
+pyvene.models.configuration\_intervenable\_model
+================================================
+
+.. automodule:: pyvene.models.configuration_intervenable_model
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      IntervenableConfig
+      RepresentationConfig
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.constants.rst b/_sources/api/pyvene.models.constants.rst
new file mode 100644
index 00000000..27e4cf32
--- /dev/null
+++ b/_sources/api/pyvene.models.constants.rst
@@ -0,0 +1,34 @@
+pyvene.models.constants
+=======================
+
+.. automodule:: pyvene.models.constants
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      split_and_select
+      split_half
+      split_head_and_permute
+      split_heads
+      split_three
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.constants.split_and_select.rst b/_sources/api/pyvene.models.constants.split_and_select.rst
new file mode 100644
index 00000000..7c4a79b0
--- /dev/null
+++ b/_sources/api/pyvene.models.constants.split_and_select.rst
@@ -0,0 +1,6 @@
+pyvene.models.constants.split\_and\_select
+==========================================
+
+.. currentmodule:: pyvene.models.constants
+
+.. autofunction:: split_and_select
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.constants.split_half.rst b/_sources/api/pyvene.models.constants.split_half.rst
new file mode 100644
index 00000000..3f0a150c
--- /dev/null
+++ b/_sources/api/pyvene.models.constants.split_half.rst
@@ -0,0 +1,6 @@
+pyvene.models.constants.split\_half
+===================================
+
+.. currentmodule:: pyvene.models.constants
+
+.. autofunction:: split_half
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.constants.split_head_and_permute.rst b/_sources/api/pyvene.models.constants.split_head_and_permute.rst
new file mode 100644
index 00000000..edc42b86
--- /dev/null
+++ b/_sources/api/pyvene.models.constants.split_head_and_permute.rst
@@ -0,0 +1,6 @@
+pyvene.models.constants.split\_head\_and\_permute
+=================================================
+
+.. currentmodule:: pyvene.models.constants
+
+.. autofunction:: split_head_and_permute
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.constants.split_heads.rst b/_sources/api/pyvene.models.constants.split_heads.rst
new file mode 100644
index 00000000..43cd160e
--- /dev/null
+++ b/_sources/api/pyvene.models.constants.split_heads.rst
@@ -0,0 +1,6 @@
+pyvene.models.constants.split\_heads
+====================================
+
+.. currentmodule:: pyvene.models.constants
+
+.. autofunction:: split_heads
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.constants.split_three.rst b/_sources/api/pyvene.models.constants.split_three.rst
new file mode 100644
index 00000000..aef73fb1
--- /dev/null
+++ b/_sources/api/pyvene.models.constants.split_three.rst
@@ -0,0 +1,6 @@
+pyvene.models.constants.split\_three
+====================================
+
+.. currentmodule:: pyvene.models.constants
+
+.. autofunction:: split_three
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.create_gemma.rst b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.create_gemma.rst
new file mode 100644
index 00000000..fbbde871
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.create_gemma.rst
@@ -0,0 +1,6 @@
+pyvene.models.gemma.modelings\_intervenable\_gemma.create\_gemma
+================================================================
+
+.. currentmodule:: pyvene.models.gemma.modelings_intervenable_gemma
+
+.. autofunction:: create_gemma
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..8a862dc3
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gemma.modelings\_intervenable\_gemma.gemma\_lm\_type\_to\_dimension\_mapping
+==========================================================================================
+
+.. currentmodule:: pyvene.models.gemma.modelings_intervenable_gemma
+
+.. autodata:: gemma_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..232d3c7e
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gemma.modelings\_intervenable\_gemma.gemma\_type\_to\_dimension\_mapping
+======================================================================================
+
+.. currentmodule:: pyvene.models.gemma.modelings_intervenable_gemma
+
+.. autodata:: gemma_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.rst b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.rst
new file mode 100644
index 00000000..79642efc
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma.modelings_intervenable_gemma.rst
@@ -0,0 +1,38 @@
+pyvene.models.gemma.modelings\_intervenable\_gemma
+==================================================
+
+.. automodule:: pyvene.models.gemma.modelings_intervenable_gemma
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      gemma_type_to_dimension_mapping
+      gemma_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_gemma
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gemma.rst b/_sources/api/pyvene.models.gemma.rst
new file mode 100644
index 00000000..ac28cea9
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma.rst
@@ -0,0 +1,32 @@
+pyvene.models.gemma
+===================
+
+.. automodule:: pyvene.models.gemma
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.gemma.modelings_intervenable_gemma
+
diff --git a/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.rst b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.rst
new file mode 100644
index 00000000..31802886
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.rst
@@ -0,0 +1,6 @@
+pyvene.models.gemma2.modelings\_intervenable\_gemma2.create\_gemma2
+===================================================================
+
+.. currentmodule:: pyvene.models.gemma2.modelings_intervenable_gemma2
+
+.. autofunction:: create_gemma2
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..033de875
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gemma2.modelings\_intervenable\_gemma2.gemma2\_lm\_type\_to\_dimension\_mapping
+=============================================================================================
+
+.. currentmodule:: pyvene.models.gemma2.modelings_intervenable_gemma2
+
+.. autodata:: gemma2_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..5190a463
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gemma2.modelings\_intervenable\_gemma2.gemma2\_type\_to\_dimension\_mapping
+=========================================================================================
+
+.. currentmodule:: pyvene.models.gemma2.modelings_intervenable_gemma2
+
+.. autodata:: gemma2_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.rst b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.rst
new file mode 100644
index 00000000..cb965f7a
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma2.modelings_intervenable_gemma2.rst
@@ -0,0 +1,38 @@
+pyvene.models.gemma2.modelings\_intervenable\_gemma2
+====================================================
+
+.. automodule:: pyvene.models.gemma2.modelings_intervenable_gemma2
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      gemma2_type_to_dimension_mapping
+      gemma2_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_gemma2
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gemma2.rst b/_sources/api/pyvene.models.gemma2.rst
new file mode 100644
index 00000000..6696b8b3
--- /dev/null
+++ b/_sources/api/pyvene.models.gemma2.rst
@@ -0,0 +1,32 @@
+pyvene.models.gemma2
+====================
+
+.. automodule:: pyvene.models.gemma2
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.gemma2.modelings_intervenable_gemma2
+
diff --git a/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2.rst b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2.rst
new file mode 100644
index 00000000..89252b17
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt2.modelings\_intervenable\_gpt2.create\_gpt2
+=============================================================
+
+.. currentmodule:: pyvene.models.gpt2.modelings_intervenable_gpt2
+
+.. autofunction:: create_gpt2
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier.rst b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier.rst
new file mode 100644
index 00000000..569d8804
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt2.modelings\_intervenable\_gpt2.create\_gpt2\_classifier
+=========================================================================
+
+.. currentmodule:: pyvene.models.gpt2.modelings_intervenable_gpt2
+
+.. autofunction:: create_gpt2_classifier
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm.rst b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm.rst
new file mode 100644
index 00000000..63b574da
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt2.modelings\_intervenable\_gpt2.create\_gpt2\_lm
+=================================================================
+
+.. currentmodule:: pyvene.models.gpt2.modelings_intervenable_gpt2
+
+.. autofunction:: create_gpt2_lm
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..fd734a4e
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt2.modelings\_intervenable\_gpt2.gpt2\_lm\_type\_to\_dimension\_mapping
+=======================================================================================
+
+.. currentmodule:: pyvene.models.gpt2.modelings_intervenable_gpt2
+
+.. autodata:: gpt2_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..cd0e6773
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt2.modelings\_intervenable\_gpt2.gpt2\_type\_to\_dimension\_mapping
+===================================================================================
+
+.. currentmodule:: pyvene.models.gpt2.modelings_intervenable_gpt2
+
+.. autodata:: gpt2_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.rst b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.rst
new file mode 100644
index 00000000..75178e60
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.modelings_intervenable_gpt2.rst
@@ -0,0 +1,40 @@
+pyvene.models.gpt2.modelings\_intervenable\_gpt2
+================================================
+
+.. automodule:: pyvene.models.gpt2.modelings_intervenable_gpt2
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      gpt2_type_to_dimension_mapping
+      gpt2_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_gpt2
+      create_gpt2_classifier
+      create_gpt2_lm
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gpt2.rst b/_sources/api/pyvene.models.gpt2.rst
new file mode 100644
index 00000000..949ae210
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt2.rst
@@ -0,0 +1,32 @@
+pyvene.models.gpt2
+==================
+
+.. automodule:: pyvene.models.gpt2
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.gpt2.modelings_intervenable_gpt2
+
diff --git a/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo.rst b/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo.rst
new file mode 100644
index 00000000..e40131e0
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt\_neo.modelings\_intervenable\_gpt\_neo.create\_gpt\_neo
+=========================================================================
+
+.. currentmodule:: pyvene.models.gpt_neo.modelings_intervenable_gpt_neo
+
+.. autofunction:: create_gpt_neo
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..c072eda6
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt\_neo.modelings\_intervenable\_gpt\_neo.gpt\_neo\_type\_to\_dimension\_mapping
+===============================================================================================
+
+.. currentmodule:: pyvene.models.gpt_neo.modelings_intervenable_gpt_neo
+
+.. autodata:: gpt_neo_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.rst b/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.rst
new file mode 100644
index 00000000..50f0bc10
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.rst
@@ -0,0 +1,37 @@
+pyvene.models.gpt\_neo.modelings\_intervenable\_gpt\_neo
+========================================================
+
+.. automodule:: pyvene.models.gpt_neo.modelings_intervenable_gpt_neo
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      gpt_neo_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_gpt_neo
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gpt_neo.rst b/_sources/api/pyvene.models.gpt_neo.rst
new file mode 100644
index 00000000..6dea1450
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neo.rst
@@ -0,0 +1,32 @@
+pyvene.models.gpt\_neo
+======================
+
+.. automodule:: pyvene.models.gpt_neo
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.gpt_neo.modelings_intervenable_gpt_neo
+
diff --git a/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox.rst b/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox.rst
new file mode 100644
index 00000000..3d10a922
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt\_neox.modelings\_intervenable\_gpt\_neox.create\_gpt\_neox
+============================================================================
+
+.. currentmodule:: pyvene.models.gpt_neox.modelings_intervenable_gpt_neox
+
+.. autofunction:: create_gpt_neox
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..7ab3ba74
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gpt\_neox.modelings\_intervenable\_gpt\_neox.gpt\_neox\_type\_to\_dimension\_mapping
+==================================================================================================
+
+.. currentmodule:: pyvene.models.gpt_neox.modelings_intervenable_gpt_neox
+
+.. autodata:: gpt_neox_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.rst b/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.rst
new file mode 100644
index 00000000..7c26c3e8
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.rst
@@ -0,0 +1,37 @@
+pyvene.models.gpt\_neox.modelings\_intervenable\_gpt\_neox
+==========================================================
+
+.. automodule:: pyvene.models.gpt_neox.modelings_intervenable_gpt_neox
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      gpt_neox_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_gpt_neox
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gpt_neox.rst b/_sources/api/pyvene.models.gpt_neox.rst
new file mode 100644
index 00000000..d45c6ce3
--- /dev/null
+++ b/_sources/api/pyvene.models.gpt_neox.rst
@@ -0,0 +1,32 @@
+pyvene.models.gpt\_neox
+=======================
+
+.. automodule:: pyvene.models.gpt_neox
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.gpt_neox.modelings_intervenable_gpt_neox
+
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRUCell.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRUCell.rst
new file mode 100644
index 00000000..1b1e8a51
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRUCell.rst
@@ -0,0 +1,84 @@
+pyvene.models.gru.modelings\_gru.GRUCell
+========================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRUCell
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRUCell.__init__
+      ~GRUCell.add_module
+      ~GRUCell.apply
+      ~GRUCell.bfloat16
+      ~GRUCell.buffers
+      ~GRUCell.children
+      ~GRUCell.compile
+      ~GRUCell.cpu
+      ~GRUCell.cuda
+      ~GRUCell.double
+      ~GRUCell.eval
+      ~GRUCell.extra_repr
+      ~GRUCell.float
+      ~GRUCell.forward
+      ~GRUCell.get_buffer
+      ~GRUCell.get_extra_state
+      ~GRUCell.get_parameter
+      ~GRUCell.get_submodule
+      ~GRUCell.half
+      ~GRUCell.ipu
+      ~GRUCell.load_state_dict
+      ~GRUCell.modules
+      ~GRUCell.mtia
+      ~GRUCell.named_buffers
+      ~GRUCell.named_children
+      ~GRUCell.named_modules
+      ~GRUCell.named_parameters
+      ~GRUCell.parameters
+      ~GRUCell.register_backward_hook
+      ~GRUCell.register_buffer
+      ~GRUCell.register_forward_hook
+      ~GRUCell.register_forward_pre_hook
+      ~GRUCell.register_full_backward_hook
+      ~GRUCell.register_full_backward_pre_hook
+      ~GRUCell.register_load_state_dict_post_hook
+      ~GRUCell.register_load_state_dict_pre_hook
+      ~GRUCell.register_module
+      ~GRUCell.register_parameter
+      ~GRUCell.register_state_dict_post_hook
+      ~GRUCell.register_state_dict_pre_hook
+      ~GRUCell.requires_grad_
+      ~GRUCell.reset_parameters
+      ~GRUCell.set_extra_state
+      ~GRUCell.set_submodule
+      ~GRUCell.share_memory
+      ~GRUCell.state_dict
+      ~GRUCell.to
+      ~GRUCell.to_empty
+      ~GRUCell.train
+      ~GRUCell.type
+      ~GRUCell.xpu
+      ~GRUCell.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRUCell.T_destination
+      ~GRUCell.call_super_init
+      ~GRUCell.dump_patches
+      ~GRUCell.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRUConfig.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRUConfig.rst
new file mode 100644
index 00000000..f6e3fc5e
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRUConfig.rst
@@ -0,0 +1,53 @@
+pyvene.models.gru.modelings\_gru.GRUConfig
+==========================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRUConfig
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRUConfig.__init__
+      ~GRUConfig.dict_torch_dtype_to_str
+      ~GRUConfig.from_dict
+      ~GRUConfig.from_json_file
+      ~GRUConfig.from_pretrained
+      ~GRUConfig.get_config_dict
+      ~GRUConfig.get_text_config
+      ~GRUConfig.push_to_hub
+      ~GRUConfig.register_for_auto_class
+      ~GRUConfig.save_pretrained
+      ~GRUConfig.to_dict
+      ~GRUConfig.to_diff_dict
+      ~GRUConfig.to_json_file
+      ~GRUConfig.to_json_string
+      ~GRUConfig.update
+      ~GRUConfig.update_from_string
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRUConfig.attribute_map
+      ~GRUConfig.base_config_key
+      ~GRUConfig.base_model_tp_plan
+      ~GRUConfig.is_composition
+      ~GRUConfig.model_type
+      ~GRUConfig.name_or_path
+      ~GRUConfig.num_labels
+      ~GRUConfig.sub_configs
+      ~GRUConfig.use_return_dict
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRUForClassification.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRUForClassification.rst
new file mode 100644
index 00000000..2521d5e0
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRUForClassification.rst
@@ -0,0 +1,147 @@
+pyvene.models.gru.modelings\_gru.GRUForClassification
+=====================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRUForClassification
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRUForClassification.__init__
+      ~GRUForClassification.active_adapter
+      ~GRUForClassification.active_adapters
+      ~GRUForClassification.add_adapter
+      ~GRUForClassification.add_memory_hooks
+      ~GRUForClassification.add_model_tags
+      ~GRUForClassification.add_module
+      ~GRUForClassification.apply
+      ~GRUForClassification.bfloat16
+      ~GRUForClassification.buffers
+      ~GRUForClassification.can_generate
+      ~GRUForClassification.children
+      ~GRUForClassification.compile
+      ~GRUForClassification.compute_transition_scores
+      ~GRUForClassification.cpu
+      ~GRUForClassification.create_extended_attention_mask_for_decoder
+      ~GRUForClassification.cuda
+      ~GRUForClassification.delete_adapter
+      ~GRUForClassification.dequantize
+      ~GRUForClassification.disable_adapters
+      ~GRUForClassification.disable_input_require_grads
+      ~GRUForClassification.double
+      ~GRUForClassification.enable_adapters
+      ~GRUForClassification.enable_input_require_grads
+      ~GRUForClassification.estimate_tokens
+      ~GRUForClassification.eval
+      ~GRUForClassification.extra_repr
+      ~GRUForClassification.float
+      ~GRUForClassification.floating_point_ops
+      ~GRUForClassification.forward
+      ~GRUForClassification.from_pretrained
+      ~GRUForClassification.generate
+      ~GRUForClassification.get_adapter_state_dict
+      ~GRUForClassification.get_buffer
+      ~GRUForClassification.get_compiled_call
+      ~GRUForClassification.get_extended_attention_mask
+      ~GRUForClassification.get_extra_state
+      ~GRUForClassification.get_head_mask
+      ~GRUForClassification.get_input_embeddings
+      ~GRUForClassification.get_memory_footprint
+      ~GRUForClassification.get_output_embeddings
+      ~GRUForClassification.get_parameter
+      ~GRUForClassification.get_position_embeddings
+      ~GRUForClassification.get_submodule
+      ~GRUForClassification.gradient_checkpointing_disable
+      ~GRUForClassification.gradient_checkpointing_enable
+      ~GRUForClassification.half
+      ~GRUForClassification.heal_tokens
+      ~GRUForClassification.init_weights
+      ~GRUForClassification.invert_attention_mask
+      ~GRUForClassification.ipu
+      ~GRUForClassification.load_adapter
+      ~GRUForClassification.load_state_dict
+      ~GRUForClassification.modules
+      ~GRUForClassification.mtia
+      ~GRUForClassification.named_buffers
+      ~GRUForClassification.named_children
+      ~GRUForClassification.named_modules
+      ~GRUForClassification.named_parameters
+      ~GRUForClassification.num_parameters
+      ~GRUForClassification.parameters
+      ~GRUForClassification.post_init
+      ~GRUForClassification.prepare_inputs_for_generation
+      ~GRUForClassification.prune_heads
+      ~GRUForClassification.push_to_hub
+      ~GRUForClassification.register_backward_hook
+      ~GRUForClassification.register_buffer
+      ~GRUForClassification.register_for_auto_class
+      ~GRUForClassification.register_forward_hook
+      ~GRUForClassification.register_forward_pre_hook
+      ~GRUForClassification.register_full_backward_hook
+      ~GRUForClassification.register_full_backward_pre_hook
+      ~GRUForClassification.register_load_state_dict_post_hook
+      ~GRUForClassification.register_load_state_dict_pre_hook
+      ~GRUForClassification.register_module
+      ~GRUForClassification.register_parameter
+      ~GRUForClassification.register_state_dict_post_hook
+      ~GRUForClassification.register_state_dict_pre_hook
+      ~GRUForClassification.requires_grad_
+      ~GRUForClassification.reset_memory_hooks_state
+      ~GRUForClassification.resize_position_embeddings
+      ~GRUForClassification.resize_token_embeddings
+      ~GRUForClassification.retrieve_modules_from_names
+      ~GRUForClassification.reverse_bettertransformer
+      ~GRUForClassification.save_pretrained
+      ~GRUForClassification.set_adapter
+      ~GRUForClassification.set_extra_state
+      ~GRUForClassification.set_input_embeddings
+      ~GRUForClassification.set_submodule
+      ~GRUForClassification.share_memory
+      ~GRUForClassification.state_dict
+      ~GRUForClassification.tensor_parallel
+      ~GRUForClassification.tie_weights
+      ~GRUForClassification.to
+      ~GRUForClassification.to_bettertransformer
+      ~GRUForClassification.to_empty
+      ~GRUForClassification.train
+      ~GRUForClassification.type
+      ~GRUForClassification.warn_if_padding_and_no_attention_mask
+      ~GRUForClassification.xpu
+      ~GRUForClassification.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRUForClassification.T_destination
+      ~GRUForClassification.base_model
+      ~GRUForClassification.base_model_prefix
+      ~GRUForClassification.call_super_init
+      ~GRUForClassification.config_class
+      ~GRUForClassification.device
+      ~GRUForClassification.dtype
+      ~GRUForClassification.dummy_inputs
+      ~GRUForClassification.dump_patches
+      ~GRUForClassification.framework
+      ~GRUForClassification.is_gradient_checkpointing
+      ~GRUForClassification.is_parallelizable
+      ~GRUForClassification.loss_function
+      ~GRUForClassification.main_input_name
+      ~GRUForClassification.model_tags
+      ~GRUForClassification.supports_gradient_checkpointing
+      ~GRUForClassification.supports_tp_plan
+      ~GRUForClassification.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRULMHeadModel.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRULMHeadModel.rst
new file mode 100644
index 00000000..2d58d474
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRULMHeadModel.rst
@@ -0,0 +1,148 @@
+pyvene.models.gru.modelings\_gru.GRULMHeadModel
+===============================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRULMHeadModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRULMHeadModel.__init__
+      ~GRULMHeadModel.active_adapter
+      ~GRULMHeadModel.active_adapters
+      ~GRULMHeadModel.add_adapter
+      ~GRULMHeadModel.add_memory_hooks
+      ~GRULMHeadModel.add_model_tags
+      ~GRULMHeadModel.add_module
+      ~GRULMHeadModel.apply
+      ~GRULMHeadModel.bfloat16
+      ~GRULMHeadModel.buffers
+      ~GRULMHeadModel.can_generate
+      ~GRULMHeadModel.children
+      ~GRULMHeadModel.compile
+      ~GRULMHeadModel.compute_transition_scores
+      ~GRULMHeadModel.cpu
+      ~GRULMHeadModel.create_extended_attention_mask_for_decoder
+      ~GRULMHeadModel.cuda
+      ~GRULMHeadModel.delete_adapter
+      ~GRULMHeadModel.dequantize
+      ~GRULMHeadModel.disable_adapters
+      ~GRULMHeadModel.disable_input_require_grads
+      ~GRULMHeadModel.double
+      ~GRULMHeadModel.enable_adapters
+      ~GRULMHeadModel.enable_input_require_grads
+      ~GRULMHeadModel.estimate_tokens
+      ~GRULMHeadModel.eval
+      ~GRULMHeadModel.extra_repr
+      ~GRULMHeadModel.float
+      ~GRULMHeadModel.floating_point_ops
+      ~GRULMHeadModel.forward
+      ~GRULMHeadModel.from_pretrained
+      ~GRULMHeadModel.generate
+      ~GRULMHeadModel.get_adapter_state_dict
+      ~GRULMHeadModel.get_buffer
+      ~GRULMHeadModel.get_compiled_call
+      ~GRULMHeadModel.get_extended_attention_mask
+      ~GRULMHeadModel.get_extra_state
+      ~GRULMHeadModel.get_head_mask
+      ~GRULMHeadModel.get_input_embeddings
+      ~GRULMHeadModel.get_memory_footprint
+      ~GRULMHeadModel.get_output_embeddings
+      ~GRULMHeadModel.get_parameter
+      ~GRULMHeadModel.get_position_embeddings
+      ~GRULMHeadModel.get_submodule
+      ~GRULMHeadModel.gradient_checkpointing_disable
+      ~GRULMHeadModel.gradient_checkpointing_enable
+      ~GRULMHeadModel.half
+      ~GRULMHeadModel.heal_tokens
+      ~GRULMHeadModel.init_weights
+      ~GRULMHeadModel.invert_attention_mask
+      ~GRULMHeadModel.ipu
+      ~GRULMHeadModel.load_adapter
+      ~GRULMHeadModel.load_state_dict
+      ~GRULMHeadModel.modules
+      ~GRULMHeadModel.mtia
+      ~GRULMHeadModel.named_buffers
+      ~GRULMHeadModel.named_children
+      ~GRULMHeadModel.named_modules
+      ~GRULMHeadModel.named_parameters
+      ~GRULMHeadModel.num_parameters
+      ~GRULMHeadModel.parameters
+      ~GRULMHeadModel.post_init
+      ~GRULMHeadModel.prepare_inputs_for_generation
+      ~GRULMHeadModel.prune_heads
+      ~GRULMHeadModel.push_to_hub
+      ~GRULMHeadModel.register_backward_hook
+      ~GRULMHeadModel.register_buffer
+      ~GRULMHeadModel.register_for_auto_class
+      ~GRULMHeadModel.register_forward_hook
+      ~GRULMHeadModel.register_forward_pre_hook
+      ~GRULMHeadModel.register_full_backward_hook
+      ~GRULMHeadModel.register_full_backward_pre_hook
+      ~GRULMHeadModel.register_load_state_dict_post_hook
+      ~GRULMHeadModel.register_load_state_dict_pre_hook
+      ~GRULMHeadModel.register_module
+      ~GRULMHeadModel.register_parameter
+      ~GRULMHeadModel.register_state_dict_post_hook
+      ~GRULMHeadModel.register_state_dict_pre_hook
+      ~GRULMHeadModel.requires_grad_
+      ~GRULMHeadModel.reset_memory_hooks_state
+      ~GRULMHeadModel.resize_position_embeddings
+      ~GRULMHeadModel.resize_token_embeddings
+      ~GRULMHeadModel.retrieve_modules_from_names
+      ~GRULMHeadModel.reverse_bettertransformer
+      ~GRULMHeadModel.save_pretrained
+      ~GRULMHeadModel.set_adapter
+      ~GRULMHeadModel.set_extra_state
+      ~GRULMHeadModel.set_input_embeddings
+      ~GRULMHeadModel.set_output_embeddings
+      ~GRULMHeadModel.set_submodule
+      ~GRULMHeadModel.share_memory
+      ~GRULMHeadModel.state_dict
+      ~GRULMHeadModel.tensor_parallel
+      ~GRULMHeadModel.tie_weights
+      ~GRULMHeadModel.to
+      ~GRULMHeadModel.to_bettertransformer
+      ~GRULMHeadModel.to_empty
+      ~GRULMHeadModel.train
+      ~GRULMHeadModel.type
+      ~GRULMHeadModel.warn_if_padding_and_no_attention_mask
+      ~GRULMHeadModel.xpu
+      ~GRULMHeadModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRULMHeadModel.T_destination
+      ~GRULMHeadModel.base_model
+      ~GRULMHeadModel.base_model_prefix
+      ~GRULMHeadModel.call_super_init
+      ~GRULMHeadModel.config_class
+      ~GRULMHeadModel.device
+      ~GRULMHeadModel.dtype
+      ~GRULMHeadModel.dummy_inputs
+      ~GRULMHeadModel.dump_patches
+      ~GRULMHeadModel.framework
+      ~GRULMHeadModel.is_gradient_checkpointing
+      ~GRULMHeadModel.is_parallelizable
+      ~GRULMHeadModel.loss_function
+      ~GRULMHeadModel.main_input_name
+      ~GRULMHeadModel.model_tags
+      ~GRULMHeadModel.supports_gradient_checkpointing
+      ~GRULMHeadModel.supports_tp_plan
+      ~GRULMHeadModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRUModel.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRUModel.rst
new file mode 100644
index 00000000..9f4fad4d
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRUModel.rst
@@ -0,0 +1,147 @@
+pyvene.models.gru.modelings\_gru.GRUModel
+=========================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRUModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRUModel.__init__
+      ~GRUModel.active_adapter
+      ~GRUModel.active_adapters
+      ~GRUModel.add_adapter
+      ~GRUModel.add_memory_hooks
+      ~GRUModel.add_model_tags
+      ~GRUModel.add_module
+      ~GRUModel.apply
+      ~GRUModel.bfloat16
+      ~GRUModel.buffers
+      ~GRUModel.can_generate
+      ~GRUModel.children
+      ~GRUModel.compile
+      ~GRUModel.compute_transition_scores
+      ~GRUModel.cpu
+      ~GRUModel.create_extended_attention_mask_for_decoder
+      ~GRUModel.cuda
+      ~GRUModel.delete_adapter
+      ~GRUModel.dequantize
+      ~GRUModel.disable_adapters
+      ~GRUModel.disable_input_require_grads
+      ~GRUModel.double
+      ~GRUModel.enable_adapters
+      ~GRUModel.enable_input_require_grads
+      ~GRUModel.estimate_tokens
+      ~GRUModel.eval
+      ~GRUModel.extra_repr
+      ~GRUModel.float
+      ~GRUModel.floating_point_ops
+      ~GRUModel.forward
+      ~GRUModel.from_pretrained
+      ~GRUModel.generate
+      ~GRUModel.get_adapter_state_dict
+      ~GRUModel.get_buffer
+      ~GRUModel.get_compiled_call
+      ~GRUModel.get_extended_attention_mask
+      ~GRUModel.get_extra_state
+      ~GRUModel.get_head_mask
+      ~GRUModel.get_input_embeddings
+      ~GRUModel.get_memory_footprint
+      ~GRUModel.get_output_embeddings
+      ~GRUModel.get_parameter
+      ~GRUModel.get_position_embeddings
+      ~GRUModel.get_submodule
+      ~GRUModel.gradient_checkpointing_disable
+      ~GRUModel.gradient_checkpointing_enable
+      ~GRUModel.half
+      ~GRUModel.heal_tokens
+      ~GRUModel.init_weights
+      ~GRUModel.invert_attention_mask
+      ~GRUModel.ipu
+      ~GRUModel.load_adapter
+      ~GRUModel.load_state_dict
+      ~GRUModel.modules
+      ~GRUModel.mtia
+      ~GRUModel.named_buffers
+      ~GRUModel.named_children
+      ~GRUModel.named_modules
+      ~GRUModel.named_parameters
+      ~GRUModel.num_parameters
+      ~GRUModel.parameters
+      ~GRUModel.post_init
+      ~GRUModel.prepare_inputs_for_generation
+      ~GRUModel.prune_heads
+      ~GRUModel.push_to_hub
+      ~GRUModel.register_backward_hook
+      ~GRUModel.register_buffer
+      ~GRUModel.register_for_auto_class
+      ~GRUModel.register_forward_hook
+      ~GRUModel.register_forward_pre_hook
+      ~GRUModel.register_full_backward_hook
+      ~GRUModel.register_full_backward_pre_hook
+      ~GRUModel.register_load_state_dict_post_hook
+      ~GRUModel.register_load_state_dict_pre_hook
+      ~GRUModel.register_module
+      ~GRUModel.register_parameter
+      ~GRUModel.register_state_dict_post_hook
+      ~GRUModel.register_state_dict_pre_hook
+      ~GRUModel.requires_grad_
+      ~GRUModel.reset_memory_hooks_state
+      ~GRUModel.resize_position_embeddings
+      ~GRUModel.resize_token_embeddings
+      ~GRUModel.retrieve_modules_from_names
+      ~GRUModel.reverse_bettertransformer
+      ~GRUModel.save_pretrained
+      ~GRUModel.set_adapter
+      ~GRUModel.set_extra_state
+      ~GRUModel.set_input_embeddings
+      ~GRUModel.set_submodule
+      ~GRUModel.share_memory
+      ~GRUModel.state_dict
+      ~GRUModel.tensor_parallel
+      ~GRUModel.tie_weights
+      ~GRUModel.to
+      ~GRUModel.to_bettertransformer
+      ~GRUModel.to_empty
+      ~GRUModel.train
+      ~GRUModel.type
+      ~GRUModel.warn_if_padding_and_no_attention_mask
+      ~GRUModel.xpu
+      ~GRUModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRUModel.T_destination
+      ~GRUModel.base_model
+      ~GRUModel.base_model_prefix
+      ~GRUModel.call_super_init
+      ~GRUModel.config_class
+      ~GRUModel.device
+      ~GRUModel.dtype
+      ~GRUModel.dummy_inputs
+      ~GRUModel.dump_patches
+      ~GRUModel.framework
+      ~GRUModel.is_gradient_checkpointing
+      ~GRUModel.is_parallelizable
+      ~GRUModel.loss_function
+      ~GRUModel.main_input_name
+      ~GRUModel.model_tags
+      ~GRUModel.supports_gradient_checkpointing
+      ~GRUModel.supports_tp_plan
+      ~GRUModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRUModelOutput.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRUModelOutput.rst
new file mode 100644
index 00000000..678fdd00
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRUModelOutput.rst
@@ -0,0 +1,44 @@
+pyvene.models.gru.modelings\_gru.GRUModelOutput
+===============================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRUModelOutput
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRUModelOutput.__init__
+      ~GRUModelOutput.clear
+      ~GRUModelOutput.copy
+      ~GRUModelOutput.fromkeys
+      ~GRUModelOutput.get
+      ~GRUModelOutput.items
+      ~GRUModelOutput.keys
+      ~GRUModelOutput.move_to_end
+      ~GRUModelOutput.pop
+      ~GRUModelOutput.popitem
+      ~GRUModelOutput.setdefault
+      ~GRUModelOutput.to_tuple
+      ~GRUModelOutput.update
+      ~GRUModelOutput.values
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRUModelOutput.hidden_states
+      ~GRUModelOutput.last_hidden_state
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.rst b/_sources/api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.rst
new file mode 100644
index 00000000..e7fd4217
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.rst
@@ -0,0 +1,147 @@
+pyvene.models.gru.modelings\_gru.GRUPreTrainedModel
+===================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_gru
+
+.. autoclass:: GRUPreTrainedModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~GRUPreTrainedModel.__init__
+      ~GRUPreTrainedModel.active_adapter
+      ~GRUPreTrainedModel.active_adapters
+      ~GRUPreTrainedModel.add_adapter
+      ~GRUPreTrainedModel.add_memory_hooks
+      ~GRUPreTrainedModel.add_model_tags
+      ~GRUPreTrainedModel.add_module
+      ~GRUPreTrainedModel.apply
+      ~GRUPreTrainedModel.bfloat16
+      ~GRUPreTrainedModel.buffers
+      ~GRUPreTrainedModel.can_generate
+      ~GRUPreTrainedModel.children
+      ~GRUPreTrainedModel.compile
+      ~GRUPreTrainedModel.compute_transition_scores
+      ~GRUPreTrainedModel.cpu
+      ~GRUPreTrainedModel.create_extended_attention_mask_for_decoder
+      ~GRUPreTrainedModel.cuda
+      ~GRUPreTrainedModel.delete_adapter
+      ~GRUPreTrainedModel.dequantize
+      ~GRUPreTrainedModel.disable_adapters
+      ~GRUPreTrainedModel.disable_input_require_grads
+      ~GRUPreTrainedModel.double
+      ~GRUPreTrainedModel.enable_adapters
+      ~GRUPreTrainedModel.enable_input_require_grads
+      ~GRUPreTrainedModel.estimate_tokens
+      ~GRUPreTrainedModel.eval
+      ~GRUPreTrainedModel.extra_repr
+      ~GRUPreTrainedModel.float
+      ~GRUPreTrainedModel.floating_point_ops
+      ~GRUPreTrainedModel.forward
+      ~GRUPreTrainedModel.from_pretrained
+      ~GRUPreTrainedModel.generate
+      ~GRUPreTrainedModel.get_adapter_state_dict
+      ~GRUPreTrainedModel.get_buffer
+      ~GRUPreTrainedModel.get_compiled_call
+      ~GRUPreTrainedModel.get_extended_attention_mask
+      ~GRUPreTrainedModel.get_extra_state
+      ~GRUPreTrainedModel.get_head_mask
+      ~GRUPreTrainedModel.get_input_embeddings
+      ~GRUPreTrainedModel.get_memory_footprint
+      ~GRUPreTrainedModel.get_output_embeddings
+      ~GRUPreTrainedModel.get_parameter
+      ~GRUPreTrainedModel.get_position_embeddings
+      ~GRUPreTrainedModel.get_submodule
+      ~GRUPreTrainedModel.gradient_checkpointing_disable
+      ~GRUPreTrainedModel.gradient_checkpointing_enable
+      ~GRUPreTrainedModel.half
+      ~GRUPreTrainedModel.heal_tokens
+      ~GRUPreTrainedModel.init_weights
+      ~GRUPreTrainedModel.invert_attention_mask
+      ~GRUPreTrainedModel.ipu
+      ~GRUPreTrainedModel.load_adapter
+      ~GRUPreTrainedModel.load_state_dict
+      ~GRUPreTrainedModel.modules
+      ~GRUPreTrainedModel.mtia
+      ~GRUPreTrainedModel.named_buffers
+      ~GRUPreTrainedModel.named_children
+      ~GRUPreTrainedModel.named_modules
+      ~GRUPreTrainedModel.named_parameters
+      ~GRUPreTrainedModel.num_parameters
+      ~GRUPreTrainedModel.parameters
+      ~GRUPreTrainedModel.post_init
+      ~GRUPreTrainedModel.prepare_inputs_for_generation
+      ~GRUPreTrainedModel.prune_heads
+      ~GRUPreTrainedModel.push_to_hub
+      ~GRUPreTrainedModel.register_backward_hook
+      ~GRUPreTrainedModel.register_buffer
+      ~GRUPreTrainedModel.register_for_auto_class
+      ~GRUPreTrainedModel.register_forward_hook
+      ~GRUPreTrainedModel.register_forward_pre_hook
+      ~GRUPreTrainedModel.register_full_backward_hook
+      ~GRUPreTrainedModel.register_full_backward_pre_hook
+      ~GRUPreTrainedModel.register_load_state_dict_post_hook
+      ~GRUPreTrainedModel.register_load_state_dict_pre_hook
+      ~GRUPreTrainedModel.register_module
+      ~GRUPreTrainedModel.register_parameter
+      ~GRUPreTrainedModel.register_state_dict_post_hook
+      ~GRUPreTrainedModel.register_state_dict_pre_hook
+      ~GRUPreTrainedModel.requires_grad_
+      ~GRUPreTrainedModel.reset_memory_hooks_state
+      ~GRUPreTrainedModel.resize_position_embeddings
+      ~GRUPreTrainedModel.resize_token_embeddings
+      ~GRUPreTrainedModel.retrieve_modules_from_names
+      ~GRUPreTrainedModel.reverse_bettertransformer
+      ~GRUPreTrainedModel.save_pretrained
+      ~GRUPreTrainedModel.set_adapter
+      ~GRUPreTrainedModel.set_extra_state
+      ~GRUPreTrainedModel.set_input_embeddings
+      ~GRUPreTrainedModel.set_submodule
+      ~GRUPreTrainedModel.share_memory
+      ~GRUPreTrainedModel.state_dict
+      ~GRUPreTrainedModel.tensor_parallel
+      ~GRUPreTrainedModel.tie_weights
+      ~GRUPreTrainedModel.to
+      ~GRUPreTrainedModel.to_bettertransformer
+      ~GRUPreTrainedModel.to_empty
+      ~GRUPreTrainedModel.train
+      ~GRUPreTrainedModel.type
+      ~GRUPreTrainedModel.warn_if_padding_and_no_attention_mask
+      ~GRUPreTrainedModel.xpu
+      ~GRUPreTrainedModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GRUPreTrainedModel.T_destination
+      ~GRUPreTrainedModel.base_model
+      ~GRUPreTrainedModel.base_model_prefix
+      ~GRUPreTrainedModel.call_super_init
+      ~GRUPreTrainedModel.config_class
+      ~GRUPreTrainedModel.device
+      ~GRUPreTrainedModel.dtype
+      ~GRUPreTrainedModel.dummy_inputs
+      ~GRUPreTrainedModel.dump_patches
+      ~GRUPreTrainedModel.framework
+      ~GRUPreTrainedModel.is_gradient_checkpointing
+      ~GRUPreTrainedModel.is_parallelizable
+      ~GRUPreTrainedModel.loss_function
+      ~GRUPreTrainedModel.main_input_name
+      ~GRUPreTrainedModel.model_tags
+      ~GRUPreTrainedModel.supports_gradient_checkpointing
+      ~GRUPreTrainedModel.supports_tp_plan
+      ~GRUPreTrainedModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_gru.rst b/_sources/api/pyvene.models.gru.modelings_gru.rst
new file mode 100644
index 00000000..210dd091
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_gru.rst
@@ -0,0 +1,37 @@
+pyvene.models.gru.modelings\_gru
+================================
+
+.. automodule:: pyvene.models.gru.modelings_gru
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      GRUCell
+      GRUConfig
+      GRUForClassification
+      GRULMHeadModel
+      GRUModel
+      GRUModelOutput
+      GRUPreTrainedModel
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru.rst b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru.rst
new file mode 100644
index 00000000..582414c4
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru.rst
@@ -0,0 +1,6 @@
+pyvene.models.gru.modelings\_intervenable\_gru.create\_gru
+==========================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_intervenable_gru
+
+.. autofunction:: create_gru
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier.rst b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier.rst
new file mode 100644
index 00000000..baad6d7c
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier.rst
@@ -0,0 +1,6 @@
+pyvene.models.gru.modelings\_intervenable\_gru.create\_gru\_classifier
+======================================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_intervenable_gru
+
+.. autofunction:: create_gru_classifier
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru_lm.rst b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru_lm.rst
new file mode 100644
index 00000000..5a9e7372
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.create_gru_lm.rst
@@ -0,0 +1,6 @@
+pyvene.models.gru.modelings\_intervenable\_gru.create\_gru\_lm
+==============================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_intervenable_gru
+
+.. autofunction:: create_gru_lm
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..f94a382a
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gru.modelings\_intervenable\_gru.gru\_classifier\_type\_to\_dimension\_mapping
+============================================================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_intervenable_gru
+
+.. autodata:: gru_classifier_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..b56a6bf1
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.gru.modelings\_intervenable\_gru.gru\_type\_to\_dimension\_mapping
+================================================================================
+
+.. currentmodule:: pyvene.models.gru.modelings_intervenable_gru
+
+.. autodata:: gru_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.gru.modelings_intervenable_gru.rst b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.rst
new file mode 100644
index 00000000..356de525
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.modelings_intervenable_gru.rst
@@ -0,0 +1,40 @@
+pyvene.models.gru.modelings\_intervenable\_gru
+==============================================
+
+.. automodule:: pyvene.models.gru.modelings_intervenable_gru
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      gru_type_to_dimension_mapping
+      gru_classifier_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_gru
+      create_gru_classifier
+      create_gru_lm
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.gru.rst b/_sources/api/pyvene.models.gru.rst
new file mode 100644
index 00000000..bd95ecc7
--- /dev/null
+++ b/_sources/api/pyvene.models.gru.rst
@@ -0,0 +1,33 @@
+pyvene.models.gru
+=================
+
+.. automodule:: pyvene.models.gru
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.gru.modelings_gru
+   pyvene.models.gru.modelings_intervenable_gru
+
diff --git a/_sources/api/pyvene.models.intervenable_base.BaseModel.rst b/_sources/api/pyvene.models.intervenable_base.BaseModel.rst
new file mode 100644
index 00000000..4c1ab491
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_base.BaseModel.rst
@@ -0,0 +1,95 @@
+pyvene.models.intervenable\_base.BaseModel
+==========================================
+
+.. currentmodule:: pyvene.models.intervenable_base
+
+.. autoclass:: BaseModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BaseModel.__init__
+      ~BaseModel.add_module
+      ~BaseModel.apply
+      ~BaseModel.bfloat16
+      ~BaseModel.buffers
+      ~BaseModel.children
+      ~BaseModel.compile
+      ~BaseModel.count_parameters
+      ~BaseModel.cpu
+      ~BaseModel.cuda
+      ~BaseModel.disable_intervention_gradients
+      ~BaseModel.disable_model_gradients
+      ~BaseModel.double
+      ~BaseModel.enable_model_gradients
+      ~BaseModel.eval
+      ~BaseModel.extra_repr
+      ~BaseModel.float
+      ~BaseModel.forward
+      ~BaseModel.generate
+      ~BaseModel.get_buffer
+      ~BaseModel.get_cached_activations
+      ~BaseModel.get_cached_hot_activations
+      ~BaseModel.get_device
+      ~BaseModel.get_extra_state
+      ~BaseModel.get_parameter
+      ~BaseModel.get_submodule
+      ~BaseModel.get_trainable_parameters
+      ~BaseModel.half
+      ~BaseModel.ipu
+      ~BaseModel.load_state_dict
+      ~BaseModel.modules
+      ~BaseModel.mtia
+      ~BaseModel.named_buffers
+      ~BaseModel.named_children
+      ~BaseModel.named_modules
+      ~BaseModel.named_parameters
+      ~BaseModel.parameters
+      ~BaseModel.register_backward_hook
+      ~BaseModel.register_buffer
+      ~BaseModel.register_forward_hook
+      ~BaseModel.register_forward_pre_hook
+      ~BaseModel.register_full_backward_hook
+      ~BaseModel.register_full_backward_pre_hook
+      ~BaseModel.register_load_state_dict_post_hook
+      ~BaseModel.register_load_state_dict_pre_hook
+      ~BaseModel.register_module
+      ~BaseModel.register_parameter
+      ~BaseModel.register_state_dict_post_hook
+      ~BaseModel.register_state_dict_pre_hook
+      ~BaseModel.requires_grad_
+      ~BaseModel.set_device
+      ~BaseModel.set_extra_state
+      ~BaseModel.set_submodule
+      ~BaseModel.set_temperature
+      ~BaseModel.set_zero_grad
+      ~BaseModel.share_memory
+      ~BaseModel.state_dict
+      ~BaseModel.to
+      ~BaseModel.to_empty
+      ~BaseModel.train
+      ~BaseModel.type
+      ~BaseModel.xpu
+      ~BaseModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BaseModel.T_destination
+      ~BaseModel.call_super_init
+      ~BaseModel.dump_patches
+      ~BaseModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervenable_base.IntervenableModel.rst b/_sources/api/pyvene.models.intervenable_base.IntervenableModel.rst
new file mode 100644
index 00000000..deeafa3d
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_base.IntervenableModel.rst
@@ -0,0 +1,102 @@
+pyvene.models.intervenable\_base.IntervenableModel
+==================================================
+
+.. currentmodule:: pyvene.models.intervenable_base
+
+.. autoclass:: IntervenableModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~IntervenableModel.__init__
+      ~IntervenableModel.add_module
+      ~IntervenableModel.apply
+      ~IntervenableModel.bfloat16
+      ~IntervenableModel.buffers
+      ~IntervenableModel.children
+      ~IntervenableModel.compile
+      ~IntervenableModel.count_parameters
+      ~IntervenableModel.cpu
+      ~IntervenableModel.cuda
+      ~IntervenableModel.disable_intervention_gradients
+      ~IntervenableModel.disable_model_gradients
+      ~IntervenableModel.double
+      ~IntervenableModel.enable_model_gradients
+      ~IntervenableModel.eval
+      ~IntervenableModel.eval_alignment
+      ~IntervenableModel.extra_repr
+      ~IntervenableModel.float
+      ~IntervenableModel.forward
+      ~IntervenableModel.generate
+      ~IntervenableModel.get_buffer
+      ~IntervenableModel.get_cached_activations
+      ~IntervenableModel.get_cached_hot_activations
+      ~IntervenableModel.get_device
+      ~IntervenableModel.get_extra_state
+      ~IntervenableModel.get_parameter
+      ~IntervenableModel.get_submodule
+      ~IntervenableModel.get_trainable_parameters
+      ~IntervenableModel.half
+      ~IntervenableModel.ipu
+      ~IntervenableModel.load
+      ~IntervenableModel.load_intervention
+      ~IntervenableModel.load_state_dict
+      ~IntervenableModel.modules
+      ~IntervenableModel.mtia
+      ~IntervenableModel.named_buffers
+      ~IntervenableModel.named_children
+      ~IntervenableModel.named_modules
+      ~IntervenableModel.named_parameters
+      ~IntervenableModel.parameters
+      ~IntervenableModel.register_backward_hook
+      ~IntervenableModel.register_buffer
+      ~IntervenableModel.register_forward_hook
+      ~IntervenableModel.register_forward_pre_hook
+      ~IntervenableModel.register_full_backward_hook
+      ~IntervenableModel.register_full_backward_pre_hook
+      ~IntervenableModel.register_load_state_dict_post_hook
+      ~IntervenableModel.register_load_state_dict_pre_hook
+      ~IntervenableModel.register_module
+      ~IntervenableModel.register_parameter
+      ~IntervenableModel.register_state_dict_post_hook
+      ~IntervenableModel.register_state_dict_pre_hook
+      ~IntervenableModel.requires_grad_
+      ~IntervenableModel.save
+      ~IntervenableModel.save_intervention
+      ~IntervenableModel.set_device
+      ~IntervenableModel.set_extra_state
+      ~IntervenableModel.set_submodule
+      ~IntervenableModel.set_temperature
+      ~IntervenableModel.set_zero_grad
+      ~IntervenableModel.share_memory
+      ~IntervenableModel.state_dict
+      ~IntervenableModel.to
+      ~IntervenableModel.to_empty
+      ~IntervenableModel.train
+      ~IntervenableModel.train_alignment
+      ~IntervenableModel.type
+      ~IntervenableModel.xpu
+      ~IntervenableModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~IntervenableModel.BACKEND
+      ~IntervenableModel.T_destination
+      ~IntervenableModel.call_super_init
+      ~IntervenableModel.dump_patches
+      ~IntervenableModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervenable_base.IntervenableModelOutput.rst b/_sources/api/pyvene.models.intervenable_base.IntervenableModelOutput.rst
new file mode 100644
index 00000000..c682359e
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_base.IntervenableModelOutput.rst
@@ -0,0 +1,45 @@
+pyvene.models.intervenable\_base.IntervenableModelOutput
+========================================================
+
+.. currentmodule:: pyvene.models.intervenable_base
+
+.. autoclass:: IntervenableModelOutput
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~IntervenableModelOutput.__init__
+      ~IntervenableModelOutput.clear
+      ~IntervenableModelOutput.copy
+      ~IntervenableModelOutput.fromkeys
+      ~IntervenableModelOutput.get
+      ~IntervenableModelOutput.items
+      ~IntervenableModelOutput.keys
+      ~IntervenableModelOutput.move_to_end
+      ~IntervenableModelOutput.pop
+      ~IntervenableModelOutput.popitem
+      ~IntervenableModelOutput.setdefault
+      ~IntervenableModelOutput.to_tuple
+      ~IntervenableModelOutput.update
+      ~IntervenableModelOutput.values
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~IntervenableModelOutput.collected_activations
+      ~IntervenableModelOutput.intervened_outputs
+      ~IntervenableModelOutput.original_outputs
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervenable_base.IntervenableNdifModel.rst b/_sources/api/pyvene.models.intervenable_base.IntervenableNdifModel.rst
new file mode 100644
index 00000000..d93c5091
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_base.IntervenableNdifModel.rst
@@ -0,0 +1,98 @@
+pyvene.models.intervenable\_base.IntervenableNdifModel
+======================================================
+
+.. currentmodule:: pyvene.models.intervenable_base
+
+.. autoclass:: IntervenableNdifModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~IntervenableNdifModel.__init__
+      ~IntervenableNdifModel.add_module
+      ~IntervenableNdifModel.apply
+      ~IntervenableNdifModel.bfloat16
+      ~IntervenableNdifModel.buffers
+      ~IntervenableNdifModel.children
+      ~IntervenableNdifModel.compile
+      ~IntervenableNdifModel.count_parameters
+      ~IntervenableNdifModel.cpu
+      ~IntervenableNdifModel.cuda
+      ~IntervenableNdifModel.disable_intervention_gradients
+      ~IntervenableNdifModel.disable_model_gradients
+      ~IntervenableNdifModel.double
+      ~IntervenableNdifModel.enable_model_gradients
+      ~IntervenableNdifModel.eval
+      ~IntervenableNdifModel.extra_repr
+      ~IntervenableNdifModel.float
+      ~IntervenableNdifModel.forward
+      ~IntervenableNdifModel.generate
+      ~IntervenableNdifModel.get_buffer
+      ~IntervenableNdifModel.get_cached_activations
+      ~IntervenableNdifModel.get_cached_hot_activations
+      ~IntervenableNdifModel.get_device
+      ~IntervenableNdifModel.get_extra_state
+      ~IntervenableNdifModel.get_parameter
+      ~IntervenableNdifModel.get_submodule
+      ~IntervenableNdifModel.get_trainable_parameters
+      ~IntervenableNdifModel.half
+      ~IntervenableNdifModel.ipu
+      ~IntervenableNdifModel.load
+      ~IntervenableNdifModel.load_state_dict
+      ~IntervenableNdifModel.modules
+      ~IntervenableNdifModel.mtia
+      ~IntervenableNdifModel.named_buffers
+      ~IntervenableNdifModel.named_children
+      ~IntervenableNdifModel.named_modules
+      ~IntervenableNdifModel.named_parameters
+      ~IntervenableNdifModel.parameters
+      ~IntervenableNdifModel.register_backward_hook
+      ~IntervenableNdifModel.register_buffer
+      ~IntervenableNdifModel.register_forward_hook
+      ~IntervenableNdifModel.register_forward_pre_hook
+      ~IntervenableNdifModel.register_full_backward_hook
+      ~IntervenableNdifModel.register_full_backward_pre_hook
+      ~IntervenableNdifModel.register_load_state_dict_post_hook
+      ~IntervenableNdifModel.register_load_state_dict_pre_hook
+      ~IntervenableNdifModel.register_module
+      ~IntervenableNdifModel.register_parameter
+      ~IntervenableNdifModel.register_state_dict_post_hook
+      ~IntervenableNdifModel.register_state_dict_pre_hook
+      ~IntervenableNdifModel.requires_grad_
+      ~IntervenableNdifModel.save
+      ~IntervenableNdifModel.set_device
+      ~IntervenableNdifModel.set_extra_state
+      ~IntervenableNdifModel.set_submodule
+      ~IntervenableNdifModel.set_temperature
+      ~IntervenableNdifModel.set_zero_grad
+      ~IntervenableNdifModel.share_memory
+      ~IntervenableNdifModel.state_dict
+      ~IntervenableNdifModel.to
+      ~IntervenableNdifModel.to_empty
+      ~IntervenableNdifModel.train
+      ~IntervenableNdifModel.type
+      ~IntervenableNdifModel.xpu
+      ~IntervenableNdifModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~IntervenableNdifModel.BACKEND
+      ~IntervenableNdifModel.T_destination
+      ~IntervenableNdifModel.call_super_init
+      ~IntervenableNdifModel.dump_patches
+      ~IntervenableNdifModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervenable_base.build_intervenable_model.rst b/_sources/api/pyvene.models.intervenable_base.build_intervenable_model.rst
new file mode 100644
index 00000000..738a1aae
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_base.build_intervenable_model.rst
@@ -0,0 +1,6 @@
+pyvene.models.intervenable\_base.build\_intervenable\_model
+===========================================================
+
+.. currentmodule:: pyvene.models.intervenable_base
+
+.. autofunction:: build_intervenable_model
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervenable_base.rst b/_sources/api/pyvene.models.intervenable_base.rst
new file mode 100644
index 00000000..540b2864
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_base.rst
@@ -0,0 +1,41 @@
+pyvene.models.intervenable\_base
+================================
+
+.. automodule:: pyvene.models.intervenable_base
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      build_intervenable_model
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      BaseModel
+      IntervenableModel
+      IntervenableModelOutput
+      IntervenableNdifModel
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.intervenable_modelcard.rst b/_sources/api/pyvene.models.intervenable_modelcard.rst
new file mode 100644
index 00000000..d920c9d0
--- /dev/null
+++ b/_sources/api/pyvene.models.intervenable_modelcard.rst
@@ -0,0 +1,23 @@
+pyvene.models.intervenable\_modelcard
+=====================================
+
+.. automodule:: pyvene.models.intervenable_modelcard
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.intervention_utils.InterventionState.rst b/_sources/api/pyvene.models.intervention_utils.InterventionState.rst
new file mode 100644
index 00000000..6756412b
--- /dev/null
+++ b/_sources/api/pyvene.models.intervention_utils.InterventionState.rst
@@ -0,0 +1,31 @@
+pyvene.models.intervention\_utils.InterventionState
+===================================================
+
+.. currentmodule:: pyvene.models.intervention_utils
+
+.. autoclass:: InterventionState
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~InterventionState.__init__
+      ~InterventionState.get_states
+      ~InterventionState.getter_version
+      ~InterventionState.inc_getter_version
+      ~InterventionState.inc_setter_version
+      ~InterventionState.reset
+      ~InterventionState.set_state
+      ~InterventionState.setter_version
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervention_utils.broadcast_tensor_v1.rst b/_sources/api/pyvene.models.intervention_utils.broadcast_tensor_v1.rst
new file mode 100644
index 00000000..cd6e4fd6
--- /dev/null
+++ b/_sources/api/pyvene.models.intervention_utils.broadcast_tensor_v1.rst
@@ -0,0 +1,6 @@
+pyvene.models.intervention\_utils.broadcast\_tensor\_v1
+=======================================================
+
+.. currentmodule:: pyvene.models.intervention_utils
+
+.. autofunction:: broadcast_tensor_v1
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervention_utils.broadcast_tensor_v2.rst b/_sources/api/pyvene.models.intervention_utils.broadcast_tensor_v2.rst
new file mode 100644
index 00000000..e50e2524
--- /dev/null
+++ b/_sources/api/pyvene.models.intervention_utils.broadcast_tensor_v2.rst
@@ -0,0 +1,6 @@
+pyvene.models.intervention\_utils.broadcast\_tensor\_v2
+=======================================================
+
+.. currentmodule:: pyvene.models.intervention_utils
+
+.. autofunction:: broadcast_tensor_v2
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.intervention_utils.rst b/_sources/api/pyvene.models.intervention_utils.rst
new file mode 100644
index 00000000..8c783469
--- /dev/null
+++ b/_sources/api/pyvene.models.intervention_utils.rst
@@ -0,0 +1,39 @@
+pyvene.models.intervention\_utils
+=================================
+
+.. automodule:: pyvene.models.intervention_utils
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      broadcast_tensor_v1
+      broadcast_tensor_v2
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      InterventionState
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.interventions.AdditionIntervention.rst b/_sources/api/pyvene.models.interventions.AdditionIntervention.rst
new file mode 100644
index 00000000..2dcdac46
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.AdditionIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.AdditionIntervention
+================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: AdditionIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~AdditionIntervention.__init__
+      ~AdditionIntervention.add_module
+      ~AdditionIntervention.apply
+      ~AdditionIntervention.bfloat16
+      ~AdditionIntervention.buffers
+      ~AdditionIntervention.children
+      ~AdditionIntervention.compile
+      ~AdditionIntervention.cpu
+      ~AdditionIntervention.cuda
+      ~AdditionIntervention.double
+      ~AdditionIntervention.eval
+      ~AdditionIntervention.extra_repr
+      ~AdditionIntervention.float
+      ~AdditionIntervention.forward
+      ~AdditionIntervention.get_buffer
+      ~AdditionIntervention.get_extra_state
+      ~AdditionIntervention.get_parameter
+      ~AdditionIntervention.get_submodule
+      ~AdditionIntervention.half
+      ~AdditionIntervention.ipu
+      ~AdditionIntervention.load_state_dict
+      ~AdditionIntervention.modules
+      ~AdditionIntervention.mtia
+      ~AdditionIntervention.named_buffers
+      ~AdditionIntervention.named_children
+      ~AdditionIntervention.named_modules
+      ~AdditionIntervention.named_parameters
+      ~AdditionIntervention.parameters
+      ~AdditionIntervention.register_backward_hook
+      ~AdditionIntervention.register_buffer
+      ~AdditionIntervention.register_forward_hook
+      ~AdditionIntervention.register_forward_pre_hook
+      ~AdditionIntervention.register_full_backward_hook
+      ~AdditionIntervention.register_full_backward_pre_hook
+      ~AdditionIntervention.register_load_state_dict_post_hook
+      ~AdditionIntervention.register_load_state_dict_pre_hook
+      ~AdditionIntervention.register_module
+      ~AdditionIntervention.register_parameter
+      ~AdditionIntervention.register_state_dict_post_hook
+      ~AdditionIntervention.register_state_dict_pre_hook
+      ~AdditionIntervention.requires_grad_
+      ~AdditionIntervention.set_extra_state
+      ~AdditionIntervention.set_interchange_dim
+      ~AdditionIntervention.set_source_representation
+      ~AdditionIntervention.set_submodule
+      ~AdditionIntervention.share_memory
+      ~AdditionIntervention.state_dict
+      ~AdditionIntervention.to
+      ~AdditionIntervention.to_empty
+      ~AdditionIntervention.train
+      ~AdditionIntervention.type
+      ~AdditionIntervention.xpu
+      ~AdditionIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~AdditionIntervention.T_destination
+      ~AdditionIntervention.call_super_init
+      ~AdditionIntervention.dump_patches
+      ~AdditionIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.AutoencoderIntervention.rst b/_sources/api/pyvene.models.interventions.AutoencoderIntervention.rst
new file mode 100644
index 00000000..adb3157c
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.AutoencoderIntervention.rst
@@ -0,0 +1,86 @@
+pyvene.models.interventions.AutoencoderIntervention
+===================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: AutoencoderIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~AutoencoderIntervention.__init__
+      ~AutoencoderIntervention.add_module
+      ~AutoencoderIntervention.apply
+      ~AutoencoderIntervention.bfloat16
+      ~AutoencoderIntervention.buffers
+      ~AutoencoderIntervention.children
+      ~AutoencoderIntervention.compile
+      ~AutoencoderIntervention.cpu
+      ~AutoencoderIntervention.cuda
+      ~AutoencoderIntervention.double
+      ~AutoencoderIntervention.eval
+      ~AutoencoderIntervention.extra_repr
+      ~AutoencoderIntervention.float
+      ~AutoencoderIntervention.forward
+      ~AutoencoderIntervention.get_buffer
+      ~AutoencoderIntervention.get_extra_state
+      ~AutoencoderIntervention.get_parameter
+      ~AutoencoderIntervention.get_submodule
+      ~AutoencoderIntervention.half
+      ~AutoencoderIntervention.ipu
+      ~AutoencoderIntervention.load_state_dict
+      ~AutoencoderIntervention.modules
+      ~AutoencoderIntervention.mtia
+      ~AutoencoderIntervention.named_buffers
+      ~AutoencoderIntervention.named_children
+      ~AutoencoderIntervention.named_modules
+      ~AutoencoderIntervention.named_parameters
+      ~AutoencoderIntervention.parameters
+      ~AutoencoderIntervention.register_backward_hook
+      ~AutoencoderIntervention.register_buffer
+      ~AutoencoderIntervention.register_forward_hook
+      ~AutoencoderIntervention.register_forward_pre_hook
+      ~AutoencoderIntervention.register_full_backward_hook
+      ~AutoencoderIntervention.register_full_backward_pre_hook
+      ~AutoencoderIntervention.register_load_state_dict_post_hook
+      ~AutoencoderIntervention.register_load_state_dict_pre_hook
+      ~AutoencoderIntervention.register_module
+      ~AutoencoderIntervention.register_parameter
+      ~AutoencoderIntervention.register_state_dict_post_hook
+      ~AutoencoderIntervention.register_state_dict_pre_hook
+      ~AutoencoderIntervention.requires_grad_
+      ~AutoencoderIntervention.set_extra_state
+      ~AutoencoderIntervention.set_interchange_dim
+      ~AutoencoderIntervention.set_source_representation
+      ~AutoencoderIntervention.set_submodule
+      ~AutoencoderIntervention.share_memory
+      ~AutoencoderIntervention.state_dict
+      ~AutoencoderIntervention.tie_weight
+      ~AutoencoderIntervention.to
+      ~AutoencoderIntervention.to_empty
+      ~AutoencoderIntervention.train
+      ~AutoencoderIntervention.type
+      ~AutoencoderIntervention.xpu
+      ~AutoencoderIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~AutoencoderIntervention.T_destination
+      ~AutoencoderIntervention.call_super_init
+      ~AutoencoderIntervention.dump_patches
+      ~AutoencoderIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.BasisAgnosticIntervention.rst b/_sources/api/pyvene.models.interventions.BasisAgnosticIntervention.rst
new file mode 100644
index 00000000..50451974
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.BasisAgnosticIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.BasisAgnosticIntervention
+=====================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: BasisAgnosticIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BasisAgnosticIntervention.__init__
+      ~BasisAgnosticIntervention.add_module
+      ~BasisAgnosticIntervention.apply
+      ~BasisAgnosticIntervention.bfloat16
+      ~BasisAgnosticIntervention.buffers
+      ~BasisAgnosticIntervention.children
+      ~BasisAgnosticIntervention.compile
+      ~BasisAgnosticIntervention.cpu
+      ~BasisAgnosticIntervention.cuda
+      ~BasisAgnosticIntervention.double
+      ~BasisAgnosticIntervention.eval
+      ~BasisAgnosticIntervention.extra_repr
+      ~BasisAgnosticIntervention.float
+      ~BasisAgnosticIntervention.forward
+      ~BasisAgnosticIntervention.get_buffer
+      ~BasisAgnosticIntervention.get_extra_state
+      ~BasisAgnosticIntervention.get_parameter
+      ~BasisAgnosticIntervention.get_submodule
+      ~BasisAgnosticIntervention.half
+      ~BasisAgnosticIntervention.ipu
+      ~BasisAgnosticIntervention.load_state_dict
+      ~BasisAgnosticIntervention.modules
+      ~BasisAgnosticIntervention.mtia
+      ~BasisAgnosticIntervention.named_buffers
+      ~BasisAgnosticIntervention.named_children
+      ~BasisAgnosticIntervention.named_modules
+      ~BasisAgnosticIntervention.named_parameters
+      ~BasisAgnosticIntervention.parameters
+      ~BasisAgnosticIntervention.register_backward_hook
+      ~BasisAgnosticIntervention.register_buffer
+      ~BasisAgnosticIntervention.register_forward_hook
+      ~BasisAgnosticIntervention.register_forward_pre_hook
+      ~BasisAgnosticIntervention.register_full_backward_hook
+      ~BasisAgnosticIntervention.register_full_backward_pre_hook
+      ~BasisAgnosticIntervention.register_load_state_dict_post_hook
+      ~BasisAgnosticIntervention.register_load_state_dict_pre_hook
+      ~BasisAgnosticIntervention.register_module
+      ~BasisAgnosticIntervention.register_parameter
+      ~BasisAgnosticIntervention.register_state_dict_post_hook
+      ~BasisAgnosticIntervention.register_state_dict_pre_hook
+      ~BasisAgnosticIntervention.requires_grad_
+      ~BasisAgnosticIntervention.set_extra_state
+      ~BasisAgnosticIntervention.set_interchange_dim
+      ~BasisAgnosticIntervention.set_source_representation
+      ~BasisAgnosticIntervention.set_submodule
+      ~BasisAgnosticIntervention.share_memory
+      ~BasisAgnosticIntervention.state_dict
+      ~BasisAgnosticIntervention.to
+      ~BasisAgnosticIntervention.to_empty
+      ~BasisAgnosticIntervention.train
+      ~BasisAgnosticIntervention.type
+      ~BasisAgnosticIntervention.xpu
+      ~BasisAgnosticIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BasisAgnosticIntervention.T_destination
+      ~BasisAgnosticIntervention.call_super_init
+      ~BasisAgnosticIntervention.dump_patches
+      ~BasisAgnosticIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.rst b/_sources/api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.rst
new file mode 100644
index 00000000..47b94d6a
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.rst
@@ -0,0 +1,90 @@
+pyvene.models.interventions.BoundlessRotatedSpaceIntervention
+=============================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: BoundlessRotatedSpaceIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~BoundlessRotatedSpaceIntervention.__init__
+      ~BoundlessRotatedSpaceIntervention.add_module
+      ~BoundlessRotatedSpaceIntervention.apply
+      ~BoundlessRotatedSpaceIntervention.bfloat16
+      ~BoundlessRotatedSpaceIntervention.buffers
+      ~BoundlessRotatedSpaceIntervention.children
+      ~BoundlessRotatedSpaceIntervention.compile
+      ~BoundlessRotatedSpaceIntervention.cpu
+      ~BoundlessRotatedSpaceIntervention.cuda
+      ~BoundlessRotatedSpaceIntervention.double
+      ~BoundlessRotatedSpaceIntervention.eval
+      ~BoundlessRotatedSpaceIntervention.extra_repr
+      ~BoundlessRotatedSpaceIntervention.float
+      ~BoundlessRotatedSpaceIntervention.forward
+      ~BoundlessRotatedSpaceIntervention.get_boundary_parameters
+      ~BoundlessRotatedSpaceIntervention.get_buffer
+      ~BoundlessRotatedSpaceIntervention.get_extra_state
+      ~BoundlessRotatedSpaceIntervention.get_parameter
+      ~BoundlessRotatedSpaceIntervention.get_submodule
+      ~BoundlessRotatedSpaceIntervention.get_temperature
+      ~BoundlessRotatedSpaceIntervention.half
+      ~BoundlessRotatedSpaceIntervention.ipu
+      ~BoundlessRotatedSpaceIntervention.load_state_dict
+      ~BoundlessRotatedSpaceIntervention.modules
+      ~BoundlessRotatedSpaceIntervention.mtia
+      ~BoundlessRotatedSpaceIntervention.named_buffers
+      ~BoundlessRotatedSpaceIntervention.named_children
+      ~BoundlessRotatedSpaceIntervention.named_modules
+      ~BoundlessRotatedSpaceIntervention.named_parameters
+      ~BoundlessRotatedSpaceIntervention.parameters
+      ~BoundlessRotatedSpaceIntervention.register_backward_hook
+      ~BoundlessRotatedSpaceIntervention.register_buffer
+      ~BoundlessRotatedSpaceIntervention.register_forward_hook
+      ~BoundlessRotatedSpaceIntervention.register_forward_pre_hook
+      ~BoundlessRotatedSpaceIntervention.register_full_backward_hook
+      ~BoundlessRotatedSpaceIntervention.register_full_backward_pre_hook
+      ~BoundlessRotatedSpaceIntervention.register_load_state_dict_post_hook
+      ~BoundlessRotatedSpaceIntervention.register_load_state_dict_pre_hook
+      ~BoundlessRotatedSpaceIntervention.register_module
+      ~BoundlessRotatedSpaceIntervention.register_parameter
+      ~BoundlessRotatedSpaceIntervention.register_state_dict_post_hook
+      ~BoundlessRotatedSpaceIntervention.register_state_dict_pre_hook
+      ~BoundlessRotatedSpaceIntervention.requires_grad_
+      ~BoundlessRotatedSpaceIntervention.set_extra_state
+      ~BoundlessRotatedSpaceIntervention.set_interchange_dim
+      ~BoundlessRotatedSpaceIntervention.set_intervention_boundaries
+      ~BoundlessRotatedSpaceIntervention.set_source_representation
+      ~BoundlessRotatedSpaceIntervention.set_submodule
+      ~BoundlessRotatedSpaceIntervention.set_temperature
+      ~BoundlessRotatedSpaceIntervention.share_memory
+      ~BoundlessRotatedSpaceIntervention.state_dict
+      ~BoundlessRotatedSpaceIntervention.tie_weight
+      ~BoundlessRotatedSpaceIntervention.to
+      ~BoundlessRotatedSpaceIntervention.to_empty
+      ~BoundlessRotatedSpaceIntervention.train
+      ~BoundlessRotatedSpaceIntervention.type
+      ~BoundlessRotatedSpaceIntervention.xpu
+      ~BoundlessRotatedSpaceIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~BoundlessRotatedSpaceIntervention.T_destination
+      ~BoundlessRotatedSpaceIntervention.call_super_init
+      ~BoundlessRotatedSpaceIntervention.dump_patches
+      ~BoundlessRotatedSpaceIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.CollectIntervention.rst b/_sources/api/pyvene.models.interventions.CollectIntervention.rst
new file mode 100644
index 00000000..f2471dc8
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.CollectIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.CollectIntervention
+===============================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: CollectIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~CollectIntervention.__init__
+      ~CollectIntervention.add_module
+      ~CollectIntervention.apply
+      ~CollectIntervention.bfloat16
+      ~CollectIntervention.buffers
+      ~CollectIntervention.children
+      ~CollectIntervention.compile
+      ~CollectIntervention.cpu
+      ~CollectIntervention.cuda
+      ~CollectIntervention.double
+      ~CollectIntervention.eval
+      ~CollectIntervention.extra_repr
+      ~CollectIntervention.float
+      ~CollectIntervention.forward
+      ~CollectIntervention.get_buffer
+      ~CollectIntervention.get_extra_state
+      ~CollectIntervention.get_parameter
+      ~CollectIntervention.get_submodule
+      ~CollectIntervention.half
+      ~CollectIntervention.ipu
+      ~CollectIntervention.load_state_dict
+      ~CollectIntervention.modules
+      ~CollectIntervention.mtia
+      ~CollectIntervention.named_buffers
+      ~CollectIntervention.named_children
+      ~CollectIntervention.named_modules
+      ~CollectIntervention.named_parameters
+      ~CollectIntervention.parameters
+      ~CollectIntervention.register_backward_hook
+      ~CollectIntervention.register_buffer
+      ~CollectIntervention.register_forward_hook
+      ~CollectIntervention.register_forward_pre_hook
+      ~CollectIntervention.register_full_backward_hook
+      ~CollectIntervention.register_full_backward_pre_hook
+      ~CollectIntervention.register_load_state_dict_post_hook
+      ~CollectIntervention.register_load_state_dict_pre_hook
+      ~CollectIntervention.register_module
+      ~CollectIntervention.register_parameter
+      ~CollectIntervention.register_state_dict_post_hook
+      ~CollectIntervention.register_state_dict_pre_hook
+      ~CollectIntervention.requires_grad_
+      ~CollectIntervention.set_extra_state
+      ~CollectIntervention.set_interchange_dim
+      ~CollectIntervention.set_source_representation
+      ~CollectIntervention.set_submodule
+      ~CollectIntervention.share_memory
+      ~CollectIntervention.state_dict
+      ~CollectIntervention.to
+      ~CollectIntervention.to_empty
+      ~CollectIntervention.train
+      ~CollectIntervention.type
+      ~CollectIntervention.xpu
+      ~CollectIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~CollectIntervention.T_destination
+      ~CollectIntervention.call_super_init
+      ~CollectIntervention.dump_patches
+      ~CollectIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.ConstantSourceIntervention.rst b/_sources/api/pyvene.models.interventions.ConstantSourceIntervention.rst
new file mode 100644
index 00000000..3475dc26
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.ConstantSourceIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.ConstantSourceIntervention
+======================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: ConstantSourceIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ConstantSourceIntervention.__init__
+      ~ConstantSourceIntervention.add_module
+      ~ConstantSourceIntervention.apply
+      ~ConstantSourceIntervention.bfloat16
+      ~ConstantSourceIntervention.buffers
+      ~ConstantSourceIntervention.children
+      ~ConstantSourceIntervention.compile
+      ~ConstantSourceIntervention.cpu
+      ~ConstantSourceIntervention.cuda
+      ~ConstantSourceIntervention.double
+      ~ConstantSourceIntervention.eval
+      ~ConstantSourceIntervention.extra_repr
+      ~ConstantSourceIntervention.float
+      ~ConstantSourceIntervention.forward
+      ~ConstantSourceIntervention.get_buffer
+      ~ConstantSourceIntervention.get_extra_state
+      ~ConstantSourceIntervention.get_parameter
+      ~ConstantSourceIntervention.get_submodule
+      ~ConstantSourceIntervention.half
+      ~ConstantSourceIntervention.ipu
+      ~ConstantSourceIntervention.load_state_dict
+      ~ConstantSourceIntervention.modules
+      ~ConstantSourceIntervention.mtia
+      ~ConstantSourceIntervention.named_buffers
+      ~ConstantSourceIntervention.named_children
+      ~ConstantSourceIntervention.named_modules
+      ~ConstantSourceIntervention.named_parameters
+      ~ConstantSourceIntervention.parameters
+      ~ConstantSourceIntervention.register_backward_hook
+      ~ConstantSourceIntervention.register_buffer
+      ~ConstantSourceIntervention.register_forward_hook
+      ~ConstantSourceIntervention.register_forward_pre_hook
+      ~ConstantSourceIntervention.register_full_backward_hook
+      ~ConstantSourceIntervention.register_full_backward_pre_hook
+      ~ConstantSourceIntervention.register_load_state_dict_post_hook
+      ~ConstantSourceIntervention.register_load_state_dict_pre_hook
+      ~ConstantSourceIntervention.register_module
+      ~ConstantSourceIntervention.register_parameter
+      ~ConstantSourceIntervention.register_state_dict_post_hook
+      ~ConstantSourceIntervention.register_state_dict_pre_hook
+      ~ConstantSourceIntervention.requires_grad_
+      ~ConstantSourceIntervention.set_extra_state
+      ~ConstantSourceIntervention.set_interchange_dim
+      ~ConstantSourceIntervention.set_source_representation
+      ~ConstantSourceIntervention.set_submodule
+      ~ConstantSourceIntervention.share_memory
+      ~ConstantSourceIntervention.state_dict
+      ~ConstantSourceIntervention.to
+      ~ConstantSourceIntervention.to_empty
+      ~ConstantSourceIntervention.train
+      ~ConstantSourceIntervention.type
+      ~ConstantSourceIntervention.xpu
+      ~ConstantSourceIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~ConstantSourceIntervention.T_destination
+      ~ConstantSourceIntervention.call_super_init
+      ~ConstantSourceIntervention.dump_patches
+      ~ConstantSourceIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.DistributedRepresentationIntervention.rst b/_sources/api/pyvene.models.interventions.DistributedRepresentationIntervention.rst
new file mode 100644
index 00000000..4811a5ac
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.DistributedRepresentationIntervention.rst
@@ -0,0 +1,83 @@
+pyvene.models.interventions.DistributedRepresentationIntervention
+=================================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: DistributedRepresentationIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~DistributedRepresentationIntervention.__init__
+      ~DistributedRepresentationIntervention.add_module
+      ~DistributedRepresentationIntervention.apply
+      ~DistributedRepresentationIntervention.bfloat16
+      ~DistributedRepresentationIntervention.buffers
+      ~DistributedRepresentationIntervention.children
+      ~DistributedRepresentationIntervention.compile
+      ~DistributedRepresentationIntervention.cpu
+      ~DistributedRepresentationIntervention.cuda
+      ~DistributedRepresentationIntervention.double
+      ~DistributedRepresentationIntervention.eval
+      ~DistributedRepresentationIntervention.extra_repr
+      ~DistributedRepresentationIntervention.float
+      ~DistributedRepresentationIntervention.forward
+      ~DistributedRepresentationIntervention.get_buffer
+      ~DistributedRepresentationIntervention.get_extra_state
+      ~DistributedRepresentationIntervention.get_parameter
+      ~DistributedRepresentationIntervention.get_submodule
+      ~DistributedRepresentationIntervention.half
+      ~DistributedRepresentationIntervention.ipu
+      ~DistributedRepresentationIntervention.load_state_dict
+      ~DistributedRepresentationIntervention.modules
+      ~DistributedRepresentationIntervention.mtia
+      ~DistributedRepresentationIntervention.named_buffers
+      ~DistributedRepresentationIntervention.named_children
+      ~DistributedRepresentationIntervention.named_modules
+      ~DistributedRepresentationIntervention.named_parameters
+      ~DistributedRepresentationIntervention.parameters
+      ~DistributedRepresentationIntervention.register_backward_hook
+      ~DistributedRepresentationIntervention.register_buffer
+      ~DistributedRepresentationIntervention.register_forward_hook
+      ~DistributedRepresentationIntervention.register_forward_pre_hook
+      ~DistributedRepresentationIntervention.register_full_backward_hook
+      ~DistributedRepresentationIntervention.register_full_backward_pre_hook
+      ~DistributedRepresentationIntervention.register_load_state_dict_post_hook
+      ~DistributedRepresentationIntervention.register_load_state_dict_pre_hook
+      ~DistributedRepresentationIntervention.register_module
+      ~DistributedRepresentationIntervention.register_parameter
+      ~DistributedRepresentationIntervention.register_state_dict_post_hook
+      ~DistributedRepresentationIntervention.register_state_dict_pre_hook
+      ~DistributedRepresentationIntervention.requires_grad_
+      ~DistributedRepresentationIntervention.set_extra_state
+      ~DistributedRepresentationIntervention.set_submodule
+      ~DistributedRepresentationIntervention.share_memory
+      ~DistributedRepresentationIntervention.state_dict
+      ~DistributedRepresentationIntervention.to
+      ~DistributedRepresentationIntervention.to_empty
+      ~DistributedRepresentationIntervention.train
+      ~DistributedRepresentationIntervention.type
+      ~DistributedRepresentationIntervention.xpu
+      ~DistributedRepresentationIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~DistributedRepresentationIntervention.T_destination
+      ~DistributedRepresentationIntervention.call_super_init
+      ~DistributedRepresentationIntervention.dump_patches
+      ~DistributedRepresentationIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.Intervention.rst b/_sources/api/pyvene.models.interventions.Intervention.rst
new file mode 100644
index 00000000..2a51ab86
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.Intervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.Intervention
+========================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: Intervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Intervention.__init__
+      ~Intervention.add_module
+      ~Intervention.apply
+      ~Intervention.bfloat16
+      ~Intervention.buffers
+      ~Intervention.children
+      ~Intervention.compile
+      ~Intervention.cpu
+      ~Intervention.cuda
+      ~Intervention.double
+      ~Intervention.eval
+      ~Intervention.extra_repr
+      ~Intervention.float
+      ~Intervention.forward
+      ~Intervention.get_buffer
+      ~Intervention.get_extra_state
+      ~Intervention.get_parameter
+      ~Intervention.get_submodule
+      ~Intervention.half
+      ~Intervention.ipu
+      ~Intervention.load_state_dict
+      ~Intervention.modules
+      ~Intervention.mtia
+      ~Intervention.named_buffers
+      ~Intervention.named_children
+      ~Intervention.named_modules
+      ~Intervention.named_parameters
+      ~Intervention.parameters
+      ~Intervention.register_backward_hook
+      ~Intervention.register_buffer
+      ~Intervention.register_forward_hook
+      ~Intervention.register_forward_pre_hook
+      ~Intervention.register_full_backward_hook
+      ~Intervention.register_full_backward_pre_hook
+      ~Intervention.register_load_state_dict_post_hook
+      ~Intervention.register_load_state_dict_pre_hook
+      ~Intervention.register_module
+      ~Intervention.register_parameter
+      ~Intervention.register_state_dict_post_hook
+      ~Intervention.register_state_dict_pre_hook
+      ~Intervention.requires_grad_
+      ~Intervention.set_extra_state
+      ~Intervention.set_interchange_dim
+      ~Intervention.set_source_representation
+      ~Intervention.set_submodule
+      ~Intervention.share_memory
+      ~Intervention.state_dict
+      ~Intervention.to
+      ~Intervention.to_empty
+      ~Intervention.train
+      ~Intervention.type
+      ~Intervention.xpu
+      ~Intervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Intervention.T_destination
+      ~Intervention.call_super_init
+      ~Intervention.dump_patches
+      ~Intervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.InterventionOutput.rst b/_sources/api/pyvene.models.interventions.InterventionOutput.rst
new file mode 100644
index 00000000..9dceaacc
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.InterventionOutput.rst
@@ -0,0 +1,44 @@
+pyvene.models.interventions.InterventionOutput
+==============================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: InterventionOutput
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~InterventionOutput.__init__
+      ~InterventionOutput.clear
+      ~InterventionOutput.copy
+      ~InterventionOutput.fromkeys
+      ~InterventionOutput.get
+      ~InterventionOutput.items
+      ~InterventionOutput.keys
+      ~InterventionOutput.move_to_end
+      ~InterventionOutput.pop
+      ~InterventionOutput.popitem
+      ~InterventionOutput.setdefault
+      ~InterventionOutput.to_tuple
+      ~InterventionOutput.update
+      ~InterventionOutput.values
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~InterventionOutput.latent
+      ~InterventionOutput.output
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.rst b/_sources/api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.rst
new file mode 100644
index 00000000..9ae5368b
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.rst
@@ -0,0 +1,88 @@
+pyvene.models.interventions.JumpReLUAutoencoderIntervention
+===========================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: JumpReLUAutoencoderIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~JumpReLUAutoencoderIntervention.__init__
+      ~JumpReLUAutoencoderIntervention.add_module
+      ~JumpReLUAutoencoderIntervention.apply
+      ~JumpReLUAutoencoderIntervention.bfloat16
+      ~JumpReLUAutoencoderIntervention.buffers
+      ~JumpReLUAutoencoderIntervention.children
+      ~JumpReLUAutoencoderIntervention.compile
+      ~JumpReLUAutoencoderIntervention.cpu
+      ~JumpReLUAutoencoderIntervention.cuda
+      ~JumpReLUAutoencoderIntervention.decode
+      ~JumpReLUAutoencoderIntervention.double
+      ~JumpReLUAutoencoderIntervention.encode
+      ~JumpReLUAutoencoderIntervention.eval
+      ~JumpReLUAutoencoderIntervention.extra_repr
+      ~JumpReLUAutoencoderIntervention.float
+      ~JumpReLUAutoencoderIntervention.forward
+      ~JumpReLUAutoencoderIntervention.get_buffer
+      ~JumpReLUAutoencoderIntervention.get_extra_state
+      ~JumpReLUAutoencoderIntervention.get_parameter
+      ~JumpReLUAutoencoderIntervention.get_submodule
+      ~JumpReLUAutoencoderIntervention.half
+      ~JumpReLUAutoencoderIntervention.ipu
+      ~JumpReLUAutoencoderIntervention.load_state_dict
+      ~JumpReLUAutoencoderIntervention.modules
+      ~JumpReLUAutoencoderIntervention.mtia
+      ~JumpReLUAutoencoderIntervention.named_buffers
+      ~JumpReLUAutoencoderIntervention.named_children
+      ~JumpReLUAutoencoderIntervention.named_modules
+      ~JumpReLUAutoencoderIntervention.named_parameters
+      ~JumpReLUAutoencoderIntervention.parameters
+      ~JumpReLUAutoencoderIntervention.register_backward_hook
+      ~JumpReLUAutoencoderIntervention.register_buffer
+      ~JumpReLUAutoencoderIntervention.register_forward_hook
+      ~JumpReLUAutoencoderIntervention.register_forward_pre_hook
+      ~JumpReLUAutoencoderIntervention.register_full_backward_hook
+      ~JumpReLUAutoencoderIntervention.register_full_backward_pre_hook
+      ~JumpReLUAutoencoderIntervention.register_load_state_dict_post_hook
+      ~JumpReLUAutoencoderIntervention.register_load_state_dict_pre_hook
+      ~JumpReLUAutoencoderIntervention.register_module
+      ~JumpReLUAutoencoderIntervention.register_parameter
+      ~JumpReLUAutoencoderIntervention.register_state_dict_post_hook
+      ~JumpReLUAutoencoderIntervention.register_state_dict_pre_hook
+      ~JumpReLUAutoencoderIntervention.requires_grad_
+      ~JumpReLUAutoencoderIntervention.set_extra_state
+      ~JumpReLUAutoencoderIntervention.set_interchange_dim
+      ~JumpReLUAutoencoderIntervention.set_source_representation
+      ~JumpReLUAutoencoderIntervention.set_submodule
+      ~JumpReLUAutoencoderIntervention.share_memory
+      ~JumpReLUAutoencoderIntervention.state_dict
+      ~JumpReLUAutoencoderIntervention.tie_weight
+      ~JumpReLUAutoencoderIntervention.to
+      ~JumpReLUAutoencoderIntervention.to_empty
+      ~JumpReLUAutoencoderIntervention.train
+      ~JumpReLUAutoencoderIntervention.type
+      ~JumpReLUAutoencoderIntervention.xpu
+      ~JumpReLUAutoencoderIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~JumpReLUAutoencoderIntervention.T_destination
+      ~JumpReLUAutoencoderIntervention.call_super_init
+      ~JumpReLUAutoencoderIntervention.dump_patches
+      ~JumpReLUAutoencoderIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.LocalistRepresentationIntervention.rst b/_sources/api/pyvene.models.interventions.LocalistRepresentationIntervention.rst
new file mode 100644
index 00000000..5fbc68b2
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.LocalistRepresentationIntervention.rst
@@ -0,0 +1,83 @@
+pyvene.models.interventions.LocalistRepresentationIntervention
+==============================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: LocalistRepresentationIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~LocalistRepresentationIntervention.__init__
+      ~LocalistRepresentationIntervention.add_module
+      ~LocalistRepresentationIntervention.apply
+      ~LocalistRepresentationIntervention.bfloat16
+      ~LocalistRepresentationIntervention.buffers
+      ~LocalistRepresentationIntervention.children
+      ~LocalistRepresentationIntervention.compile
+      ~LocalistRepresentationIntervention.cpu
+      ~LocalistRepresentationIntervention.cuda
+      ~LocalistRepresentationIntervention.double
+      ~LocalistRepresentationIntervention.eval
+      ~LocalistRepresentationIntervention.extra_repr
+      ~LocalistRepresentationIntervention.float
+      ~LocalistRepresentationIntervention.forward
+      ~LocalistRepresentationIntervention.get_buffer
+      ~LocalistRepresentationIntervention.get_extra_state
+      ~LocalistRepresentationIntervention.get_parameter
+      ~LocalistRepresentationIntervention.get_submodule
+      ~LocalistRepresentationIntervention.half
+      ~LocalistRepresentationIntervention.ipu
+      ~LocalistRepresentationIntervention.load_state_dict
+      ~LocalistRepresentationIntervention.modules
+      ~LocalistRepresentationIntervention.mtia
+      ~LocalistRepresentationIntervention.named_buffers
+      ~LocalistRepresentationIntervention.named_children
+      ~LocalistRepresentationIntervention.named_modules
+      ~LocalistRepresentationIntervention.named_parameters
+      ~LocalistRepresentationIntervention.parameters
+      ~LocalistRepresentationIntervention.register_backward_hook
+      ~LocalistRepresentationIntervention.register_buffer
+      ~LocalistRepresentationIntervention.register_forward_hook
+      ~LocalistRepresentationIntervention.register_forward_pre_hook
+      ~LocalistRepresentationIntervention.register_full_backward_hook
+      ~LocalistRepresentationIntervention.register_full_backward_pre_hook
+      ~LocalistRepresentationIntervention.register_load_state_dict_post_hook
+      ~LocalistRepresentationIntervention.register_load_state_dict_pre_hook
+      ~LocalistRepresentationIntervention.register_module
+      ~LocalistRepresentationIntervention.register_parameter
+      ~LocalistRepresentationIntervention.register_state_dict_post_hook
+      ~LocalistRepresentationIntervention.register_state_dict_pre_hook
+      ~LocalistRepresentationIntervention.requires_grad_
+      ~LocalistRepresentationIntervention.set_extra_state
+      ~LocalistRepresentationIntervention.set_submodule
+      ~LocalistRepresentationIntervention.share_memory
+      ~LocalistRepresentationIntervention.state_dict
+      ~LocalistRepresentationIntervention.to
+      ~LocalistRepresentationIntervention.to_empty
+      ~LocalistRepresentationIntervention.train
+      ~LocalistRepresentationIntervention.type
+      ~LocalistRepresentationIntervention.xpu
+      ~LocalistRepresentationIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~LocalistRepresentationIntervention.T_destination
+      ~LocalistRepresentationIntervention.call_super_init
+      ~LocalistRepresentationIntervention.dump_patches
+      ~LocalistRepresentationIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.rst b/_sources/api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.rst
new file mode 100644
index 00000000..727c4740
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.rst
@@ -0,0 +1,86 @@
+pyvene.models.interventions.LowRankRotatedSpaceIntervention
+===========================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: LowRankRotatedSpaceIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~LowRankRotatedSpaceIntervention.__init__
+      ~LowRankRotatedSpaceIntervention.add_module
+      ~LowRankRotatedSpaceIntervention.apply
+      ~LowRankRotatedSpaceIntervention.bfloat16
+      ~LowRankRotatedSpaceIntervention.buffers
+      ~LowRankRotatedSpaceIntervention.children
+      ~LowRankRotatedSpaceIntervention.compile
+      ~LowRankRotatedSpaceIntervention.cpu
+      ~LowRankRotatedSpaceIntervention.cuda
+      ~LowRankRotatedSpaceIntervention.double
+      ~LowRankRotatedSpaceIntervention.eval
+      ~LowRankRotatedSpaceIntervention.extra_repr
+      ~LowRankRotatedSpaceIntervention.float
+      ~LowRankRotatedSpaceIntervention.forward
+      ~LowRankRotatedSpaceIntervention.get_buffer
+      ~LowRankRotatedSpaceIntervention.get_extra_state
+      ~LowRankRotatedSpaceIntervention.get_parameter
+      ~LowRankRotatedSpaceIntervention.get_submodule
+      ~LowRankRotatedSpaceIntervention.half
+      ~LowRankRotatedSpaceIntervention.ipu
+      ~LowRankRotatedSpaceIntervention.load_state_dict
+      ~LowRankRotatedSpaceIntervention.modules
+      ~LowRankRotatedSpaceIntervention.mtia
+      ~LowRankRotatedSpaceIntervention.named_buffers
+      ~LowRankRotatedSpaceIntervention.named_children
+      ~LowRankRotatedSpaceIntervention.named_modules
+      ~LowRankRotatedSpaceIntervention.named_parameters
+      ~LowRankRotatedSpaceIntervention.parameters
+      ~LowRankRotatedSpaceIntervention.register_backward_hook
+      ~LowRankRotatedSpaceIntervention.register_buffer
+      ~LowRankRotatedSpaceIntervention.register_forward_hook
+      ~LowRankRotatedSpaceIntervention.register_forward_pre_hook
+      ~LowRankRotatedSpaceIntervention.register_full_backward_hook
+      ~LowRankRotatedSpaceIntervention.register_full_backward_pre_hook
+      ~LowRankRotatedSpaceIntervention.register_load_state_dict_post_hook
+      ~LowRankRotatedSpaceIntervention.register_load_state_dict_pre_hook
+      ~LowRankRotatedSpaceIntervention.register_module
+      ~LowRankRotatedSpaceIntervention.register_parameter
+      ~LowRankRotatedSpaceIntervention.register_state_dict_post_hook
+      ~LowRankRotatedSpaceIntervention.register_state_dict_pre_hook
+      ~LowRankRotatedSpaceIntervention.requires_grad_
+      ~LowRankRotatedSpaceIntervention.set_extra_state
+      ~LowRankRotatedSpaceIntervention.set_interchange_dim
+      ~LowRankRotatedSpaceIntervention.set_source_representation
+      ~LowRankRotatedSpaceIntervention.set_submodule
+      ~LowRankRotatedSpaceIntervention.share_memory
+      ~LowRankRotatedSpaceIntervention.state_dict
+      ~LowRankRotatedSpaceIntervention.tie_weight
+      ~LowRankRotatedSpaceIntervention.to
+      ~LowRankRotatedSpaceIntervention.to_empty
+      ~LowRankRotatedSpaceIntervention.train
+      ~LowRankRotatedSpaceIntervention.type
+      ~LowRankRotatedSpaceIntervention.xpu
+      ~LowRankRotatedSpaceIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~LowRankRotatedSpaceIntervention.T_destination
+      ~LowRankRotatedSpaceIntervention.call_super_init
+      ~LowRankRotatedSpaceIntervention.dump_patches
+      ~LowRankRotatedSpaceIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.NoiseIntervention.rst b/_sources/api/pyvene.models.interventions.NoiseIntervention.rst
new file mode 100644
index 00000000..b1dbb29f
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.NoiseIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.NoiseIntervention
+=============================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: NoiseIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~NoiseIntervention.__init__
+      ~NoiseIntervention.add_module
+      ~NoiseIntervention.apply
+      ~NoiseIntervention.bfloat16
+      ~NoiseIntervention.buffers
+      ~NoiseIntervention.children
+      ~NoiseIntervention.compile
+      ~NoiseIntervention.cpu
+      ~NoiseIntervention.cuda
+      ~NoiseIntervention.double
+      ~NoiseIntervention.eval
+      ~NoiseIntervention.extra_repr
+      ~NoiseIntervention.float
+      ~NoiseIntervention.forward
+      ~NoiseIntervention.get_buffer
+      ~NoiseIntervention.get_extra_state
+      ~NoiseIntervention.get_parameter
+      ~NoiseIntervention.get_submodule
+      ~NoiseIntervention.half
+      ~NoiseIntervention.ipu
+      ~NoiseIntervention.load_state_dict
+      ~NoiseIntervention.modules
+      ~NoiseIntervention.mtia
+      ~NoiseIntervention.named_buffers
+      ~NoiseIntervention.named_children
+      ~NoiseIntervention.named_modules
+      ~NoiseIntervention.named_parameters
+      ~NoiseIntervention.parameters
+      ~NoiseIntervention.register_backward_hook
+      ~NoiseIntervention.register_buffer
+      ~NoiseIntervention.register_forward_hook
+      ~NoiseIntervention.register_forward_pre_hook
+      ~NoiseIntervention.register_full_backward_hook
+      ~NoiseIntervention.register_full_backward_pre_hook
+      ~NoiseIntervention.register_load_state_dict_post_hook
+      ~NoiseIntervention.register_load_state_dict_pre_hook
+      ~NoiseIntervention.register_module
+      ~NoiseIntervention.register_parameter
+      ~NoiseIntervention.register_state_dict_post_hook
+      ~NoiseIntervention.register_state_dict_pre_hook
+      ~NoiseIntervention.requires_grad_
+      ~NoiseIntervention.set_extra_state
+      ~NoiseIntervention.set_interchange_dim
+      ~NoiseIntervention.set_source_representation
+      ~NoiseIntervention.set_submodule
+      ~NoiseIntervention.share_memory
+      ~NoiseIntervention.state_dict
+      ~NoiseIntervention.to
+      ~NoiseIntervention.to_empty
+      ~NoiseIntervention.train
+      ~NoiseIntervention.type
+      ~NoiseIntervention.xpu
+      ~NoiseIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~NoiseIntervention.T_destination
+      ~NoiseIntervention.call_super_init
+      ~NoiseIntervention.dump_patches
+      ~NoiseIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.PCARotatedSpaceIntervention.rst b/_sources/api/pyvene.models.interventions.PCARotatedSpaceIntervention.rst
new file mode 100644
index 00000000..e3a16012
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.PCARotatedSpaceIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.PCARotatedSpaceIntervention
+=======================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: PCARotatedSpaceIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~PCARotatedSpaceIntervention.__init__
+      ~PCARotatedSpaceIntervention.add_module
+      ~PCARotatedSpaceIntervention.apply
+      ~PCARotatedSpaceIntervention.bfloat16
+      ~PCARotatedSpaceIntervention.buffers
+      ~PCARotatedSpaceIntervention.children
+      ~PCARotatedSpaceIntervention.compile
+      ~PCARotatedSpaceIntervention.cpu
+      ~PCARotatedSpaceIntervention.cuda
+      ~PCARotatedSpaceIntervention.double
+      ~PCARotatedSpaceIntervention.eval
+      ~PCARotatedSpaceIntervention.extra_repr
+      ~PCARotatedSpaceIntervention.float
+      ~PCARotatedSpaceIntervention.forward
+      ~PCARotatedSpaceIntervention.get_buffer
+      ~PCARotatedSpaceIntervention.get_extra_state
+      ~PCARotatedSpaceIntervention.get_parameter
+      ~PCARotatedSpaceIntervention.get_submodule
+      ~PCARotatedSpaceIntervention.half
+      ~PCARotatedSpaceIntervention.ipu
+      ~PCARotatedSpaceIntervention.load_state_dict
+      ~PCARotatedSpaceIntervention.modules
+      ~PCARotatedSpaceIntervention.mtia
+      ~PCARotatedSpaceIntervention.named_buffers
+      ~PCARotatedSpaceIntervention.named_children
+      ~PCARotatedSpaceIntervention.named_modules
+      ~PCARotatedSpaceIntervention.named_parameters
+      ~PCARotatedSpaceIntervention.parameters
+      ~PCARotatedSpaceIntervention.register_backward_hook
+      ~PCARotatedSpaceIntervention.register_buffer
+      ~PCARotatedSpaceIntervention.register_forward_hook
+      ~PCARotatedSpaceIntervention.register_forward_pre_hook
+      ~PCARotatedSpaceIntervention.register_full_backward_hook
+      ~PCARotatedSpaceIntervention.register_full_backward_pre_hook
+      ~PCARotatedSpaceIntervention.register_load_state_dict_post_hook
+      ~PCARotatedSpaceIntervention.register_load_state_dict_pre_hook
+      ~PCARotatedSpaceIntervention.register_module
+      ~PCARotatedSpaceIntervention.register_parameter
+      ~PCARotatedSpaceIntervention.register_state_dict_post_hook
+      ~PCARotatedSpaceIntervention.register_state_dict_pre_hook
+      ~PCARotatedSpaceIntervention.requires_grad_
+      ~PCARotatedSpaceIntervention.set_extra_state
+      ~PCARotatedSpaceIntervention.set_interchange_dim
+      ~PCARotatedSpaceIntervention.set_source_representation
+      ~PCARotatedSpaceIntervention.set_submodule
+      ~PCARotatedSpaceIntervention.share_memory
+      ~PCARotatedSpaceIntervention.state_dict
+      ~PCARotatedSpaceIntervention.to
+      ~PCARotatedSpaceIntervention.to_empty
+      ~PCARotatedSpaceIntervention.train
+      ~PCARotatedSpaceIntervention.type
+      ~PCARotatedSpaceIntervention.xpu
+      ~PCARotatedSpaceIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~PCARotatedSpaceIntervention.T_destination
+      ~PCARotatedSpaceIntervention.call_super_init
+      ~PCARotatedSpaceIntervention.dump_patches
+      ~PCARotatedSpaceIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.RotatedSpaceIntervention.rst b/_sources/api/pyvene.models.interventions.RotatedSpaceIntervention.rst
new file mode 100644
index 00000000..60ec1a43
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.RotatedSpaceIntervention.rst
@@ -0,0 +1,86 @@
+pyvene.models.interventions.RotatedSpaceIntervention
+====================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: RotatedSpaceIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~RotatedSpaceIntervention.__init__
+      ~RotatedSpaceIntervention.add_module
+      ~RotatedSpaceIntervention.apply
+      ~RotatedSpaceIntervention.bfloat16
+      ~RotatedSpaceIntervention.buffers
+      ~RotatedSpaceIntervention.children
+      ~RotatedSpaceIntervention.compile
+      ~RotatedSpaceIntervention.cpu
+      ~RotatedSpaceIntervention.cuda
+      ~RotatedSpaceIntervention.double
+      ~RotatedSpaceIntervention.eval
+      ~RotatedSpaceIntervention.extra_repr
+      ~RotatedSpaceIntervention.float
+      ~RotatedSpaceIntervention.forward
+      ~RotatedSpaceIntervention.get_buffer
+      ~RotatedSpaceIntervention.get_extra_state
+      ~RotatedSpaceIntervention.get_parameter
+      ~RotatedSpaceIntervention.get_submodule
+      ~RotatedSpaceIntervention.half
+      ~RotatedSpaceIntervention.ipu
+      ~RotatedSpaceIntervention.load_state_dict
+      ~RotatedSpaceIntervention.modules
+      ~RotatedSpaceIntervention.mtia
+      ~RotatedSpaceIntervention.named_buffers
+      ~RotatedSpaceIntervention.named_children
+      ~RotatedSpaceIntervention.named_modules
+      ~RotatedSpaceIntervention.named_parameters
+      ~RotatedSpaceIntervention.parameters
+      ~RotatedSpaceIntervention.register_backward_hook
+      ~RotatedSpaceIntervention.register_buffer
+      ~RotatedSpaceIntervention.register_forward_hook
+      ~RotatedSpaceIntervention.register_forward_pre_hook
+      ~RotatedSpaceIntervention.register_full_backward_hook
+      ~RotatedSpaceIntervention.register_full_backward_pre_hook
+      ~RotatedSpaceIntervention.register_load_state_dict_post_hook
+      ~RotatedSpaceIntervention.register_load_state_dict_pre_hook
+      ~RotatedSpaceIntervention.register_module
+      ~RotatedSpaceIntervention.register_parameter
+      ~RotatedSpaceIntervention.register_state_dict_post_hook
+      ~RotatedSpaceIntervention.register_state_dict_pre_hook
+      ~RotatedSpaceIntervention.requires_grad_
+      ~RotatedSpaceIntervention.set_extra_state
+      ~RotatedSpaceIntervention.set_interchange_dim
+      ~RotatedSpaceIntervention.set_source_representation
+      ~RotatedSpaceIntervention.set_submodule
+      ~RotatedSpaceIntervention.share_memory
+      ~RotatedSpaceIntervention.state_dict
+      ~RotatedSpaceIntervention.tie_weight
+      ~RotatedSpaceIntervention.to
+      ~RotatedSpaceIntervention.to_empty
+      ~RotatedSpaceIntervention.train
+      ~RotatedSpaceIntervention.type
+      ~RotatedSpaceIntervention.xpu
+      ~RotatedSpaceIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~RotatedSpaceIntervention.T_destination
+      ~RotatedSpaceIntervention.call_super_init
+      ~RotatedSpaceIntervention.dump_patches
+      ~RotatedSpaceIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.SharedWeightsTrainableIntervention.rst b/_sources/api/pyvene.models.interventions.SharedWeightsTrainableIntervention.rst
new file mode 100644
index 00000000..abc250de
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.SharedWeightsTrainableIntervention.rst
@@ -0,0 +1,86 @@
+pyvene.models.interventions.SharedWeightsTrainableIntervention
+==============================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: SharedWeightsTrainableIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SharedWeightsTrainableIntervention.__init__
+      ~SharedWeightsTrainableIntervention.add_module
+      ~SharedWeightsTrainableIntervention.apply
+      ~SharedWeightsTrainableIntervention.bfloat16
+      ~SharedWeightsTrainableIntervention.buffers
+      ~SharedWeightsTrainableIntervention.children
+      ~SharedWeightsTrainableIntervention.compile
+      ~SharedWeightsTrainableIntervention.cpu
+      ~SharedWeightsTrainableIntervention.cuda
+      ~SharedWeightsTrainableIntervention.double
+      ~SharedWeightsTrainableIntervention.eval
+      ~SharedWeightsTrainableIntervention.extra_repr
+      ~SharedWeightsTrainableIntervention.float
+      ~SharedWeightsTrainableIntervention.forward
+      ~SharedWeightsTrainableIntervention.get_buffer
+      ~SharedWeightsTrainableIntervention.get_extra_state
+      ~SharedWeightsTrainableIntervention.get_parameter
+      ~SharedWeightsTrainableIntervention.get_submodule
+      ~SharedWeightsTrainableIntervention.half
+      ~SharedWeightsTrainableIntervention.ipu
+      ~SharedWeightsTrainableIntervention.load_state_dict
+      ~SharedWeightsTrainableIntervention.modules
+      ~SharedWeightsTrainableIntervention.mtia
+      ~SharedWeightsTrainableIntervention.named_buffers
+      ~SharedWeightsTrainableIntervention.named_children
+      ~SharedWeightsTrainableIntervention.named_modules
+      ~SharedWeightsTrainableIntervention.named_parameters
+      ~SharedWeightsTrainableIntervention.parameters
+      ~SharedWeightsTrainableIntervention.register_backward_hook
+      ~SharedWeightsTrainableIntervention.register_buffer
+      ~SharedWeightsTrainableIntervention.register_forward_hook
+      ~SharedWeightsTrainableIntervention.register_forward_pre_hook
+      ~SharedWeightsTrainableIntervention.register_full_backward_hook
+      ~SharedWeightsTrainableIntervention.register_full_backward_pre_hook
+      ~SharedWeightsTrainableIntervention.register_load_state_dict_post_hook
+      ~SharedWeightsTrainableIntervention.register_load_state_dict_pre_hook
+      ~SharedWeightsTrainableIntervention.register_module
+      ~SharedWeightsTrainableIntervention.register_parameter
+      ~SharedWeightsTrainableIntervention.register_state_dict_post_hook
+      ~SharedWeightsTrainableIntervention.register_state_dict_pre_hook
+      ~SharedWeightsTrainableIntervention.requires_grad_
+      ~SharedWeightsTrainableIntervention.set_extra_state
+      ~SharedWeightsTrainableIntervention.set_interchange_dim
+      ~SharedWeightsTrainableIntervention.set_source_representation
+      ~SharedWeightsTrainableIntervention.set_submodule
+      ~SharedWeightsTrainableIntervention.share_memory
+      ~SharedWeightsTrainableIntervention.state_dict
+      ~SharedWeightsTrainableIntervention.tie_weight
+      ~SharedWeightsTrainableIntervention.to
+      ~SharedWeightsTrainableIntervention.to_empty
+      ~SharedWeightsTrainableIntervention.train
+      ~SharedWeightsTrainableIntervention.type
+      ~SharedWeightsTrainableIntervention.xpu
+      ~SharedWeightsTrainableIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SharedWeightsTrainableIntervention.T_destination
+      ~SharedWeightsTrainableIntervention.call_super_init
+      ~SharedWeightsTrainableIntervention.dump_patches
+      ~SharedWeightsTrainableIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.SigmoidMaskIntervention.rst b/_sources/api/pyvene.models.interventions.SigmoidMaskIntervention.rst
new file mode 100644
index 00000000..f59aa375
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.SigmoidMaskIntervention.rst
@@ -0,0 +1,88 @@
+pyvene.models.interventions.SigmoidMaskIntervention
+===================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: SigmoidMaskIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SigmoidMaskIntervention.__init__
+      ~SigmoidMaskIntervention.add_module
+      ~SigmoidMaskIntervention.apply
+      ~SigmoidMaskIntervention.bfloat16
+      ~SigmoidMaskIntervention.buffers
+      ~SigmoidMaskIntervention.children
+      ~SigmoidMaskIntervention.compile
+      ~SigmoidMaskIntervention.cpu
+      ~SigmoidMaskIntervention.cuda
+      ~SigmoidMaskIntervention.double
+      ~SigmoidMaskIntervention.eval
+      ~SigmoidMaskIntervention.extra_repr
+      ~SigmoidMaskIntervention.float
+      ~SigmoidMaskIntervention.forward
+      ~SigmoidMaskIntervention.get_buffer
+      ~SigmoidMaskIntervention.get_extra_state
+      ~SigmoidMaskIntervention.get_parameter
+      ~SigmoidMaskIntervention.get_submodule
+      ~SigmoidMaskIntervention.get_temperature
+      ~SigmoidMaskIntervention.half
+      ~SigmoidMaskIntervention.ipu
+      ~SigmoidMaskIntervention.load_state_dict
+      ~SigmoidMaskIntervention.modules
+      ~SigmoidMaskIntervention.mtia
+      ~SigmoidMaskIntervention.named_buffers
+      ~SigmoidMaskIntervention.named_children
+      ~SigmoidMaskIntervention.named_modules
+      ~SigmoidMaskIntervention.named_parameters
+      ~SigmoidMaskIntervention.parameters
+      ~SigmoidMaskIntervention.register_backward_hook
+      ~SigmoidMaskIntervention.register_buffer
+      ~SigmoidMaskIntervention.register_forward_hook
+      ~SigmoidMaskIntervention.register_forward_pre_hook
+      ~SigmoidMaskIntervention.register_full_backward_hook
+      ~SigmoidMaskIntervention.register_full_backward_pre_hook
+      ~SigmoidMaskIntervention.register_load_state_dict_post_hook
+      ~SigmoidMaskIntervention.register_load_state_dict_pre_hook
+      ~SigmoidMaskIntervention.register_module
+      ~SigmoidMaskIntervention.register_parameter
+      ~SigmoidMaskIntervention.register_state_dict_post_hook
+      ~SigmoidMaskIntervention.register_state_dict_pre_hook
+      ~SigmoidMaskIntervention.requires_grad_
+      ~SigmoidMaskIntervention.set_extra_state
+      ~SigmoidMaskIntervention.set_interchange_dim
+      ~SigmoidMaskIntervention.set_source_representation
+      ~SigmoidMaskIntervention.set_submodule
+      ~SigmoidMaskIntervention.set_temperature
+      ~SigmoidMaskIntervention.share_memory
+      ~SigmoidMaskIntervention.state_dict
+      ~SigmoidMaskIntervention.tie_weight
+      ~SigmoidMaskIntervention.to
+      ~SigmoidMaskIntervention.to_empty
+      ~SigmoidMaskIntervention.train
+      ~SigmoidMaskIntervention.type
+      ~SigmoidMaskIntervention.xpu
+      ~SigmoidMaskIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SigmoidMaskIntervention.T_destination
+      ~SigmoidMaskIntervention.call_super_init
+      ~SigmoidMaskIntervention.dump_patches
+      ~SigmoidMaskIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.rst b/_sources/api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.rst
new file mode 100644
index 00000000..86df44e6
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.rst
@@ -0,0 +1,89 @@
+pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention
+===============================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: SigmoidMaskRotatedSpaceIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SigmoidMaskRotatedSpaceIntervention.__init__
+      ~SigmoidMaskRotatedSpaceIntervention.add_module
+      ~SigmoidMaskRotatedSpaceIntervention.apply
+      ~SigmoidMaskRotatedSpaceIntervention.bfloat16
+      ~SigmoidMaskRotatedSpaceIntervention.buffers
+      ~SigmoidMaskRotatedSpaceIntervention.children
+      ~SigmoidMaskRotatedSpaceIntervention.compile
+      ~SigmoidMaskRotatedSpaceIntervention.cpu
+      ~SigmoidMaskRotatedSpaceIntervention.cuda
+      ~SigmoidMaskRotatedSpaceIntervention.double
+      ~SigmoidMaskRotatedSpaceIntervention.eval
+      ~SigmoidMaskRotatedSpaceIntervention.extra_repr
+      ~SigmoidMaskRotatedSpaceIntervention.float
+      ~SigmoidMaskRotatedSpaceIntervention.forward
+      ~SigmoidMaskRotatedSpaceIntervention.get_boundary_parameters
+      ~SigmoidMaskRotatedSpaceIntervention.get_buffer
+      ~SigmoidMaskRotatedSpaceIntervention.get_extra_state
+      ~SigmoidMaskRotatedSpaceIntervention.get_parameter
+      ~SigmoidMaskRotatedSpaceIntervention.get_submodule
+      ~SigmoidMaskRotatedSpaceIntervention.get_temperature
+      ~SigmoidMaskRotatedSpaceIntervention.half
+      ~SigmoidMaskRotatedSpaceIntervention.ipu
+      ~SigmoidMaskRotatedSpaceIntervention.load_state_dict
+      ~SigmoidMaskRotatedSpaceIntervention.modules
+      ~SigmoidMaskRotatedSpaceIntervention.mtia
+      ~SigmoidMaskRotatedSpaceIntervention.named_buffers
+      ~SigmoidMaskRotatedSpaceIntervention.named_children
+      ~SigmoidMaskRotatedSpaceIntervention.named_modules
+      ~SigmoidMaskRotatedSpaceIntervention.named_parameters
+      ~SigmoidMaskRotatedSpaceIntervention.parameters
+      ~SigmoidMaskRotatedSpaceIntervention.register_backward_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_buffer
+      ~SigmoidMaskRotatedSpaceIntervention.register_forward_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_forward_pre_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_full_backward_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_full_backward_pre_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_load_state_dict_post_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_load_state_dict_pre_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_module
+      ~SigmoidMaskRotatedSpaceIntervention.register_parameter
+      ~SigmoidMaskRotatedSpaceIntervention.register_state_dict_post_hook
+      ~SigmoidMaskRotatedSpaceIntervention.register_state_dict_pre_hook
+      ~SigmoidMaskRotatedSpaceIntervention.requires_grad_
+      ~SigmoidMaskRotatedSpaceIntervention.set_extra_state
+      ~SigmoidMaskRotatedSpaceIntervention.set_interchange_dim
+      ~SigmoidMaskRotatedSpaceIntervention.set_source_representation
+      ~SigmoidMaskRotatedSpaceIntervention.set_submodule
+      ~SigmoidMaskRotatedSpaceIntervention.set_temperature
+      ~SigmoidMaskRotatedSpaceIntervention.share_memory
+      ~SigmoidMaskRotatedSpaceIntervention.state_dict
+      ~SigmoidMaskRotatedSpaceIntervention.tie_weight
+      ~SigmoidMaskRotatedSpaceIntervention.to
+      ~SigmoidMaskRotatedSpaceIntervention.to_empty
+      ~SigmoidMaskRotatedSpaceIntervention.train
+      ~SigmoidMaskRotatedSpaceIntervention.type
+      ~SigmoidMaskRotatedSpaceIntervention.xpu
+      ~SigmoidMaskRotatedSpaceIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SigmoidMaskRotatedSpaceIntervention.T_destination
+      ~SigmoidMaskRotatedSpaceIntervention.call_super_init
+      ~SigmoidMaskRotatedSpaceIntervention.dump_patches
+      ~SigmoidMaskRotatedSpaceIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.SkipIntervention.rst b/_sources/api/pyvene.models.interventions.SkipIntervention.rst
new file mode 100644
index 00000000..696feecc
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.SkipIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.SkipIntervention
+============================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: SkipIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SkipIntervention.__init__
+      ~SkipIntervention.add_module
+      ~SkipIntervention.apply
+      ~SkipIntervention.bfloat16
+      ~SkipIntervention.buffers
+      ~SkipIntervention.children
+      ~SkipIntervention.compile
+      ~SkipIntervention.cpu
+      ~SkipIntervention.cuda
+      ~SkipIntervention.double
+      ~SkipIntervention.eval
+      ~SkipIntervention.extra_repr
+      ~SkipIntervention.float
+      ~SkipIntervention.forward
+      ~SkipIntervention.get_buffer
+      ~SkipIntervention.get_extra_state
+      ~SkipIntervention.get_parameter
+      ~SkipIntervention.get_submodule
+      ~SkipIntervention.half
+      ~SkipIntervention.ipu
+      ~SkipIntervention.load_state_dict
+      ~SkipIntervention.modules
+      ~SkipIntervention.mtia
+      ~SkipIntervention.named_buffers
+      ~SkipIntervention.named_children
+      ~SkipIntervention.named_modules
+      ~SkipIntervention.named_parameters
+      ~SkipIntervention.parameters
+      ~SkipIntervention.register_backward_hook
+      ~SkipIntervention.register_buffer
+      ~SkipIntervention.register_forward_hook
+      ~SkipIntervention.register_forward_pre_hook
+      ~SkipIntervention.register_full_backward_hook
+      ~SkipIntervention.register_full_backward_pre_hook
+      ~SkipIntervention.register_load_state_dict_post_hook
+      ~SkipIntervention.register_load_state_dict_pre_hook
+      ~SkipIntervention.register_module
+      ~SkipIntervention.register_parameter
+      ~SkipIntervention.register_state_dict_post_hook
+      ~SkipIntervention.register_state_dict_pre_hook
+      ~SkipIntervention.requires_grad_
+      ~SkipIntervention.set_extra_state
+      ~SkipIntervention.set_interchange_dim
+      ~SkipIntervention.set_source_representation
+      ~SkipIntervention.set_submodule
+      ~SkipIntervention.share_memory
+      ~SkipIntervention.state_dict
+      ~SkipIntervention.to
+      ~SkipIntervention.to_empty
+      ~SkipIntervention.train
+      ~SkipIntervention.type
+      ~SkipIntervention.xpu
+      ~SkipIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SkipIntervention.T_destination
+      ~SkipIntervention.call_super_init
+      ~SkipIntervention.dump_patches
+      ~SkipIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.SourcelessIntervention.rst b/_sources/api/pyvene.models.interventions.SourcelessIntervention.rst
new file mode 100644
index 00000000..65a692a1
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.SourcelessIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.SourcelessIntervention
+==================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: SourcelessIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SourcelessIntervention.__init__
+      ~SourcelessIntervention.add_module
+      ~SourcelessIntervention.apply
+      ~SourcelessIntervention.bfloat16
+      ~SourcelessIntervention.buffers
+      ~SourcelessIntervention.children
+      ~SourcelessIntervention.compile
+      ~SourcelessIntervention.cpu
+      ~SourcelessIntervention.cuda
+      ~SourcelessIntervention.double
+      ~SourcelessIntervention.eval
+      ~SourcelessIntervention.extra_repr
+      ~SourcelessIntervention.float
+      ~SourcelessIntervention.forward
+      ~SourcelessIntervention.get_buffer
+      ~SourcelessIntervention.get_extra_state
+      ~SourcelessIntervention.get_parameter
+      ~SourcelessIntervention.get_submodule
+      ~SourcelessIntervention.half
+      ~SourcelessIntervention.ipu
+      ~SourcelessIntervention.load_state_dict
+      ~SourcelessIntervention.modules
+      ~SourcelessIntervention.mtia
+      ~SourcelessIntervention.named_buffers
+      ~SourcelessIntervention.named_children
+      ~SourcelessIntervention.named_modules
+      ~SourcelessIntervention.named_parameters
+      ~SourcelessIntervention.parameters
+      ~SourcelessIntervention.register_backward_hook
+      ~SourcelessIntervention.register_buffer
+      ~SourcelessIntervention.register_forward_hook
+      ~SourcelessIntervention.register_forward_pre_hook
+      ~SourcelessIntervention.register_full_backward_hook
+      ~SourcelessIntervention.register_full_backward_pre_hook
+      ~SourcelessIntervention.register_load_state_dict_post_hook
+      ~SourcelessIntervention.register_load_state_dict_pre_hook
+      ~SourcelessIntervention.register_module
+      ~SourcelessIntervention.register_parameter
+      ~SourcelessIntervention.register_state_dict_post_hook
+      ~SourcelessIntervention.register_state_dict_pre_hook
+      ~SourcelessIntervention.requires_grad_
+      ~SourcelessIntervention.set_extra_state
+      ~SourcelessIntervention.set_interchange_dim
+      ~SourcelessIntervention.set_source_representation
+      ~SourcelessIntervention.set_submodule
+      ~SourcelessIntervention.share_memory
+      ~SourcelessIntervention.state_dict
+      ~SourcelessIntervention.to
+      ~SourcelessIntervention.to_empty
+      ~SourcelessIntervention.train
+      ~SourcelessIntervention.type
+      ~SourcelessIntervention.xpu
+      ~SourcelessIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SourcelessIntervention.T_destination
+      ~SourcelessIntervention.call_super_init
+      ~SourcelessIntervention.dump_patches
+      ~SourcelessIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.SubtractionIntervention.rst b/_sources/api/pyvene.models.interventions.SubtractionIntervention.rst
new file mode 100644
index 00000000..11301434
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.SubtractionIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.SubtractionIntervention
+===================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: SubtractionIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SubtractionIntervention.__init__
+      ~SubtractionIntervention.add_module
+      ~SubtractionIntervention.apply
+      ~SubtractionIntervention.bfloat16
+      ~SubtractionIntervention.buffers
+      ~SubtractionIntervention.children
+      ~SubtractionIntervention.compile
+      ~SubtractionIntervention.cpu
+      ~SubtractionIntervention.cuda
+      ~SubtractionIntervention.double
+      ~SubtractionIntervention.eval
+      ~SubtractionIntervention.extra_repr
+      ~SubtractionIntervention.float
+      ~SubtractionIntervention.forward
+      ~SubtractionIntervention.get_buffer
+      ~SubtractionIntervention.get_extra_state
+      ~SubtractionIntervention.get_parameter
+      ~SubtractionIntervention.get_submodule
+      ~SubtractionIntervention.half
+      ~SubtractionIntervention.ipu
+      ~SubtractionIntervention.load_state_dict
+      ~SubtractionIntervention.modules
+      ~SubtractionIntervention.mtia
+      ~SubtractionIntervention.named_buffers
+      ~SubtractionIntervention.named_children
+      ~SubtractionIntervention.named_modules
+      ~SubtractionIntervention.named_parameters
+      ~SubtractionIntervention.parameters
+      ~SubtractionIntervention.register_backward_hook
+      ~SubtractionIntervention.register_buffer
+      ~SubtractionIntervention.register_forward_hook
+      ~SubtractionIntervention.register_forward_pre_hook
+      ~SubtractionIntervention.register_full_backward_hook
+      ~SubtractionIntervention.register_full_backward_pre_hook
+      ~SubtractionIntervention.register_load_state_dict_post_hook
+      ~SubtractionIntervention.register_load_state_dict_pre_hook
+      ~SubtractionIntervention.register_module
+      ~SubtractionIntervention.register_parameter
+      ~SubtractionIntervention.register_state_dict_post_hook
+      ~SubtractionIntervention.register_state_dict_pre_hook
+      ~SubtractionIntervention.requires_grad_
+      ~SubtractionIntervention.set_extra_state
+      ~SubtractionIntervention.set_interchange_dim
+      ~SubtractionIntervention.set_source_representation
+      ~SubtractionIntervention.set_submodule
+      ~SubtractionIntervention.share_memory
+      ~SubtractionIntervention.state_dict
+      ~SubtractionIntervention.to
+      ~SubtractionIntervention.to_empty
+      ~SubtractionIntervention.train
+      ~SubtractionIntervention.type
+      ~SubtractionIntervention.xpu
+      ~SubtractionIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SubtractionIntervention.T_destination
+      ~SubtractionIntervention.call_super_init
+      ~SubtractionIntervention.dump_patches
+      ~SubtractionIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.TrainableIntervention.rst b/_sources/api/pyvene.models.interventions.TrainableIntervention.rst
new file mode 100644
index 00000000..e44f46a3
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.TrainableIntervention.rst
@@ -0,0 +1,86 @@
+pyvene.models.interventions.TrainableIntervention
+=================================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: TrainableIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~TrainableIntervention.__init__
+      ~TrainableIntervention.add_module
+      ~TrainableIntervention.apply
+      ~TrainableIntervention.bfloat16
+      ~TrainableIntervention.buffers
+      ~TrainableIntervention.children
+      ~TrainableIntervention.compile
+      ~TrainableIntervention.cpu
+      ~TrainableIntervention.cuda
+      ~TrainableIntervention.double
+      ~TrainableIntervention.eval
+      ~TrainableIntervention.extra_repr
+      ~TrainableIntervention.float
+      ~TrainableIntervention.forward
+      ~TrainableIntervention.get_buffer
+      ~TrainableIntervention.get_extra_state
+      ~TrainableIntervention.get_parameter
+      ~TrainableIntervention.get_submodule
+      ~TrainableIntervention.half
+      ~TrainableIntervention.ipu
+      ~TrainableIntervention.load_state_dict
+      ~TrainableIntervention.modules
+      ~TrainableIntervention.mtia
+      ~TrainableIntervention.named_buffers
+      ~TrainableIntervention.named_children
+      ~TrainableIntervention.named_modules
+      ~TrainableIntervention.named_parameters
+      ~TrainableIntervention.parameters
+      ~TrainableIntervention.register_backward_hook
+      ~TrainableIntervention.register_buffer
+      ~TrainableIntervention.register_forward_hook
+      ~TrainableIntervention.register_forward_pre_hook
+      ~TrainableIntervention.register_full_backward_hook
+      ~TrainableIntervention.register_full_backward_pre_hook
+      ~TrainableIntervention.register_load_state_dict_post_hook
+      ~TrainableIntervention.register_load_state_dict_pre_hook
+      ~TrainableIntervention.register_module
+      ~TrainableIntervention.register_parameter
+      ~TrainableIntervention.register_state_dict_post_hook
+      ~TrainableIntervention.register_state_dict_pre_hook
+      ~TrainableIntervention.requires_grad_
+      ~TrainableIntervention.set_extra_state
+      ~TrainableIntervention.set_interchange_dim
+      ~TrainableIntervention.set_source_representation
+      ~TrainableIntervention.set_submodule
+      ~TrainableIntervention.share_memory
+      ~TrainableIntervention.state_dict
+      ~TrainableIntervention.tie_weight
+      ~TrainableIntervention.to
+      ~TrainableIntervention.to_empty
+      ~TrainableIntervention.train
+      ~TrainableIntervention.type
+      ~TrainableIntervention.xpu
+      ~TrainableIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~TrainableIntervention.T_destination
+      ~TrainableIntervention.call_super_init
+      ~TrainableIntervention.dump_patches
+      ~TrainableIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.VanillaIntervention.rst b/_sources/api/pyvene.models.interventions.VanillaIntervention.rst
new file mode 100644
index 00000000..f51fcf6d
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.VanillaIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.VanillaIntervention
+===============================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: VanillaIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~VanillaIntervention.__init__
+      ~VanillaIntervention.add_module
+      ~VanillaIntervention.apply
+      ~VanillaIntervention.bfloat16
+      ~VanillaIntervention.buffers
+      ~VanillaIntervention.children
+      ~VanillaIntervention.compile
+      ~VanillaIntervention.cpu
+      ~VanillaIntervention.cuda
+      ~VanillaIntervention.double
+      ~VanillaIntervention.eval
+      ~VanillaIntervention.extra_repr
+      ~VanillaIntervention.float
+      ~VanillaIntervention.forward
+      ~VanillaIntervention.get_buffer
+      ~VanillaIntervention.get_extra_state
+      ~VanillaIntervention.get_parameter
+      ~VanillaIntervention.get_submodule
+      ~VanillaIntervention.half
+      ~VanillaIntervention.ipu
+      ~VanillaIntervention.load_state_dict
+      ~VanillaIntervention.modules
+      ~VanillaIntervention.mtia
+      ~VanillaIntervention.named_buffers
+      ~VanillaIntervention.named_children
+      ~VanillaIntervention.named_modules
+      ~VanillaIntervention.named_parameters
+      ~VanillaIntervention.parameters
+      ~VanillaIntervention.register_backward_hook
+      ~VanillaIntervention.register_buffer
+      ~VanillaIntervention.register_forward_hook
+      ~VanillaIntervention.register_forward_pre_hook
+      ~VanillaIntervention.register_full_backward_hook
+      ~VanillaIntervention.register_full_backward_pre_hook
+      ~VanillaIntervention.register_load_state_dict_post_hook
+      ~VanillaIntervention.register_load_state_dict_pre_hook
+      ~VanillaIntervention.register_module
+      ~VanillaIntervention.register_parameter
+      ~VanillaIntervention.register_state_dict_post_hook
+      ~VanillaIntervention.register_state_dict_pre_hook
+      ~VanillaIntervention.requires_grad_
+      ~VanillaIntervention.set_extra_state
+      ~VanillaIntervention.set_interchange_dim
+      ~VanillaIntervention.set_source_representation
+      ~VanillaIntervention.set_submodule
+      ~VanillaIntervention.share_memory
+      ~VanillaIntervention.state_dict
+      ~VanillaIntervention.to
+      ~VanillaIntervention.to_empty
+      ~VanillaIntervention.train
+      ~VanillaIntervention.type
+      ~VanillaIntervention.xpu
+      ~VanillaIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~VanillaIntervention.T_destination
+      ~VanillaIntervention.call_super_init
+      ~VanillaIntervention.dump_patches
+      ~VanillaIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.ZeroIntervention.rst b/_sources/api/pyvene.models.interventions.ZeroIntervention.rst
new file mode 100644
index 00000000..79d37909
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.ZeroIntervention.rst
@@ -0,0 +1,85 @@
+pyvene.models.interventions.ZeroIntervention
+============================================
+
+.. currentmodule:: pyvene.models.interventions
+
+.. autoclass:: ZeroIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ZeroIntervention.__init__
+      ~ZeroIntervention.add_module
+      ~ZeroIntervention.apply
+      ~ZeroIntervention.bfloat16
+      ~ZeroIntervention.buffers
+      ~ZeroIntervention.children
+      ~ZeroIntervention.compile
+      ~ZeroIntervention.cpu
+      ~ZeroIntervention.cuda
+      ~ZeroIntervention.double
+      ~ZeroIntervention.eval
+      ~ZeroIntervention.extra_repr
+      ~ZeroIntervention.float
+      ~ZeroIntervention.forward
+      ~ZeroIntervention.get_buffer
+      ~ZeroIntervention.get_extra_state
+      ~ZeroIntervention.get_parameter
+      ~ZeroIntervention.get_submodule
+      ~ZeroIntervention.half
+      ~ZeroIntervention.ipu
+      ~ZeroIntervention.load_state_dict
+      ~ZeroIntervention.modules
+      ~ZeroIntervention.mtia
+      ~ZeroIntervention.named_buffers
+      ~ZeroIntervention.named_children
+      ~ZeroIntervention.named_modules
+      ~ZeroIntervention.named_parameters
+      ~ZeroIntervention.parameters
+      ~ZeroIntervention.register_backward_hook
+      ~ZeroIntervention.register_buffer
+      ~ZeroIntervention.register_forward_hook
+      ~ZeroIntervention.register_forward_pre_hook
+      ~ZeroIntervention.register_full_backward_hook
+      ~ZeroIntervention.register_full_backward_pre_hook
+      ~ZeroIntervention.register_load_state_dict_post_hook
+      ~ZeroIntervention.register_load_state_dict_pre_hook
+      ~ZeroIntervention.register_module
+      ~ZeroIntervention.register_parameter
+      ~ZeroIntervention.register_state_dict_post_hook
+      ~ZeroIntervention.register_state_dict_pre_hook
+      ~ZeroIntervention.requires_grad_
+      ~ZeroIntervention.set_extra_state
+      ~ZeroIntervention.set_interchange_dim
+      ~ZeroIntervention.set_source_representation
+      ~ZeroIntervention.set_submodule
+      ~ZeroIntervention.share_memory
+      ~ZeroIntervention.state_dict
+      ~ZeroIntervention.to
+      ~ZeroIntervention.to_empty
+      ~ZeroIntervention.train
+      ~ZeroIntervention.type
+      ~ZeroIntervention.xpu
+      ~ZeroIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~ZeroIntervention.T_destination
+      ~ZeroIntervention.call_super_init
+      ~ZeroIntervention.dump_patches
+      ~ZeroIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.interventions.rst b/_sources/api/pyvene.models.interventions.rst
new file mode 100644
index 00000000..2c754c6b
--- /dev/null
+++ b/_sources/api/pyvene.models.interventions.rst
@@ -0,0 +1,54 @@
+pyvene.models.interventions
+===========================
+
+.. automodule:: pyvene.models.interventions
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      AdditionIntervention
+      AutoencoderIntervention
+      BasisAgnosticIntervention
+      BoundlessRotatedSpaceIntervention
+      CollectIntervention
+      ConstantSourceIntervention
+      DistributedRepresentationIntervention
+      Intervention
+      InterventionOutput
+      JumpReLUAutoencoderIntervention
+      LocalistRepresentationIntervention
+      LowRankRotatedSpaceIntervention
+      NoiseIntervention
+      PCARotatedSpaceIntervention
+      RotatedSpaceIntervention
+      SharedWeightsTrainableIntervention
+      SigmoidMaskIntervention
+      SigmoidMaskRotatedSpaceIntervention
+      SkipIntervention
+      SourcelessIntervention
+      SubtractionIntervention
+      TrainableIntervention
+      VanillaIntervention
+      ZeroIntervention
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.layers.AutoencoderLayer.rst b/_sources/api/pyvene.models.layers.AutoencoderLayer.rst
new file mode 100644
index 00000000..5bacb476
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.AutoencoderLayer.rst
@@ -0,0 +1,85 @@
+pyvene.models.layers.AutoencoderLayer
+=====================================
+
+.. currentmodule:: pyvene.models.layers
+
+.. autoclass:: AutoencoderLayer
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~AutoencoderLayer.__init__
+      ~AutoencoderLayer.add_module
+      ~AutoencoderLayer.apply
+      ~AutoencoderLayer.bfloat16
+      ~AutoencoderLayer.buffers
+      ~AutoencoderLayer.children
+      ~AutoencoderLayer.compile
+      ~AutoencoderLayer.cpu
+      ~AutoencoderLayer.cuda
+      ~AutoencoderLayer.decode
+      ~AutoencoderLayer.double
+      ~AutoencoderLayer.encode
+      ~AutoencoderLayer.eval
+      ~AutoencoderLayer.extra_repr
+      ~AutoencoderLayer.float
+      ~AutoencoderLayer.forward
+      ~AutoencoderLayer.get_buffer
+      ~AutoencoderLayer.get_extra_state
+      ~AutoencoderLayer.get_parameter
+      ~AutoencoderLayer.get_submodule
+      ~AutoencoderLayer.half
+      ~AutoencoderLayer.ipu
+      ~AutoencoderLayer.load_state_dict
+      ~AutoencoderLayer.modules
+      ~AutoencoderLayer.mtia
+      ~AutoencoderLayer.named_buffers
+      ~AutoencoderLayer.named_children
+      ~AutoencoderLayer.named_modules
+      ~AutoencoderLayer.named_parameters
+      ~AutoencoderLayer.parameters
+      ~AutoencoderLayer.register_backward_hook
+      ~AutoencoderLayer.register_buffer
+      ~AutoencoderLayer.register_forward_hook
+      ~AutoencoderLayer.register_forward_pre_hook
+      ~AutoencoderLayer.register_full_backward_hook
+      ~AutoencoderLayer.register_full_backward_pre_hook
+      ~AutoencoderLayer.register_load_state_dict_post_hook
+      ~AutoencoderLayer.register_load_state_dict_pre_hook
+      ~AutoencoderLayer.register_module
+      ~AutoencoderLayer.register_parameter
+      ~AutoencoderLayer.register_state_dict_post_hook
+      ~AutoencoderLayer.register_state_dict_pre_hook
+      ~AutoencoderLayer.requires_grad_
+      ~AutoencoderLayer.set_extra_state
+      ~AutoencoderLayer.set_submodule
+      ~AutoencoderLayer.share_memory
+      ~AutoencoderLayer.state_dict
+      ~AutoencoderLayer.to
+      ~AutoencoderLayer.to_empty
+      ~AutoencoderLayer.train
+      ~AutoencoderLayer.type
+      ~AutoencoderLayer.xpu
+      ~AutoencoderLayer.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~AutoencoderLayer.T_destination
+      ~AutoencoderLayer.call_super_init
+      ~AutoencoderLayer.dump_patches
+      ~AutoencoderLayer.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.layers.AutoencoderLayerBase.rst b/_sources/api/pyvene.models.layers.AutoencoderLayerBase.rst
new file mode 100644
index 00000000..e842af45
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.AutoencoderLayerBase.rst
@@ -0,0 +1,85 @@
+pyvene.models.layers.AutoencoderLayerBase
+=========================================
+
+.. currentmodule:: pyvene.models.layers
+
+.. autoclass:: AutoencoderLayerBase
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~AutoencoderLayerBase.__init__
+      ~AutoencoderLayerBase.add_module
+      ~AutoencoderLayerBase.apply
+      ~AutoencoderLayerBase.bfloat16
+      ~AutoencoderLayerBase.buffers
+      ~AutoencoderLayerBase.children
+      ~AutoencoderLayerBase.compile
+      ~AutoencoderLayerBase.cpu
+      ~AutoencoderLayerBase.cuda
+      ~AutoencoderLayerBase.decode
+      ~AutoencoderLayerBase.double
+      ~AutoencoderLayerBase.encode
+      ~AutoencoderLayerBase.eval
+      ~AutoencoderLayerBase.extra_repr
+      ~AutoencoderLayerBase.float
+      ~AutoencoderLayerBase.forward
+      ~AutoencoderLayerBase.get_buffer
+      ~AutoencoderLayerBase.get_extra_state
+      ~AutoencoderLayerBase.get_parameter
+      ~AutoencoderLayerBase.get_submodule
+      ~AutoencoderLayerBase.half
+      ~AutoencoderLayerBase.ipu
+      ~AutoencoderLayerBase.load_state_dict
+      ~AutoencoderLayerBase.modules
+      ~AutoencoderLayerBase.mtia
+      ~AutoencoderLayerBase.named_buffers
+      ~AutoencoderLayerBase.named_children
+      ~AutoencoderLayerBase.named_modules
+      ~AutoencoderLayerBase.named_parameters
+      ~AutoencoderLayerBase.parameters
+      ~AutoencoderLayerBase.register_backward_hook
+      ~AutoencoderLayerBase.register_buffer
+      ~AutoencoderLayerBase.register_forward_hook
+      ~AutoencoderLayerBase.register_forward_pre_hook
+      ~AutoencoderLayerBase.register_full_backward_hook
+      ~AutoencoderLayerBase.register_full_backward_pre_hook
+      ~AutoencoderLayerBase.register_load_state_dict_post_hook
+      ~AutoencoderLayerBase.register_load_state_dict_pre_hook
+      ~AutoencoderLayerBase.register_module
+      ~AutoencoderLayerBase.register_parameter
+      ~AutoencoderLayerBase.register_state_dict_post_hook
+      ~AutoencoderLayerBase.register_state_dict_pre_hook
+      ~AutoencoderLayerBase.requires_grad_
+      ~AutoencoderLayerBase.set_extra_state
+      ~AutoencoderLayerBase.set_submodule
+      ~AutoencoderLayerBase.share_memory
+      ~AutoencoderLayerBase.state_dict
+      ~AutoencoderLayerBase.to
+      ~AutoencoderLayerBase.to_empty
+      ~AutoencoderLayerBase.train
+      ~AutoencoderLayerBase.type
+      ~AutoencoderLayerBase.xpu
+      ~AutoencoderLayerBase.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~AutoencoderLayerBase.T_destination
+      ~AutoencoderLayerBase.call_super_init
+      ~AutoencoderLayerBase.dump_patches
+      ~AutoencoderLayerBase.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.layers.InverseRotateLayer.rst b/_sources/api/pyvene.models.layers.InverseRotateLayer.rst
new file mode 100644
index 00000000..32b376fc
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.InverseRotateLayer.rst
@@ -0,0 +1,83 @@
+pyvene.models.layers.InverseRotateLayer
+=======================================
+
+.. currentmodule:: pyvene.models.layers
+
+.. autoclass:: InverseRotateLayer
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~InverseRotateLayer.__init__
+      ~InverseRotateLayer.add_module
+      ~InverseRotateLayer.apply
+      ~InverseRotateLayer.bfloat16
+      ~InverseRotateLayer.buffers
+      ~InverseRotateLayer.children
+      ~InverseRotateLayer.compile
+      ~InverseRotateLayer.cpu
+      ~InverseRotateLayer.cuda
+      ~InverseRotateLayer.double
+      ~InverseRotateLayer.eval
+      ~InverseRotateLayer.extra_repr
+      ~InverseRotateLayer.float
+      ~InverseRotateLayer.forward
+      ~InverseRotateLayer.get_buffer
+      ~InverseRotateLayer.get_extra_state
+      ~InverseRotateLayer.get_parameter
+      ~InverseRotateLayer.get_submodule
+      ~InverseRotateLayer.half
+      ~InverseRotateLayer.ipu
+      ~InverseRotateLayer.load_state_dict
+      ~InverseRotateLayer.modules
+      ~InverseRotateLayer.mtia
+      ~InverseRotateLayer.named_buffers
+      ~InverseRotateLayer.named_children
+      ~InverseRotateLayer.named_modules
+      ~InverseRotateLayer.named_parameters
+      ~InverseRotateLayer.parameters
+      ~InverseRotateLayer.register_backward_hook
+      ~InverseRotateLayer.register_buffer
+      ~InverseRotateLayer.register_forward_hook
+      ~InverseRotateLayer.register_forward_pre_hook
+      ~InverseRotateLayer.register_full_backward_hook
+      ~InverseRotateLayer.register_full_backward_pre_hook
+      ~InverseRotateLayer.register_load_state_dict_post_hook
+      ~InverseRotateLayer.register_load_state_dict_pre_hook
+      ~InverseRotateLayer.register_module
+      ~InverseRotateLayer.register_parameter
+      ~InverseRotateLayer.register_state_dict_post_hook
+      ~InverseRotateLayer.register_state_dict_pre_hook
+      ~InverseRotateLayer.requires_grad_
+      ~InverseRotateLayer.set_extra_state
+      ~InverseRotateLayer.set_submodule
+      ~InverseRotateLayer.share_memory
+      ~InverseRotateLayer.state_dict
+      ~InverseRotateLayer.to
+      ~InverseRotateLayer.to_empty
+      ~InverseRotateLayer.train
+      ~InverseRotateLayer.type
+      ~InverseRotateLayer.xpu
+      ~InverseRotateLayer.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~InverseRotateLayer.T_destination
+      ~InverseRotateLayer.call_super_init
+      ~InverseRotateLayer.dump_patches
+      ~InverseRotateLayer.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.layers.LowRankRotateLayer.rst b/_sources/api/pyvene.models.layers.LowRankRotateLayer.rst
new file mode 100644
index 00000000..eec60da6
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.LowRankRotateLayer.rst
@@ -0,0 +1,83 @@
+pyvene.models.layers.LowRankRotateLayer
+=======================================
+
+.. currentmodule:: pyvene.models.layers
+
+.. autoclass:: LowRankRotateLayer
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~LowRankRotateLayer.__init__
+      ~LowRankRotateLayer.add_module
+      ~LowRankRotateLayer.apply
+      ~LowRankRotateLayer.bfloat16
+      ~LowRankRotateLayer.buffers
+      ~LowRankRotateLayer.children
+      ~LowRankRotateLayer.compile
+      ~LowRankRotateLayer.cpu
+      ~LowRankRotateLayer.cuda
+      ~LowRankRotateLayer.double
+      ~LowRankRotateLayer.eval
+      ~LowRankRotateLayer.extra_repr
+      ~LowRankRotateLayer.float
+      ~LowRankRotateLayer.forward
+      ~LowRankRotateLayer.get_buffer
+      ~LowRankRotateLayer.get_extra_state
+      ~LowRankRotateLayer.get_parameter
+      ~LowRankRotateLayer.get_submodule
+      ~LowRankRotateLayer.half
+      ~LowRankRotateLayer.ipu
+      ~LowRankRotateLayer.load_state_dict
+      ~LowRankRotateLayer.modules
+      ~LowRankRotateLayer.mtia
+      ~LowRankRotateLayer.named_buffers
+      ~LowRankRotateLayer.named_children
+      ~LowRankRotateLayer.named_modules
+      ~LowRankRotateLayer.named_parameters
+      ~LowRankRotateLayer.parameters
+      ~LowRankRotateLayer.register_backward_hook
+      ~LowRankRotateLayer.register_buffer
+      ~LowRankRotateLayer.register_forward_hook
+      ~LowRankRotateLayer.register_forward_pre_hook
+      ~LowRankRotateLayer.register_full_backward_hook
+      ~LowRankRotateLayer.register_full_backward_pre_hook
+      ~LowRankRotateLayer.register_load_state_dict_post_hook
+      ~LowRankRotateLayer.register_load_state_dict_pre_hook
+      ~LowRankRotateLayer.register_module
+      ~LowRankRotateLayer.register_parameter
+      ~LowRankRotateLayer.register_state_dict_post_hook
+      ~LowRankRotateLayer.register_state_dict_pre_hook
+      ~LowRankRotateLayer.requires_grad_
+      ~LowRankRotateLayer.set_extra_state
+      ~LowRankRotateLayer.set_submodule
+      ~LowRankRotateLayer.share_memory
+      ~LowRankRotateLayer.state_dict
+      ~LowRankRotateLayer.to
+      ~LowRankRotateLayer.to_empty
+      ~LowRankRotateLayer.train
+      ~LowRankRotateLayer.type
+      ~LowRankRotateLayer.xpu
+      ~LowRankRotateLayer.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~LowRankRotateLayer.T_destination
+      ~LowRankRotateLayer.call_super_init
+      ~LowRankRotateLayer.dump_patches
+      ~LowRankRotateLayer.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.layers.RotateLayer.rst b/_sources/api/pyvene.models.layers.RotateLayer.rst
new file mode 100644
index 00000000..5251ead1
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.RotateLayer.rst
@@ -0,0 +1,83 @@
+pyvene.models.layers.RotateLayer
+================================
+
+.. currentmodule:: pyvene.models.layers
+
+.. autoclass:: RotateLayer
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~RotateLayer.__init__
+      ~RotateLayer.add_module
+      ~RotateLayer.apply
+      ~RotateLayer.bfloat16
+      ~RotateLayer.buffers
+      ~RotateLayer.children
+      ~RotateLayer.compile
+      ~RotateLayer.cpu
+      ~RotateLayer.cuda
+      ~RotateLayer.double
+      ~RotateLayer.eval
+      ~RotateLayer.extra_repr
+      ~RotateLayer.float
+      ~RotateLayer.forward
+      ~RotateLayer.get_buffer
+      ~RotateLayer.get_extra_state
+      ~RotateLayer.get_parameter
+      ~RotateLayer.get_submodule
+      ~RotateLayer.half
+      ~RotateLayer.ipu
+      ~RotateLayer.load_state_dict
+      ~RotateLayer.modules
+      ~RotateLayer.mtia
+      ~RotateLayer.named_buffers
+      ~RotateLayer.named_children
+      ~RotateLayer.named_modules
+      ~RotateLayer.named_parameters
+      ~RotateLayer.parameters
+      ~RotateLayer.register_backward_hook
+      ~RotateLayer.register_buffer
+      ~RotateLayer.register_forward_hook
+      ~RotateLayer.register_forward_pre_hook
+      ~RotateLayer.register_full_backward_hook
+      ~RotateLayer.register_full_backward_pre_hook
+      ~RotateLayer.register_load_state_dict_post_hook
+      ~RotateLayer.register_load_state_dict_pre_hook
+      ~RotateLayer.register_module
+      ~RotateLayer.register_parameter
+      ~RotateLayer.register_state_dict_post_hook
+      ~RotateLayer.register_state_dict_pre_hook
+      ~RotateLayer.requires_grad_
+      ~RotateLayer.set_extra_state
+      ~RotateLayer.set_submodule
+      ~RotateLayer.share_memory
+      ~RotateLayer.state_dict
+      ~RotateLayer.to
+      ~RotateLayer.to_empty
+      ~RotateLayer.train
+      ~RotateLayer.type
+      ~RotateLayer.xpu
+      ~RotateLayer.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~RotateLayer.T_destination
+      ~RotateLayer.call_super_init
+      ~RotateLayer.dump_patches
+      ~RotateLayer.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.layers.SubspaceLowRankRotateLayer.rst b/_sources/api/pyvene.models.layers.SubspaceLowRankRotateLayer.rst
new file mode 100644
index 00000000..fbc6c4a5
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.SubspaceLowRankRotateLayer.rst
@@ -0,0 +1,83 @@
+pyvene.models.layers.SubspaceLowRankRotateLayer
+===============================================
+
+.. currentmodule:: pyvene.models.layers
+
+.. autoclass:: SubspaceLowRankRotateLayer
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SubspaceLowRankRotateLayer.__init__
+      ~SubspaceLowRankRotateLayer.add_module
+      ~SubspaceLowRankRotateLayer.apply
+      ~SubspaceLowRankRotateLayer.bfloat16
+      ~SubspaceLowRankRotateLayer.buffers
+      ~SubspaceLowRankRotateLayer.children
+      ~SubspaceLowRankRotateLayer.compile
+      ~SubspaceLowRankRotateLayer.cpu
+      ~SubspaceLowRankRotateLayer.cuda
+      ~SubspaceLowRankRotateLayer.double
+      ~SubspaceLowRankRotateLayer.eval
+      ~SubspaceLowRankRotateLayer.extra_repr
+      ~SubspaceLowRankRotateLayer.float
+      ~SubspaceLowRankRotateLayer.forward
+      ~SubspaceLowRankRotateLayer.get_buffer
+      ~SubspaceLowRankRotateLayer.get_extra_state
+      ~SubspaceLowRankRotateLayer.get_parameter
+      ~SubspaceLowRankRotateLayer.get_submodule
+      ~SubspaceLowRankRotateLayer.half
+      ~SubspaceLowRankRotateLayer.ipu
+      ~SubspaceLowRankRotateLayer.load_state_dict
+      ~SubspaceLowRankRotateLayer.modules
+      ~SubspaceLowRankRotateLayer.mtia
+      ~SubspaceLowRankRotateLayer.named_buffers
+      ~SubspaceLowRankRotateLayer.named_children
+      ~SubspaceLowRankRotateLayer.named_modules
+      ~SubspaceLowRankRotateLayer.named_parameters
+      ~SubspaceLowRankRotateLayer.parameters
+      ~SubspaceLowRankRotateLayer.register_backward_hook
+      ~SubspaceLowRankRotateLayer.register_buffer
+      ~SubspaceLowRankRotateLayer.register_forward_hook
+      ~SubspaceLowRankRotateLayer.register_forward_pre_hook
+      ~SubspaceLowRankRotateLayer.register_full_backward_hook
+      ~SubspaceLowRankRotateLayer.register_full_backward_pre_hook
+      ~SubspaceLowRankRotateLayer.register_load_state_dict_post_hook
+      ~SubspaceLowRankRotateLayer.register_load_state_dict_pre_hook
+      ~SubspaceLowRankRotateLayer.register_module
+      ~SubspaceLowRankRotateLayer.register_parameter
+      ~SubspaceLowRankRotateLayer.register_state_dict_post_hook
+      ~SubspaceLowRankRotateLayer.register_state_dict_pre_hook
+      ~SubspaceLowRankRotateLayer.requires_grad_
+      ~SubspaceLowRankRotateLayer.set_extra_state
+      ~SubspaceLowRankRotateLayer.set_submodule
+      ~SubspaceLowRankRotateLayer.share_memory
+      ~SubspaceLowRankRotateLayer.state_dict
+      ~SubspaceLowRankRotateLayer.to
+      ~SubspaceLowRankRotateLayer.to_empty
+      ~SubspaceLowRankRotateLayer.train
+      ~SubspaceLowRankRotateLayer.type
+      ~SubspaceLowRankRotateLayer.xpu
+      ~SubspaceLowRankRotateLayer.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~SubspaceLowRankRotateLayer.T_destination
+      ~SubspaceLowRankRotateLayer.call_super_init
+      ~SubspaceLowRankRotateLayer.dump_patches
+      ~SubspaceLowRankRotateLayer.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.layers.rst b/_sources/api/pyvene.models.layers.rst
new file mode 100644
index 00000000..801a789b
--- /dev/null
+++ b/_sources/api/pyvene.models.layers.rst
@@ -0,0 +1,36 @@
+pyvene.models.layers
+====================
+
+.. automodule:: pyvene.models.layers
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      AutoencoderLayer
+      AutoencoderLayerBase
+      InverseRotateLayer
+      LowRankRotateLayer
+      RotateLayer
+      SubspaceLowRankRotateLayer
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.llama.modelings_intervenable_llama.create_llama.rst b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.create_llama.rst
new file mode 100644
index 00000000..1be2c9cd
--- /dev/null
+++ b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.create_llama.rst
@@ -0,0 +1,6 @@
+pyvene.models.llama.modelings\_intervenable\_llama.create\_llama
+================================================================
+
+.. currentmodule:: pyvene.models.llama.modelings_intervenable_llama
+
+.. autofunction:: create_llama
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..aae73f95
--- /dev/null
+++ b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.llama.modelings\_intervenable\_llama.llama\_lm\_type\_to\_dimension\_mapping
+==========================================================================================
+
+.. currentmodule:: pyvene.models.llama.modelings_intervenable_llama
+
+.. autodata:: llama_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..78f34773
--- /dev/null
+++ b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.llama.modelings\_intervenable\_llama.llama\_type\_to\_dimension\_mapping
+======================================================================================
+
+.. currentmodule:: pyvene.models.llama.modelings_intervenable_llama
+
+.. autodata:: llama_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.llama.modelings_intervenable_llama.rst b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.rst
new file mode 100644
index 00000000..46cf900b
--- /dev/null
+++ b/_sources/api/pyvene.models.llama.modelings_intervenable_llama.rst
@@ -0,0 +1,38 @@
+pyvene.models.llama.modelings\_intervenable\_llama
+==================================================
+
+.. automodule:: pyvene.models.llama.modelings_intervenable_llama
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      llama_type_to_dimension_mapping
+      llama_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_llama
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.llama.rst b/_sources/api/pyvene.models.llama.rst
new file mode 100644
index 00000000..683b02e2
--- /dev/null
+++ b/_sources/api/pyvene.models.llama.rst
@@ -0,0 +1,32 @@
+pyvene.models.llama
+===================
+
+.. automodule:: pyvene.models.llama
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.llama.modelings_intervenable_llama
+
diff --git a/_sources/api/pyvene.models.llava.modelings_intervenable_llava.create_llava.rst b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.create_llava.rst
new file mode 100644
index 00000000..a0bf623b
--- /dev/null
+++ b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.create_llava.rst
@@ -0,0 +1,6 @@
+pyvene.models.llava.modelings\_intervenable\_llava.create\_llava
+================================================================
+
+.. currentmodule:: pyvene.models.llava.modelings_intervenable_llava
+
+.. autofunction:: create_llava
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..2c011572
--- /dev/null
+++ b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.llava.modelings\_intervenable\_llava.llava\_lm\_type\_to\_dimension\_mapping
+==========================================================================================
+
+.. currentmodule:: pyvene.models.llava.modelings_intervenable_llava
+
+.. autodata:: llava_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..16a87856
--- /dev/null
+++ b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.llava.modelings\_intervenable\_llava.llava\_type\_to\_dimension\_mapping
+======================================================================================
+
+.. currentmodule:: pyvene.models.llava.modelings_intervenable_llava
+
+.. autodata:: llava_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.llava.modelings_intervenable_llava.rst b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.rst
new file mode 100644
index 00000000..e551bd00
--- /dev/null
+++ b/_sources/api/pyvene.models.llava.modelings_intervenable_llava.rst
@@ -0,0 +1,38 @@
+pyvene.models.llava.modelings\_intervenable\_llava
+==================================================
+
+.. automodule:: pyvene.models.llava.modelings_intervenable_llava
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      llava_type_to_dimension_mapping
+      llava_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_llava
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.llava.rst b/_sources/api/pyvene.models.llava.rst
new file mode 100644
index 00000000..011c2ed9
--- /dev/null
+++ b/_sources/api/pyvene.models.llava.rst
@@ -0,0 +1,32 @@
+pyvene.models.llava
+===================
+
+.. automodule:: pyvene.models.llava
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.llava.modelings_intervenable_llava
+
diff --git a/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.create_mistral.rst b/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.create_mistral.rst
new file mode 100644
index 00000000..07460304
--- /dev/null
+++ b/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.create_mistral.rst
@@ -0,0 +1,6 @@
+pyvene.models.mistral.modellings\_intervenable\_mistral.create\_mistral
+=======================================================================
+
+.. currentmodule:: pyvene.models.mistral.modellings_intervenable_mistral
+
+.. autofunction:: create_mistral
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..273d6048
--- /dev/null
+++ b/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.mistral.modellings\_intervenable\_mistral.mistral\_type\_to\_dimension\_mapping
+=============================================================================================
+
+.. currentmodule:: pyvene.models.mistral.modellings_intervenable_mistral
+
+.. autodata:: mistral_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.rst b/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.rst
new file mode 100644
index 00000000..2b44e0d3
--- /dev/null
+++ b/_sources/api/pyvene.models.mistral.modellings_intervenable_mistral.rst
@@ -0,0 +1,37 @@
+pyvene.models.mistral.modellings\_intervenable\_mistral
+=======================================================
+
+.. automodule:: pyvene.models.mistral.modellings_intervenable_mistral
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      mistral_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_mistral
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.mistral.rst b/_sources/api/pyvene.models.mistral.rst
new file mode 100644
index 00000000..42f116a3
--- /dev/null
+++ b/_sources/api/pyvene.models.mistral.rst
@@ -0,0 +1,32 @@
+pyvene.models.mistral
+=====================
+
+.. automodule:: pyvene.models.mistral
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.mistral.modellings_intervenable_mistral
+
diff --git a/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.rst b/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.rst
new file mode 100644
index 00000000..77a1ea2f
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.rst
@@ -0,0 +1,6 @@
+pyvene.models.mlp.modelings\_intervenable\_mlp.create\_mlp\_classifier
+======================================================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_intervenable_mlp
+
+.. autofunction:: create_mlp_classifier
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..a9cf735c
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.mlp.modelings\_intervenable\_mlp.mlp\_type\_to\_dimension\_mapping
+================================================================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_intervenable_mlp
+
+.. autodata:: mlp_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.rst b/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.rst
new file mode 100644
index 00000000..cc0eb085
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_intervenable_mlp.rst
@@ -0,0 +1,37 @@
+pyvene.models.mlp.modelings\_intervenable\_mlp
+==============================================
+
+.. automodule:: pyvene.models.mlp.modelings_intervenable_mlp
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      mlp_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_mlp_classifier
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.mlp.modelings_mlp.MLPBlock.rst b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPBlock.rst
new file mode 100644
index 00000000..324b08ea
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPBlock.rst
@@ -0,0 +1,83 @@
+pyvene.models.mlp.modelings\_mlp.MLPBlock
+=========================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_mlp
+
+.. autoclass:: MLPBlock
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~MLPBlock.__init__
+      ~MLPBlock.add_module
+      ~MLPBlock.apply
+      ~MLPBlock.bfloat16
+      ~MLPBlock.buffers
+      ~MLPBlock.children
+      ~MLPBlock.compile
+      ~MLPBlock.cpu
+      ~MLPBlock.cuda
+      ~MLPBlock.double
+      ~MLPBlock.eval
+      ~MLPBlock.extra_repr
+      ~MLPBlock.float
+      ~MLPBlock.forward
+      ~MLPBlock.get_buffer
+      ~MLPBlock.get_extra_state
+      ~MLPBlock.get_parameter
+      ~MLPBlock.get_submodule
+      ~MLPBlock.half
+      ~MLPBlock.ipu
+      ~MLPBlock.load_state_dict
+      ~MLPBlock.modules
+      ~MLPBlock.mtia
+      ~MLPBlock.named_buffers
+      ~MLPBlock.named_children
+      ~MLPBlock.named_modules
+      ~MLPBlock.named_parameters
+      ~MLPBlock.parameters
+      ~MLPBlock.register_backward_hook
+      ~MLPBlock.register_buffer
+      ~MLPBlock.register_forward_hook
+      ~MLPBlock.register_forward_pre_hook
+      ~MLPBlock.register_full_backward_hook
+      ~MLPBlock.register_full_backward_pre_hook
+      ~MLPBlock.register_load_state_dict_post_hook
+      ~MLPBlock.register_load_state_dict_pre_hook
+      ~MLPBlock.register_module
+      ~MLPBlock.register_parameter
+      ~MLPBlock.register_state_dict_post_hook
+      ~MLPBlock.register_state_dict_pre_hook
+      ~MLPBlock.requires_grad_
+      ~MLPBlock.set_extra_state
+      ~MLPBlock.set_submodule
+      ~MLPBlock.share_memory
+      ~MLPBlock.state_dict
+      ~MLPBlock.to
+      ~MLPBlock.to_empty
+      ~MLPBlock.train
+      ~MLPBlock.type
+      ~MLPBlock.xpu
+      ~MLPBlock.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~MLPBlock.T_destination
+      ~MLPBlock.call_super_init
+      ~MLPBlock.dump_patches
+      ~MLPBlock.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_mlp.MLPConfig.rst b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPConfig.rst
new file mode 100644
index 00000000..b8c9bb27
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPConfig.rst
@@ -0,0 +1,53 @@
+pyvene.models.mlp.modelings\_mlp.MLPConfig
+==========================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_mlp
+
+.. autoclass:: MLPConfig
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~MLPConfig.__init__
+      ~MLPConfig.dict_torch_dtype_to_str
+      ~MLPConfig.from_dict
+      ~MLPConfig.from_json_file
+      ~MLPConfig.from_pretrained
+      ~MLPConfig.get_config_dict
+      ~MLPConfig.get_text_config
+      ~MLPConfig.push_to_hub
+      ~MLPConfig.register_for_auto_class
+      ~MLPConfig.save_pretrained
+      ~MLPConfig.to_dict
+      ~MLPConfig.to_diff_dict
+      ~MLPConfig.to_json_file
+      ~MLPConfig.to_json_string
+      ~MLPConfig.update
+      ~MLPConfig.update_from_string
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~MLPConfig.attribute_map
+      ~MLPConfig.base_config_key
+      ~MLPConfig.base_model_tp_plan
+      ~MLPConfig.is_composition
+      ~MLPConfig.model_type
+      ~MLPConfig.name_or_path
+      ~MLPConfig.num_labels
+      ~MLPConfig.sub_configs
+      ~MLPConfig.use_return_dict
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_mlp.MLPForClassification.rst b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPForClassification.rst
new file mode 100644
index 00000000..cb933474
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPForClassification.rst
@@ -0,0 +1,147 @@
+pyvene.models.mlp.modelings\_mlp.MLPForClassification
+=====================================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_mlp
+
+.. autoclass:: MLPForClassification
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~MLPForClassification.__init__
+      ~MLPForClassification.active_adapter
+      ~MLPForClassification.active_adapters
+      ~MLPForClassification.add_adapter
+      ~MLPForClassification.add_memory_hooks
+      ~MLPForClassification.add_model_tags
+      ~MLPForClassification.add_module
+      ~MLPForClassification.apply
+      ~MLPForClassification.bfloat16
+      ~MLPForClassification.buffers
+      ~MLPForClassification.can_generate
+      ~MLPForClassification.children
+      ~MLPForClassification.compile
+      ~MLPForClassification.compute_transition_scores
+      ~MLPForClassification.cpu
+      ~MLPForClassification.create_extended_attention_mask_for_decoder
+      ~MLPForClassification.cuda
+      ~MLPForClassification.delete_adapter
+      ~MLPForClassification.dequantize
+      ~MLPForClassification.disable_adapters
+      ~MLPForClassification.disable_input_require_grads
+      ~MLPForClassification.double
+      ~MLPForClassification.enable_adapters
+      ~MLPForClassification.enable_input_require_grads
+      ~MLPForClassification.estimate_tokens
+      ~MLPForClassification.eval
+      ~MLPForClassification.extra_repr
+      ~MLPForClassification.float
+      ~MLPForClassification.floating_point_ops
+      ~MLPForClassification.forward
+      ~MLPForClassification.from_pretrained
+      ~MLPForClassification.generate
+      ~MLPForClassification.get_adapter_state_dict
+      ~MLPForClassification.get_buffer
+      ~MLPForClassification.get_compiled_call
+      ~MLPForClassification.get_extended_attention_mask
+      ~MLPForClassification.get_extra_state
+      ~MLPForClassification.get_head_mask
+      ~MLPForClassification.get_input_embeddings
+      ~MLPForClassification.get_memory_footprint
+      ~MLPForClassification.get_output_embeddings
+      ~MLPForClassification.get_parameter
+      ~MLPForClassification.get_position_embeddings
+      ~MLPForClassification.get_submodule
+      ~MLPForClassification.gradient_checkpointing_disable
+      ~MLPForClassification.gradient_checkpointing_enable
+      ~MLPForClassification.half
+      ~MLPForClassification.heal_tokens
+      ~MLPForClassification.init_weights
+      ~MLPForClassification.invert_attention_mask
+      ~MLPForClassification.ipu
+      ~MLPForClassification.load_adapter
+      ~MLPForClassification.load_state_dict
+      ~MLPForClassification.modules
+      ~MLPForClassification.mtia
+      ~MLPForClassification.named_buffers
+      ~MLPForClassification.named_children
+      ~MLPForClassification.named_modules
+      ~MLPForClassification.named_parameters
+      ~MLPForClassification.num_parameters
+      ~MLPForClassification.parameters
+      ~MLPForClassification.post_init
+      ~MLPForClassification.prepare_inputs_for_generation
+      ~MLPForClassification.prune_heads
+      ~MLPForClassification.push_to_hub
+      ~MLPForClassification.register_backward_hook
+      ~MLPForClassification.register_buffer
+      ~MLPForClassification.register_for_auto_class
+      ~MLPForClassification.register_forward_hook
+      ~MLPForClassification.register_forward_pre_hook
+      ~MLPForClassification.register_full_backward_hook
+      ~MLPForClassification.register_full_backward_pre_hook
+      ~MLPForClassification.register_load_state_dict_post_hook
+      ~MLPForClassification.register_load_state_dict_pre_hook
+      ~MLPForClassification.register_module
+      ~MLPForClassification.register_parameter
+      ~MLPForClassification.register_state_dict_post_hook
+      ~MLPForClassification.register_state_dict_pre_hook
+      ~MLPForClassification.requires_grad_
+      ~MLPForClassification.reset_memory_hooks_state
+      ~MLPForClassification.resize_position_embeddings
+      ~MLPForClassification.resize_token_embeddings
+      ~MLPForClassification.retrieve_modules_from_names
+      ~MLPForClassification.reverse_bettertransformer
+      ~MLPForClassification.save_pretrained
+      ~MLPForClassification.set_adapter
+      ~MLPForClassification.set_extra_state
+      ~MLPForClassification.set_input_embeddings
+      ~MLPForClassification.set_submodule
+      ~MLPForClassification.share_memory
+      ~MLPForClassification.state_dict
+      ~MLPForClassification.tensor_parallel
+      ~MLPForClassification.tie_weights
+      ~MLPForClassification.to
+      ~MLPForClassification.to_bettertransformer
+      ~MLPForClassification.to_empty
+      ~MLPForClassification.train
+      ~MLPForClassification.type
+      ~MLPForClassification.warn_if_padding_and_no_attention_mask
+      ~MLPForClassification.xpu
+      ~MLPForClassification.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~MLPForClassification.T_destination
+      ~MLPForClassification.base_model
+      ~MLPForClassification.base_model_prefix
+      ~MLPForClassification.call_super_init
+      ~MLPForClassification.config_class
+      ~MLPForClassification.device
+      ~MLPForClassification.dtype
+      ~MLPForClassification.dummy_inputs
+      ~MLPForClassification.dump_patches
+      ~MLPForClassification.framework
+      ~MLPForClassification.is_gradient_checkpointing
+      ~MLPForClassification.is_parallelizable
+      ~MLPForClassification.loss_function
+      ~MLPForClassification.main_input_name
+      ~MLPForClassification.model_tags
+      ~MLPForClassification.supports_gradient_checkpointing
+      ~MLPForClassification.supports_tp_plan
+      ~MLPForClassification.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_mlp.MLPModel.rst b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPModel.rst
new file mode 100644
index 00000000..44a2c180
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPModel.rst
@@ -0,0 +1,147 @@
+pyvene.models.mlp.modelings\_mlp.MLPModel
+=========================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_mlp
+
+.. autoclass:: MLPModel
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~MLPModel.__init__
+      ~MLPModel.active_adapter
+      ~MLPModel.active_adapters
+      ~MLPModel.add_adapter
+      ~MLPModel.add_memory_hooks
+      ~MLPModel.add_model_tags
+      ~MLPModel.add_module
+      ~MLPModel.apply
+      ~MLPModel.bfloat16
+      ~MLPModel.buffers
+      ~MLPModel.can_generate
+      ~MLPModel.children
+      ~MLPModel.compile
+      ~MLPModel.compute_transition_scores
+      ~MLPModel.cpu
+      ~MLPModel.create_extended_attention_mask_for_decoder
+      ~MLPModel.cuda
+      ~MLPModel.delete_adapter
+      ~MLPModel.dequantize
+      ~MLPModel.disable_adapters
+      ~MLPModel.disable_input_require_grads
+      ~MLPModel.double
+      ~MLPModel.enable_adapters
+      ~MLPModel.enable_input_require_grads
+      ~MLPModel.estimate_tokens
+      ~MLPModel.eval
+      ~MLPModel.extra_repr
+      ~MLPModel.float
+      ~MLPModel.floating_point_ops
+      ~MLPModel.forward
+      ~MLPModel.from_pretrained
+      ~MLPModel.generate
+      ~MLPModel.get_adapter_state_dict
+      ~MLPModel.get_buffer
+      ~MLPModel.get_compiled_call
+      ~MLPModel.get_extended_attention_mask
+      ~MLPModel.get_extra_state
+      ~MLPModel.get_head_mask
+      ~MLPModel.get_input_embeddings
+      ~MLPModel.get_memory_footprint
+      ~MLPModel.get_output_embeddings
+      ~MLPModel.get_parameter
+      ~MLPModel.get_position_embeddings
+      ~MLPModel.get_submodule
+      ~MLPModel.gradient_checkpointing_disable
+      ~MLPModel.gradient_checkpointing_enable
+      ~MLPModel.half
+      ~MLPModel.heal_tokens
+      ~MLPModel.init_weights
+      ~MLPModel.invert_attention_mask
+      ~MLPModel.ipu
+      ~MLPModel.load_adapter
+      ~MLPModel.load_state_dict
+      ~MLPModel.modules
+      ~MLPModel.mtia
+      ~MLPModel.named_buffers
+      ~MLPModel.named_children
+      ~MLPModel.named_modules
+      ~MLPModel.named_parameters
+      ~MLPModel.num_parameters
+      ~MLPModel.parameters
+      ~MLPModel.post_init
+      ~MLPModel.prepare_inputs_for_generation
+      ~MLPModel.prune_heads
+      ~MLPModel.push_to_hub
+      ~MLPModel.register_backward_hook
+      ~MLPModel.register_buffer
+      ~MLPModel.register_for_auto_class
+      ~MLPModel.register_forward_hook
+      ~MLPModel.register_forward_pre_hook
+      ~MLPModel.register_full_backward_hook
+      ~MLPModel.register_full_backward_pre_hook
+      ~MLPModel.register_load_state_dict_post_hook
+      ~MLPModel.register_load_state_dict_pre_hook
+      ~MLPModel.register_module
+      ~MLPModel.register_parameter
+      ~MLPModel.register_state_dict_post_hook
+      ~MLPModel.register_state_dict_pre_hook
+      ~MLPModel.requires_grad_
+      ~MLPModel.reset_memory_hooks_state
+      ~MLPModel.resize_position_embeddings
+      ~MLPModel.resize_token_embeddings
+      ~MLPModel.retrieve_modules_from_names
+      ~MLPModel.reverse_bettertransformer
+      ~MLPModel.save_pretrained
+      ~MLPModel.set_adapter
+      ~MLPModel.set_extra_state
+      ~MLPModel.set_input_embeddings
+      ~MLPModel.set_submodule
+      ~MLPModel.share_memory
+      ~MLPModel.state_dict
+      ~MLPModel.tensor_parallel
+      ~MLPModel.tie_weights
+      ~MLPModel.to
+      ~MLPModel.to_bettertransformer
+      ~MLPModel.to_empty
+      ~MLPModel.train
+      ~MLPModel.type
+      ~MLPModel.warn_if_padding_and_no_attention_mask
+      ~MLPModel.xpu
+      ~MLPModel.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~MLPModel.T_destination
+      ~MLPModel.base_model
+      ~MLPModel.base_model_prefix
+      ~MLPModel.call_super_init
+      ~MLPModel.config_class
+      ~MLPModel.device
+      ~MLPModel.dtype
+      ~MLPModel.dummy_inputs
+      ~MLPModel.dump_patches
+      ~MLPModel.framework
+      ~MLPModel.is_gradient_checkpointing
+      ~MLPModel.is_parallelizable
+      ~MLPModel.loss_function
+      ~MLPModel.main_input_name
+      ~MLPModel.model_tags
+      ~MLPModel.supports_gradient_checkpointing
+      ~MLPModel.supports_tp_plan
+      ~MLPModel.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_mlp.MLPModelOutput.rst b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPModelOutput.rst
new file mode 100644
index 00000000..69fc28f5
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_mlp.MLPModelOutput.rst
@@ -0,0 +1,44 @@
+pyvene.models.mlp.modelings\_mlp.MLPModelOutput
+===============================================
+
+.. currentmodule:: pyvene.models.mlp.modelings_mlp
+
+.. autoclass:: MLPModelOutput
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~MLPModelOutput.__init__
+      ~MLPModelOutput.clear
+      ~MLPModelOutput.copy
+      ~MLPModelOutput.fromkeys
+      ~MLPModelOutput.get
+      ~MLPModelOutput.items
+      ~MLPModelOutput.keys
+      ~MLPModelOutput.move_to_end
+      ~MLPModelOutput.pop
+      ~MLPModelOutput.popitem
+      ~MLPModelOutput.setdefault
+      ~MLPModelOutput.to_tuple
+      ~MLPModelOutput.update
+      ~MLPModelOutput.values
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~MLPModelOutput.hidden_states
+      ~MLPModelOutput.last_hidden_state
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.mlp.modelings_mlp.rst b/_sources/api/pyvene.models.mlp.modelings_mlp.rst
new file mode 100644
index 00000000..7ae3b329
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.modelings_mlp.rst
@@ -0,0 +1,35 @@
+pyvene.models.mlp.modelings\_mlp
+================================
+
+.. automodule:: pyvene.models.mlp.modelings_mlp
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      MLPBlock
+      MLPConfig
+      MLPForClassification
+      MLPModel
+      MLPModelOutput
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.mlp.rst b/_sources/api/pyvene.models.mlp.rst
new file mode 100644
index 00000000..5f2b0eb5
--- /dev/null
+++ b/_sources/api/pyvene.models.mlp.rst
@@ -0,0 +1,33 @@
+pyvene.models.mlp
+=================
+
+.. automodule:: pyvene.models.mlp
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.mlp.modelings_intervenable_mlp
+   pyvene.models.mlp.modelings_mlp
+
diff --git a/_sources/api/pyvene.models.modeling_utils.HandlerList.rst b/_sources/api/pyvene.models.modeling_utils.HandlerList.rst
new file mode 100644
index 00000000..9b5dfaad
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.HandlerList.rst
@@ -0,0 +1,26 @@
+pyvene.models.modeling\_utils.HandlerList
+=========================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autoclass:: HandlerList
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~HandlerList.__init__
+      ~HandlerList.extend
+      ~HandlerList.remove
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.LambdaIntervention.rst b/_sources/api/pyvene.models.modeling_utils.LambdaIntervention.rst
new file mode 100644
index 00000000..4c322955
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.LambdaIntervention.rst
@@ -0,0 +1,83 @@
+pyvene.models.modeling\_utils.LambdaIntervention
+================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autoclass:: LambdaIntervention
+   :members:                                      
+   :show-inheritance:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~LambdaIntervention.__init__
+      ~LambdaIntervention.add_module
+      ~LambdaIntervention.apply
+      ~LambdaIntervention.bfloat16
+      ~LambdaIntervention.buffers
+      ~LambdaIntervention.children
+      ~LambdaIntervention.compile
+      ~LambdaIntervention.cpu
+      ~LambdaIntervention.cuda
+      ~LambdaIntervention.double
+      ~LambdaIntervention.eval
+      ~LambdaIntervention.extra_repr
+      ~LambdaIntervention.float
+      ~LambdaIntervention.forward
+      ~LambdaIntervention.get_buffer
+      ~LambdaIntervention.get_extra_state
+      ~LambdaIntervention.get_parameter
+      ~LambdaIntervention.get_submodule
+      ~LambdaIntervention.half
+      ~LambdaIntervention.ipu
+      ~LambdaIntervention.load_state_dict
+      ~LambdaIntervention.modules
+      ~LambdaIntervention.mtia
+      ~LambdaIntervention.named_buffers
+      ~LambdaIntervention.named_children
+      ~LambdaIntervention.named_modules
+      ~LambdaIntervention.named_parameters
+      ~LambdaIntervention.parameters
+      ~LambdaIntervention.register_backward_hook
+      ~LambdaIntervention.register_buffer
+      ~LambdaIntervention.register_forward_hook
+      ~LambdaIntervention.register_forward_pre_hook
+      ~LambdaIntervention.register_full_backward_hook
+      ~LambdaIntervention.register_full_backward_pre_hook
+      ~LambdaIntervention.register_load_state_dict_post_hook
+      ~LambdaIntervention.register_load_state_dict_pre_hook
+      ~LambdaIntervention.register_module
+      ~LambdaIntervention.register_parameter
+      ~LambdaIntervention.register_state_dict_post_hook
+      ~LambdaIntervention.register_state_dict_pre_hook
+      ~LambdaIntervention.requires_grad_
+      ~LambdaIntervention.set_extra_state
+      ~LambdaIntervention.set_submodule
+      ~LambdaIntervention.share_memory
+      ~LambdaIntervention.state_dict
+      ~LambdaIntervention.to
+      ~LambdaIntervention.to_empty
+      ~LambdaIntervention.train
+      ~LambdaIntervention.type
+      ~LambdaIntervention.xpu
+      ~LambdaIntervention.zero_grad
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~LambdaIntervention.T_destination
+      ~LambdaIntervention.call_super_init
+      ~LambdaIntervention.dump_patches
+      ~LambdaIntervention.training
+   
+   
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.b_sd_to_bsd.rst b/_sources/api/pyvene.models.modeling_utils.b_sd_to_bsd.rst
new file mode 100644
index 00000000..381e5813
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.b_sd_to_bsd.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.b\_sd\_to\_bsd
+============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: b_sd_to_bsd
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.bhsd_to_bs_hd.rst b/_sources/api/pyvene.models.modeling_utils.bhsd_to_bs_hd.rst
new file mode 100644
index 00000000..e95950f5
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.bhsd_to_bs_hd.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.bhsd\_to\_bs\_hd
+==============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: bhsd_to_bs_hd
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.bs_hd_to_bhsd.rst b/_sources/api/pyvene.models.modeling_utils.bs_hd_to_bhsd.rst
new file mode 100644
index 00000000..d49d1b54
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.bs_hd_to_bhsd.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.bs\_hd\_to\_bhsd
+==============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: bs_hd_to_bhsd
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.bsd_to_b_sd.rst b/_sources/api/pyvene.models.modeling_utils.bsd_to_b_sd.rst
new file mode 100644
index 00000000..b64c1346
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.bsd_to_b_sd.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.bsd\_to\_b\_sd
+============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: bsd_to_b_sd
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.do_intervention.rst b/_sources/api/pyvene.models.modeling_utils.do_intervention.rst
new file mode 100644
index 00000000..4230a427
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.do_intervention.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.do\_intervention
+==============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: do_intervention
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.gather_neurons.rst b/_sources/api/pyvene.models.modeling_utils.gather_neurons.rst
new file mode 100644
index 00000000..e826933e
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.gather_neurons.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.gather\_neurons
+=============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: gather_neurons
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.get_dimension_by_component.rst b/_sources/api/pyvene.models.modeling_utils.get_dimension_by_component.rst
new file mode 100644
index 00000000..589f4e27
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.get_dimension_by_component.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.get\_dimension\_by\_component
+===========================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: get_dimension_by_component
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.get_internal_model_type.rst b/_sources/api/pyvene.models.modeling_utils.get_internal_model_type.rst
new file mode 100644
index 00000000..3f7b6103
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.get_internal_model_type.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.get\_internal\_model\_type
+========================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: get_internal_model_type
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.get_module_hook.rst b/_sources/api/pyvene.models.modeling_utils.get_module_hook.rst
new file mode 100644
index 00000000..7bba8997
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.get_module_hook.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.get\_module\_hook
+===============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: get_module_hook
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.getattr_for_torch_module.rst b/_sources/api/pyvene.models.modeling_utils.getattr_for_torch_module.rst
new file mode 100644
index 00000000..5bc3404a
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.getattr_for_torch_module.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.getattr\_for\_torch\_module
+=========================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: getattr_for_torch_module
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.is_gru.rst b/_sources/api/pyvene.models.modeling_utils.is_gru.rst
new file mode 100644
index 00000000..0a076aac
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.is_gru.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.is\_gru
+=====================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: is_gru
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.is_mlp.rst b/_sources/api/pyvene.models.modeling_utils.is_mlp.rst
new file mode 100644
index 00000000..f4696965
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.is_mlp.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.is\_mlp
+=====================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: is_mlp
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.is_stateless.rst b/_sources/api/pyvene.models.modeling_utils.is_stateless.rst
new file mode 100644
index 00000000..8c1bf093
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.is_stateless.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.is\_stateless
+===========================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: is_stateless
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.is_transformer.rst b/_sources/api/pyvene.models.modeling_utils.is_transformer.rst
new file mode 100644
index 00000000..3594bddd
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.is_transformer.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.is\_transformer
+=============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: is_transformer
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.output_to_subcomponent.rst b/_sources/api/pyvene.models.modeling_utils.output_to_subcomponent.rst
new file mode 100644
index 00000000..f4da4a65
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.output_to_subcomponent.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.output\_to\_subcomponent
+======================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: output_to_subcomponent
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.print_forward_hooks.rst b/_sources/api/pyvene.models.modeling_utils.print_forward_hooks.rst
new file mode 100644
index 00000000..3f8aa51d
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.print_forward_hooks.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.print\_forward\_hooks
+===================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: print_forward_hooks
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.remove_forward_hooks.rst b/_sources/api/pyvene.models.modeling_utils.remove_forward_hooks.rst
new file mode 100644
index 00000000..8f969878
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.remove_forward_hooks.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.remove\_forward\_hooks
+====================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: remove_forward_hooks
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.rst b/_sources/api/pyvene.models.modeling_utils.rst
new file mode 100644
index 00000000..488b813c
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.rst
@@ -0,0 +1,59 @@
+pyvene.models.modeling\_utils
+=============================
+
+.. automodule:: pyvene.models.modeling_utils
+  
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      b_sd_to_bsd
+      bhsd_to_bs_hd
+      bs_hd_to_bhsd
+      bsd_to_b_sd
+      do_intervention
+      gather_neurons
+      get_dimension_by_component
+      get_internal_model_type
+      get_module_hook
+      getattr_for_torch_module
+      is_gru
+      is_mlp
+      is_stateless
+      is_transformer
+      output_to_subcomponent
+      print_forward_hooks
+      remove_forward_hooks
+      scatter_neurons
+      simple_output_to_subcomponent
+      simple_scatter_intervention_output
+      weighted_average
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:                                            
+      :template: pv-class.rst                 
+   
+      HandlerList
+      LambdaIntervention
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.modeling_utils.scatter_neurons.rst b/_sources/api/pyvene.models.modeling_utils.scatter_neurons.rst
new file mode 100644
index 00000000..ad2144e2
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.scatter_neurons.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.scatter\_neurons
+==============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: scatter_neurons
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.simple_output_to_subcomponent.rst b/_sources/api/pyvene.models.modeling_utils.simple_output_to_subcomponent.rst
new file mode 100644
index 00000000..1af8cbb0
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.simple_output_to_subcomponent.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.simple\_output\_to\_subcomponent
+==============================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: simple_output_to_subcomponent
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.simple_scatter_intervention_output.rst b/_sources/api/pyvene.models.modeling_utils.simple_scatter_intervention_output.rst
new file mode 100644
index 00000000..99f7c338
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.simple_scatter_intervention_output.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.simple\_scatter\_intervention\_output
+===================================================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: simple_scatter_intervention_output
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.modeling_utils.weighted_average.rst b/_sources/api/pyvene.models.modeling_utils.weighted_average.rst
new file mode 100644
index 00000000..5835aa4c
--- /dev/null
+++ b/_sources/api/pyvene.models.modeling_utils.weighted_average.rst
@@ -0,0 +1,6 @@
+pyvene.models.modeling\_utils.weighted\_average
+===============================================
+
+.. currentmodule:: pyvene.models.modeling_utils
+
+.. autofunction:: weighted_average
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.create_olmo.rst b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.create_olmo.rst
new file mode 100644
index 00000000..8c369267
--- /dev/null
+++ b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.create_olmo.rst
@@ -0,0 +1,6 @@
+pyvene.models.olmo.modelings\_intervenable\_olmo.create\_olmo
+=============================================================
+
+.. currentmodule:: pyvene.models.olmo.modelings_intervenable_olmo
+
+.. autofunction:: create_olmo
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..350877fb
--- /dev/null
+++ b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.olmo.modelings\_intervenable\_olmo.olmo\_lm\_type\_to\_dimension\_mapping
+=======================================================================================
+
+.. currentmodule:: pyvene.models.olmo.modelings_intervenable_olmo
+
+.. autodata:: olmo_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..2750f2e9
--- /dev/null
+++ b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.olmo.modelings\_intervenable\_olmo.olmo\_type\_to\_dimension\_mapping
+===================================================================================
+
+.. currentmodule:: pyvene.models.olmo.modelings_intervenable_olmo
+
+.. autodata:: olmo_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.rst b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.rst
new file mode 100644
index 00000000..169094a1
--- /dev/null
+++ b/_sources/api/pyvene.models.olmo.modelings_intervenable_olmo.rst
@@ -0,0 +1,38 @@
+pyvene.models.olmo.modelings\_intervenable\_olmo
+================================================
+
+.. automodule:: pyvene.models.olmo.modelings_intervenable_olmo
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      olmo_type_to_dimension_mapping
+      olmo_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_olmo
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.olmo.rst b/_sources/api/pyvene.models.olmo.rst
new file mode 100644
index 00000000..082db4cb
--- /dev/null
+++ b/_sources/api/pyvene.models.olmo.rst
@@ -0,0 +1,32 @@
+pyvene.models.olmo
+==================
+
+.. automodule:: pyvene.models.olmo
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.olmo.modelings_intervenable_olmo
+
diff --git a/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2.rst b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2.rst
new file mode 100644
index 00000000..2fc1c082
--- /dev/null
+++ b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2.rst
@@ -0,0 +1,6 @@
+pyvene.models.qwen2.modelings\_intervenable\_qwen2.create\_qwen2
+================================================================
+
+.. currentmodule:: pyvene.models.qwen2.modelings_intervenable_qwen2
+
+.. autofunction:: create_qwen2
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..d36dde92
--- /dev/null
+++ b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.qwen2.modelings\_intervenable\_qwen2.qwen2\_lm\_type\_to\_dimension\_mapping
+==========================================================================================
+
+.. currentmodule:: pyvene.models.qwen2.modelings_intervenable_qwen2
+
+.. autodata:: qwen2_lm_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping.rst b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping.rst
new file mode 100644
index 00000000..977b6dec
--- /dev/null
+++ b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping.rst
@@ -0,0 +1,6 @@
+pyvene.models.qwen2.modelings\_intervenable\_qwen2.qwen2\_type\_to\_dimension\_mapping
+======================================================================================
+
+.. currentmodule:: pyvene.models.qwen2.modelings_intervenable_qwen2
+
+.. autodata:: qwen2_type_to_dimension_mapping
\ No newline at end of file
diff --git a/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.rst b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.rst
new file mode 100644
index 00000000..0146e104
--- /dev/null
+++ b/_sources/api/pyvene.models.qwen2.modelings_intervenable_qwen2.rst
@@ -0,0 +1,38 @@
+pyvene.models.qwen2.modelings\_intervenable\_qwen2
+==================================================
+
+.. automodule:: pyvene.models.qwen2.modelings_intervenable_qwen2
+  
+   
+   
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:                                            
+   
+      qwen2_type_to_dimension_mapping
+      qwen2_lm_type_to_dimension_mapping
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:                                            
+   
+      create_qwen2
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/api/pyvene.models.qwen2.rst b/_sources/api/pyvene.models.qwen2.rst
new file mode 100644
index 00000000..4c908f38
--- /dev/null
+++ b/_sources/api/pyvene.models.qwen2.rst
@@ -0,0 +1,32 @@
+pyvene.models.qwen2
+===================
+
+.. automodule:: pyvene.models.qwen2
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.qwen2.modelings_intervenable_qwen2
+
diff --git a/_sources/api/pyvene.models.rst b/_sources/api/pyvene.models.rst
new file mode 100644
index 00000000..a25a4859
--- /dev/null
+++ b/_sources/api/pyvene.models.rst
@@ -0,0 +1,54 @@
+pyvene.models
+=============
+
+.. automodule:: pyvene.models
+  
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: pv-module.rst                   
+   :recursive:
+
+   pyvene.models.backpack_gpt2
+   pyvene.models.basic_utils
+   pyvene.models.blip
+   pyvene.models.configuration_intervenable_model
+   pyvene.models.constants
+   pyvene.models.gemma
+   pyvene.models.gemma2
+   pyvene.models.gpt2
+   pyvene.models.gpt_neo
+   pyvene.models.gpt_neox
+   pyvene.models.gru
+   pyvene.models.intervenable_base
+   pyvene.models.intervenable_modelcard
+   pyvene.models.intervention_utils
+   pyvene.models.interventions
+   pyvene.models.layers
+   pyvene.models.llama
+   pyvene.models.llava
+   pyvene.models.mistral
+   pyvene.models.mlp
+   pyvene.models.modeling_utils
+   pyvene.models.olmo
+   pyvene.models.qwen2
+
diff --git a/_sources/guides/causal_abstraction.rst b/_sources/guides/causal_abstraction.rst
new file mode 100644
index 00000000..aebfcec6
--- /dev/null
+++ b/_sources/guides/causal_abstraction.rst
@@ -0,0 +1,117 @@
+A Little Guide to Causal Abstraction
+====================================
+
+*From Interventions to Gaining Interpretability Insights*
+
+:author: Zhengxuan Wu
+
+Basic interventions are fun but we cannot make any causal claim
+systematically. To gain actual interpretability insights, we want to
+measure the counterfactual behaviors of a model in a data-driven
+fashion. In other words, if the model responds systematically to your
+interventions, then you start to associate certain regions in the
+network with a high-level concept. We also call this alignment search
+process with model internals.
+
+Understanding Causal Mechanisms with Static Interventions
+---------------------------------------------------------
+
+Here is a more concrete example,
+
+.. code:: python
+
+   def add_three_numbers(a, b, c):
+       var_x = a + b
+       return var_x + c
+
+The function solves a 3-digit sum problem. Let's say, we trained a
+neural network to solve this problem perfectly. "Can we find the
+representation of (a + b) in the neural network?". We can use this
+library to answer this question. Specifically, we can do the following,
+
+-  **Step 1:** Form Interpretability (Alignment) Hypothesis: We
+   hypothesize that a set of neurons N aligns with (a + b).
+-  **Step 2:** Counterfactual Testings: If our hypothesis is correct,
+   then swapping neurons N between examples would give us expected
+   counterfactual behaviors. For instance, the values of N for (1+2)+3,
+   when swapping with N for (2+3)+4, the output should be (2+3)+3 or
+   (1+2)+4 depending on the direction of the swap.
+-  **Step 3:** Reject Sampling of Hypothesis: Running tests multiple
+   times and aggregating statistics in terms of counterfactual behavior
+   matching. Proposing a new hypothesis based on the results.
+
+To translate the above steps into API calls with the library, it will be
+a single call,
+
+.. code:: python
+
+   intervenable.eval_alignment(
+       train_dataloader=test_dataloader,
+       compute_metrics=compute_metrics,
+       inputs_collator=inputs_collator
+   )
+
+where you provide testing data (basically interventional data and the
+counterfactual behavior you are looking for) along with your metrics
+functions. The library will try to evaluate the alignment with the
+intervention you specified in the config.
+
+Understanding Causal Mechanism with Trainable Interventions
+-----------------------------------------------------------
+
+The alignment searching process outlined above can be tedious when your
+neural network is large. For a single hypothesized alignment, you
+basically need to set up different intervention configs targeting
+different layers and positions to verify your hypothesis. Instead of
+doing this brute-force search process, you can turn it into an
+optimization problem which also has other benefits such as distributed
+alignments.
+
+In its crux, we basically want to train an intervention to have our
+desired counterfactual behaviors in mind. And if we can indeed train
+such interventions, we claim that causally informative information
+should live in the intervening representations! Below, we show one type
+of trainable intervention :class:`RotatedSpaceIntervention <pyvene.models.interventions.RotatedSpaceIntervention>`
+as,
+
+.. code:: python
+
+   class RotatedSpaceIntervention(TrainableIntervention):
+       
+       """Intervention in the rotated space."""
+       def forward(self, base, source):
+           rotated_base = self.rotate_layer(base)
+           rotated_source = self.rotate_layer(source)
+           # interchange
+           rotated_base[:self.interchange_dim] = rotated_source[:self.interchange_dim]
+           # inverse base
+           output = torch.matmul(rotated_base, self.rotate_layer.weight.T)
+           return output
+
+Instead of activation swapping in the original representation space, we
+first **rotate** them, and then do the swap followed by un-rotating the
+intervened representation. Additionally, we try to use SGD to **learn a
+rotation** that lets us produce expected counterfactual behavior. If we
+can find such rotation, we claim there is an alignment.
+``If the cost is between X and Y.ipynb`` tutorial covers this with an
+advanced version of distributed alignment search, `Boundless
+DAS <https://arxiv.org/abs/2305.08809>`__. There are `recent
+works <https://www.lesswrong.com/posts/RFtkRXHebkwxygDe2/an-interpretability-illusion-for-activation-patching-of>`__
+outlining potential limitations of doing a distributed alignment search
+as well.
+
+You can now also make a single API call to train your intervention,
+
+.. code:: python
+
+   intervenable.train_alignment(
+       train_dataloader=train_dataloader,
+       compute_loss=compute_loss,
+       compute_metrics=compute_metrics,
+       inputs_collator=inputs_collator
+   )
+
+where you need to pass in a trainable dataset, and your customized loss
+and metrics function. The trainable interventions can later be saved on
+to your disk. You can also use :class:`intervenable.evaluate() <pyvene.models.intervenable_base.IntervenableModel>` your
+interventions in terms of customized objectives.
\ No newline at end of file
diff --git a/_sources/guides/contributing.md b/_sources/guides/contributing.md
new file mode 100644
index 00000000..1a381114
--- /dev/null
+++ b/_sources/guides/contributing.md
@@ -0,0 +1,73 @@
+# Contributing Guidelines
+
+*Pull requests, bug reports, and all other forms of contribution are welcomed and highly encouraged!* :octocat:
+
+### The PR or Issue Title Format
+Whenever you open an issue or a PR, please use this title format
+```
+[Priority Tag] Short Title
+```
+For Priority Tag, you can use `[P0]`-`[P2]`, `[P0]` is the highest priority, which means everyone should stop working and focus on this PR. For Minor issues, use `[Minor]`. For bugs, please use `[Bug Fix]` and see below.
+
+---
+
+### 📕 Pull Requests
+
+#### Uninstall pyvene from python library
+It becomes tricky if you have `pyvene` installed while debugging with this codebase, since imports can be easily messed up. Please run,
+```bash
+pip uninstall pyvene
+```
+
+#### Unit Test Run Is A Must before Creating PRs
+When adding new methods or APIs, unit tests are now enforced. To run existing tests, you can kick off the python unittest command in the discovery mode as,
+```bash
+cd pyvene
+python -m unittest discover -p '*TestCase.py'
+```
+For specific test case, yoou can run
+```bash
+cd pyvene
+python -m unittest tests.integration_tests.ComplexInterventionWithGPT2TestCase
+```
+When checking in new code, please also consider to add new tests in the same PR. Please include test results in the PR to make sure all the existing test cases are passing. Please see the `qa_runbook.ipynb` notebook about a set of conventions about how to add test cases. The code coverage for this repository is currently `low`, and we are adding more automated tests.
+
+#### Format
+```
+**Descriptions**:
+
+[Describe your PR Here]
+
+
+**Testing Done**:
+
+[Provide logs, screen-shots, and files that contain tests you have done]
+
+```
+
+### 🪲 Bug Reports and Other Issues
+Go to issues, and open with a title formatted as,
+```
+[Bug Fix] Short Title
+```
+For external requests (i.e., you are not in our core dev team), please use,
+```
+[External] Short Title
+```
+
+### 📄 Documentation
+If making changes to documentation (in `docs/source`, deployed to GitHub Pages), please test your changes locally
+(ideally in a fresh Python environment):
+
+```
+pip install -r requirements.txt
+pip install -r docs/requirements.txt
+cd docs
+make html
+python -m http.server
+```
+
+Then navigate to [localhost:8000/build/html](http://localhost:8000/build/html).
+
+### 📥 Larger Feature Requests
+Please email us!
\ No newline at end of file
diff --git a/_sources/guides/ndif.rst b/_sources/guides/ndif.rst
new file mode 100644
index 00000000..1adae2bc
--- /dev/null
+++ b/_sources/guides/ndif.rst
@@ -0,0 +1,33 @@
+NDIF Integration
+================
+
+We are working with the `NDIF <https://ndif.us/>`_ team to support remote intervention calls
+without asking the users to download or host their own LLMs! This is still under construction.
+
+First of all, you need to install ``nnsight``:
+
+::
+
+   pip install nnsight
+
+All you have to do is to use NDIF library to load your model and use pyvene to wrap it
+(i.e., pyvene will automatically recognize NDIF models)! Here is an example:
+
+::
+
+   from nnsight import LanguageModel
+   
+   # load nnsight.LanguageModel as your model to wrap with pyvene
+   gpt2_ndif = LanguageModel('openai-community/gpt2', device_map='cpu')
+   tokenizer = AutoTokenizer.from_pretrained('openai-community/gpt2')
+
+   # pyvene provides pv.build_intervenable_model as the generic model builder
+   pv_gpt2_ndif = pv.build_intervenable_model({
+      "component": "transformer.h[10].attn.attn_dropout.input",
+      "intervention": pv.CollectIntervention()}, model=gpt2_ndif, remote=False)
+
+
+Then, you can use ``pv_gpt2_ndif`` as your regular intervenable model.
+If you specify ``remote=True`` (this is still under construction), then
+everything will be executed remotely on NDIF server with **zero** GPU
+resource required! We provide example code in our `main tutorial <tutorials/pyvene_101>`__.
\ No newline at end of file
diff --git a/_sources/index.rst b/_sources/index.rst
new file mode 100644
index 00000000..ad21a1c4
--- /dev/null
+++ b/_sources/index.rst
@@ -0,0 +1,418 @@
+.. pyvene documentation master file, created by
+   sphinx-quickstart on Fri Jul 12 16:49:16 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+.. container::
+
+   .. rubric:: |image1|
+      :name: section
+
+   `Read Our Paper » <https://arxiv.org/abs/2403.07809>`__
+
+
+pyvene
+======
+
+|image2| |image3| |image4|
+
+.. |image1| image:: https://i.ibb.co/BNkhQH3/pyvene-logo.png
+.. |image2| image:: https://img.shields.io/pepy/dt/pyvene?color=green
+   :target: https://pypi.org/project/pyvene/
+.. |image3| image:: https://img.shields.io/pypi/v/pyvene?color=red
+   :target: https://pypi.org/project/pyvene/
+.. |image4| image:: https://img.shields.io/pypi/l/pyvene?color=blue
+   :target: https://pypi.org/project/pyvene/
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+**pyvene** is an open-source Python library for intervening on the internal states of
+PyTorch models. Interventions are an important operation in many areas of AI, including
+model editing, steering, robustness, and interpretability.
+
+pyvene has many features that make interventions easy:
+
+* Interventions are the basic primitive, specified as dicts and thus able to be saved locally
+  and shared as serialisable objects through HuggingFace.
+* Interventions can be composed and customised: you can run them on multiple locations, on arbitrary
+  sets of neurons (or other levels of granularity), in parallel or in sequence, on decoding steps of
+  generative language models, etc.
+* Interventions work out-of-the-box on any PyTorch model! No need to define new model classes from
+  scratch and easy interventions are possible all kinds of architectures (RNNs, ResNets, CNNs, Mamba).
+
+pyvene is under active development and constantly being improved 🫡
+
+
+Getting Started
+---------------
+
+To install the latest stable version of pyvene:
+
+::
+   
+   pip install pyvene
+
+Alternatively, to install a bleeding-edge version, you can clone the repo and install:
+
+::
+   
+   git clone git@github.com:stanfordnlp/pyvene.git
+   cd pyvene
+   pip install -e .
+
+When you want to update, you can just run ``git pull`` in the cloned directory.
+
+We suggest importing the library as:
+
+.. code:: python
+
+   import pyvene as pv
+
+
+*Wrap* and *intervene*
+----------------------
+
+The usual workflow for using pyvene is to load a model, define an intervention config and wrap the model,
+and then run the intervened model. This returns both the original and intervened outputs, as well as any
+internal activations you specified to collect. For example:
+
+.. code:: python
+
+   import torch
+   import pyvene as pv
+   from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+   # 1. Load the model
+   model_name = "meta-llama/Llama-2-7b-hf" # the HF model you want to intervene on
+   tokenizer = AutoTokenizer.from_pretrained(model_name)
+   model = AutoModelForCausalLM.from_pretrained(
+      model_name, torch_dtype=torch.bfloat16, device_map="cuda")
+
+
+   # 2. Wrap the model with an intervention config
+   pv_model = pv.IntervenableModel({
+      "component": "model.layers[15].mlp.output",   # where to intervene (here, the MLP output in layer 15)
+      "intervention": pv.ZeroIntervention           # what intervention to apply (here, zeroing out the activation)
+   }, model=model)
+
+
+   # 3. Run the intervened model
+   orig_outputs, intervened_outputs = pv_model(
+      tokenizer("The capital of Spain is", return_tensors="pt").to('cuda'),
+      output_original_output=True
+   )
+
+
+   # 4. Compare outputs
+   print(intervened_outputs.logits - orig_outputs.logits)
+
+
+which returns
+
+::
+
+   tensor([[[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],
+            [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],
+            [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],
+            [ 0.4375,  1.0625,  0.3750,  ..., -0.1562,  0.4844,  0.2969],
+            [ 0.0938,  0.1250,  0.1875,  ...,  0.2031,  0.0625,  0.2188],
+            [ 0.0000, -0.0625, -0.0312,  ...,  0.0000,  0.0000, -0.0156]]],
+         device='cuda:0')
+
+*Share* and *load* from HuggingFace
+-----------------------------------
+
+pyvene has support for sharing and loading intervention schemata via HuggingFace.
+
+The following codeblock can reproduce `honest_llama-2
+chat <https://github.com/likenneth/honest_llama/tree/master>`__ from the
+paper `Inference-Time Intervention: Eliciting Truthful Answers from a
+Language Model <https://arxiv.org/abs/2306.03341>`__. The added
+activations are only **~0.14MB** on disk!
+
+.. code:: python
+
+   import torch
+   from transformers import AutoTokenizer, AutoModelForCausalLM
+   import pyvene as pv
+
+
+   # 1. Load base model
+   tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+   model = AutoModelForCausalLM.from_pretrained(
+       "meta-llama/Llama-2-7b-chat-hf",
+       torch_dtype=torch.bfloat16,
+   ).to("cuda")
+
+
+   # 2. Load intervention from HF and wrap model
+   pv_model = pv.IntervenableModel.load(
+       "zhengxuanzenwu/intervenable_honest_llama2_chat_7B", # the activation diff ~0.14MB
+       model,
+   )
+
+
+   # 3. Let's run it!
+   print("llama-2-chat loaded with interventions:")
+   q = "What's a cure for insomnia that always works?"
+   prompt = tokenizer(q, return_tensors="pt").to("cuda")
+   _, iti_response_shared = pv_model.generate(prompt, max_new_tokens=64, do_sample=False)
+   print(tokenizer.decode(iti_response_shared[0], skip_special_tokens=True))
+
+With this, once you discover some clever intervention schemes, you can
+share with others quickly without sharing the actual base LMs or the
+intervention code!
+
+.. _intervenablemodel-as-regular-nnmodule:
+
+*IntervenableModel* is just an *nn.Module*
+------------------------------------------
+
+pyvene wraps PyTorch models in the :class:`IntervenableModel <pyvene.models.intervenable_base.IntervenableModel>` class. This is just a subclass of
+``nn.Module``, so you can use it just like any other PyTorch model! For example:
+
+.. code:: python
+
+   import torch
+   import torch.nn as nn
+   from typing import List, Optional, Tuple, Union, Dict
+
+   class ModelWithIntervenables(nn.Module):
+       def __init__(self):
+           super(ModelWithIntervenables, self).__init__()
+           self.pv_gpt2 = pv_gpt2
+           self.relu = nn.ReLU()
+           self.fc = nn.Linear(768, 1)
+           # Your other downstream components go here
+
+       def forward(
+           self, 
+           base,
+           sources: Optional[List] = None,
+           unit_locations: Optional[Dict] = None,
+           activations_sources: Optional[Dict] = None,
+           subspaces: Optional[List] = None,
+       ):
+           _, counterfactual_x = self.pv_gpt2(
+               base,
+               sources,
+               unit_locations,
+               activations_sources,
+               subspaces
+           )
+           return self.fc(self.relu(counterfactual_x.last_hidden_state))
+
+Complex *Intervention Schema* as an *Object*
+--------------------------------------------
+
+One key abstraction that pyvene provides is the encapsulation of the
+intervention schema. While abstraction provides good user-interfaces,
+pyvene can support relatively complex intervention schema. The
+following helper function generates the schema for *path
+patching* for individual attention heads, for replicating experiments
+from the paper `Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small <https://arxiv.org/abs/2211.00593>`__:
+
+.. code:: python
+
+   import pyvene as pv
+
+   def path_patching_config(
+       layer, last_layer, 
+       component="head_attention_value_output", unit="h.pos", 
+   ):
+       intervening_component = [
+           {"layer": layer, "component": component, "unit": unit, "group_key": 0}]
+       restoring_components = []
+       if not stream.startswith("mlp_"):
+           restoring_components += [
+               {"layer": layer, "component": "mlp_output", "group_key": 1}]
+       for i in range(layer+1, last_layer):
+           restoring_components += [
+               {"layer": i, "component": "attention_output", "group_key": 1}
+               {"layer": i, "component": "mlp_output", "group_key": 1}
+           ]
+       intervenable_config = IntervenableConfig(intervening_component + restoring_components)
+       return intervenable_config
+
+You can now use the config generated by this function to wrap a model. And
+after you have done your intervention, you can share your path patching
+config with others:
+
+.. code:: python
+
+   _, tokenizer, gpt2 = pv.create_gpt2()
+
+   pv_gpt2 = pv.IntervenableModel(
+       path_patching_config(4, gpt2.config.n_layer), 
+       model=gpt2
+   )
+   # saving the path
+   pv_gpt2.save(
+       save_directory="./your_gpt2_path/"
+   )
+   # loading the path
+   pv_gpt2 = pv.IntervenableModel.load(
+       "./tmp/",
+       model=gpt2)
+
+Selected Tutorials
+------------------
+
+.. list-table:: Tutorials
+   :widths: 10 20 20 50
+   :header-rows: 1
+
+   * - Level
+     - Tutorial
+     - Run in Colab
+     - Description
+   * - Beginner
+     - `pyvene 101 <tutorials/pyvene_101.html>`__
+     - 
+         .. image:: https://colab.research.google.com/assets/colab-badge.svg
+            :align: center
+            :target: https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/pyvene_101.ipynb
+     - Introduce you to the basics of pyvene
+   * - Intermediate
+     - `ROME Causal Tracing <tutorials/advanced_tutorials/Causal_Tracing.html>`__
+     - 
+         .. image:: https://colab.research.google.com/assets/colab-badge.svg
+            :align: center
+            :target: https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/advanced_tutorials/Causal_Tracing.ipynb
+     - Reproduce ROME's Results on Factual Associations with GPT2-XL
+   * - Intermediate
+     - `Intervention vs. Probing <tutorials/advanced_tutorials/Probing_Gender.html>`__
+     - 
+         .. image:: https://colab.research.google.com/assets/colab-badge.svg
+            :align: center
+            :target: https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/advanced_tutorials/Probing_Gender.ipynb
+     - Illustrates how to run trainable interventions and probing with pythia-6.9B
+   * - Advanced
+     - `Trainable Interventions for Causal Abstraction <tutorials/advanced_tutorials/DAS_Main_Introduction.html>`__
+     - 
+         .. image:: https://colab.research.google.com/assets/colab-badge.svg
+            :align: center
+            :target: https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/advanced_tutorials/DAS_Main_Introduction.ipynb
+     - Illustrates how to train an intervention to discover causal mechanisms of a neural model
+      
+
+Contributing to This Library
+----------------------------
+
+Please see `our guidelines <guides/contributing.html>`__ about how to contribute
+to this repository.
+
+*Pull requests, bug reports, and all other forms of contribution are
+welcomed and highly encouraged!*
+
+Citation
+--------
+
+If you use this repository, please cite our library paper:
+
+.. code:: bibtex
+
+   @inproceedings{wu-etal-2024-pyvene,
+      title = "pyvene: A Library for Understanding and Improving {P}y{T}orch Models via Interventions",
+      author = "Wu, Zhengxuan and Geiger, Atticus and Arora, Aryaman and Huang, Jing and Wang, Zheng and Goodman, Noah and Manning, Christopher and Potts, Christopher",
+      editor = "Chang, Kai-Wei and Lee, Annie and Rajani, Nazneen",
+      booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)",
+      month = jun,
+      year = "2024",
+      address = "Mexico City, Mexico",
+      publisher = "Association for Computational Linguistics",
+      url = "https://aclanthology.org/2024.naacl-demo.16",
+      pages = "158--165",
+   }
+
+
+Related Works on Discovering Causal Mechanism of LLMs
+-----------------------------------------------------
+
+If you would like to read more works on this area, here is a list of
+papers that try to align or discover the causal mechanisms of LLMs.
+
+-  `Causal Abstractions of Neural
+   Networks <https://arxiv.org/abs/2106.02997>`__: This paper introduces
+   interchange intervention (a.k.a. activation patching or causal
+   scrubbing). It tries to align a causal model with the model's
+   representations.
+-  `Inducing Causal Structure for Interpretable Neural
+   Networks <https://arxiv.org/abs/2112.00826>`__: Interchange
+   intervention training (IIT) induces causal structures into the
+   model's representations.
+-  `Localizing Model Behavior with Path
+   Patching <https://arxiv.org/abs/2304.05969>`__: Path patching (or
+   causal scrubbing) to uncover causal paths in neural model.
+-  `Towards Automated Circuit Discovery for Mechanistic
+   Interpretability <https://arxiv.org/abs/2304.14997>`__: Scalable
+   method to prune out a small set of connections in a neural network
+   that can still complete a task.
+-  `Interpretability in the Wild: a Circuit for Indirect Object
+   Identification in GPT-2 small <https://arxiv.org/abs/2211.00593>`__:
+   Path patching plus posthoc representation study to uncover a circuit
+   that solves the indirect object identification (IOI) task.
+-  `Rigorously Assessing Natural Language Explanations of
+   Neurons <https://arxiv.org/abs/2309.10312>`__: Using causal
+   abstraction to validate `neuron explanations released by
+   OpenAI <https://openai.com/research/language-models-can-explain-neurons-in-language-models>`__.
+
+Star History
+------------
+
+|Star History Chart|
+
+.. |Star History Chart| image:: https://api.star-history.com/svg?repos=stanfordnlp/pyvene,stanfordnlp/pyreft&type=Date
+   :target: https://star-history.com/#stanfordnlp/pyvene&stanfordnlp/pyreft&Date
+
+.. toctree::
+   :hidden:
+   :caption: Guides
+
+   guides/contributing
+   guides/causal_abstraction
+   guides/ndif
+
+
+.. toctree::
+   :hidden:
+   :caption: Basic tutorials
+
+   tutorials/pyvene_101
+   tutorials/basic_tutorials/Add_Activations_to_Streams
+   tutorials/basic_tutorials/Basic_Intervention
+   tutorials/basic_tutorials/Nested_Intervention
+   tutorials/basic_tutorials/Subspace_Partition_with_Intervention
+   tutorials/basic_tutorials/Intervention_Training
+
+.. toctree::
+   :hidden:
+   :caption: Advanced tutorials
+
+   tutorials/advanced_tutorials/Causal_Tracing
+   tutorials/advanced_tutorials/Probing_Gender
+   tutorials/advanced_tutorials/DAS_Main_Introduction
+   tutorials/advanced_tutorials/Boundless_DAS
+   tutorials/advanced_tutorials/IOI_Replication
+   tutorials/advanced_tutorials/IOI_with_DAS
+   tutorials/advanced_tutorials/IOI_with_Mask_Intervention
+   tutorials/advanced_tutorials/Interventions_with_BLIP
+   tutorials/advanced_tutorials/MQNLI
+   tutorials/advanced_tutorials/Voting_Mechanism
+
+.. toctree::
+   :hidden:
+   :caption: API
+
+   api/core
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
\ No newline at end of file
diff --git a/_sources/tutorials/advanced_tutorials/Boundless_DAS.ipynb b/_sources/tutorials/advanced_tutorials/Boundless_DAS.ipynb
new file mode 100644
index 00000000..7f6a84eb
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/Boundless_DAS.ipynb
@@ -0,0 +1,624 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "63ff6846",
+   "metadata": {},
+   "source": [
+    "## Boundless DAS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3ae11b28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"10/05/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d898fce",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "This tutorial aims to reproduce one key result of [the Boundless DAS paper](https://arxiv.org/pdf/2305.08809). It uses the same pricing tag dataset as in the paper. Additionally, it focuses on finding alignment for the left boundary check only. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8af5dff0",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3e3c09e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-11 01:35:34,365] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9a39c2b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from tqdm import tqdm, trange\n",
+    "from datasets import Dataset\n",
+    "from torch.utils.data import DataLoader\n",
+    "from transformers import get_linear_schedule_with_warmup\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from tutorial_price_tagging_utils import (\n",
+    "    factual_sampler,\n",
+    "    bound_alignment_sampler,\n",
+    "    lower_bound_alignment_example_sampler,\n",
+    ")\n",
+    "\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    BoundlessRotatedSpaceIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")\n",
+    "from pyvene import create_llama\n",
+    "from pyvene import set_seed, count_parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "970a8f7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ae4a40cfb8d44a38578fd45815ab319",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading config.json:   0%|          | 0.00/550 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1220a638dba243d49cc5f951cd4d9364",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer_config.json:   0%|          | 0.00/726 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9fd255929ed487b8440ed3dae85df58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37973ed21b144db99a242a249ffba7ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "307b905f058e48f19eab80f03dcc2496",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53793dbe43e54aa694d2cc0aff7b9a1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
+      "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n",
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbf4bf617f9a4099bf3525e0c5334ed6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/34 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, llama = create_llama()\n",
+    "_ = llama.to(\"cuda\")  # single gpu\n",
+    "_ = llama.eval()  # always no grad on the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99f9a7da",
+   "metadata": {},
+   "source": [
+    "### Factual performance of instruct-tuned LLaMA-7B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7f5d9c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_prealign = factual_sampler(tokenizer, 5000, game=\"pricing_tag\")\n",
+    "prealign_dataset = Dataset.from_dict(\n",
+    "    {\"input_ids\": raw_prealign[0], \"labels\": raw_prealign[1]}\n",
+    ")\n",
+    "prealign_dataset.set_format(\"torch\", columns=[\"input_ids\", \"labels\"])\n",
+    "prealign_dataloader = DataLoader(prealign_dataset, batch_size=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e4cb38ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 625/625 [00:48<00:00, 12.82it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[WARNING: THIS NEEDS TO BE GOOD!] prealign task accuracy: 0.92\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_count = 0\n",
+    "correct_count = 0\n",
+    "with torch.no_grad():\n",
+    "    for step, inputs in enumerate(tqdm(prealign_dataloader)):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(llama.device)\n",
+    "\n",
+    "        # aligning forward!\n",
+    "        outputs = llama(\n",
+    "            input_ids=inputs[\"input_ids\"],\n",
+    "            labels=inputs[\"labels\"],\n",
+    "        )\n",
+    "\n",
+    "        actual_test_labels = inputs[\"labels\"][:, -1]\n",
+    "        pred_test_labels = torch.argmax(outputs.logits[:, -1], dim=-1)\n",
+    "\n",
+    "        correct_labels = actual_test_labels == pred_test_labels\n",
+    "\n",
+    "        total_count += len(correct_labels)\n",
+    "        correct_count += correct_labels.sum().tolist()\n",
+    "current_acc = round(correct_count / total_count, 2)\n",
+    "print(f\"[WARNING: THIS NEEDS TO BE GOOD!] prealign task accuracy: {current_acc}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59d30b97",
+   "metadata": {},
+   "source": [
+    "### Create training dataset for our trainable intervention (Boundless DAS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "79caade7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "set_seed(42)\n",
+    "\n",
+    "###################\n",
+    "# data loaders\n",
+    "###################\n",
+    "raw_data = bound_alignment_sampler(\n",
+    "    tokenizer, 10000, [lower_bound_alignment_example_sampler]\n",
+    ")\n",
+    "\n",
+    "raw_train = (\n",
+    "    raw_data[0][:8000],\n",
+    "    raw_data[1][:8000],\n",
+    "    raw_data[2][:8000],\n",
+    "    raw_data[3][:8000],\n",
+    ")\n",
+    "raw_eval = (\n",
+    "    raw_data[0][8000:9000],\n",
+    "    raw_data[1][8000:9000],\n",
+    "    raw_data[2][8000:9000],\n",
+    "    raw_data[3][8000:9000],\n",
+    ")\n",
+    "raw_test = (\n",
+    "    raw_data[0][9000:],\n",
+    "    raw_data[1][9000:],\n",
+    "    raw_data[2][9000:],\n",
+    "    raw_data[3][9000:],\n",
+    ")\n",
+    "train_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_train[0],\n",
+    "        \"source_input_ids\": raw_train[1],\n",
+    "        \"labels\": raw_train[2],\n",
+    "        \"intervention_ids\": raw_train[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "train_dataloader = DataLoader(\n",
+    "    train_dataset,\n",
+    "    batch_size=16,\n",
+    ")\n",
+    "eval_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_eval[0],\n",
+    "        \"source_input_ids\": raw_eval[1],\n",
+    "        \"labels\": raw_eval[2],\n",
+    "        \"intervention_ids\": raw_eval[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "eval_dataloader = DataLoader(\n",
+    "    eval_dataset,\n",
+    "    batch_size=16,\n",
+    ")\n",
+    "test_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_test[0],\n",
+    "        \"source_input_ids\": raw_test[1],\n",
+    "        \"labels\": raw_test[2],\n",
+    "        \"intervention_ids\": raw_test[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "test_dataloader = DataLoader(\n",
+    "    test_dataset,\n",
+    "    batch_size=16,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47e83259",
+   "metadata": {},
+   "source": [
+    "### Boundless DAS on Position-aligned Tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "296d0a11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simple_boundless_das_position_config(model_type, intervention_type, layer):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                layer,              # layer\n",
+    "                intervention_type,  # intervention type\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=BoundlessRotatedSpaceIntervention,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "config = simple_boundless_das_position_config(\n",
+    "    type(llama), \"block_output\", 15\n",
+    ")\n",
+    "intervenable = IntervenableModel(config, llama)\n",
+    "intervenable.set_device(\"cuda\")\n",
+    "intervenable.disable_model_gradients()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "740e3724",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_total = int(len(train_dataloader) * 3)\n",
+    "warm_up_steps = 0.1 * t_total\n",
+    "optimizer_params = []\n",
+    "for k, v in intervenable.interventions.items():\n",
+    "    optimizer_params += [{\"params\": v.rotate_layer.parameters()}]\n",
+    "    optimizer_params += [{\"params\": v.intervention_boundaries, \"lr\": 1e-2}]\n",
+    "optimizer = torch.optim.Adam(optimizer_params, lr=1e-3)\n",
+    "scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer, num_warmup_steps=warm_up_steps, num_training_steps=t_total\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# You can define your custom compute_metrics function.\n",
+    "def compute_metrics(eval_preds, eval_labels):\n",
+    "    total_count = 0\n",
+    "    correct_count = 0\n",
+    "    for eval_pred, eval_label in zip(eval_preds, eval_labels):\n",
+    "        actual_test_labels = eval_label[:, -1]\n",
+    "        pred_test_labels = torch.argmax(eval_pred[:, -1], dim=-1)\n",
+    "        correct_labels = actual_test_labels == pred_test_labels\n",
+    "        total_count += len(correct_labels)\n",
+    "        correct_count += correct_labels.sum().tolist()\n",
+    "    accuracy = round(correct_count / total_count, 2)\n",
+    "    return {\"accuracy\": accuracy}\n",
+    "\n",
+    "\n",
+    "epochs = 3\n",
+    "gradient_accumulation_steps = 4\n",
+    "total_step = 0\n",
+    "target_total_step = len(train_dataloader) * epochs\n",
+    "temperature_start = 50.0\n",
+    "temperature_end = 0.1\n",
+    "temperature_schedule = (\n",
+    "    torch.linspace(temperature_start, temperature_end, target_total_step)\n",
+    "    .to(torch.bfloat16)\n",
+    "    .to(\"cuda\")\n",
+    ")\n",
+    "intervenable.set_temperature(temperature_schedule[total_step])\n",
+    "\n",
+    "\n",
+    "def calculate_loss(logits, labels):\n",
+    "    shift_logits = logits[..., :, :].contiguous()\n",
+    "    shift_labels = labels[..., :].contiguous()\n",
+    "    # Flatten the tokens\n",
+    "    loss_fct = CrossEntropyLoss()\n",
+    "    shift_logits = shift_logits.view(-1, intervenable.model_config.vocab_size)\n",
+    "    shift_labels = shift_labels.view(-1)\n",
+    "    # Enable model parallelism\n",
+    "    shift_labels = shift_labels.to(shift_logits.device)\n",
+    "    loss = loss_fct(shift_logits, shift_labels)\n",
+    "\n",
+    "    for k, v in intervenable.interventions.items():\n",
+    "        boundary_loss = 1.0 * v.intervention_boundaries.sum()\n",
+    "    loss += boundary_loss\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5cc2a247",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llama trainable parameters:  0\n",
+      "intervention trainable parameters:  16777218\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0: 100%|██████████████████████████████████████████████████████████████████████████████████████| 500/500 [07:05<00:00,  1.18it/s, loss=0.5, acc=0.88]\n",
+      "Epoch: 1: 100%|█████████████████████████████████████████████████████████████████████████████████████| 500/500 [07:58<00:00,  1.04it/s, loss=0.39, acc=0.94]\n",
+      "Epoch: 2: 100%|█████████████████████████████████████████████████████████████████████████████████████| 500/500 [08:19<00:00,  1.00it/s, loss=0.35, acc=0.94]\n",
+      "Epoch: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [23:23<00:00, 467.83s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable.model.train()  # train enables drop-off but no grads\n",
+    "print(\"llama trainable parameters: \", count_parameters(intervenable.model))\n",
+    "print(\"intervention trainable parameters: \", intervenable.count_parameters())\n",
+    "train_iterator = trange(0, int(epochs), desc=\"Epoch\")\n",
+    "for epoch in train_iterator:\n",
+    "    epoch_iterator = tqdm(\n",
+    "        train_dataloader, desc=f\"Epoch: {epoch}\", position=0, leave=True\n",
+    "    )\n",
+    "    for step, inputs in enumerate(epoch_iterator):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(\"cuda\")\n",
+    "        b_s = inputs[\"input_ids\"].shape[0]\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": inputs[\"source_input_ids\"]}],\n",
+    "            {\"sources->base\": 80},  # swap 80th token\n",
+    "        )\n",
+    "        eval_metrics = compute_metrics(\n",
+    "            [counterfactual_outputs.logits], [inputs[\"labels\"]]\n",
+    "        )\n",
+    "\n",
+    "        # loss and backprop\n",
+    "        loss = calculate_loss(counterfactual_outputs.logits, inputs[\"labels\"])\n",
+    "        loss_str = round(loss.item(), 2)\n",
+    "        epoch_iterator.set_postfix({\"loss\": loss_str, \"acc\": eval_metrics[\"accuracy\"]})\n",
+    "\n",
+    "        if gradient_accumulation_steps > 1:\n",
+    "            loss = loss / gradient_accumulation_steps\n",
+    "        loss.backward()\n",
+    "        if total_step % gradient_accumulation_steps == 0:\n",
+    "            if not (gradient_accumulation_steps > 1 and total_step == 0):\n",
+    "                optimizer.step()\n",
+    "                scheduler.step()\n",
+    "                intervenable.set_zero_grad()\n",
+    "                intervenable.set_temperature(temperature_schedule[total_step])\n",
+    "        total_step += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3323b113",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:45<00:00,  1.38it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.96}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluation on the test set\n",
+    "eval_labels = []\n",
+    "eval_preds = []\n",
+    "with torch.no_grad():\n",
+    "    epoch_iterator = tqdm(test_dataloader, desc=f\"Test\")\n",
+    "    for step, inputs in enumerate(epoch_iterator):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(\"cuda\")\n",
+    "        b_s = inputs[\"input_ids\"].shape[0]\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": inputs[\"source_input_ids\"]}],\n",
+    "            {\"sources->base\": 80},  # swap 80th token\n",
+    "        )\n",
+    "        eval_labels += [inputs[\"labels\"]]\n",
+    "        eval_preds += [counterfactual_outputs.logits]\n",
+    "eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "print(eval_metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fbd6296",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/advanced_tutorials/Causal_Tracing.ipynb b/_sources/tutorials/advanced_tutorials/Causal_Tracing.ipynb
new file mode 100644
index 00000000..58589f7b
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/Causal_Tracing.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Causal Tracing (ROME)\n",
+    "\n",
+    "Causal tracing was a methodology for locating where facts are stored in transformer LMs, introduced in the paper [\"Locating and Editing Factual Associations in GPT\" (Meng et al., 2023)](https://arxiv.org/abs/2202.05262). In this notebook, we will implement their method using this library and replicate the first causal tracing example in the paper (full figure 1 on page 2)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/advance_tutorials/Causal_Tracing.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Aryaman Arora\"\n",
+    "__version__ = \"11/08/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    VanillaIntervention, Intervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    "    ConstantSourceIntervention,\n",
+    "    LocalistRepresentationIntervention\n",
+    ")\n",
+    "from pyvene import create_gpt2\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    "    xlab, ylab, ylim,\n",
+    "    scale_y_discrete, scale_y_continuous, ggsave\n",
+    ")\n",
+    "from plotnine.scales import scale_y_reverse, scale_fill_cmap\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "titles={\n",
+    "    \"block_output\": \"single restored layer in GPT2-XL\",\n",
+    "    \"mlp_activation\": \"center of interval of 10 patched mlp layer\",\n",
+    "    \"attention_output\": \"center of interval of 10 patched attn layer\"\n",
+    "}\n",
+    "\n",
+    "colors={\n",
+    "    \"block_output\": \"Purples\",\n",
+    "    \"mlp_activation\": \"Greens\",\n",
+    "    \"attention_output\": \"Reds\"\n",
+    "} "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Factual recall\n",
+    "\n",
+    "Let's set up the model (gpt2-xl) and test it on the fact we want to causal trace: \"The Space Needle is in downtown **Seattle**\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "The Space Needle is in downtown\n",
+      "_Seattle             0.9763794541358948\n",
+      "_Bellev              0.0027682818472385406\n",
+      "_Portland            0.0021577849984169006\n",
+      ",                    0.0015149445971474051\n",
+      "_Vancouver           0.0014351375866681337\n",
+      "_San                 0.0013575783232226968\n",
+      "_Minneapolis         0.000938268203753978\n",
+      ".                    0.0007443446083925664\n",
+      "_Tacoma              0.0006097281584516168\n",
+      "_Washington          0.0005885555874556303\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
+    "config, tokenizer, gpt = create_gpt2(name=\"gpt2-xl\")\n",
+    "gpt.to(device)\n",
+    "\n",
+    "base = \"The Space Needle is in downtown\"\n",
+    "inputs = [\n",
+    "    tokenizer(base, return_tensors=\"pt\").to(device),\n",
+    "]\n",
+    "print(base)\n",
+    "res = gpt(**inputs[0])\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Corrupted run\n",
+    "\n",
+    "The first step in implementing causal tracing is to corrupt the input embeddings for the subject tokens by adding Gaussian noise to them. In Meng et al., the standard deviation of the Gaussian we sample from is computed as thrice the standard deviation of embeddings over a big dataset. We encode this as a constant, `self.noise_level`.\n",
+    "\n",
+    "Note that the `source` argument is ignored unlike in causal interventions, since we are adding noise without reference to any other input.\n",
+    "\n",
+    "Our intervention config intervenes on the `block_input` of the 0th layer, i.e. the embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NoiseIntervention(ConstantSourceIntervention, LocalistRepresentationIntervention):\n",
+    "    def __init__(self, embed_dim, **kwargs):\n",
+    "        super().__init__()\n",
+    "        self.interchange_dim = embed_dim\n",
+    "        rs = np.random.RandomState(1)\n",
+    "        prng = lambda *shape: rs.randn(*shape)\n",
+    "        self.noise = torch.from_numpy(\n",
+    "            prng(1, 4, embed_dim)).to(device)\n",
+    "        self.noise_level = 0.13462981581687927\n",
+    "\n",
+    "    def forward(self, base, source=None, subspaces=None):\n",
+    "        base[..., : self.interchange_dim] += self.noise * self.noise_level\n",
+    "        return base\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return f\"NoiseIntervention(embed_dim={self.embed_dim})\"\n",
+    "\n",
+    "\n",
+    "def corrupted_config(model_type):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                0,              # layer\n",
+    "                \"block_input\",  # intervention type\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=NoiseIntervention,\n",
+    "    )\n",
+    "    return config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check that this reduced the probability of the output \"_Seattle\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_Los                 0.03294256329536438\n",
+      "_San                 0.03194474056363106\n",
+      "_Seattle             0.026176469400525093\n",
+      "_Toronto             0.02585919387638569\n",
+      "_Chicago             0.024749040603637695\n",
+      "_Houston             0.024224288761615753\n",
+      "_Atlanta             0.01866454817354679\n",
+      "_Austin              0.017735302448272705\n",
+      "_St                  0.017606761306524277\n",
+      "_Denver              0.01740877516567707\n"
+     ]
+    }
+   ],
+   "source": [
+    "base = tokenizer(\"The Space Needle is in downtown\", return_tensors=\"pt\").to(device)\n",
+    "config = corrupted_config(type(gpt))\n",
+    "intervenable = IntervenableModel(config, gpt)\n",
+    "_, counterfactual_outputs = intervenable(\n",
+    "    base, unit_locations={\"base\": ([[[0, 1, 2, 3]]])}\n",
+    ")\n",
+    "distrib = embed_to_distrib(gpt, counterfactual_outputs.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Restored run\n",
+    "\n",
+    "We now make a config that performs the following:\n",
+    "1. Corrupt input embeddings for some positions.\n",
+    "2. Restore the hidden state at a particular layer for some (potentially different positions).\n",
+    "\n",
+    "This is how Meng et al. check where in the model the fact moves through."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def restore_corrupted_with_interval_config(\n",
+    "    layer, stream=\"mlp_activation\", window=10, num_layers=48):\n",
+    "    start = max(0, layer - window // 2)\n",
+    "    end = min(num_layers, layer - (-window // 2))\n",
+    "    config = IntervenableConfig(\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                0,       # layer\n",
+    "                \"block_input\",  # intervention type\n",
+    "            ),\n",
+    "        ] + [\n",
+    "            RepresentationConfig(\n",
+    "                i,       # layer\n",
+    "                stream,  # intervention type\n",
+    "        ) for i in range(start, end)],\n",
+    "        intervention_types=\\\n",
+    "            [NoiseIntervention]+[VanillaIntervention]*(end-start),\n",
+    "    )\n",
+    "    return config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's run this over all layers and positions! We will corrupt positions 0, 1, 2, 3 (\"The Space Needle\", i.e. the subject of the fact) and restore at a single position at every layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7312\n"
+     ]
+    }
+   ],
+   "source": [
+    "# should finish within 1 min with a standard 12G GPU\n",
+    "token = tokenizer.encode(\" Seattle\")[0]\n",
+    "print(token)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for stream in [\"block_output\", \"mlp_activation\", \"attention_output\"]:\n",
+    "    data = []\n",
+    "    for layer_i in tqdm(range(gpt.config.n_layer)):\n",
+    "        for pos_i in range(7):\n",
+    "            config = restore_corrupted_with_interval_config(\n",
+    "                layer_i, stream, \n",
+    "                window=1 if stream == \"block_output\" else 10\n",
+    "            )\n",
+    "            n_restores = len(config.representations) - 1\n",
+    "            intervenable = IntervenableModel(config, gpt)\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                base,\n",
+    "                [None] + [base]*n_restores,\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [None] + [[[pos_i]]]*n_restores,\n",
+    "                        [[[0, 1, 2, 3]]] + [[[pos_i]]]*n_restores,\n",
+    "                    )\n",
+    "                },\n",
+    "            )\n",
+    "            distrib = embed_to_distrib(\n",
+    "                gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "            )\n",
+    "            prob = distrib[0][-1][token].detach().cpu().item()\n",
+    "            data.append({\"layer\": layer_i, \"pos\": pos_i, \"prob\": prob})\n",
+    "    df = pd.DataFrame(data)\n",
+    "    df.to_csv(f\"./tutorial_data/pyvene_rome_{stream}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot below should now replicate Meng et al."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.\n",
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_block_output.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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++60+//xzO8sDAAAAAMAxtgX0jRs3asGCBfL5fPrNb36jf/7zn0pKSqp13WHDhsk0Ta1Zs8au8gAAAAAAcJRtl7gvXbpUhmFo8uTJOv/88+tdt0+fPpKkHTt22FEaAAAAAACOs60H/bvvvpMkjR49usF1ExISFBsbqwMHDlhdFgAAAAAArmBbQM/NzVVsbKwSEhIatb7P52PKJgAAAACAZ9gW0OPj41VUVKTy8vIG183Ly1NhYaESExNtqAwAAAAAAOfZFtCPPvpoSf+71L0+n3zyiUzTVGpqqsVVAQAAAADgDrYF9DPOOEOmaWru3Ln19qJv3bpVr7zyigzD0LBhw+wqDwAAAAAAR9k2ivvZZ5+tDz74QJs3b9Ztt92mMWPGyO/3S5K2bdumjIwMrVq1Sp999pnKy8t13HHH6ZRTTrGrPAAAAAAAHGWYNo7ElpOTo/vuu0/fffedDMOodR3TNNWnTx/dcccdjR5QriFZWVkh2U9ztUlMDnrbOl4uhJG6zvmWhIEd3YW3w7ua8+sm2PPGA7/igBbvYE620yVIklJSUpwuAXCMrQFdkvx+vz7++GMtW7ZM6enpKisrk1Q5antqaqrOPvtsjRgxQhERESFrk4AONyCgw268Hd5FQAcQDAI64DzbA/qhKioqlJ+fL7/fr8TExJCG8kMR0OEGBHTYjbfDuwjoAIJBQAecZ9s96LXx+XxMpQYAAAAAgGwcxR0AAAAAANTNtoC+Zs0aTZkyRY8++miD695///2aMmWKvv32WxsqAwAAAADAebYF9E8//VSFhYU688wzG1z3zDPPVEFBgT777DMbKgMAAAAAwHm2BfRt27ZJkvr379/guieeeKIMw9DWrVutLgsAAAAAAFewLaBnZWUpPj5eMTExDa4bExOj+Ph47d+/34bKAAAAAABwnq2juJeWllqyLgAAAAAA4c62HvSUlBSVlZVpx44dDa67Y8cOlZaWMgciAAAAAMAzbAvoAwYMkGmamjt3br3rmaapOXPmyDAMHXfccTZVBwAAAACAs2wL6OPHj1dERIRWr16tBx54QL/88kuNdX755Rc98MADWr16tXw+n8aPH29XeQAAAAAAOMowTdO0q7H3339fTz/9tAzDkCR16dJFHTp0kCRlZGRo9+7dkip70a+55hqde+65IWk3KysrJPtprjaJyUFv+9+XDGHM8MCbaOOvEzQCb4d3NefXTbDnjQd+xQEt3sGcbKdLkCRuc4Wn2RrQJenLL7/Uc889V2dobt++va688kqdcsopIWuTgA43IKDDbrwd3kVABxAMAjrgPNsDuiT5/X6tX79eW7du1cGDByVJycnJ6t27t4477jhFRESEtD0COtyAgA678XZ4FwEdQDAI6IDzHAnooTZnzhzNmzevzuWTJk3StGnTbKyodhX+4Lflg0/4I6DDbrwd3kVABxAMw7bRqeoX6s46IJzYOg+6VQoKCpSRkVHn8sLCQlf8Ra/wNyOhAwAAABZyw+dlwOtaRECPj48PDDZXm7i4OPkJxwAAAECd3PJ5mS8K4GW2X+JeUlKilStXasuWLTpw4ICKi4vrvCzWMAzNmjWr2W1yDzrcgEvcYTfeDu/iEncAweAedMB5tvagf/vtt3r00UeVm5sr0zQDgaXqQ/2hAebQ5QAAAAAAtHS2DQWxd+9e3X///crJyVGXLl00btw4maap6OhoXXjhhRo5cqSOPPJImaap1q1b64ILLtBFF11kV3kAAAAAYKmysjL16dNHhmHo5ptvdrqcFsswDBmGobPOOsvSdoqLi3XUUUfJMAzNnDkzJPu0rQd90aJFKi4u1qBBg3T77bcrIiJCS5cuVWxsrC655JLAesuXL9fTTz+t7du3684777SrPAAAAACw1OOPP66tW7cqKSlJd9xxR73r/vzzz3r55Zf14YcfatOmTcrOzpbf71fr1q3VtWtXHXvssRo0aJDOOussDRo0SD6fS4bhb4SqMHv00Udr+vTpIV/fLjExMbr33ns1ffp0PfTQQ5o+fbqOPvroZu3TtnvQr7nmGu3bt0+PP/64unfvLkmaMGGCkpOT9eKLL1Zbd8mSJXrhhRd05ZVXauzYsc1um3vQ4QZeuGWDe9DdhbfDu7gHHUAwuAfdWgcOHFCPHj2Uk5OjO++8U/fcc0+d6z788MP6y1/+ouLi4kbt+5ZbbtFDDz0UqlItV/W5eOjQofr4448dX785/H6/UlNTtWPHDk2dOlWvvPJKs/Zn29cs+/fvl8/nq/aNgmEYKisrq7HuyJEjZRiGPvroI7vKAwAAAADL/PWvf1VOTo5iYmJ044031rnebbfdpltvvTUQzk899VTdfffdeuWVV/T666/rueee080336zTTz89MOK9W0bg96KIiIjA7Qrz5s3Tli1bmrU/2y5xb9WqlaKjo6v1IsbExKigoEDl5eVq1apVtedjY2O1Z88eu8oDAAAAAEvk5+fr73//uyTpggsuULt27Wpd79tvvw30hEdHR2v+/Pk677zz6txvVlaW5s6dq+jo6NAXjUa79NJLdeutt6qgoECPPfaYnnvuuaD3ZVsPetu2bVVYWKiKiorAc1Vzl//www/V1j148KAKCgpq7V0HAAAAgHAyd+5cHTx4UJI0bdq0eterykszZsyoN5xLlbcD3HjjjbrmmmtCViuaLjExURMnTpQkzZkzRzk5OUHvy7aA3rVrV1VUVOinn34KPNe3b1+ZpqmXX345cAlHeXm5nn/+eUnSUUcdZVd5AAAAACBJ+vjjjwMjgVcNUPbdd9/p+uuvV+/evRUfH6/k5GSdcsopevzxx1VSUlLv/qrG3GrXrl29I4tv3bo18PPQoUObexjV/PDDD/p//+//6aSTTlL79u0VFRWlI444QsOHD9fs2bNVWFjY4D7Wrl2r+++/X2PGjFH37t0VFxen6OhoHXnkkRo5cqRmz56t/Pz8Orevek2rfPLJJ4HnDn1UvV5NXd+p10WSzj//fEmVI7u/+uqrQddj2yXuAwcO1BdffKGvv/46cB/62LFj9cEHH2jTpk2aNm2aunTpooyMDOXl5ckwDI0aNcqu8gAAAACgVq+++qouv/zyamGtsLBQX331lb766is9++yzeu+999StW7ca2+7du1dffvmlJFW7b7w25eXlgZ9/+eWXkNReUVGhO+64Q4888ki1/UtSRkaGMjIy9NFHH+mvf/2rFi9erEGDBtW6n3vuuUd/+ctfal22b98+7du3T8uWLdPDDz+sRYsW6eSTTw5J/VYJ1etSZejQoTIMQ6ZpauHChbrqqquCqsu2gH766afrwIEDSkpKCjzXpUsXzZgxQ0899ZSKi4uVnp4uqfKbkvHjx2vkyJF2lQcAAAAANXzzzTd68MEHVVZWpilTpmjEiBGKjY3Vpk2b9MILL2jv3r3aunWrhg0bprVr16pNmzbVtn///fcDP59yyin1ttWrV6/Az08//bSmTJmiqKioZtU/bdo0zZkzR1LlbccXXXSRBg0apMTERGVkZOjtt9/Wu+++q927d2vYsGFavXq1jjnmmBr7KSwsVEREhE4++WSdfvrpOuaYY5SUlCS/36+dO3fqrbfe0ueff649e/bo3HPP1bp169S1a9dq+1i0aJEkBS7d79evn2bNmlWjrRNOOCGo9Z14Xaq0bdtWqamp+v777/Xxxx+ruLhYMTExTa7LtmnW6pObm6tvvvlGWVlZio+P14ABA9S5c+eQ7Z9p1uAGTLMGu/F2eBfTrAEIBtOs/c/HH3+sYcOGBf4cFxent99+u8bl6QcPHtTo0aP1xRdfSJKuvvpqPfPMM9XWufbaawPPLV++XMOHD6+z3S+++EKnnXZa4M9paWn63e9+p1GjRgU1v/Y//vGPwP3p48aN08svv1ytw7TKwoULddFFF6m8vFynn366VqxYUWOdVatWqXPnzurUqVOd7c2ZM0fTpk1TRUWFrrjiisCty4dzepq1UL4uh5o+fbpeeuklSZXvZUNfyNTGFbPZJyYmatiwYbrgggs0evTokIZzAAAAAGiOBx98sNZ7x5OSkvT6668rISFBUuW95pmZmdXW2bBhQ+DnY489tt52Tj31VN10002BP2/cuFHXXnutunfvrvbt22vUqFG688479f777zd433tJSYnuvvtuSVKfPn30xhtv1BpCJenXv/61brnlFknS559/rq+++qrGOieddFK94VySpk6dqilTpkiS5s+f78pBv0P9uhyqT58+gZ/Xr18fVH2uCOgAAAAA4EZJSUn67W9/W+fyzp0765JLLpFUGf6WLl1abfnOnTsDP7dt27bB9qqm6To8DGdlZen999/XrFmzNGrUKB1xxBG66aab6rxa+IMPPtDevXslVY4I39Cl8oeOLn/oZflNNWTIEEmVl8QHG1KtZOXrcuj7e+j73hS23YN+qPz8fK1atUo//PBDYAj6Nm3aqGfPnjrppJMC30ABAAAAgJOGDBnS4L3EZ599tv7xj39Ikr7++mtdfvnlgWUHDhyQVHmZfGPvSb7yyiv1m9/8Rh988IHeeecdrVy5Ups2bao2mFlOTo7+9re/acGCBVq6dKlOPPHEavv49NNPAz/n5eVp8eLF9bZ5aG/35s2ba13HNE29++67euONN/TNN99o165dysvLqzHIWpXdu3c3OLia3ax4XaocOr991fveVLYGdL/fr3nz5mnJkiWBA626Z7XqPoHIyEhNmDBBkydPVqtWjnx/AAAAAACSpNTU1Cats2fPnmrLqi5Fb926dZPajYqK0tixYzV27FhJldN3ffvtt/rss8/02muvadWqVZIqR1AfO3astmzZouTk/415dWgP7h//+McmtV1buNy3b58mTZqkzz//vNH7yc3NbVK7dgj163KoxMTEwM9FRUVN2ncV2xJwRUWFZs2apbVr18o0TUVHR6tnz56BbxkOHDig9PR0lZSU6I033tD27dt15513emJgLQAAAADuFB8f36R18vLyqi2Ljo5WUVFRs8NqTEyMBg8erMGDB+uPf/yjXnnlFU2bNk2maeqXX37R008/rdtvvz2w/sGDB4Nuq7S0tNqfy8vLNWrUKH377beSpOTkZI0bN079+/dXx44dFRsbG5g+7sMPP9T//d//SarsoHWbUL4uh6u6OlySYmNjg2rDtoC+dOlSrVmzRj6fT1OmTNH48eNrXOJRUlKiJUuWaN68eVqzZo3eeustjRs3zq4SAQAAAKCagoKCJq1zeE95u3bttHv3bhUVFQU99VZtLr30Uq1YsULPPvusJGnZsmXVAvqhtw2vX79e/fv3D7qtV199NRDOR4wYoUWLFtV5RcDPP/8cdDt2COXrcrj9+/cHfm7MeAO1sW2QuOXLl8swDE2fPl0XXnhhrSdmdHS0LrjgAk2fPl2maeo///mPXeUBAAAAQA3p6elNWufwwd26d+8e+DnY+5LrcvbZZwd+rhr4rEqXLl0CP+/atatZ7XzwwQeBn//2t7/Ve7n+jh07mtWW1UL5uhzu0Pc3mGnxJBsD+p49e+Tz+XTuuec2uO65556riIiIGvdvAAAAAICdPvvsswanNDu0Y3Hw4MHVlh3aQ7tly5aQ1hYZGRn4+fCBtocOHRr4+d13321WO/v27Qv83KtXr3rXfe+99xrcX9VtzFXjkYV6/fqE8nU53KGDyA0YMCCofdgW0GNjYxUbG9vgMPZS5YAIMTExIbv8AwAAAACCcfDgQT3//PN1Lt+7d6/mzp0rqfKK4KpB3aqccsopgZ8bmkf7l19+aVJtb775ZuDnwy/VPvfcc9W+fXtJ0gsvvNCoKwHqcug99vXt59VXX9WmTZsa3F/VlwmNuX0gmPXrE8rX5XBffvmlpMrxAlwf0I855hgVFBQoMzOzwXUzMjJUUFCg3r1721AZAAAAANTt1ltvrTY9V5Xc3FxdeOGFgYHhLrvsskD4qzJy5MhAD3BDAX3GjBkaPny43nzzzXoHJDNNU7Nnz9ZLL70UeO7SSy+ttk58fLxmzpwpqXJO8nPOOUdr166tt/309HT94Q9/UEZGRrXnTzrppMDPt99+e62Dv3300Ue66qqr6t1/larL/rdu3dqo0c6bun59Qvm6HOrAgQPatm2bpMpe+mA7m20bJG7SpElau3at/v73v+u2226rcwo1v9+vZ555RhEREZo0aZJd5QEAAABADWPHjtWyZcs0fPhwTZ48WSNGjFBsbKw2b96sf/7zn4Hbcrt3766HHnqoxvZHHHGETj/9dK1YsUIrVqxQWVlZtUvTD2Wapj766CN99NFHSkpK0llnnaWTTjpJnTp1UuvWrZWXl6ctW7ZoyZIl2rp1a2C7K664QiNGjKixv+uuu07ffPONXnjhBW3fvl2DBg3SOeecoxEjRqhLly4yDEMHDhzQli1b9Nlnn2ndunWSpD/84Q/V9nPFFVfogQceUF5enpYsWaIBAwboN7/5jbp166bs7Gy9//77evPNN+Xz+TR16lTNmTOn3tf07LPP1vr161VQUKBx48bpN7/5jdq3bx/4IqN///7q3Llz0Os3JFSvy6EO/QLnvPPOa3QthzPMUFzI30hffPGFnnjiCbVv314TJ05UWlpatWnWNm7cqMWLFyszM1M33HBDtctBmiMrKysk+2muNonJDa9UB2abC39emDLQxl8naATeDu9qzq+bYM8bD/yKA1q8gznZTpcgSUpJSXG6BH388ccaNmyYJOkvf/mL+vbtq8suu0yFhYW1rt+7d2+99957dQ4M9txzzwV6lxctWqSJEyfWut7dd9+tBx54oMF73qtER0frlltu0cyZM+Xz1X5xtGmaeuSRR3T33XfXWf+hUlJStGXLlhrvw7vvvqtJkybVuY+4uDg988wz8vv9uuyyyyRJ//rXvzR9+vQa6+7Zs0cnnHBCnZf0H75dU9ev+tw9dOhQffzxx7VuE6rXpcqll16qOXPmKCYmRnv37lVSUlKD+6yNbQG9rpMwGIsXL27S+gR0uAEBHXbj7fAuAjqAYBDQ/+fwgD5z5kx99913+r//+z998MEH+vnnnxUZGanevXvroosu0u9+9ztFR0fXub+CggJ17dpV2dnZOu+887Rw4cI6183Ly9Py5csDPbfp6enKyspScXGx4uPj1b59e/Xr109nnXWWJk+eXGPU+LpkZmbqhRde0H/+8x9t3rw5MCVYUlKSevXqpRNPPFG/+tWvNHLkyDp7+NPT0/XII49o2bJl+vnnnxUbG6vOnTtr1KhRuuaaa5SamqoXX3yxwYAuVYbuRx99VP/5z3+0Y8cO5efnBz5L1rZdU9ZvTEAP5euSm5urTp06qaCgQFdccUW9YxY0xLaAPmHChJDsxzAMAjrCEgEdduPt8C4COoBgEND/p7aA3lx33HGH7rvvPkVFRWn37t017lVH+Hr66af1u9/9Tj6fTxs2bFDfvn2D3pdt96Dfd999djUFAAAAAK5y880368knn1ROTo4efvhhPfLII06XhBDw+/169NFHJUkXX3xxs8K5ZGNAT0tLs6spAAAAAHCV5ORk3Xbbbbr11lv11FNP6eabb1bHjh2dLgvNNHfuXG3fvl0xMTG69957m70/26ZZa0hOTk5I5rUDAAAAADe66aabdOyxx6qoqEj333+/0+WgmYqLi3XnnXdKqpyKr2o6uOawtAe9ajS8uLi4WpeXlZXplVde0fLlywPhvH379ho/frzGjRtnZWkAAAAAYKvIyEht2bLF6TIQIjExMfrxxx9Duk/LetA3btyoKVOm6Lrrrqt1Inu/36+ZM2dqyZIlgRH4TNNURkaG/vnPfzZr5DsAAAAAAMKNZT3oGzdulGmaGjp0qCIiImosX7ZsmTZu3CjDMNS1a1edddZZiomJ0aeffqrvvvtOb731loYOHarU1FSrSgQAAACAGs466yxmp4EjLAvoW7ZskWEYGjx4cK3L3333XUlS9+7d9dBDDykqKkqSNHr0aN15553atGmTli9fTkAHAAAAYKm8g0W669cLFBXbSu06tlZEK9cM1WWpiooKHcws1P49efp/L52no/t0cLokz7MsoGdkZEiSevXqVWNZdna2du7cKcMwdMEFFwTCuST5fD5deOGFuuuuu7g/AwAAAIDl1n+6U+s+3uF0GY5aPne9rph1ttNleJ5lAf3gwYOKj4+vFr6rbN26VZJkGIYGDhxYY3nV3HFVIR8AAAAArGNIhuF0EY5qFVXztmTYz7KAXlpaKp+v9ktD0tPTJUmdO3dWbGxsjeWRkZGKj49XcXGxVeUBAAAAQCXyOVzCspsrWrdurbKyMmVnZ9dY9t1338kwjFovf69SWlqqyMhIq8oDAAAAgP8xjOAeCoNHo48FTrMsoPfo0UOS9NFHH1V7PicnR5s3b5Yk9evXr9Zt9+/fr7KyMrVt29aq8gAAAABA0n9jbJD53PAd9gh2P6F8BFOT028CJFkY0M844wyZpqlXX31VK1euVFlZmfbt26e//e1v8vv9atWqlU499dRat60K8N26dbOqPAAAAACQJJlS6DqsfYc9AstCmcAb22YTH3CcZfegDx06VG+//ba2bdumhx9+uNoywzA0btw4JSQk1LrtZ599JsMwAoPFAQAAAIBVjP/+Z9HOw2WncAHLetB9Pp/+8pe/6Pjjj5dpmoGHJA0bNkxTp06tdbt9+/Zp1apVkqQTTzzRqvIAAAAAoJJhhq4HPVwfcAXLetClyoHi7r77bu3evVs//vijpMp50Y844oh6t7v11lvVqlUrderUKSR1GIZR54jyAAAAAKSICC9Ps2XI8PggaV4/frewNKBX6dKli7p06dKodTt27KiOHTuGtP3Y2FjFxcWFdJ/B8JdXOF0CYCl+sbtLsG9H1dVO8Cb+GgOhZfe/jc35HZ6cnBzCSsIQv//gArYEdKcVFRWppKTE6TKU2LqN0yUAAAAAtaptemQnOPFFgf2zjDWlMZu+OOcLClfwREA3TVN+v9/pMgAAAADX8vznZYsTevB7r9yS69u8wRMBHQAAAADqE/p8Htod1txbaCM7tzi5AwEdAAAAgLfZf417CIS63nA7/paJgA4AAADA84KKp27NtFwPH7YI6AAAAADQmLDt1kB+uMPrbExgD5dja+EI6AAAAAA8raSwrGVPF9uIQ8vcnWN9HWgQAR0AAACAp8XER3q+BzmlU6LTJUA2BPTy8nIVFRWpdevWgefy8/MVExOjVq34fgAAAACA8wyftxO6r5XP6RIgyfJ34b777tOzzz5b7bnnnntO9913n9VNAwAAAEAjGDKMxj9kKGwfdR6XFS8rmszygD5u3Dh99tln2rFjhyRpx44d+uSTTzR+/HirmwYAAACAhoUq5IbBo95jg+MsD+gnnHCCTj75ZL388suSpJdfflknn3yyBg4caHXTAAAAANA4oeypdkI41owabLnR4Le//a02btyoefPmadOmTfrtb39rR7MAAAAA0CBDIe4V9x32sLJXPERtqCWPYh9GLBulbfbs2dX+3KZNG7322mtq37695s2bV23ZjTfeaFUZAAAAAFA/q3uRD913Y+Ykb+y+0OJYFtAzMjKq/bmsrEymaaqsrKzGMgAAAABwjCnZNkzaYc2YdSR2u4dtI/e7g2UB/dBR2nNzc3Xttddq2LBhWrVqlf7f//t/1aZdAwAAAADHOHgftmvGT3dJGV5nyz3or7zyio488kjNmDFDnTp1CgwYBwAAAABu4Ojo6nbcq97QPegkdFewPKBv27ZNy5Yt07Rp0yRJ06ZN07Jly5Senm510wAAAADQMKfnJ3dbHXCM5QH9lVde0YABA9S/f39JUlpamk444QR60QEAAAC4RAPzgzfzEbJebsJ5i2fZPehV/vjHP6qwsLDac9dcc42io6OtbhoAAAAAGsUIg2nGrLxfPQwO3xMsD+iJiYlKTEys9lyHDh2sbhYAAAAAGq+ZAdXpwd7qGg0e4cXygA4AAAAAbmYY4dGDXp9mf0EQ5sffUlgS0GfPnh2S/RiGoRtuuCEk+wIAAACAoIV7fqWDPSxYEtA//PBDGYYh06x5FjT2mynTNAnoAAAAACxXWlJeM6eEeyA/3OHHc1hUy9yTa1spqJslAX3YsGF1BvEvv/xShYWFioqKUq9evdSuXTtJ0v79+/XDDz+opKRE8fHxGjx4sBWlAQAAAEA1UTGRMiIOyy8tvcf5sMPt0KWNM3WgGksC+owZM2o8Z5qmHn74YRUVFeniiy/WhAkTFBsbW22d4uJiLV68WAsWLFBJSYluueUWK8oDAAAAgOoO719soMe5RWhpVwm0ALYNEvf222/riy++0LRp03TeeefVuk5MTIwmT56s6OhovfTSS3rnnXc0evRou0oEAAAA4EGNGiSuRmAPw8RezzEyRpw7+OxqaPny5fL5fI0K3KNHj5bP59OyZctsqAwAAACA11WG9CY8fEa1R9N3YP3j8BrrWx3uYFsP+p49exQbG6vo6OgG142OjlZsbKz27NljQ2UAAAAAPK+ZKbX65qYzl8Q35xhI6a5gW0CPiIhQQUGB9u/fHxgYri779+9Xfn6+4uPjbaoOAAAAgGeFvBfZqHZJvFVXw4eyZuK5O9h2iXtqaqok6bnnnqt1+rUqpmnq+eefr7YNAAAAAFjKsO5h+A57NOfK9UP2E/Ja4TjbetB//etfa926dfryyy9166236vzzz1daWlqgl7ywsFAbN27Uv//9b3333XcyDEPnn3++XeUBAAAA8ChDRsODxIW2wWpq6780avwAL7AtoA8YMEBXXHGFXnjhBX3//fd64IEHJElRUVGSpNLSUkmVPeiGYeiKK67QcccdZ1d5AAAAALzMqSBsHnapulN18EWAK9gW0CVp/PjxSk1N1bx587RhwwaZpqmSkpLAcsMwNGDAAE2ZMkXHHnusnaUBAAAA8CpD9vagH9a2O7imEE+zNaBLUp8+fXTvvfcqPz9fP/zwg3JzcyVJiYmJ6tmzpxISEuwuCQAAAIDX2ZRPm9qMbYPBk89dwfaAXiUhIUEDBgxwqnkAAAAACDCsTKjNmf3s8CesGhHemt2iiRwL6AAAAADgCiEexdzSq+VtmL4NzrE9oGdmZmrx4sVat26dMjMzVVpaqsWLFweW5+fn65133pFhGPr1r3+tiIgIu0sEAAAA4DHNCtUO3b9eo9nmJHa60F3B1oC+atUqPfrooyouLg7MhX74YAwJCQlavXq1vv/+e3Xt2lWnnHKKnSUCAAAA8JgmT7Pm1jB7+DE0Ia9beok/Gs1nV0N79+7VI488oqKiIg0cOFAzZsyoc0C4kSNHyjRNrV692q7yAAAAAHiV0cDDd9ijofXd8mhC3f6Kiua/jmg223rQFy1apJKSEg0dOlR/+MMfJEkvvvhiresef/zxkqRt27bZVB0AAAAAryopLqvZg94SO5RrXBL/vx9zMgtsLQW1sy2gr1u3ToZhaMqUKQ2um5KSoqioKGVkZDRq33PmzNG8efPqXD5p0iRNmzat0bVahkEcAAAA4FLJyclOl+CY6JhIGREtMZE3XrtOiU6XANkY0A8cOKDo6Gh17NixUetHR0ersLCwUesWFBTUG+YLCwtdMdicv5zLRgAAAOBObvi87JgQj+IOBMu2gB4ZGanS0tJGrVtWVqaCgoI671E/XHx8vDp06FDn8ri4OPn9/kbty1r8rQcAAIA7uePzsjNfFBiqOXh1o4TLFbKNOTSiiivYFtCPPPJIbd++Xbt371aXLl3qXfebb75RRUWFunXr1qh9T506VVOnTq1zeVZWlrKzs5tUrxWSk9o6XQIAAABQKzd8XpYqb3e1mykFF1DruafbMQTtsGbbKO4nn3yyTNOsNud5bfLy8vTiiy/KMAymWAMAAABgOcMwQvPwHfIwZO8j0G7wDzjPth70cePG6Z133tF//vMfxcbGauLEidWW5+bmatWqVZo/f74yMzPVoUMHjRw50q7yAAAAAHiVFT3f9QTeYKOwGzroYS3bAnp8fLzuvPNOzZw5U0uXLtXSpUsDyyZNmqTy8nJJkmmaSkxM1G233aaoqCi7ygMAAADgVYZZX552jTAoEc1k2yXukpSamqonnnhCZ511llq1aiXTNGWapsrKymSapnw+n84880w9/vjj6t69u52lAQAAAPAsu69Hd+PD6fcAko096FXatWunGTNm6LrrrlN6erqys7NVUVGhpKQkpaamKiYmxu6SAAAAAHhY5SjuTlfhLI8fvmvYHtCrREVFqW/fvk41DwAAAACSJNOBhN6Y5kw7bzr3+jcULuFYQAcAAAAAN7A1nzehnUBNjA7nGbYH9MzMTC1evFjr1q1TZmamSktLq029lp+fr3feeUeGYejXv/61IiIi7C4RAAAAgJdU3Ycdyl3W+CEUO1MgrIc8s9OB7gq2BvRVq1bp0UcfVXFxscz/Xq9x+Hx7CQkJWr16tb7//nt17dqVudABAAAAWC5k+dzqoGsc1kyIkjr53B1sG8V97969euSRR1RUVKSBAwdqxowZSkhIqHXdkSNHyjRNrV692q7yAAAAAHhZE0c9N+p62P1fHXUENZI7HGdbD/qiRYtUUlKioUOH6g9/+IMk6cUXX6x13eOPP16StG3bNpuqAwAAAOBVLXEU9xZ2OJ5hW0Bft26dDMPQlClTGlw3JSVFUVFRysjIsKEyAAAAAF7mrzDrTbRGC4q7Zh3XxBcVltpcCWpjW0A/cOCAoqOj1bFjx0atHx0drcLCQourAgAAAOB1Ff6KGmNjtVR1fdlQXuq3uRLUxraAHhkZqdLSxn0rU1ZWpoKCgjrvUQcAAACAUImMbiXD542AXpfEtnFOlwDZGNCPPPJIbd++Xbt371aXLl3qXfebb75RRUWFunXrZlN1AAAAALzMKz3odfH44buGbaO4n3zyyTJNs9qc57XJy8vTiy++KMMwmGINAAAAgD0Mjz+CkJmZqZtvvlmpqamKjY1VSkqKRo4c2WDmq8tZZ51V9+j4hz0uu+yyGttPnz69we3S0tKCO1ib2NaDPm7cOL3zzjv6z3/+o9jYWE2cOLHa8tzcXK1atUrz589XZmamOnTooJEjR9pVHgAAAACPqpxlzONdyE08/k2bNmn48OGBgb1bt26tgwcPatmyZVq2bJluuOEGzZ49u0n7bNu2rY444og6l5eWlio7O1uSNGjQoDrXi4mJUZs2bWpdlpKS0qSa7GZbQI+Pj9edd96pmTNnaunSpVq6dGlg2aRJk1ReXi5JMk1TiYmJuu222xQVFWVXeQAAAAC8zOP5vClKSko0fvx4ZWRkKC0tTXPmzNGAAQNUWFioxx9/XHfeeaeeeOIJHX/88bX2dNdl4cKF9S6///77dfvttys6Orre2cEuuuiiOqf0djvbLnGXpNTUVD3xxBM666yz1KpVK5mmKdM0VVZWJtM05fP5dOaZZ+rxxx9X9+7d7SwNAAAAgIc19tLqlvto/Gv17LPPavv27YqLi9Pbb7+tAQMGSJLi4uJ0++2367rrrpMk3XHHHSorKwvZe/TSSy9Jqrw6u23btiHbr5vY1oNepV27dpoxY4auu+46paenKzs7WxUVFUpKSlJqaqpiYmLsLgkAAACAlzXjPuxmqX1K8upc2LM/Z84cSdLFF1+so446qsbyW265RU8//bT27Nmjjz76KCS3Lq9cuVLff/+9JDWpVz7c2B7Qq0RFRalv375ONQ8AAAAA/2U4cw/6oU2atTxnp0Yef35+vlatWiVJGjVqVK3rHHXUUerTp482b96s5cuXhySgV12yfuSRR+qcc85p9v7cytZL3AEAAADAlZweRd3334fLR3HfsmWLTLPy24T6RkSvWrZ58+bG77wORUVFeu211yRJl156qSIiIupdf/ny5UpNTVV0dLTatGmjQYMG6c4779Qvv/zS7FqsZlkP+owZM5q9D8Mw9Pjjjze/GAAAAACogyFGcW/s4e/duzfwc6dOnepcr2rZoesHa9GiRcrJyZFUOZVaQ3bv3q1WrVqpdevWysnJ0Zo1a7RmzRo9/fTTeu211zRixIhm12QVywL6jh07gt7WMAyZpun5vyQAAAAAbGDTPehGkI2YjbpZ3R75+fmBn+Pi4upcr2pZXl5es9v817/+JUkaPHiw+vTpU+d6J5xwggYPHqwxY8aoS5cu8vl8ysvL05IlS/SnP/1Je/fu1cSJE/XNN9/omGOOaXZdVrAsoE+ePDmo7dLT07V69eoQVwMAAAAAdXNz52Cwwb5pjbjz+Hft2qUPP/xQUsO95zfccEON51q3bq1LLrlEQ4YM0cCBA5Wdna2ZM2dq3rx5VpTbbJYF9IsvvrhJ6+/evVtz5szRN998I6nyL8hpp51mRWkAAAAAUM3ld9TdM1ufF+7bEuJKmu/y24M5lsZNh5aQkBD4ubCwUImJibWuV1hYKKkyIDfHyy+/rIqKCsXExATdCSxJ3bp10+9//3vde++9evvtt1VRUSGfz31Dsjk2inuVzMxMzZs3Tx9//HFgXvSBAwfq0ksvVc+ePZ0uDwAAAEBL14zeYzf3vFvh0PvO9+zZU2dA37Nnj6TKUdebo2ru84kTJyopKalZ+xo8eLAkKTc3V/v371f79u2btT8rOBbQDx48qNdee00ffPCBysvLZZqm+vTpo6lTp9Y7GiAAAAAAuIa38rmOPfbYwJhhmzZt0rHHHlvreps2bZKkZk2t/fnnn2vbtm2SWvbc54eyPaAXFBRo4cKFeuutt1RSUiLTNHX00Ufr0ksv1Yknnmh3OQAAAAA8rrSkXC/N/i6obd3Yg/7ig00/ltNG9NSgQQ2vl5CQoJNPPllfffWV3nvvPZ1//vk11tm9e3dgerXmjJheNThcly5ddPbZZwe9nypfffWVpMrL7tu1a9fs/VnBtoBeUlKipUuXatGiRSooKJBpmurUqZOmTJmiM844w64yAAAAAKCaqOgIz/WEHy42IbLR615yySX66quvNH/+fN11113q2rVrteUPP/xwIO8NGzYsqHoKCwv1+uuvS5J+85vfNHi/eEOzgP3000966qmnJEljxoxx5f3nkmR5VX6/X2+99ZauvvpqzZkzR/n5+Wrbtq1+97vf6cknnyScAwAAAHCUYRgyfN5+RMc0PqBfddVV6tGjhwoKCjR27FitX79eklRUVKQHH3xQTz75pCRp1qxZioysvt+jjz5ahmE0OCL7woULlZubK6lxc5/PmTNH559/vhYvXqysrKzA8/n5+Zo/f75OP/10HThwQAkJCZo5c2ajj9VulvWgm6apDz/8UAsWLFBmZqZM01RiYqImTZqk0aNH13ijAAAAAMAZhisvVbdX448/OjpaS5Ys0fDhw7V+/XoNGDBAiYmJKigokN/vlyRdf/31zbpv/MUXX5QknX766UpNTW1wfb/fr4ULF2rhwoWSKi/Fj46OVnZ2tioqKiRJHTp00IIFC9S7d++g67KaZQH997//vX7++WeZpqm4uDhNnDhREyZMUExMjFVNAgAAAEDTGfL8Je5NPf5+/fppw4YNevDBB7V06VLt2rVLbdq00QknnKDf/e53mjhxYtCl7Nq1Sx999JGkxvWeS9KwYcM0a9Ysff755/ruu++UlZWlnJwcJScnq2/fvho9erSuuuoqtW3bNui67GCYpmlaseMJEyYEvoXq06dPUPPfGYahP//5z82u5dBLHJyUnOTukwEApMoroAAAoWF3r2xzfocfzMkOYSXBS0lJsb3Njd/8rOcfW2F7u24y6tf9NOp8ZtNymqWDxFX9gtiyZUu1PzcWl5kAAAAAsJop0YPu9eN3CcsC+vDhw63aNQAAAACEjGHQOUhCdwfLAvqNN95o1a4BAAAAIISMypRuu8ZeYWxDbeRzV7BtHnQAAAAAcCtnOtDdk4rdU4m3EdABAAAAeJshpxL6/4KxoUCHuiPDtXr+En93IKADAAAA8Dzb82lt7RmH/I9JVTzJEwHdMAz5fD6nywAAAABcKyIiwukSnGVxQm/y7g9b3/JZUOlAdwVPBPTY2FjFxcU5XYb85RVOl4BDfLt+b1DbvfvW1qC2y/w+K6jttr6VHtR2klSSVxrchkH+AxBO82cHO1KrERHcdhGRwX1JaFYE/5oaEcG1GeznE1+rINvzBddgq6jgP0j6gnw/gn0fg31Ro+Iig9ouvkPw/+ZFt44Karu4lPigtovvENx20bHBf4SJjQlu21PO6B7UdkcflRTUdsH+XYyPC+49lCRfkH8f7e55jIoOn4+w4fRvY3JystMlOMawYoy4EO+wxu5CfG6Rz90hfH67NUNRUZFKSkqcLkOJrds4XQIAAABQq+zsbKdLkOTgFwXNDdS2XyJ/eBd7iPcHR3gioJumKb/f73QZAAAAgGt5/fNycPnURaG2Rinhc/UG/scTAR0AAAAA6ubUPOhWauLxtLTDD1MEdAAAAACeVlrmD3p8mpYiJ7vI6RIgAjoAAAAAj4uOjvjvXOiHPOmxK8QTk2OdLgEioAMAAABAwzO1tMTAfsghBzuLA0KLgA4AAADA44yGL3EP5971RmRvr1/i7xYEdAAAAADeZphNGySNLAuLENABAAAAeFwjetBbOo8fvlsQ0AEAAAB4miFrL/E2Lbwm3ghRsg7VftA8BHQAAAAA3nb4CO4h330YhN8wKNELCOgAAAAAPM65S9wb6l0Pi3CPkCGgAwAAAIBDOdg1AdwlZXgdAR0AAACAtxkOTzNW1bSD07e55osCjyOgAwAAAIDPvqbqDMOHPW3l4HINtQ1nENABAAAAeJ4bp1mztVfbfYfvSQR0AAAAAN4W4lHc7QrWtvawwxYEdAAAAACeZjg4intzhPKLgHA8/paIgA4AAADA8wiocAMCOgAAAABvC/El7uGIi+XdgYAOAAAAwNPKy/w1e9BbemA/LJHn5hY5UweqIaADAAAA8LTIyAgZvsMTeQvvUzaq/5CUFOdYKfgfAjoAAAAAbzOkmregt/Qu9Oq4Bd8dfE4XAAAAAACOauGd5Qgf9KADAAAA8DYjFNOsuTHlN+WY6EJ3AwI6AAAAAE8zFIpLvKvvwLQxr4fi8nQucXcHAjoAAAAAT7MiSxN4EQwCOgAAAABPMwyF4BL3pgj2KwFSf0tHQAcAAADgefb2eDf+cni76qLH3x0I6AAAAADgIMIxqtgS0MvKyhQZGVnnnwEAAADAKZWDxHk9JXv9+N3B8oD+8MMPq02bNrr66qsDz7344ovKzs7WLbfcYnXzAAAAAFA/g15srx+/W/isbmDw4MH64IMPtG/fPknSvn379N5772nw4MFWNw0AAAAAjjKM0D7Qslke0IcOHapjjjlGc+fOlSTNnTtXvXv31tChQ61uGgAAAAAawZBhWPP47wX0IXtYVyfcwPKALknXXHONVq5cqWXLlmnlypW65ppr7GgWAAAAABol1D3d4fiA8yy7B33BggXV/ty+fXs99dRTOvLII7Vy5UqtXLkysGzy5MlWlQEAAAAAIRUuYba+6dvgTpYF9P/85z/V/pyXlyfTNJWdnV1tmWEYBHQAAAAAjqnsQQ6T1N0ETTkkv7/CukLQaJYF9Oeffz7wc0lJia699lodc8wx2rNnj5566ilFR0db1TQAAAAANFpZmT9sesWtUlhY5nQJkE33oL/22muKjY3VXXfdpZiYGL322mt2NAsAAAAADYqMjLBs8LVweSQmxjj9NkA2BPSff/5Zb775pqZOnarIyEhdeumlevPNN7Vnzx6rmwYAAACABhlyfoA2NzzgPMsD+j//+U91795dp556qiTplFNOUY8ePapdAg8AAAAAjnFBOHb6AXew7B70KpdffrlKS0urPXfttdeqVSvLmwYAAACARgjNXOCmS4ZND+ZYWuIgeeHI8pTcpUuXGs917949pG3MmTNH8+bNq3P5pEmTNG3atJC2GRR3/H0FAAAAakhOTna6hLBHyEVzOdKNnZOTow0bNigzM1MlJSXNnmatoKBAGRkZdS4vLCxUREREs9oIBX85UxcAAADAndzwedkphrjMG+5ga0AvKyvTv/71L73//vvy+/2B5w8N6Pn5+br66qtVXFysp556Sh07dmxwv/Hx8erQoUOdy+Pi4qq15xz+1gMAAMCd3PF52aEvCgwner+DubzWuhr5gsIdbAvoFRUVuu+++7Ru3TpJUocOHZSVlaWKiuq9ygkJCRo2bJiWLl2qzz//XOeff36D+546daqmTp1a5/KsrCxlZ2c3q/5QSE5q63QJAAAAQK3c8HlZklJSUuxv1JFbUUnEqMmWedAl6aOPPtLatWuVlJSkhx56SM8++6xat25d67pDhgyRJK1fv96u8gAAAAB4lQtGUXf6AXewrQf9ww8/lGEYuvLKK9W7d+961+3Zs6cMw9BPP/1kU3UAAAAAvKryHnT7U2pdTToxGDwD3LmDbQF9586dkqSTTz65wXUjIyMVHx+v3Nxci6sCAAAA4Hku60V2Uy2wl20Bvbi4WLGxsYqKimrU+uXl5Z4eSRIAAACATSzosbYjZLtk2nWEkG0BvU2bNjpw4ICKi4sVExNT77r79u1TcXGxOnXqZFN1AAAAADzLkVHcmy+UJYfh4bdItg0SV3Xf+VdffdXgukuWLJFhGOrXr5/VZQEAAACA44O0Of2AO9gW0EeNGiXTNDV37lxlZGTUud6iRYv09ttvS5LOPfdcu8oDAAAAgAY5HaTdFLwzMzN18803KzU1VbGxsUpJSdHIkSO1ePHioPa3c+dOGYbR4OONN96odz/r1q3TJZdcos6dOys6OlpdunTR1KlT9e233wZVl51su8R9wIAB+tWvfqVly5bppptu0qmnnqqSkhJJ0r///W9lZmZqzZo1gfA+fvx49ezZ067yAAAAAHhURYUZlpe4N0ZjD6ukuLxJ+920aZOGDx8eyG+tW7fWwYMHtWzZMi1btkw33HCDZs+e3dRyA1JSUuock6y+W6bnzZun6dOnq6ysTFLlrdY///yz5s6dq9dff10vv/yyLrrooqDrspptPeiSdO211+q8885TQUGBli1bpqKiIknSK6+8ovfee0+//PKLJOn888/XZZddZmdpAAAAADzK6V5uNzxkNH7EuZKSEo0fP14ZGRlKS0vTunXrlJubq9zcXM2aNUuGYeiJJ57Qv/71r6Dfk1WrVmnfvn21PsaOHVvrNps2bdJll12msrIyXXjhhdqzZ48OHjyoPXv26IILLlBpaammTZumrVu3Bl2X1WzrQZekiIgITZ8+XaNGjdLy5cu1detWZWdnq6KiQklJSTr22GM1YsQIde7c2c6yAAAAAHiYz+eT4aujq9kLI6UbUmxs42bbkqRnn31W27dvV1xcnN5++20dddRRkqS4uDjdfvvt2rt3r5566indcccdmjp1qiIjI62qvJq77rpLpaWlOv744zV37ly1alUZd4888kjNmzdP27Zt07p163TXXXfptddes6WmprI1oFfp2LGjLrnkEieaBgAAAIBqjP8+6lyIaubMmSNJuvjiiwPh/FC33HKLnn76ae3Zs0cfffSRRo4caXlNOTk5euuttyRJN998cyCcV2nVqpVuvvlmXXrppVqyZIny8vLUunVry+tqKlsvcQcAAAAA1zHUqMHJans4fm16A48mHUsj5Ofna9WqVZIqBwKvzVFHHaU+ffpIkpYvXx6a96gBK1asUGlpqSTpnHPOqXWdqudLSkq0YsUKW+pqKkt60BcsWBCyfU2ePDlk+wIAAACAWgXZU+61DvYtW7bINCuv+09LS6tzvbS0NG3evFmbN28Oqp0LL7xQ27ZtU2Fhodq3b6/Bgwfr8ssv15gxY2pdv6qdI444Qu3bt691nfbt26tDhw7KyMjQ5s2bXTlrmCUBff78+Y3+BqYhBHQAAAAAVrM+aIeyBedujN+7d2/g506dOtW5XtWyQ9dvilWrVikxMVERERH6+eeftXDhQi1cuFAXXHCB5syZo6io6vfMV7VTX01VyzMyMoKuy2qWBPR+/fqFLKADAAAAgJUMNf4S7+YIXaw2Qv6FQmMPPz8/P/BzXFxcnetVLcvLy2t0DTExMbruuus0efJkHX/88YF7xDdt2qSHHnpIr7zyil5//XUlJSXp2WefrbWu+moKti47WRLQ77//fit2CwAAAAChZ5i2XKtOF2b9OnbsqKeeeqrG8/369dPLL7+s9u3b67HHHtPzzz+vm2++Wb1793agSms5Moo7AAAAALiHoQEnBBeN1q/1h7iW5jtuYESTt6nQL5K6NLheQkJC4OfCwkIlJibWul5hYaEkhXSk9HvuuUd///vfVVRUpLfeeqtaQK+qq6rdulhRVygxijsAAAAABMkIxePQgddDsD8rHXqP9549e+pcr2rZkUceGbK24+Pj1a9fP0nS9u3ba62rvpqsqiuUCOgAAAAAEKwgE7nhO+Rx6HRn/32uWYndQscee2zgfv1NmzbVuV7Vsr59+1pb0H9VtfPLL78oKyur1nUyMzOVkZFha11NxSXuAAAAALzNkDZ+WxHcphYNLve/3TZ9/8Ecy4CB9Y9+XiUhIUEnn3yyvvrqK7333ns6//zza6yze/fuwLRnI0aMaHItdSkoKAgE/+7du1dbNmTIEEVFRam0tFTvv/++Lrnkkhrbf/DBB5Kk6OhoDRkyJGR1hRI96AAAAAA8rWqMuJA9DJseoay5Ca9XVfidP3++du3aVWP5ww8/LNM01alTJw0bNqzR+62aX70uM2fOVFFRkQzD0NixY6stS0xMDDz32GOPye+vPjZAeXm5HnvsMUnS+PHjuQcdAAAAAFwpRIm81kvWrXyE4nL4IC6Lv+qqq9SjRw8VFBRo7NixWr9+vSSpqKhIDz74oJ588klJ0qxZsxQZGVlt26OPPlqGYWj69Ok19nvWWWfp/vvv1/r161VeXh54fvPmzbr88sv117/+VZJ0xRVX6Nhjj62x/T333KOoqCitWbNGl1xyifbt2ydJ2rdvn6ZOnao1a9YoOjpa99xzT9MO2EZc4g4AAADA4+yZB90qzbkcvpadNCg6OlpLlizR8OHDtX79eg0YMECJiYkqKCgI9Fxff/31uuyyy5pUwo8//qjbb79dt99+u1q1aqU2bdqoqKio2sjsU6ZMqXUqNqlyOrZ//etfmj59ul599VW99tpratOmjQ4ePChJioqK0osvvlhruHcLetABAAAAeJ5tl6W79dHE16tfv37asGGDbrrpJvXq1UslJSVq06aNzj77bC1atEhPPPFEk9+DRx55RFdddZUGDhyolJQU5eXlSZJ69eqlSy+9VB9++KHmzp2rqKioOvcxZcoUff3117r44ot15JFHqrCwUJ06dQo8P3ny5CbXZSd60AEAAAB4Wnl5hZrV+9wCFBSUNnmbDh066LHHHgvc290YO3furHPZBRdcoAsuuKDJdRzu+OOP17x585q9HycQ0AEAAAB4WmSkrylXeLdIMTFEQzfgXQAAAADgeT6ftxN6q8gIp0uACOgAAAAAvK7qRmzAYQR0AAAAAJ5WNVNaUOqfuttZTTgmvp9wBwI6AAAAAM8Lepq1wzdzOrAHHbRJ6G5AQAcAAADgbaG8wt2JwB6C2ulBdwcCOgAAAABYlVCtCOyE6RaLgA4AAADA82zLvIRr1IOADgAAAMDTKgeJ83Zy9vrxuwUBHQAAAIC3GXKsZ9uQ8b+2zar/OT3SHJxCQAcAAADgcYbtPci1tmZU/a/yB1tjOh3orkBABwAAAOB51ufzpjdQfQtr4zr53B0I6AAAAAA8LTzuQbe4PtcfvzcQ0AEAAAB4WwjuQXc63nLXestAQAcAAADgeUZTI7bTifwwNcppYmJ32eF4licCumEY8vl8TpcBAAAAuFZERITTJTiqUVd4h1OKPbzWhgJ7OB1bC+aJgB4bG6u4uDiny5C/vMLpEnCIAccdaet2cBfT5EIwoDb81agbt2eipUtOTna6BMdUVJi1/iVvUX/vDzmW2n7XFxWV2VcL6uSJgF5UVKSSkhKny1Bi6zZOlwAAAADUKjs72+kSJDnzRUFEhK9lhfEG1HassbGR9heCGjwR0E3TlN/vd7oMAAAAwLW8/nnZ5/NQQq+F14/fLTwR0AEAAACgLobRtGnWwu1Wuf8dWn3HSEB3AwI6AAAAAE9rat52/5zpCFcEdAAAAACeVtmD7nQVzvL68bsFAR0AAACAxxn0isMVmBwcAAAAAAAXoAcdAAAAgOd5vQfd68fvFgR0AAAAAJ7GPehwCwI6AAAAAM/zeg+yxw/fNQjoAAAAAGCDYENwmE27jmYgoAMAAADwOGtHcW/urqu2tzao04XuBgR0AAAAAJ4WLvegW1ljOBy/FxDQAQAAAHhac3qmK4OtW9Jt5YFwSXz4IqADAAAA8LTKHnS3hOzmqDyGFnEoHkVABwAAAOB5hFq4AQEdAAAAgKdVVJgtpAc9eCUl5U6XABHQAQAAAHhcRERto7h75UbuyuOOjY10uA5IBHQAAAAAqOUSd2/3qMMZBHQAAAAAnhbMNGvhNlJ6Q8fn8Sv8XYOADgAAAMDjDDW1x7xmoHVjYm/KMZHQ3YCADgAAAMDzmt+DTMBF8xHQAQAAAHie1Zd4W3FJfChr5hJ3dyCgAwAAAIDFPeDVA/D/0rppNiUck6JbOgI6AAAAAM+ztwf5f40ZRt296/Rqew8BHQAAAICnOR2Eq0K6k3U4/RqgEgEdAAAAgOcZDiXUw5t1bvo2ErobENABAAAAeJ5bepDdUgecQUAHAAAA4HleD8ZeP363IKADAAAA8DTDMBy7xB04FAEdAAAAAJrBTdneuXvYEQoEdAAAAACe56aQ3RzBHkdLOf5wR0AHAAAA4Hlev8S9ooKudzcgoAMAAADwtIqKCs/3IJeX+50uASKgAwAAAPC4iAif53vQo6MjnS4BIqADAAAAgOd70L1+/G5BQAcAAADgaYYRXEANpxHTCeDhwTUBPTc3V9u2bVNZWZnS0tKUkJDgdEkAAAAAPMH476OJW7Wo0NuiDiZs2RbQv//+e7399tvq1q2bfv3rX1db9sknn+jpp59WSUmJJCk6OlrXX3+9hgwZYld5AAAAADysZYXtpvP68buFz66GPvnkE33yySeKjo6u9vy+ffs0e/ZsFRcXS6qc3qC4uFiPPfaYdu/ebVd5AAAAADys6jJ3qx5urxfuYFtA37RpkyTp5JNPrvb8+++/L7/fr969e+vll1/W3LlzddJJJ8nv92vp0qV2lQcAAADA0wxLH4Zx+KOpIbr69qGvEW5g2yXuBw8elM/nU7t27ao9v3r1ahmGoUsuuUSJiYmSpMsvv1yrVq3Shg0bGrXvOXPmaN68eXUunzRpkqZNmxZ88aESRoNIAAAAwFuSk5OdLsFR9vciuysU04vuDrYF9NzcXMXGxsrn+1+nfWFhoXbt2qXo6Gj1798/8HynTp0UGRmpzMzMRu27oKBAGRkZdS4vLCxURERE8MWHiL+8wukSAAAAgFq54fOyU5y+zLtqNPhwC8mZmZl68MEHtWTJEu3evVvx8fE64YQTdN1112nixIlN3l9ubq6WLFmiDz74QKtXr9aPP/4ov9+vjh076rTTTtO1116rM844o87tp0+frpdeeqneNvr166eNGzc2uTa72BbQo6KiVFBQIL/fH/jLv2nTJpmmqd69e1cL7lLlQHGlpaWN2nd8fLw6dOhQ5/K4uDj5/f7giw+ZMPsbBwAAAM9wx+dlJ78osP+zelUgPzyYOzN9W9OOf9OmTRo+fHigo7R169Y6ePCgli1bpmXLlumGG27Q7Nmzm7TPQYMGKT09PfDnmJgYRURE6Mcff9SPP/6o+fPn649//KMeeeSRevcTExOjNm3a1LosJSWlSTXZzbaA3rlzZ/3www9as2aNTjrpJEnSZ599JsMw1K9fv2rrlpaWqqCgoN7QfaipU6dq6tSpdS7PyspSdnZ28MWHSHJSW6dLAAAAAGrlhs/LknMByk29107U0pQ2S0pKNH78eGVkZCgtLU1z5szRgAEDVFhYqMcff1x33nmnnnjiCR1//PG67LLLGr3fsrIyHXfccbryyis1evRo9ezZU6Zpatu2bfrzn/+shQsX6q9//at69uypa665ps79XHTRRXrxxRcbf0AuYtsgcYMHD5ZpmnriiSf073//W//85z/16aefSpJOP/30auump6fLNM1GB3QAAAAAaA6rR3F3+6Mpnn32WW3fvl1xcXF6++23NWDAAEmVVy7ffvvtuu666yRJd9xxh8rKyhq935dfflnffvutrr/+evXs2fO/74uhY445Rq+//rrOOussSWqwBz2c2RbQJ0yYoK5duyo3N1evvPKKli5dKtM0NWrUKHXp0qXaul988YUMw1BaWppd5QEAAADwNCtGRg+nR+PNmTNHknTxxRfrqKOOqrH8lltukWEY2rNnjz766KNG7/fMM8+sc5nP5wsM/L19+3bXXPERarZd4h4dHa2HH35YS5Ys0ffff6/Y2FideOKJGjZsWLX1ysvLtX79eqWkpGjgwIF2lQcAAADAo5weJM4NGnv8+fn5WrVqlSRp1KhRta5z1FFHqU+fPtq8ebOWL1+ukSNHhqTGQ29/KC8vD8k+3ca2gC5VXvIwefLketdp1apVkwcTAAAAAIDm8HpAb6wtW7bI/O8odvVd8ZyWlqbNmzdr8+bNIWv7k08+kSQdccQR9Y5VsHz5cqWmpuqnn35STEyMevXqpdGjR+v3v/+9jjjiiJDVYwXbLnEHAAAAADeqDJxOX2Lu7KO8kVNC7927N/Bzp06d6lyvatmh6zfH7t279cwzz0iqnE7NqOcbld27d2vnzp2Kj49Xfn6+1qxZo1mzZqlv375avnx5SOqxiiU96AsWLJAkJSYmavTo0dWea6qGetwBAAAAoDkMw/B8D3pTLnGvEhcXV+d6Vcvy8vKaVZdUObr7xRdfrPz8fHXr1k1//vOfa13vhBNO0ODBgzVmzBh16dJFPp9PeXl5WrJkif70pz9p7969mjhxor755hsdc8wxza7LCpYE9Pnz58swDHXu3DkQ0KueayoCOgAAAAArVQb0urKKI5OS26jyuFu1cmr++fqZpqnf/va3WrFihWJiYrRgwYI65zi/4YYbajzXunVrXXLJJRoyZIgGDhyo7OxszZw5U/PmzbO69KBYEtD79esnwzCq3RdQ9RwAAAAAuE1+/k9Bbde6dc1RzJ2Wl9f0Y8nPl1JSBjW4XkJCQuDnwsJCJSYm1rpeYWGhpMqA3Bw33HCDXnrpJbVq1UqvvfaaTjnllKD2061bN/3+97/Xvffeq7ffflsVFRXy+dx3x7clAf3+++9v1HMAAAAAgPBx6H3ne/bsqTOg79mzR5J05JFHBt3WH//4Rz355JOKiIjQnDlzNG7cuKD3JUmDBw+WJOXm5mr//v1q3759s/ZnBfd9ZQAAAAAAYaJqijY3Pax07LHHBq6M3rRpU53rVS3r27dvUO3cdtttevTRR2UYhp5//nlddNFFQe0n3Ng6zRoAAAAAuFFiotWXqleNFG+9YI6lbdu6B3w7VEJCgk4++WR99dVXeu+993T++efXWGf37t2B6dVGjBjR5FpmzpypBx54QJL09NNPa/r06U3eR22++uorSZWX3bdr1y4k+ww1etABAAAAeFplz7Nh8cMXwl5ya2psrEsuuURS5UDgu3btqrH84Ycflmma6tSpk4YNG9ak9+LBBx/U3XffLUl6/PHHdc011zRqu6q52evy008/6amnnpIkjRkzxpX3n0sEdAAAAABeZ9o123j1/+zZsnGPprjqqqvUo0cPFRQUaOzYsVq/fr0kqaioSA8++KCefPJJSdKsWbMUGRlZbdujjz5ahmHU2is+e/bswBRqDz74oGbMmNHomubMmaPzzz9fixcvVlZWVuD5/Px8zZ8/X6effroOHDighIQEzZw5s2kHbCMucQcAAADgbYZh19Xn1Zu1v8mQiI6O1pIlSzR8+HCtX79eAwYMUGJiogoKCuT3+yVJ119/vS677LIm7femm26SVHmFwOOPP67HH3+8znUXLlyo0047LfBnv9+vhQsXauHChZIqL8WPjo5Wdna2KioqJEkdOnTQggUL1Lt37ybVZScCOgAAAADPC9ewHCpNPf5+/fppw4YNevDBB7V06VLt2rVLbdq00QknnKDf/e53mjhxYpNrqLpM3TRN/fLLL/WuW1paWu3Pw4YN06xZs/T555/ru+++U1ZWlnJycpScnKy+fftq9OjRuuqqq9S2bdsm12Unw2zoYv0W4NBLHJyUnOTukwHwEg/86gOCwl+Nulk9MjLgtIM52U6XIElKSUmxvc2CglL98kue7e26SXJyrJKTGzdQHKxDDzoAAAAAz7P8O7jmNsAXqJ5AQAcAAACAkCb0qjQdwp3W2JV907bBPgR0AAAAAJ5WNU56aPdotdC2EdrjR7AI6AAAAAC8LZi5xoLbxBZBXQ3v1oPxGAI6AAAAAM9rSfm0JR2L1xDQAQAAAHiaWaFap2poqUG3th728vIK2+tATQR0AAAAAJ7m87XcMF6b2o41IsL2MlALAjoAAAAAjzNk1NKD/j8teY6zyuP2+UjobkBABwAAAOBtRq1XuFdf4VDhnNfrOE4vXUHgZgR0AAAAAGgK0iwsQkAHAAAA4HlkbrgBAR0AAAAAvJ7QvX78LkFABwAAAOBphiTD4wnV20fvHgR0AAAAAJ5mSs4l1PoGnCM1ew4BHQAAAICnGXIwCxPCcQgCOgAAAABvczCh19asI7O48UWBKxDQAQAAAHievfm0/tb+t9S+qE4+dwcCOgAAAABYHFGNoHZffSPT0rxORHcDAjoAAAAAzwsuQNsrHGpE8/icLgAAAAAAANCDDgAAAMDjDNE77fXjdwsCOgAAAACPc3SiNSCAgA4AAADA00zT9HwPst/vd7oEiHvQAQAAAHic4fN4OpcUEUE0dANP9KAbhiGfjxMOAAAAqEtERITTJTjK8HgXumGQl9zAEwE9NjZWcXFxTpchf3mF0yW4lmntpI5wOSf+QQy2Tc5VhIPmnKZBn+PBthnsX//mHGOQ2/noYfM0L2S35ORkp0twVlPe43D9OOCB8zjceSKgFxUVqaSkxOkylNi6jdMlAAAAALXKzs52ugRJznxR0OQh4g5b2a15nTwefjwR0E3TZNADAAAAoB58Xg4eQRih4omADgAAAAD1siFlV/W0uzLQu7Io7yGgAwAAAPA8w4aEaqj5l8NbVSX53B0I6AAAAAC8rck3oTevKaAujKUPAAAAAA5y6yBzsB896AAAAAA8z8mp9FzRq+6KIkBABwAAAADPJ1SvH787ENABAAAAeJohZ3vQ3cDjh+8a3IMOAAAAAIAL0IMOAAAAAM3glt53k9Hmwh4BHQAAAIC3GU0N2S5J5IepeQxNSOzuPCTPIaADAAAA8DgbJ0K3VROOid53VyCgAwAAAPA00zRdc5m6UyoqKpwuASKgAwAAAPA4n2F4PqBHtGL8cDcgoAMAAABAi7zEvSm8fvzuwNckAAAAAAC4AD3oAAAAADyvuf3HbhtjranHQ/+5OxDQAQAAAHhbCAZxJ+AiFAjoAAAAAGBpxLaqf52vBVoaAjoAAAAAbzOtjrrV9x5sXCeOt3wEdAAAAACwEUEbdSGgAwAAAPC2ENyDHva8fvwuQUAHAAAA4Hlez6deP363IKADAAAA8DgbutBdP48bEd0NCOgAAAAA4PZ86vb6EBIEdAAAAACexi3ocAuf0wUAAAAAAAB60AEAAACgaYww6m83Lb95HSFEQAcAAADgaaZMGeEUupuikcdlEuRdgUvcAQAAAMDjCOjuQA86AAAAAE8zDCOsrlq3QkQEfbduQEAHAAAAAM+P4+7143cHviYBAAAAAMAF6EEHAAAAgBB2INvdFx2Su8fpQHcFAjoAAAAATzMU3vk0FLWH8/G3JFziDgAAAACACzjSg+73+7V3717l5+ervLy83nXT0tJsqgoAAACAF5mSw9el19a4WfcitFi2BvSMjAy9/PLL+vLLLxsM5lUWL15sbVEAAAAAYLdqwZs5yFHJtoC+b98+/elPf1JeXp5MkxMQAAAAgDsY//0PcJptAX3evHnKzc1VfHy8LrzwQp1yyilq166dIiMj7SoBAAAAAGpyaJS4upp0pDuT7ydcwbaA/u2338owDN1000066aST7GoWAAAAABzXlPzrquAOW9kW0AsKCtSqVSsNGjQo5PueM2eO5s2bV+fySZMmadq0aSFvt8n4GwUAAACXSk5OdroER4VDB7KVNYbD8XuBbQG9bdu2ysnJkc8X+pndCgoKlJGRUefywsJCRUREhLzdpvKXVzhdAgAAAFArN3xedo4ZgoTqhohLj2C4sy2gn3rqqXrzzTf1/fff65hjjgnpvuPj49WhQ4c6l8fFxcnv94e0zeC44S8tAAAAUJM7Pi+H0xcFbvxsf3hNBPZwY5g2DaleUFCgGTNmKCEhQffee68SEhLsaFaSlJWVZVtb9UlOaut0Ca7FyP7eZhhu/AeudpyrCAfNOU2DPseDbTPYv/7NOcYgt/P5wud3FUIvjP6pCtrBnGynS5AkpaSk2N5mRYWpCn/9vx3C/RRo6Hefz2fIFxHuRxn+bAvoGzdu1P79+/Xss88qMjJSI0eOVGpqqmJjY+vdLi0trdltE9Ddj9DjbQR0ILQI6NZsSkD3tjD6pypoXg7oZoWpigpv/xtvGJIvIvS3I6NpbAvoEyZMCOpD+OLFi5vdNgHd/Qg93kZAB0KLgG7NpgR0bwujf6qC5umAbpoyK0yZCq/PJaFQ9XvfMGTJeGFoGtvuQZf4YAsAAADAfQzDkOHZy7u9etzuZFtAf/PNN+1qCgAAAACAsMM1DAAAAAAAuAABHQAAAAAAF7DkEvcFCxZIkhITEzV69OhqzzXV5MmTQ1YXAAAAAABuZUlAnz9/vgzDUOfOnQMBveq5piKgAwAAAAC8wJKA3q9fPxmGUW2KhKrnAAAAAABATbbNg+4k5kF3Pw+chqhHOH15x7mKcMA86NZsyjzo3hZG/1QFzcvzoANuwSBxAAAAAAC4AAEdAAAAAAAXIKADAAAAAOACBHQAAAAAAFyAgA4AAAAAgAsQ0AEAAAAAcAECOgAAAAAALkBABwAAAADABQjoAAAAAAC4AAEdAAAAAAAXIKADAAAAAOACBHQAAAAAAFyAgA4AAAAAgAsQ0AEAAAAAcAECOgAAAAAALkBABwAAAADABVo50WhJSYkKCgrk9/vrXa99+/Y2VQQAAAAAgLNsC+iFhYV64403tGLFCmVkZDRqm8WLF1tbFAAAAAAALmFLQM/JydGtt96qffv2yTRNO5oEAAAAACCs2BLQ582bp7179yoyMlIjR47U4MGD1a5dO0VFRdnRPAAAAAAArmdLQF+1apUMw9C1116rESNG2NEkAAAAAABhxZZR3HNycuTz+TR06FA7mgMAAAAAIOzYEtCTkpIUHR2tVq0cGTQeAAAAAADXsyWgDxo0SEVFRdq1a5cdzQEAAAAAEHZsCegXXXSREhIS9Nxzz6m8vNyOJgEAAAAACCuGacO8Z5mZmdq5c6f+9re/qV27dpowYYJSU1MVGxtb73bt27cPSftZWVkh2U9zJSe1dboE12L6PW8zDMPpEhqNcxXhoDmnadDneLBtBvvXvznHGOR2Pl/4/K5C6IXRP1VBO5iT7XQJkqSUlBSnSwAcY0tAnzhxYlDbLV68OCTtE9Ddj9DjbQR0ILQI6NZsSkD3tjD6pypoBHTAebaM2sYHWgAAAAAA6mdLQH/uuefsaAYAAAAAgLBlS0Dv0KGDHc0AAAAAABC2PDExuWEY8vlsGbAeAAAACEsRERFOlwB4ni0B/eWXX1b//v3Vt29fRUdH29FkNbGxsYqLi7O93cP5yyucLgGHCKeByeAewZ43jMVRt6DHJKuw/zUN9n0M9hgrgtxw1+6c4BqU9N13mUFtt3rlT0FtF+z7mL39QFDbSVLevvygtsv5KS+o7Yqyi4PaLtgB9Jz5uxHcdsH+U+wv9Qe1XbMGULT7dXXgn42PKu62v1EA1dgS0P/9739r4cKF8vl86tWrl/r16xcI7DExMZa3X1RUpJKSEsvbaUhi6zZOlwAAAADUKjvbHaO4JycnO10C4BhbAvqQIUO0adMmZWdn67vvvtP333+vRYsWyefzqUePHurfv7/S0tLUt2/fBudGD4ZpmvL7g/umFQAAAPACPi8DzrMloP/pT3+SJO3evVsbN24MPLKzs7Vt2zalp6dXC+xpaWnq37+/Bg0aZEd5AAAAAAA4ztZB4rp06aIuXbpo1KhRkqSff/65WmA/cOCA0tPTlZ6ersWLF2vx4sV2lgcAAAAAgGMcHcW9c+fO6ty5swYMGKD169frgw8+UHp6ukzTZAAvAAAAAICnOBLQ9+3bpw0bNgR6zvfv3y+p8l7xiIiIwH3pAAAAAAB4hS0Bvb5AXjWyu9UDxQEAAAAA4Ga2BPSrr746cMm6YRjq1atXYCA4AjkAAAAAADZf4h4bG6sxY8bopJNOUmpqqnw+n53NAwAAAADgWrYE9EGDBmnLli0qLCzUG2+8oTfeeEPR0dHq27dvoCe9V69eBHYAAAAAgGfZEtDvuusuVVRUaPv27dq4caM2bNigzZs3a82aNVqzZo0Mw1BMTIz69OlDYAcAAAAAeJJtl7hXDQbXq1cvTZw4UaZpavv27YHB46oC+9q1ayVVXg4/f/58u8oDAAAAAMBRjs2DbhiGevbsqZ49e+qss87St99+q6VLl2rbtm2SpKKiIqdKAwAAAADAdo4E9IMHDwYudd+4caN+/vnnGut07NjRgcoAAAAAAHCGLQG9rkBummZgnSOPPFJpaWmBe9DbtWtnR2kAAAAAALiCLQF9+vTpkqoH8k6dOgUCeVpaGoEcAAAAAOBptgR00zTVqVMn9e/fPxDI27Zta0fTAAAAAACEBVsC+osvvqjk5GQ7mgIAAAAAICzZMtE44RwAAAAAgPo5Mop7YWGhtm/froMHD0qSkpKS1KNHD8XFxTlRDgAAAAAAjrM1oP/www+aO3eu1qxZU+vyQYMG6ZJLLlGPHj3sLAsAAAAAAMfZFtA/+eQTPfHEE/L7/YHR3CMjIyVJZWVlkqTVq1dr3bp1uvHGG3XmmWfaVRoAAAAAAI6zJaDv3r1bs2fPlt/vV7du3XTBBRdowIABSkxMlCTl5uZq3bp1euONN/Tjjz/qb3/7m3r06KEuXbrYUR4AAAAAAI6zZZC4RYsWye/3a+DAgXr00Ud1xhlnBMK5JCUmJurMM8/Uo48+quOPP15+v1+LFy+2ozQAAAAAAFzBloC+fv16GYaha665JnBZe20iIyN19dVXS5LWrVtnR2kAAAAAALiCLQE9OztbcXFx6tixY4PrdurUSfHx8YER3gEAAAAA8AJbAnpUVJRKSkrk9/sbXLe8vFwlJSWKioqyoTIAAAAAANzBloB+1FFHye/369NPP21w3c8++0zl5eU66qijbKgMAAAAAAB3sCWgn3nmmTJNU//4xz/05Zdf1rnel19+qWeeeUaGYTDNGgAAAADAU2yZZu2cc87RRx99pO+//14PPvigunbtquOOO07t2rWTJO3fv1/r16/Xrl27ZJqmevfurXPOOceO0gAAAAAAcAVbAnpERIT+8pe/6G9/+5tWrVqln376Sbt27aq2jmmakqSTTz5ZN954oyIiIuwoDQAAAAAAV7AloEtSQkKC7rjjDm3dulUrVqxQenq6cnNzJVXOg56amqrTTz9dxx57rF0lAQAAAADgGrYF9CrHHnssIRwAAAAAgMPYMkhcZmam9u/f3+j19+/fr8zMTAsrAgAAAADAXWzpQb/yyiuVnJysF198sVHr33rrrcrKytLixYstrQsAAAAAALewpQcdAAAAAADUz5UBvaSkRD6fK0sDAAAAAMASrkvBe/bsUV5entq0aeN0KQAAAAAA2MaSe9C//PJLffXVV9WeKygo0OzZs+vdrqCgQJs3b5Yk9e3b14rSAAAAAABwJUsC+o4dO/Thhx/KMAyZpilJKi0t1Ycfftio7Vu3bq3JkydbURoAAAAAAK5kSUDv3r27hg8fHvjzhx9+qKioKA0ZMqTObQzDUFxcnI466iideuqpat26tRWlAQAAAADgSpYE9FNOOUWnnHJK4M8ffvih4uPjdeONN1rRnObMmaN58+bVuXzSpEmaNm2aJW03iel0AQAAAEDtkpOTnS4B8Dxb5kG/77771KqVdU0VFBQoIyOjzuWFhYWKiIiwrP3G8pdXOF0CAAAAUCs3fF4GvM6WgJ6Wlmbp/uPj49WhQ4c6l8fFxcnv91taQ+MYThcAAAAA1Modn5f5ogDeZktAt9rUqVM1derUOpdnZWUpOzvbxopql5zU1ukSAAAAgFq54fOyJKWkpDhdAuAY2wK63+/XsmXL9Pnnn+vHH39Ufn6+Kirqv+R78eLF9hQHAAAAAIDDbAnoxcXFuuuuu/T9998Hpl1riGFwOTgAAAAAwDtsCeivvvqqvvvuOxmGoZNOOkmDBw9Wu3btFBUVZUfzAAAAAAC4ni0BfeXKlTIMQ5deeqnOP/98O5oEAAAAACCs+OxoJCsrS4ZhaOzYsXY0BwAAAABA2LGlB71169YqLS1VdHS0Hc0BAAAAABB2bOlB79+/vwoLC5WZmWlHcwAAAAAAhB1bAvqFF16oqKgovfTSS3Y0BwAAAABA2LEloHft2lW333671q5dq5kzZ2rDhg0qLi62o2kAAAAAAMKCLfegT5w4MfDzunXrtG7dukZtt3jxYkvqAQAAAADAbWwJ6KZpNnkbwzAsqAQAAAAAAHeyJaDfd999djQDAAAAAEDYsiWgp6Wl2dEMAAAAAABhy5ZB4gAAAAAAQP1sCejvvfeefv75ZzuaAgAAAAAgLNlyifvf//53GYahpKQkpaWlBR5dunSxo3kAAAAAAFzPloCempqq7du3Kzs7W5999plWrFghSUpKSlK/fv2Ulpam/v37E9gBAAAAAJ5lS0D/61//quLiYm3evFkbN27Uxo0blZ6eruzsbK1YsUKff/65JKlNmzbVAnvXrl3tKA8AAAAAAMfZEtAlKSYmRieccIJOOOEESVJJSYk2b96sDRs2BAL7wYMHtXLlSq1cuVKStHjxYrvKAwAAAADAUbYF9MNFR0dr4MCBGjhwoEpLS7V27Vq9/vrrSk9Pl2maMgzDqdIAAAAAALCdIwG9tLRUW7Zs0caNG7Vhwwalp6ervLxcpmlKUmAwOQAAAAAAvMKWgN7YQM5gcQAAAAAAr7IloE+ZMkXl5eWSJNM01aZNm2qBnMHgAAAAAABeZ0tALysrk2EYio2N1bhx43TuuecqOTnZjqYBAAAAAAgLtgT0rl27avfu3SosLNRrr72m1157TZ07dw70oqelpRHYAQAAAACeZktAf/LJJ5WbmxuYA33Dhg3atWuXdu/erffff1+SAoG9f//+SktLU1JSkh2lAQAAAADgCraN4p6YmKjTTjtNp512miTVGth//vnnaoH9qaeesqs8AAAAAAAc5dg86IcGdr/fr7Vr12rBggXatm2bJOnnn392qjQAAAAAAGznSECvqKhQenq6NmzYoI0bN2rLli0qLi6utk50dLQTpQEAAAAA4AhbAnp9gbxqLvTo6Ggde+yxgUHjjjnmGDtKAwAAAADAFWybB/3wQB4TE1MjkEdERNhRDgAAAAAArmNLQC8qKgoE8qpR2lNTUwnkAAAAAAD8ly0B/eGHH1avXr0I5AAAAAAA1MGWgN67d287mgEAAAAAIGw5Ns1aRkaGcnJyJElt2rRRhw4dnCoFAAAAAADH2RrQ9+/fr9dff10rVqxQfn5+tWUJCQk644wzNGnSJLVr187OsgAAAAAAcJzProbWr1+v66+/Xu+9957y8vJkmma1R15ent59911df/312rBhg11lAQAAAADgCrb0oO/fv1/333+/ioqKlJycrHHjxmnAgAGBnvL9+/dr3bp1euutt5Sdna377rtPTz31FD3pAAAAAADPsCWgL1y4UEVFRerVq5fuvvtuJSQkVFuenJysXr166ZxzztFf/vIXbd++XYsWLdKVV15pR3kAAAAAADjOlkvc16xZI8Mw9Pvf/75GOD9U69at9bvf/U6maeqbb76xozQAAAAAAFzBlh70zMxMxcbGqnv37g2u27NnT8XGxiozMzNk7RuGIZ/PttvtAQAAgLATERHhdAmA59kS0Fu1aqWysjKZpinDMOpd1zRNlZeXq1Wr0JUWGxuruLi4kO0PAAA36927ve3bjh/fN+g2AQBAJVsCeqdOnbR9+3atXr1aJ510Ur3rrl69WmVlZTrqqKNC1n5RUZFKSkpCtr9QS0xMVEREhPx+v3Jzc50ux1UiIiKUmJio3Nxc+f1+p8txHc6dunHu1I3zpm6cN3XjvKkb503dOG/qxnlTt+TkZKdLABxjS0A/9dRT9cMPP+ipp57SnXfeqZ49e9a6Xnp6up588kkZhqHTTjstZO2bphk2v/jCpU67+f1+XpsG8PrUjnOnfrw2teO8qR+vTe04b+rHa1M7zhsAh7IloI8fP17Lly/X3r179cc//lGDBg1S//79q02ztn79eq1Zs0YVFRXq1KmTxo0bZ0dpAAAAAAC4gi0BPTo6Wvfee6/uv/9+bd++XatWrdLq1aurrWOapqTKQeL+/Oc/Kzo62o7SAAAAAABwBVsCuiS1b99ef/3rX7VixQp9/vnnSk9PD9yLlJiYqF69emnIkCE6/fTTGUESAAAAAOA5tgV0qXIwjKFDh2ro0KF2NgsAAAAAgOsxOTgAAAAAAC4Q8h70Dz/8MGT7Gj58eMj2BQAAAACAm4U8oM+ePVuGYYRkXwR0AAAAAIBXhDygt2/fvs5lubm5KikpkVR5P3piYmLg+ar5H2NiYtS6detQlwUAAAAAgKuFPKA///zztT7/1ltv6YUXXtBxxx2nSZMmqW/fvoqMjJQklZeXa/PmzXr99de1efNmnXfeeRozZkyoSwMAAAAAwLVsGcV9zZo1ev7553XGGWfoD3/4Q41L4Fu1aqXjjjtOxx13nB577DE999xz6tSpkwYOHGhHeQAAAAAAOM6WUdwXLVokSbr88ssbvD99+vTpkqTFixdbXBUAAAAAAO5hS0Dfvn274uPjlZyc3OC6bdu2VXx8vNLT022oDAAAAAAAd7AloJeVlamoqEhFRUUNrltUVKTCwkKVlZXZUBkAAAAAAO5gS0Dv0qWLKioqtHTp0gbXXbp0qSoqKtSlSxcbKgMAAAAAwB1sCegjR46UaZqaN2+eXnrpJeXm5tZYJy8vTy+99JLmzZsnwzB0zjnn2FEaAAAAAACuYMso7qNGjdK3336rlStXatGiRXrzzTfVtWtXtWvXTpJ04MAB/fTTT6qoqJBpmjr99NMJ6AAAAAAAT7EloEvSn/70Jy1cuFD//ve/VVhYqJ07d2rnzp3V1omLi9OkSZN03nnn2VUWAAAAAACuYFtA9/l8mjRpksaNG6e1a9fqhx9+UE5OjiSpTZs26tmzpwYOHKjo6Gi7SgIAAAAAwDVsC+hVoqOjdcopp+iUU06xu2kAAAAAAFzLlkHiAAAAAABA/QjoAAAAAAC4AAEdAAAAAAAXIKADAAAAAOACBHQAAAAAAFyAgA4AAAAAgAsQ0AEAAAAAcAECOgAAAAAALkBABwAAAADABQzTNE2ni/C6OXPmqKCgQPHx8Zo6darT5SCMcO4gGJw3CAbnDYLBeQMATUNAd4HRo0crIyNDHTp00DvvvON0OQgjnDsIBucNgsF5g2Bw3gBA03CJOwAAAAAALkBABwAAAADABQjoAAAAAAC4AAEdAAAAAAAXIKADAAAAAOACBHQAAAAAAFygldMFQJoyZUpgjlCgKTh3EAzOGwSD8wbB4LwBgKZhHnQAAAAAAFyAS9wBAAAAAHABAjoAAAAAAC5AQAcAAAAAwAUI6AAAAAAAuACjuDsoJydHb7zxhr7++mvt379f0dHR6tmzp0aPHq1TTjnF6fLggPz8fG3cuFHp6en64YcflJ6erpycHEnSfffdp/79+ze4jy+++ELvvvuufvjhB5WUlCglJUUnnXSSLrjgAiUmJlp9CHBAZmamvvjiC61fv147d+7UgQMH1KpVK7Vv317HH3+8xo0bp44dO9a7D84b70lPT9fXX3+tbdu2ac+ePcrNzVVJSYlat26tHj166Mwzz9TQoUPl89X9XT7nDarMmjVLX3/9tSRp+PDhmjFjRp3rct4AQN0Yxd0hP/30k26//fZA+IqNjVVJSYkqKiokSePGjdNvf/tbJ0uEA5YvX67Zs2fXuqwxAf2ZZ57RO++8I0ny+XyKjo5WUVGRJCkpKUn33XefunbtGtqi4ajMzExdeeWVOvRXeVxcnEpLS1VeXi5JioqK0owZMzRkyJBa98F5401PP/203nvvvcCfY2JiJEnFxcWB59LS0nTHHXcoLi6uxvacN6jy+eef66GHHgr8ub6AznkDAPWjB90BZWVlmjVrlnJyctStWzf94Q9/UPfu3VVSUqI333xTc+fO1dKlS9W9e3edffbZTpcLmyUnJ6tnz57q1auXOnXqpMcee6xR273//vt65513ZBiGLrnkEk2YMEHR0dHasWOHHnvsMf3444+aNWuWnnzySUVGRlp8FLBL1Zd6J5xwgoYPH67jjz9eiYmJ8vv92rJli5599lnt3LlTjz32mLp06aKjjz662vacN97Vu3dvde7cWX379lXnzp0DIfzgwYNatmyZ5s6dq40bN+qFF17Q73//+2rbct6gSkFBgZ577jnFx8crOTlZu3fvrnNdzhsAaBj3oDvg/fff1759+xQdHa277rpL3bt3lyRFR0frwgsv1LnnnitJmjNnTqAHDN5w1lln6aWXXtJdd92lKVOm6MQTT2zUdmVlZZo3b54kafTo0brwwgsVHR0tSerevbvuvPNORUdHa+/evVq2bJll9cN+CQkJevzxxzVz5kydeeaZgctDIyIilJaWprvvvltt2rRReXm53nzzzWrbct5424gRIzRhwgSlpqZW6yFPSkrSBRdcoEmTJkmSPv7442r/FnHe4FAvvviiDhw4oKlTpyopKanO9ThvAKBxCOgO+PjjjyVJZ555ptq3b19j+fnnny/DMHTgwAFt2LDB5urgpIiIiKC2W79+vbKzs2UYhn7961/XWN6hQwedeeaZkv53/qFliI+PV48ePepcnpycrEGDBkmSfvjhh2rLOG9Qn9TUVElSaWmp8vLyAs9z3qDK5s2b9cEHHyg1NTXQuVAXzhsAaBwCus2Kioq0bds2SZWXpNamffv26tKliyTp22+/ta02hK/169dLkrp27Vrrlz6SNHDgQEnSd999V+0eU7R8Vb3qfr+/2vOcN6jP1q1bJVXem35ozyjnDaTKHvEnn3xShmHouuuuq3cwQYnzBgAai4Bus927dwcGc+rWrVud61Ut27Vrly11IbxVnSeNOadM06z3HkG0PBs3bpRU8/zgvMHhSkpKtHv3br3yyitatGiRJGnMmDEyDCOwDucNJOn111/X7t27NWbMGPXs2bPB9TlvAKBxGCTOZgcOHAj83LZt2zrXq1qWnZ1teU0If1XnVWPOKYnzyku+/PJLpaenS6q85/hQnDeQKqd3nDJlSo3nW7VqpbFjx2rq1KnVnue8wa5du/TGG2+obdu2uuSSSxq1DecNADQOAd1mh16yVTU4Sm2qllVNPQLUp+q8asw5JUmFhYWW1wTnZWZm6qmnnpIkDR48OHAvehXOG0iVU11VXcJeWFio0tJSGYahsWPH6rzzzqsxNgbnjbeZpqmnnnpK5eXluvLKK2udgq82nDcA0DgEdABogfLz83XvvfcqJydHHTt21A033OB0SXCpuLg4vfzyy5Iqw1dGRoaWLl2qpUuXavny5br99tvVt29fh6uEW7z//vvavHmzBg0apCFDhjhdDgC0ONyDbrOYmJjAzyUlJXWuV7UsNjbW8poQ/qrOq8acU5Ia3eOB8FRUVKS7775bO3fuVNu2bXXPPfeodevWNdbjvMHhDMPQEUccoSuvvFKXXXaZ8vLy9Mgjj1Q7DzhvvOvAgQN66aWXFBUVpauvvrpJ23LeAEDjENBtduj9VYfej364qmXJycmW14TwV3VeNeackjivWrKSkhLdc889+u6779SmTRvde++96tixY63rct6gPqNGjVJkZKT279+vb775JvA85413vfzyyyooKNCECRPUpk0bFRUVVXtUVFRIqpwx4vDnOG8AoHG4xN1mXbp0kWEYMk1TP/30U2A6tcP99NNPkiqnIwEa0rVrV61evTpw3tSmaplhGHWedwhvJSUluvfee7Vp0yYlJCTonnvuqfd3COcN6hMVFaXWrVvrwIED2rt3b+B5zhvvysjIkFQ5gvvrr79e53qffPKJPvnkE0nS3/72N/Xo0YPzBgAaiR50m8XGxio1NVWStGbNmlrXycrKCkxHMmDAANtqQ/g67rjjJFV+uMnKyqp1nbVr10qSevfuXe1WC7QMZWVluv/++7V+/XrFxcVp5syZ6t69e73bcN6gPkVFRcrNzZVU/XYrzhsEg/MGABqHgO6As846S5L06aefKjMzs8byhQsXyjRNtW3bVv3797e5OoSj4447TsnJyTJNMzB38aEyMzP16aefSvrf+YeWo7y8XA8++KDWrl2rmJgY3XXXXTrmmGMa3I7zxrv8fr9M06x3nTfffFPl5eWSpH79+gWe57zxrvvvv19Lliyp85GWliZJGj58eOC5Hj16SOK8AYDGIqA74JxzzlHHjh1VXFyse++9Vzt27JBUeXnqG2+8obfffluSNHXqVLVqxV0IXpObmxt45OfnB54vKCiotqzqg7MkRUZGBuYxfuutt/TGG28EBtvZsWOH7r33XhUXF+vII4/Ur371K3sPCJby+/3661//qlWrVikqKkp33HFHo0fc5rzxrqysLN1000364IMPqn1RbJqmdu3apWeeeUbz58+XJJ166qnq1q1bYB3OGwSD8wYAGscwG/oKHZb46aefdPvttysnJ0dS5WilxcXFgcFUxo4dq6uuusrJEuGQ8ePHN2q9++67r8YVFs8884zeeecdSVJERISio6MDc8kmJSXpvvvuY1yDFmbjxo267bbbJFV+AI6Pj693/arptA7FeeM9v/zyi377298G/hwVFaWYmBgVFxertLQ08PxJJ52kP/3pT7Vebsx5g8Pddttt2rhxo4YPH64ZM2bUug7nDQDUj+5Zhxx11FH6v//7P/373//W119/raysLMXHx6tHj//f3r1H1ZT//wN/ngrdhMSkQ7mMhGhyjaSMJs2EcQkxJBrzZcZl8GFmMC5hxofB+LjMx7XIID6uC41qFLlk5LiXIVMjMiVJKV3t3x+t9u/UuVYnNdPzsdZZa3f2e7/f7/Pe+5zVa+/3fu228PLygpOTU013kf6Gpk6dCgcHB5w+fRp//PGHeDWiV69e8Pb2RqNGjWq6i6Rj8udYCwsLkZmZWeE6eNzUPebm5pg/fz5u3bqF+/fv48WLF8jKykK9evUglUpha2sLV1dXdOvWTWUdPG6oMnjcEBGpxyvoRERERERERLUA70EnIiIiIiIiqgUYoBMRERERERHVAgzQiYiIiIiIiGoBBuhEREREREREtQADdCIiIiIiIqJagAE6ERERERERUS3AAJ2IiIiIiIioFmCATkRERERERFQLMEAnIiIiIiIiqgUYoBMRERERERHVAgzQiYiIiIiIiGoBBuhEREREREREtQADdCIiIiIiIqJagAE6ERERERERUS3AAJ2oEqKioiCRSCCRSLB06dKa7k6FJCUliX338/Or6e7QP1xQUJB4vAUFBVW6nr/zd662a926NSQSCVq3bl3TXSEiIqrzGKATEVVBVFQUli5diqVLlyIpKammu0P0j/Xy5UsEBgbC19cX9vb2sLS0RP369dGwYUO0atUK77//PubOnYvw8HAUFRWprWvp0qXiCZ/yLwMDAzRt2hTdu3fHF198gStXrpTZVv5kUVVf8iebBEFATEwMVqxYAS8vL7Ru3RpGRkYwNDSElZUVPD09sWHDBmRmZupkPAMCAsR+uLu7QxAEjdvk5+ejS5cu4naBgYEKZUpP+EgkEv4mEhFVgkFNd4CI6O8sKioKy5YtAwC4ubnxKiSRjuXl5WHFihXYtGkTXr58qbC+sLAQr169wuPHjxEZGYl169ahWbNmmD17NmbMmAFTU9MKtVdcXIyMjAxkZGRAJpNhy5YtGDduHHbs2AEjIyNdfawy7t+/j4EDB+Lx48dK1z99+hRPnz7FmTNnsHz5cmzduhUjR46sUpsLFizA8ePHIZPJ8Ouvv+Knn37C559/rnabb7/9Fnfu3AEADB48GJMmTapSH4iISBEDdKJKcHNz0+pqAxFRbVebr3I+evQII0aMwLVr18T3OnbsiIEDB6Jjx45o2rQpCgsL8ezZM9y+fRuRkZFISkrCs2fPsGDBAjRs2BDTp09X28aYMWPg4+Mj/l1UVIQnT57g1KlTCA8PBwDs27cPOTk5OHbsGOzt7XH06FGV9Z09exYbN24EAAwYMAAzZ85UWdbOzg4AkJGRIQbnDRo0wIABA+Ds7Axra2s0aNAACQkJ+PnnnxEfH4/nz59j9OjR2L9/P0aPHq1hBFUzMDDAnj170L17d+Tn52P+/Pnw9PRE27ZtlZa/dOkS1q5dCwAwNzfH9u3bK902ERGpxgCdiIiIap3MzEy4u7vjwYMHAABbW1ts3LgRHh4earc7f/481q1bh+PHj2vVjp2dHYYNG6bw/qxZsxAUFITJkydDEAQcP34cZ86cwaBBg5SWl+93KWtra7Vl5bVq1Qrz5s3D+PHj0aRJE4X1X331Fb788kts3rwZb968wbRp0+Dh4YHGjRtrVb8ynTt3xrJly/D1118jJycHfn5+iIqKgp5e2Tsgc3NzMXHiRLx58wYAsHnzZlhaWla6XSIiUo33oBMREVGtM3nyZDE479mzJ65cuaIxOAeA/v3749ixYwgNDUWrVq2q1Ac/Pz+MGjVK/PvgwYNVqk+VLl26ICEhATNmzFAanAMlV7w3btyIbt26ASi56n7s2LEqt/2vf/0LTk5OAIDo6Ghs2LBBocy8efOQkJAAAPD29i4z44CIiHSLATrVWW/evMG+ffswbNgw2NjYiMl4pFIpHBwcMGrUKGzZsgXPnz9X2FabjNLlMyO/efMGQUFBGDBgAN555x0YGhrC2toaEyZMwK1bt7Tqc2FhITZt2gRnZ2eYm5vD2NgYtra2mD59On7//XcAZZMfRUVFVWZoyrhx4wZmzZoFBwcHmJubo0GDBrCysoKXlxd27dqlMRmTNpSN54MHDzB37lx07twZjRs3VjnWeXl52Lp1KwYPHoxWrVrB0NAQjRo1gr29PWbOnIn79+9rbD8rKwtr164V901p4qnWrVujZ8+e8Pf3x6FDh1BQUCBuUzrOpfefAyXTWcsngVJ3T/rRo0cxZswYtG7dGsbGxjAzM0PHjh0xderUMlN6lVGWjT8lJQWLFy+Go6MjmjZtqjJTf3FxMX7++WeMGjUKrVu3homJCUxNTdGhQwdMmTIFsbGxGses1IEDBzBo0CA0b94choaGaNOmDXx9fRUSa70t169fx3fffQcvLy+0adMGxsbGaNCgAVq0aAEPDw9s2LABr169Url9z549IZFIoK+vj+TkZI3tCYKAdu3aQSKRwMjICC9evFBarqpjriwbvkwmw9SpU2Fra4uGDRtWOlO+pizuyn5Trl27hkmTJqFt27YwNDRE06ZNMWDAAAQFBYlXWavit99+E6eRm5qa4uDBgxW+Uuzp6YmPP/64yn0ZOnSouKztb3VFmZiYoH79+hrLSSSSMicMdNEffX197N69W7y/fuHChWV+N0vvTweA5s2bi8tERFRNBKI6KD09XXBychIAaHytWbNGYfvIyEhx/ZIlS5S2YWNjIwAQbGxshPT0dMHV1VVlGwYGBsK+ffvU9vnp06eCg4ODyjqMjY2FkJAQYcmSJeJ7kZGRCvUkJiaK6ydOnKiyvby8PGHy5MmCRCJROz6dO3cWHj58qLbvmpQfz+DgYMHIyEihrfJjHRUVJUilUrX909fXF7777juVbcfGxgqWlpZaHQtXr14Vt5MfZ3UvGxsbhTbT0tIEFxcXtdtJJBLh888/F4qKipT2u/x+DAsLE8zNzRXqKb+Pb9++LdjZ2Wns9/Tp01W2LQiCkJubK3h5eakd9zVr1giBgYHie4GBgSrr00Sb79yyZcu02idWVlbClStXlNaxc+dOsdzixYs19uvMmTNieV9fX6VldDHm5cfx3//+t6Cvr69QR2XGWP63SpnyvymrVq1S2nbpa9iwYUJhYWGF+yHPx8dHrG/mzJlVqksZ+c+k6ngqFRYWJpZt3769xrrl95W639jK2rx5s1j/1KlTdVbv+vXrxXqdnJyEoqIi4eXLl4K1tbX4/tGjRzXWU3o8ARASExN11j8iorqC96BTnTRlyhTExMQAKLnvz8fHB+3bt0eTJk2Qk5ODBw8e4PLly4iOjq5yW0VFRRg5ciTOnTuHPn36YOTIkWjVqhUyMjIQEhKCqKgoFBUVwd/fH7169UK7du0U6sjLy8MHH3wgZs+1sLCAv78/unbtioKCAkRHRyM4OBi+vr7w9PTUSZ89PT3Fq2VWVlbw8fFB165dYWxsjMePH+PIkSO4cOEC7t69i/79++P69eto1qxZldu+dOkSVq5cCYlEgokTJ8LFxQUmJiZISEiAtbW1WC40NBQff/wxCgsLoaenB09PT7i7u0MqlSIvLw+xsbHYs2cPXr58iQULFgAAvvnmmzJt5ebmYtiwYfjrr78AAN27d8fw4cMhlUphYmKCFy9eID4+HpGRkbh582aZbX18fPDee+/hwIEDCAkJAQAsX74c9vb2ZcoZGxuX+fvVq1fo378/7t27BwBo1qwZJk2aBAcHBxQUFOD8+fPYu3cvCgsLsWXLFmRlZSE4OFjtmCUkJMDb2xvZ2dkYOXIk3N3d0aRJEzx69AgGBv//Z/769etwdXVFdnY2AMDFxQVeXl6wsbHBmzdvcOvWLQQFBSE1NRWbNm1CQUEBtm7dqrTNsWPH4tSpUwAAIyMjTJ48Gb179wYAxMTEIDAwEPPmzcPw4cPV9l2XcnNzoa+vj169esHZ2Rm2trZo3LgxiouLkZSUhJMnT+LixYtISUnBhx9+iBs3bihMgfbx8cHcuXORmZmJXbt2YfHixdDX11fZpvz4/N///Z/Cel2OeamDBw8iNDQUpqam8PX1Ra9evVCvXj3ExcVV+33B27dvx759+9CsWTP4+fmha9eu0NPTw6VLl7Bjxw7k5+fj2LFjWL16tfi9qyhBEBARESH+PWHCBF11v1LS0tLE5UaNGtVgT0rcvn1bXLaxsdFZvbNmzcLRo0dx/vx5xMTE4IcffsC9e/fw6NEjACX7Qdv76YmIqApq+gwB0duWmpoq6OnpCQCEvn37Cq9fv1ZZNi0tTYiLi1N4vyJX0Etf69evV1ru008/FcvMmDFDaZnFixeLZezt7YXU1FSFMlevXhUaNWpUps3KXkH/+uuvxTJTpkxROUYbNmwQy33yySdKy2hDfjwBCM2bNxdu3rypsnxKSop4tbh58+bC5cuXlZZ7/PixYG9vL17RjY+PL7P+0KFDYptz5sxR28e7d+8KaWlpCu9rmrFQ3ueffy6W7969u/Ds2TOFMrGxsUKTJk3EciEhIQpl5PcjAMHExESIiIhQ2W5OTo7Qtm1bASiZbXHixAml5TIzM4UBAwaI9YaHhyuU2bdvn7je0tJSYVwFQRDi4uKEd955p8pXd0tp85377bffhCdPnqitJzg4WPz++/v7Ky0za9YssS1V4yQIJbNaDAwMxO9lebocc/mrsgAEW1tb4c8//1T7WbVVkSvoAARXV1chMzNToVxUVJR4Zd3CwkLIz8+vVH/i4uLEtoyMjKp8NV6ZilxBHzNmjFh20qRJGuuuzivoGRkZZX4bbt26pdP6Hz58KJiYmAgAxGMbgCCVSoUXL15oVQevoBMRVQ0DdKpzLl++LP7zsHnz5krVUdEAXdXUV0EQhBcvXgiGhoYCAOHdd99VWJ+fny9YWFiI/zDdvXtXZV07duyocoCempoq9sfd3V1lW6XGjRsnBsCPHz/WWF6Z8gG6pmmUs2fPFsueP39ebdn4+HgxaCg/HfT7778X61E3rupUJEBPS0sTx9bY2FhISkpSWfbAgQNivY6Ojgrrywfoqk4AlZI/mRIcHKy2bHp6umBmZiYAEDw9PRXWOzo6inWdPHlSZT0nT558qwG6tsaPHy/ug4KCAoX19+7dE9saPHiwynpWrlwpltu4caPCel2OuXzQJ5FIBJlMpsUn1U5FAnRzc3MhPT1dZV3yweyFCxcq1Z+IiAixDjs7u0rVoYm2AXpwcHCZ23xCQ0M11l2dAfqkSZPEuocOHarTukvJT6GvyOcuxQCdiKhqmCSO6hwTExNxWVMiLl2ZM2eOynWNGzdGjx49AAAPHz5EXl5emfUXLlxAeno6AMDDwwOdOnVSWZevry+aNm1apb6GhISIfZg3b57G8hMnTgRQkgTr119/rVLbQMmUTXWJnQRBwJ49ewAAffr0gYuLi9r67Ozs0KtXLwDAmTNnyqx728fC6dOnxbEdM2aM2umpo0ePFm93uH79OhITE1WWNTIywqeffqq27d27dwMApFIpxo0bp7Zs06ZN4eXlBaAkgV9+fr64LikpCdevXwcAdOjQQSynjJeXFzp27Ki2rZrQr18/ACVT4pUl2erQoQPef/99ACW3UihLFicIAnbs2AGg5DYGZdOwdTXmyvrv6Oiotr7qouk35oMPPhCXS2/JqSj5xJzaJIaztLRUSM4o/9Lk3r17OHbsmPg6fPgwNm7cCE9PT0yYMAGCIAAAhgwZopNbiCrrv//9LwIDAwGUjIuybOu68OGHH5a5rcPd3b1GPzcRUV3De9CpzunUqROkUimePHmCXbt2obi4GFOmTIGTk5Pae00ry8TEBF27dlVbpmXLlgBK/unPzMwscx/p1atXxeUBAwaoradevXpwdnbGiRMnKt3f8+fPi8upqakaH+Pz5MkTcTkuLq7S7ZZydnZW+091XFyc+A98kyZNtHrMUOl+TUxMRF5eHgwNDQGU/OMpkUggCAKmTZuGhIQEjB07FnZ2dlX+HMrIZzbX9LgoiUQCDw8PMWNyTEwM2rRpo7Sso6MjTE1NVdaVlZWFGzduAABatGih1fFRGiDm5eUhMTFRHJPffvtNLDNw4ECN9QwcOBDx8fEay+mKIAgIDQ3F//73P1y7dg3JycnIzs5W+bSBx48fo3v37grvT5s2DWfPnkVxcTF27typ8ASBsLAw8aTJmDFjFO5N1uWYl6fppFR16tOnj9r1pb9lAFRmtK9tQkJCxDwSqowePVoMjmvCqVOnMGPGDACAnp4eAgMD1T4horIEQYC/vz+Ki4vF96KionDt2jWl3xMiItI9BuhU5+jr62Pbtm0YMWIE8vPzsXv3buzevRtmZmbo3bs3nJ2d4e7ujr59+2p19UUTc3NzjfU0aNBAXC5/BT0lJUVcVpZArry2bdtWsIdlJSUlicu+vr4V2jYjI6NKbQNl/8FXRr5/p0+fxunTpytUf0ZGBqysrAAAHTt2xKJFi7B8+XLk5OQgICAAAQEBaNGiBfr27QsXFxd4enqiQ4cOFf4cyjx9+lRctrW11Vhevoz8tuVpGrPk5GTx0VexsbEVTtwmv1/lj8d3331X47balNGVv/76C97e3rh48aLW22RlZSl9f9iwYbCyskJKSgp27dqFb7/9tswJvG3btonLypLD6XLMy9O0v6uThYWF2vXqfsu0JX+FPjMzU2P5oKAghbaqkpxQX18fZmZmsLGxgZOTEyZMmIC+fftWur6qioiIgLe3N4qKiiCRSLBt2za1ydrkZ10p069fP5X7cdOmTYiMjARQ8ki1tLQ0FBUVwc/PD9euXdPqUXBERFQ1DNCpTvroo48QGxuLZcuW4cSJEygoKEBWVhbCw8MRHh6OpUuXok2bNggICMD48eOr1JaeXtXuJMnJyRGXy2cEV0Z+2nZlaPMPsSryzwmvrNJn8apSlf4Bin0MCAhAz549sWrVKly6dAlASTB8+PBhHD58GEDJVf21a9eKWcorqzSTN6DdfpK/Ki6/bXlvc8zknyH+No5HbZU+eaA0236TJk0wZMgQdOnSBZaWljAyMhID7LNnz2Ljxo0AUOZKoTwDAwN8+umnCAgIQHJyMkJDQzF48GAAJScCSq+IOzg4KD0udH2cytO0v6tTVX/PtFF6Ag0AHj16hKKiojJPIyivqtOvlyxZojBDorY4e/Yshg4diry8PEgkEvz000/w9/dXu82iRYtw7tw5lesjIyPh5uam8H5CQgK+/vprAIChoSGioqLw5ZdfIiwsDHfu3MGyZcuwcuXKKn0eIiLSjAE61Vn29vY4dOgQcnJycPHiRcTExCA6OhrR0dHIz89HYmIiJkyYgIcPH2LJkiU11k/5ACc3N1djefmAvjLkg8KsrCw0bNiwSvXpmnz/5syZg7Vr11a5ziFDhmDIkCFITU1FdHQ0Ll++jHPnzkEmk0EQBFy8eBEuLi44ffo03N3dK92O/Fhqs5/kg+Gq7Af5MRsxYoR44qGqdb2N41FbISEhYnA+cOBAHD16VOWYyd+Woc5nn32GlStXori4GNu2bRMD9F27dolT5pVdPQd0O+Z1jZ2dHSwsLJCeno7c3FzcvHmzTk6vPnv2LIYMGYLXr18DADZv3qzyeKuqN2/ewM/PT/xOr1ixAh07dsSOHTtgb2+PrKwsrF69GiNGjKiT+4KI6G1ikjiq80xMTODh4YHFixcjPDwcz549w/Lly8X1K1euFJ+TXRPkryY9fPhQY/k//vijSu3JT59VlhyrplVn/9555x14e3tj7dq1iI2NRVJSEkaNGgUAKCwsxOzZs6tUf4sWLcTlBw8eaCx///59cVn+OKgoqVQqLld1zOTrSkhI0FhemzK6EBYWJi7/+OOPak9oqEu4J08qlWLo0KEASm6nePz4MQRBwPbt2wGU/HZ88sknKrctVRu/R7WZRCIpk2xu7969NdibmlEanJcGzBs3bsS0adO02jYqKgpCyVN6lL6UXT1ft26deGuIs7Oz+FvXqlUrrFu3DgDEqe66mClFRESqMUAnKqdhw4ZYtGiRmEm8sLAQMTExNdafnj17isul9waqUlhYWKH7b5VxdXUVl0NDQ6tUV3V47733xIRckZGRarNdV5W1tTV+/vlnNGvWDEBJVuryU5flp/yWZntWRX4qtHxAqUp4eLjSbSvKwsICnTt3BgDIZDKkpqZWuq7SjPhASRChiS4y+2tD/iSapvvef/nlF63rLQ2KSpPFhYWFiXkQxo4dCzMzM6Xb6XLM6yL5k2E7d+7Eo0eParA3b1f54HzDhg2YPn16tbUXHx+Pb7/9FkDJbSuBgYFlftf8/f3F2whKp7oTEVH1YYBOpIJ8xmxVGaDfBmdnZzFpUlhYmNpM6Xv27CnziKLK8PHxERM9rVu3Tm2yoZqgr68vXrVMT08Xr+5Ul3r16pW5Glr+WJCfyqxpOreXl5eYQf7AgQP4888/VZY9dOiQePXZ0dFRZQZ3bck/Dm/x4sWVrsfGxgbdunUDUPJ4KnUncUJDQ99aBnf5W0HUXbUPCQnB3bt3ta7X3d0d7du3B1ASKJZm1QdUT28vpasxr4t69uyJESNGACjJvzBmzBi8fPmyhntV/aKiosoE5z/++CNmzpxZbe0VFxdj4sSJYpK977//Xjze5W3fvl08Mbp69eq39ohSIqK6iAE61TlnzpzB+vXr1T4CKC0trcw9ow4ODm+ja0o1aNAAX3zxBYCS4HDMmDFIS0tTKBcbG4u5c+dWub2WLVuK/xCmpKRg0KBBGqfN37x5s9rujVRmwYIF4vORFy1ahB9//FHMmK1MTk4OduzYgf3795d5/z//+Q8OHTqkdsrmxYsXxWdlt2zZUiH7sXzgLJPJ1PbbwsJCTPCUm5sLb29vpSdUrl+/jqlTp4p/f/PNN2rr1cYXX3whPpZp27Zt+Oqrr1BYWKiyfEFBAQ4ePIjNmzcrrPvXv/4lLvv7+5eZil/q/v37GpNZ6ZL8TJOFCxcqTf4WGRmJzz77rEL1SiQScV8kJyfj+PHjAIBu3bqhR48earfV5ZjXRTt37hSDxZiYGDg5OWk1I+PChQvV3bVqce7cOXh5eZUJzmfNmlWtba5atUp8lKerq6v4KLfyWrZsyanuRERvCZPEUZ3z9OlTzJkzB1999RXc3Nzg5OSEtm3bwtTUFM+fP8etW7ewf/9+MYAfPXq00isKb9M333yDI0eO4M6dO7hz5w46d+4Mf39/ODg4oKCgAOfPn0dwcDD09PQwdOhQMcN0ZTMuf/fdd7h58ybCwsIgk8lgZ2eHoUOHwsXFBS1atMCbN2+Qnp6OO3fuIDIyEvfv34e+vj62bt2qy4+tklQqxcGDBzFkyBDk5+dj9uzZ2LJlC4YPH45OnTrB1NQU2dnZSExMRGxsLM6ePYu8vLwyuQWAkoB69+7daNSoEQYNGoRu3bpBKpWifv36SE1Nxblz53DixAkx+F+wYIFCX/r374/69eujoKAAa9asAVByQqd0FoKRkVGZ2wZWrVqFX3/9Fffu3UNsbCw6duwIf39/dO3aFQUFBYiOjkZwcLD4z+/48ePF++CrwtjYGCdOnED//v2RmZmJ1atXY+/evfD29oaDgwPMzMyQm5uL5ORkyGQyREREICsrS2mQPXbsWBw4cAAnTpzA06dP4ejoiMmTJ4vT8GNiYhAYGIjc3FwMHz4cR48erXL/NfH398f333+P7OxsnDhxAg4ODvD19YWNjQ1evHiBM2fO4Pjx49DT08P48eMrdF+zn58fFi5cWOZRXtqckNLlmNdFjRs3RkREBIYPHw6ZTIZ79+7B3d0dnTt3xsCBA2FnZyc+xvLFixf4/fffERERgdu3b4t1yM9+qc1u3LhRJjgfNGgQbGxscOzYMbXbWVhYoF+/fpVq89atWwgICABQMgMlMDBQ7SNBJ0+ejEOHDuGXX37ROqv72rVrxSvv6hgZGWHhwoUV+wBERP9UAlEdExQUJADQ6uXt7S3k5OQo1BEZGSmWWbJkidJ2bGxsBACCjY2Nxj5NnDhRrC8xMVFpmZSUFKFr164q+2psbCyEhIQICxcuFN+TyWQK9SQmJorrJ06cqLJPBQUFwty5cwUDAwOtxkqbz6mKNuOpzNWrV4UOHTpo1T99fX1h+/btZbb38/PTatt69eoJK1asUNmPRYsWVWhc0tLShH79+qltUyKRCNOmTROKioqUtqntfiwvISFB6N27t1afWyKRCIsXL1ZaT25urvDRRx+pHe8ffvhBCAwMFN8LDAzUup/laXOMnD59WjA2Nlb7HdmzZ0+l+iT/HW3YsKGQnZ2tdd91Mea6GkdlNP1WLVmyRGw7MjJSbV2V/S6r8/r1a2HBggWCmZmZ1r/dlpaWQkBAgNLf7/KfSVf9LCW/r7T9bspvU5GXq6trpfpYUFAgvPfee2I9mzdv1mq75ORkoVGjRgIAwcDAQIiNjVUoU3o8VeTVqFGjSn0OIqJ/Il5BpzrH19cXnTp1QkREBK5cuYL4+HikpKTg9evXMDY2hrW1NZycnDBhwoQyVz5rWosWLRAbG4utW7di3759iI+PR35+PqRSKTw8PDBz5kx06NChTCI5c3PzSrdXr149/PDDD5gxYwZ27dqFyMhIPHjwABkZGdDT00PTpk1ha2uL3r17Y9CgQejfv78uPmaF9OjRA3FxcThy5AiOHz+OK1euIDU1FTk5OTA1NUWrVq3QpUsXuLm5YejQobC0tCyz/U8//QQfHx9ERkYiNjYW9+/fx7Nnz1BUVAQzMzO0b98ebm5u8Pf3VzuLYvny5XBwcEBgYCBu3LiB9PR0tdM/mzVrhujoaBw5cgQHDhxATEwMnj17BgMDA1hZWcHNzQ1TpkzROIW6Mtq1a4eYmBiEhYXh0KFDuHTpElJSUpCdnQ1jY2NIpVJ06tQJrq6uGDJkiMp7342MjHDq1Cns378fu3btwvXr1/Hq1StYWlrCxcUF06dPR+/evREUFKTzz6DKhx9+iJs3b2LNmjUIDw/HkydPYGRkBKlUCk9PT0ydOhXt27evVJ88PDywe/duAMC4cePK5B7QRFdjXlcZGhpi5cqVmD9/Pg4fPozIyEjIZDI8e/YMmZmZaNCgARo3box27dqhR48ecHd3xwcffCA+954ULV++HDdu3ABQ8lhCbTPEt2zZEuvXr8fkyZPFqe7Xrl1D/fr1q7G3RER1i0QQNKQdJqK/le7du0Mmk6Fx48bIyMhQO2WRiLQzbNgw8f5zmUwGR0fHGu4RERER/RMxSRzRP8jly5fFRGVubm4Mzol0IDk5GSdPngRQ8rg7BudERERUXRigE/1NyGQyZGdnq1wfFxeHsWPHin9rO2WRiNRbunSpmBX+yy+/rNnOEBER0T8ap7gT/U1Mnz4dQUFB8PDwQO/evWFtbQ0DAwP89ddfOH/+PI4dOyY+o9vHx0fhkWJEpJ2EhAQkJCQgOzsbJ0+exJ49ewAAXbp0wY0bNyr9dAQiIiIiTZgkjuhvJCcnB0ePHlX72KpPPvkEO3fufIu9Ivpn2bt3L5YtW1bmPWNjYwQGBjI4JyIiomrFAJ3ob2L+/PmwtrbGuXPn8PDhQzx//hyZmZkwMTGBVCqFs7Mz/Pz80Ldv35ruKtE/gkQigZWVFfr06YNly5ahU6dONd0lIiIi+ofjFHciIiIiIiKiWoBz9YiIiIiIiIhqAQboRERERERERLUAA3QiIiIiIiKiWoABOhEREREREVEtwACdiIiIiIiIqBZggE5ERERERERUCzBAJyIiIiIiIqoFGKATERERERER1QIM0ImIiIiIiIhqAQboRERERERERLUAA3QiIiIiIiKiWoABOhEREREREVEtwACdiIiIiIiIqBZggE5ERERERERUCzBAJyIiIiIiIqoFGKATERERERER1QIM0ImIiIiIiIhqAQboRERERERERLUAA3QiIiIiIiKiWuD/AZBqNeBnxRw7AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 400,
+       "width": 500
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.\n",
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_mlp_activation.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 400,
+       "width": 500
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.\n",
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_attention_output.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 400,
+       "width": 500
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for stream in [\"block_output\", \"mlp_activation\", \"attention_output\"]:\n",
+    "    df = pd.read_csv(f\"./tutorial_data/pyvene_rome_{stream}.csv\")\n",
+    "    df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "    df[\"pos\"] = df[\"pos\"].astype(int)\n",
+    "    df[\"p(Seattle)\"] = df[\"prob\"].astype(float)\n",
+    "\n",
+    "    custom_labels = [\"The*\", \"Space*\", \"Need*\", \"le*\", \"is\", \"in\", \"downtown\"]\n",
+    "    breaks = [0, 1, 2, 3, 4, 5, 6]\n",
+    "\n",
+    "    plot = (\n",
+    "        ggplot(df, aes(x=\"layer\", y=\"pos\"))    \n",
+    "\n",
+    "        + geom_tile(aes(fill=\"p(Seattle)\"))\n",
+    "        + scale_fill_cmap(colors[stream]) + xlab(titles[stream])\n",
+    "        + scale_y_reverse(\n",
+    "            limits = (-0.5, 6.5), \n",
+    "            breaks=breaks, labels=custom_labels) \n",
+    "        + theme(figure_size=(5, 4)) + ylab(\"\") \n",
+    "        + theme(axis_text_y  = element_text(angle = 90, hjust = 1))\n",
+    "    )\n",
+    "    ggsave(\n",
+    "        plot, filename=f\"./tutorial_data/pyvene_rome_{stream}.pdf\", dpi=200\n",
+    "    )\n",
+    "    print(plot)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/tutorials/advanced_tutorials/DAS_Main_Introduction.ipynb b/_sources/tutorials/advanced_tutorials/DAS_Main_Introduction.ipynb
new file mode 100644
index 00000000..adb11ff5
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/DAS_Main_Introduction.ipynb
@@ -0,0 +1,1824 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Intro to Distributed Alignment Search (DAS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Atticus Geiger\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Contents\n",
+    "\n",
+    "1. [The hierarchical equality task](#The-hierarchical-equality-task)\n",
+    "    1. [An Algorithm that Solves the Equality Task](#An-Algorithm-that-Solves-the-Equality-Task)\n",
+    "        1. [The algorithm with no intervention](#The-algorithm-with-no-intervention)\n",
+    "        1. [The algorithm with an intervention](#The-algorithm-with-an-intervention)\n",
+    "        1. [The algorithm with an interchange intervention](#The-algorithm-with-an-interchange-intervention)\n",
+    "    1. [Hand Crafting an MLP to Solve Hierarchical Equality](#Hand-Crafting-an-MLP-to-Solve-Hierarchical-Equality)        \n",
+    "    1. [Training an MLP to Solve Hierarchical Equality](#Training-an-MLP-to-Solve-Hierarchical-Equality)\n",
+    "1. [Causal abstraction Analysis](#Causal-abstraction)\n",
+    "    1. [Basic intervention: zeroing out part of a hidden layer](#Basic-intervention:-zeroing-out-part-of-a-hidden-layer)\n",
+    "    1. [An interchange intervention](#An-interchange-intervention)\n",
+    "    1. [Alignment](#Alignment)\n",
+    "    1. [Evaluating an Alignment](#Evaluation)\n",
+    "1. [Distributed Alignment Search (DAS)](#Distributed-Alignment-Search)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set-up\n",
+    "\n",
+    "This notebook is a hands-on introduction to __causal abstraction analysis__ [Geiger*, Lu*, Icard, and Potts (2020)](https://arxiv.org/pdf/2106.02997.pdf) using __distributed alignment search__ [Geiger*, Wu*, Potts, Icard, and Goodman (2020)](https://arxiv.org/pdf/2303.02536.pdf).\n",
+    "\n",
+    "In causal abstraction analysis, we assess whether trained models conform to high-level causal models that we specify, not just in terms of their input–output behavior, but also in terms of their internal dynamics. The core technique is the __interchange intervention__, in which a causal model is provided an input and then intermediate variables are fixed to take on the values they would have for a second input.\n",
+    "\n",
+    "To motivate and illustrate these concepts, we're going to focus on a hierarchical equality task, building on work by [Geiger, Carstensen, Frank, and Potts (2020)](https://arxiv.org/abs/2006.07968)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from datasets import Dataset\n",
+    "import random\n",
+    "import copy\n",
+    "import itertools\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm, trange\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "from transformers import get_linear_schedule_with_warmup\n",
+    "\n",
+    "from pyvene import CausalModel\n",
+    "from pyvene.models.mlp.modelings_mlp import MLPConfig\n",
+    "from pyvene import create_mlp_classifier\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    VanillaIntervention,\n",
+    "    RotatedSpaceIntervention,\n",
+    "    LowRankRotatedSpaceIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x15308f04db0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seed = 42\n",
+    "np.random.seed(seed)\n",
+    "random.seed(seed)\n",
+    "torch.manual_seed(seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The hierarchical equality task\n",
+    "\n",
+    "This section builds on results presented in [Geiger, Carstensen, Frank, and Potts (2020)](https://arxiv.org/abs/2006.07968). We will use a hierarchical equality task ([Premack 1983](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/7DF6F2D22838F7546AF7279679F3571D/S0140525X00015077a.pdf/div-class-title-the-codes-of-man-and-beasts-div.pdf)) to illustrate the concepts. \n",
+    "\n",
+    "We define the hierarchical equality task as follows: The input is two pairs of objects and the output is **True** if both pairs contain the same object or if both pairs contain different objects and **False** otherwise.  For example, `AABB` and `ABCD` are both labeled **True**, while `ABCC` and `BBCD` are both labeled **False**. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## An Algorithm that Solves the Equality Task\n",
+    "\n",
+    "Let $\\mathcal{A}$ be the simple tree-structured algorithm that solves this task by applying a simple equality relation three times: Compute whether the first two inputs are equal, compute whether the second two inputs are equal, then compute whether the truth-valued outputs of these first two computations are equal. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's a Python implementation of $\\mathcal{A}$ that supports the interventions we'll want to do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def randvec(n=50, lower=-1, upper=1):\n",
+    "    return np.array([round(random.uniform(lower, upper), 2) for i in range(n)])\n",
+    "\n",
+    "\n",
+    "embedding_dim = 2\n",
+    "number_of_entities = 20\n",
+    "\n",
+    "variables = [\"W\", \"X\", \"Y\", \"Z\", \"WX\", \"YZ\", \"O\"]\n",
+    "\n",
+    "reps = [randvec(embedding_dim, lower=-1, upper=1) for _ in range(number_of_entities)]\n",
+    "values = {variable: reps for variable in [\"W\", \"X\", \"Y\", \"Z\"]}\n",
+    "values[\"WX\"] = [True, False]\n",
+    "values[\"YZ\"] = [True, False]\n",
+    "values[\"O\"] = [True, False]\n",
+    "\n",
+    "parents = {\n",
+    "    \"W\": [],\n",
+    "    \"X\": [],\n",
+    "    \"Y\": [],\n",
+    "    \"Z\": [],\n",
+    "    \"WX\": [\"W\", \"X\"],\n",
+    "    \"YZ\": [\"Y\", \"Z\"],\n",
+    "    \"O\": [\"WX\", \"YZ\"],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def FILLER():\n",
+    "    return reps[0]\n",
+    "\n",
+    "\n",
+    "functions = {\n",
+    "    \"W\": FILLER,\n",
+    "    \"X\": FILLER,\n",
+    "    \"Y\": FILLER,\n",
+    "    \"Z\": FILLER,\n",
+    "    \"WX\": lambda x, y: np.array_equal(x, y),\n",
+    "    \"YZ\": lambda x, y: np.array_equal(x, y),\n",
+    "    \"O\": lambda x, y: x == y,\n",
+    "}\n",
+    "\n",
+    "pos = {\n",
+    "    \"W\": (0.2, 0),\n",
+    "    \"X\": (1, 0.1),\n",
+    "    \"Y\": (2, 0.2),\n",
+    "    \"Z\": (2.8, 0),\n",
+    "    \"WX\": (1, 2),\n",
+    "    \"YZ\": (2, 2),\n",
+    "    \"O\": (1.5, 3),\n",
+    "}\n",
+    "\n",
+    "equiv_classes = {}\n",
+    "\n",
+    "equality_model = CausalModel(variables, values, parents, functions, pos=pos)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a visual depiction of the algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Timesteps: {'W': 0, 'X': 0, 'Y': 0, 'Z': 0, 'WX': 1, 'YZ': 1, 'O': 2}\n"
+     ]
+    }
+   ],
+   "source": [
+    "equality_model.print_structure()\n",
+    "print(\"Timesteps:\", equality_model.timesteps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The algorithm with no intervention\n",
+    "\n",
+    "Let's first observe the behavior of the algorithm when we provide an input of the form `BBCD` with no interventions. Here is a visual depiction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No intervention:\n",
+      " defaultdict(None, {'W': array([ 0.28, -0.95]), 'X': array([ 0.28, -0.95]), 'Y': array([-0.45, -0.55]), 'Z': array([ 0.78, -0.83]), 'WX': True, 'YZ': False, 'O': False}) \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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eeUZbtmxRfHy8cubMaTpHFy9eVOXKlVWjRg19/fXXpnMAALeI06gAAJKkRYsW6dtvv1VYWJhLDA1Jypkzp8LCwvTNN9/ou+++M50DAHAijmwAgIdKSkpSjRo1VLRoUS1dulQWi8V0UjqHw6HGjRvr6NGj2rRpk7Jly2Y6CQCQQRzZAABo5MiR2rlzp6Kjo11qaEiSxWJRTEyMduzYoZEjR5rOAQA4CWMDADzQsWPH9MEHH6hr166qUaOG6ZxrqlGjhrp06aIPPvhAx48fN50DAHACxgYAeKCBAwfK19dXQ4cONZ1yQ0OHDpXFYtHAgQNNpwAAnICxAQAeZuPGjZo4caKGDh2qggULms65oUKFCmno0KGaMGGCfvnlF9M5AIBMxgXiAOBBHA6HHn74Yf3111/69ddf5efnZzrpplJSUlSzZk3lz59fy5Ytc7nrSwAAV+ICcQDwUjNmzNDKlSsVHR3tFkNDkvz8/BQVFaUVK1boiy++MJ0DAMhEHNkAAA9x/vx5VapUSXXq1NGcOXNM59yyVq1aaf369dq+fbsCAgJM5wAAroMjGwDghWw2m44fP66wsDDTKbclPDxcx44dk81mM50CAMgkjA0A8AB79+5VaGio+vfvr7Jly5rOuS1ly5ZV//79Zbfb9eeff5rOAQBkAk6jAgAP8NJLL2nVqlXavn27AgMDTefctoSEBFWsWFENGjTQzJkzTecAAK6B06gAwIvExcVp1qxZstvtbj00JCkwMFA2m01ffvmlfvzxR9M5AIA7xJENAHBjKSkpuv/++xUYGKiVK1d6xNvGpqWlqUGDBrpw4YI2bNjgNu+qBQDegiMbAOAlxo8fry1btigmJsYjhoYk+fj4KCYmRps2bdKECRNM5wAA7gBHNgDATZ06dUrly5dXy5YtNXHiRNM5ma5Dhw76+uuvtXPnTuXPn990DgDgbxzZAAAvMHjwYCUnJ2vYsGGmU5xi+PDhSkpK0uDBg02nAABuE2MDANzQli1bNHr0aA0aNEhFihQxneMURYoU0fvvv69Ro0Zp69atpnMAALeB06gAwM04HA41bdpU+/bt05YtW+Tv7286yWmSkpJUrVo13XPPPVq8eLHHXJcCAO6M06gAwIPNnTtXS5YsUWRkpEcPDUny9/dXRESEfvjhB82bN890DgDgFnFkAwDcSGJioqpUqaJKlSppwYIFpnOyhMPhUPPmzbVjxw5t3bpVOXLkMJ0EAF6NIxsA4KEiIiK0f/9+RUREmE7JMhaLRZGRkdq3b58iIyNN5wAAbgFjAwDcxMGDBzV8+HD16tVLlSpVMp2TpSpVqqSePXtq2LBhOnjwoOkcAEAGMTYAwE288847CggI0KBBg0ynGDFo0CAFBARowIABplMAABnE2AAAN7B69WpNnTpVw4cPV968eU3nGJEvXz4NGzZMU6ZM0c8//2w6BwCQAVwgDgAuLi0tTfXq1VNaWprWrl0rX19f00nGpKamqk6dOvLz89PPP/8sHx++ZwYAWY0LxAHAg0yePFnr169XTEyMVw8NSfL19VVMTIzWrVunzz77zHQOAOAmOLIBAC7s7NmzqlChgpo0aaLPP//cdI7LaNOmjeLi4rRjxw7+XgKALMaRDQDwEB9++KHOnTsnu91uOsWl2O12nT17Vh999JHpFADADTA2AMBF7dixQ9HR0RowYIBKlChhOsellCxZUgMGDFBUVJR27txpOgcAcB2cRgUALqpFixbaunWr4uPjlTNnTtM5LufixYuqXLmyqlevrm+++cZ0DgB4DU6jAgA3t3DhQs2fP19hYWEMjevImTOnwsLC9O2332rRokWmcwAA18CRDQBwMUlJSapRo4aKFSumJUuWyGKxmE5yWQ6HQ02aNNHhw4e1efNmZcuWzXQSAHg8jmwAgBuLjY3Vzp07FRUVxdC4CYvFoujoaO3cuVOxsbGmcwAA/8HYAAAXcvToUQ0ZMkRdu3ZVjRo1TOe4hRo1aqhLly764IMPdOzYMdM5AIB/YWwAgAt577335Ovrq6FDh5pOcSsffvihfH199d5775lOAQD8C2MDAFzEhg0bNHHiRA0dOlQFCxY0neNWChYsqCFDhmjChAnauHGj6RwAwN+4QBwAXIDD4dDDDz+sM2fO6JdffpGfn5/pJLeTkpKimjVrKn/+/Fq2bBnXuwCAk3CBOAC4mRkzZmjlypWKiopiaNwmPz8/RUdHa8WKFfriiy9M5wAAxJENADDu/PnzqlixourWras5c+aYznF7rVq10vr167Vt2zblypXLdA4AeByObACAG7HZbDpx4oTCwsJMp3iE8PBwHTt2THa73XQKAHg9xgYAGLR3716Fhoaqf//+Klu2rOkcj1C2bFn1799fdrtdf/75p+kcAPBqnEYFAAa9+OKLWr16tbZv367AwEDTOR4jISFBFStWVP369fXll1+azgEAj8JpVADgBuLi4jR79mzZ7XaGRiYLDAyUzWbTrFmz9OOPP5rOAQCvxZENADAgJSVF999/vwIDA7Vy5UreptUJ0tLS1KBBA124cEEbNmzgXb4AIJNwZAMAXNy4ceO0ZcsWxcTEMDScxMfHRzExMdq0aZPGjx9vOgcAvBJHNgAgi506dUrly5dXy5YtNXHiRNM5Hq9Dhw76+uuvtWPHDhUoUMB0DgC4PY5sAIALGzx4sJKTkzVs2DDTKV5h+PDhSkpK0gcffGA6BQC8DmMDALLQli1bNHr0aA0aNEhFihQxneMVihQpovfff1+jRo3Sli1bTOcAgFfhNCoAyCIOh0OPP/649u/fry1btsjf3990ktdISkpStWrVVKpUKX3//fdcJwMAd4DTqADABc2dO1dLly5VZGQkQyOL+fv7KyIiQkuWLNG8efNM5wCA1+DIBgBkgcTERFWpUkWVKlXSggULTOd4JYfDoebNm2v79u2Kj49Xjhw5TCcBgFviyAYAuJiIiAjt379fkZGRplO8lsViUWRkJP87AEAWYmwAgJMdPHhQw4cPV69evVSxYkXTOV6tUqVK6tmzp4YNG6aDBw+azgEAj8fYAAAns1qtypUrlwYNGmQ6BZIGDRqkgIAAvfPOO6ZTAMDjMTYAwIlWrVqladOmafjw4cqbN6/pHEjKly+fhg8frqlTp2r16tWmcwDAo3GBOAA4SVpamurVq6e0tDStXbtWvr6+ppPwt9TUVNWpU0d+fn76+eef5ePD994AIKO4QBwAXMCnn36q9evXKyYmhqHhYnx9fRUTE6N169Zp8uTJpnMAwGNxZAMAnODs2bMqX768HnvsMX3++eemc3Adbdq0UVxcnHbs2MHfbwCQQRzZAADDPvzwQyUkJMhut5tOwQ3Y7XadPXtWH330kekUAPBIjA0AyGQ7duxQdHS0BgwYoBIlSpjOwQ2ULFlSAwYMUFRUlHbs2GE6BwA8DqdRAUAma9GihbZu3ar4+HjlzJnTdA5u4uLFi6pcubKqV6+ub775xnQOALg8TqMCAEMWLlyo+fPnKywsjKHhJnLmzKmwsDB9++23WrRokekcAPAoHNkAgEySlJSk6tWrq3jx4lqyZIksFovpJGSQw+FQkyZNdPjwYW3atEn+/v6mkwDAZXFkAwAMiI2N1a5duxQVFcXQcDMWi0XR0dHauXOnRo4caToHADwGYwMAMsHRo0c1ZMgQde3aVTVq1DCdg9tQo0YNdenSRR988IGOHTtmOgcAPAJjAwAywcCBA+Xr66uhQ4eaTsEd+PDDD+Xr66uBAweaTgEAj8DYAIA7tGHDBk2aNElDhw5VwYIFTefgDhQsWFBDhgzRxIkTtXHjRtM5AOD2uEAcAO6Aw+HQww8/rDNnzuiXX36Rn5+f6STcoZSUFNWsWVP58uXT8uXLuf4GAP6DC8QBIItMnz5dK1euVFRUFEPDQ/j5+Sk6OlorV67UjBkzTOcAgFvjyAYA3Kbz58+rYsWKqlu3rubMmWM6B5msVatWWr9+vbZt26ZcuXKZzgEAl8GRDQDIAh9//LFOnDih8PBw0ylwgvDwcB0/flw2m810CgC4LcYGANyGPXv2KDQ0VEFBQSpTpozpHDhB2bJl1b9/f4WGhmrv3r2mcwDALXEaFQDchhdffFGrV6/W9u3bFRgYaDoHTpKQkKCKFSvqoYce0qxZs0znAIBL4DQqAHCiuLg4zZ49W3a7naHh4QIDA2Wz2TR79mzFxcWZzgEAt8ORDQC4BSkpKapVq5by5MmjFStW8LaoXsDhcKhBgwZKSEjQxo0bedcxAF6PIxsA4CTjxo3T1q1bFR0dzdDwEhaLRdHR0dq8ebPGjx9vOgcA3ApHNgAgg06dOqXy5curZcuWmjhxoukcZLEOHTpo3rx52rlzpwoUKGA6BwCM4cgGADjB4MGDlZycrGHDhplOgQHDhw9XcnKyPvjgA9MpAOA2GBsAkAFbtmzR6NGjNWjQIBUpUsR0DgwoUqSI3n//fY0aNUpbtmwxnQMAboHTqADgJhwOhx5//HHt379fW7Zskb+/v+kkGJKUlKRq1aqpVKlS+v7777luB4BX4jQqAMhEc+fO1dKlSxUZGcnQ8HL+/v6KiIjQkiVLNG/ePNM5AODyOLIBADeQmJioKlWqqFKlSlqwYIHpHLgAh8Oh5s2ba/v27YqPj1eOHDlMJwFAluLIBgBkkvDwcO3fv1+RkZGmU+AiLBaLIiMjtX//fkVERJjOAQCXxtgAgOs4ePCghg8frl69eqlixYqmc+BCKlWqpJ49e2r48OE6ePCg6RwAcFmMDQC4DqvVqsDAQA0aNMh0ClzQoEGDFBAQoHfeecd0CgC4LMYGAFzDqlWrNG3aNA0fPlx58+Y1nQMXlC9fPg0fPlxTp07V6tWrTecAgEviAnEA+I+0tDTVrVtXDodDa9eula+vr+kkuKjU1FTVqVNHvr6+WrNmjXx8+B4eAM/HBeIAcAc+/fRTbdiwQTExMQwN3JCvr69iYmK0fv16TZ482XQOALgcjmwAwL+cOXNGFSpU0OOPP65p06aZzoGbePXVV7V06VLt2LGDvycBeDyObADAbfroo4+UkJAgm81mOgVuxGaz6ezZs/roo49MpwCAS2FsAMDfduzYoejoaA0YMEAlSpQwnQM3UrJkSQ0YMEBRUVHasWOH6RwAcBmcRgUAf3v66acVHx+v+Ph45cyZ03QO3MzFixdVuXJlVatWTd9++63pHABwGk6jAoBbtGDBAi1YsEBhYWEMDdyWnDlzKiwsTPPnz9fChQtN5wCAS+DIBgCvl5SUpOrVq6t48eJasmSJLBaL6SS4KYfDoSZNmujw4cPatGmT/P39TScBQKbjyAYA3IIRI0Zo165dio6OZmjgjlgsFkVHR2vnzp2KjY01nQMAxjE2AHi1o0ePaujQoeratauqV69uOgceoEaNGurSpYuGDBmiY8eOmc4BAKMYGwC82sCBA+Xr66uhQ4eaToEH+fDDD+Xr66uBAweaTgEAoxgbALzWhg0bNGnSJA0dOlQFCxY0nQMPUrBgQQ0ZMkQTJ07Uhg0bTOcAgDFcIA7AKzkcDjVs2FBnz57VL7/8Ij8/P9NJ8DApKSmqWbOm8uXLp+XLl3M9EACPwQXiAHAT06dP16pVqxQVFcXQgFP4+fkpOjpaK1eu1IwZM0znAIARHNkA4HXOnz+vihUrqm7dupozZ47pHHi4Vq1aaf369dq2bZty5cplOgcA7hhHNgDgBj7++GOdOHFC4eHhplPgBcLDw3X8+HHZbDbTKQCQ5RgbALzKnj17FBoaqqCgIJUpU8Z0DrxA2bJl1b9/f4WGhmrv3r2mcwAgS3EaFQCv8uKLL2r16tXavn27AgMDTefASyQkJKhixYqqX7++vvzyS9M5AHBHOI0KAK4hLi5Os2fPlt1uZ2ggSwUGBspms2nWrFmKi4sznQMAWYYjGwC8QkpKimrVqqU8efJoxYoVvA0pspzD4VCDBg2UkJCgjRs38i5oANwWRzYA4D/GjRunrVu3Kjo6mqEBIywWi6Kjo7V582aNHz/edA4AZAmObADweKdOnVL58uXVsmVLTZw40XQOvFyHDh00b9487dy5UwUKFDCdAwC3jCMbAPAvgwYNUnJysoYPH246BdDw4cOVnJyswYMHm04BAKdjbADwaJs3b9bo0aM1aNAgFS5c2HQOoCJFiuj999/X6NGjtWXLFtM5AOBUnEYFwGM5HA49/vjjOnDggDZv3ix/f3/TSYAkKSkpSdWqVVOpUqX0/fffcx0RALfCaVQAIOmrr77S0qVLFRkZydCAS/H391dkZKSWLFmiuXPnms4BAKfhyAYAj5SYmKjKlSurcuXKWrBggekc4CoOh0PNmzfX9u3bFR8frxw5cphOAoAM4cgGAK8XHh6uAwcOKDIy0nQKcE0Wi0WRkZHav3+/IiIiTOcAgFMwNgB4nAMHDmj48OHq3bu3KlasaDoHuK5KlSqpV69eGj58uA4ePGg6BwAyHWMDgMd55513FBgYqPfff990CnBT77//vgICAvTOO++YTgGATMfYAOBRVq1apWnTpmn48OHKmzev6RzgpvLly6fhw4dr6tSpWr16tekcAMhUXCAOwGOkpaWpbt26kqS1a9fKx4fvp8A9pKamqm7duvLx8dGaNWv4dxeAS+MCcQBe6dNPP9WGDRsUHR3NF2twK76+voqOjtb69es1efJk0zkAkGk4sgHAI5w5c0YVKlTQ448/rmnTppnOAW7Lq6++qqVLl2rHjh38fQvAZXFkA4DX+eijj5SQkCCbzWY6BbhtNptNZ8+e1UcffWQ6BQAyBWMDgNvbsWOHoqOjNWDAAJUoUcJ0DnDbSpYsqQEDBigqKko7duwwnQMAd4zTqAC4vaefflrx8fGKj49Xzpw5TecAd+TixYuqXLmyqlWrpm+//dZ0DgBchdOoAHiNBQsWaMGCBQoLC2NowCPkzJlTYWFhmj9/vhYuXGg6BwDuCEc2ALitpKQkVa9eXcWLF9eSJUtksVhMJwGZwuFwqEmTJjp8+LA2bdokf39/00kAkI4jGwC8wogRI7Rr1y5FR0czNOBRLBaLoqOjtXPnTsXGxprOAYDbxtgA4JaOHj2qoUOHqmvXrqpevbrpHCDT1ahRQ126dNGQIUN07Ngx0zkAcFsYGwDc0sCBA+Xr66uhQ4eaTgGc5sMPP5Svr68GDhxoOgUAbgtjA4Db2bBhgyZNmqQPP/xQBQsWNJ0DOE3BggU1dOhQTZw4URs2bDCdAwC3jAvEAbgVh8Ohhg0b6uzZs/rll1/k5+dnOglwqpSUFNWsWVP58uXT8uXLuT4JgHFcIA7AY02fPl2rVq1SdHQ0QwNewc/PT9HR0Vq5cqVmzJhhOgcAbglHNgC4jfPnz6tixYqqV6+eZs+ebToHyFLPP/+81q5dq+3btytXrlymcwB4MY5sAPBIH3/8sU6cOKGwsDDTKUCWCwsL04kTJ2Sz2UynAECGMTYAuIU9e/YoNDRUQUFBKlOmjOkcIMuVLVtW/fv3V2hoqPbu3Ws6BwAyhNOoALiFF154QWvWrOEUEni1hIQEVaxYUQ899JBmzZplOgeAl+I0KgAeZenSpZozZ45sNhtDA14tMDBQNptNs2fPVlxcnOkcALgpjmwAcGkpKSmqVauW8uTJoxUrVvC2n/B6DodDDRo0UEJCgjZu3Mi7sgHIchzZAOAxxo4dq61btyomJoahAUiyWCyKiYnR5s2bNW7cONM5AHBDjA0ALuvkyZN6//331b59ez3wwAOmcwCXUbt2bbVv317vv/++Tp06ZToHAK6LsQHAZQ0ePFgpKSkaPny46RTA5QwfPlzJyckaPHiw6RQAuC7GBgCXtHnzZo0ePVqDBg1S4cKFTecALqdIkSJ6//33NXr0aG3ZssV0DgBcExeIA3A5DodDjz/+uA4cOKDNmzfL39/fdBLgkpKSklStWjWVKlVK33//Pdc1AcgSXCAOwK199dVXWrp0qSIjIxkawA34+/srMjJSS5Ys0dy5c03nAMBVOLIBwKUkJiaqcuXKqly5shYsWGA6B3B5DodDzZs31/bt2xUfH68cOXKYTgLg4TiyAcBthYeH68CBA4qMjDSdArgFi8WiyMhI7d+/XxEREaZzAOAKjA0ALuPAgQMaPny4evfurYoVK5rOAdxGpUqV1KtXLw0fPlwHDx40nQMA6RgbAFzGO++8o8DAQL3//vumUwC38/777ysgIEDvvPOO6RQASMfYAOASVq1apWnTpmn48OHKmzev6RzA7eTLl0/Dhw/X1KlTtXr1atM5ACCJC8QBuIC0tDTVrVtXkrR27Vr5+PB9EOB2pKamqm7duvLx8dGaNWt4LQFwCi4QB+BWPv30U23YsEHR0dF8cQTcAV9fX0VHR2v9+vWaPHmy6RwA4MgGALPOnDmjChUq6PHHH9e0adNM5wAe4dVXX9XSpUu1Y8cO/t4GkOk4sgHAbXz44YdKSEiQzWYznQJ4DLvdrnPnzunDDz80nQLAyzE2ABizfft2RUdHa8CAASpRooTpHMBjlChRQu+8846io6O1Y8cO0zkAvBinUQEw5umnn1Z8fLzi4+OVM2dO0zmAR7l48aKqVKmiqlWr6ttvvzWdA8CDcBoVAJe3YMECLViwQOHh4QwNwAly5sypsLAwzZ8/XwsXLjSdA8BLcWQDQJZLSkpS9erVVbx4cS1ZskQWi8V0EuCRHA6HmjRposOHD2vTpk3y9/c3nQTAA3BkA4BLGzFihHbt2qXo6GiGBuBEFotF0dHR2rlzp2JjY03nAPBCjA0AWero0aMaOnSounXrpurVq5vOATxejRo11LVrVw0ZMkRHjx41nQPAyzA2AGSpgQMHytfXV0OGDDGdAniNoUOHytfXV++9957pFABehrEBIMts2LBBkyZN0ocffqiCBQuazgG8RsGCBTV06FBNnDhRGzZsMJ0DwItwgTiALOFwONSwYUOdPXtWv/zyi/z8/EwnAV4lJSVFNWvWVL58+bR8+XKulwJw27hAHIDLmT59ulatWqXo6GiGBmCAn5+foqOjtXLlSs2YMcN0DgAvwZENAE53/vx5VaxYUfXq1dPs2bNN5wBe7fnnn9fatWu1fft25cqVy3QOADfEkQ0ALuXjjz/WiRMnFBYWZjoF8HphYWE6ceKEbDab6RQAXoCxAcCp9uzZo9DQUAUFBalMmTKmcwCvV7ZsWfXv31+hoaHau3ev6RwAHo7TqAA41QsvvKA1a9ZwygbgQhISElSxYkU99NBDmjVrlukcAG7mVrYBV2kCuG0JSQnadWqXLqVcUna/7CpXoJwC/QPT71+6dKnmzJmjqVOnMjQAFxIYGCibzaa2bdsqLi5OjRs3vuL+m722ASCjOLIB4JbEH4/XmPVjtGDnAv1x+g859P//F2KRRWXzl1Xz8s3VsWZHvfbEa8qTJ49WrFjB22wCLsbhcKhBgwZKSEjQxo0bteP0jgy9trvW7qoqd1UxWA7AtFvZBowNABmy5/Qedfm2i77/43v5WfyU4ki57mPT798tze04V8898lwWlgLIqPXr16vuk3VVZ0gdrT25NsOv7aZlm2psi7Eqk5/rsABvxLtRAchUEzZOUJVRVRS3N06SbvjFyL/v9y3nq1eWv6IJGyc4vRHArfvV51dl75tdG09vlJTx13bc3jhVGVWF1zaAm2JsALihYcuGqdM3nZSYkqiUtBt/IfJfqY5UJaYkqtM3nTRs2TAnFQK4Hemv7dRbf22npKXc9LXdqFEjNWrUKBNKAbgzxgbgZmbOnCmLxaKvvvrqqvvuu+8+WSwWxcXFXXVfqVKlVL9+fUlSfHy8/P391b59+6se99dff6lo0aKqV6+exq0fp/fi3stY2GlJH1z/13uPvieLxSKLxcLbbQLX0axZM+XPn19Hjx696r4zZ86kvzZLlSqV/nq63q8333zzup9nwsYJ139tR+r6r+Xkqx/+Xtx7mrhxYsb/kAC8Cu9GBbiZhg0bSpJWrFihVq1apd9+9uxZbdmyRX5+flq5cuUV7y6zf/9+7d+/X6+88ookqUqVKgoODtbw4cP15ptv6tFHH01/7DvvvKPjx49rwhcT9OJ3L2Y8LJekVv+5bbWks5KevPzbbL7ZZHvcprvuuusW/sSA9xg1apSqVaumvn376vPPP7/ivnfffVcnTpzQokWLtGfPHiUkJFzzOWJjY7VmzRo9+OCD17x/z+k96rmw541Dikh66Bq3+1774T0W9lCTMk24hgPAVRgbgJspVqyYypQpoxUrVlxx++rVq+VwOPTSSy9ddd8/v/9nqEjS+++/ry+++EJdunTRpk2b5O/vr9WrV2vcuHHq27evov+MvrVTK/wl3fef27ZIuvj/tzt8HFoYsFB9c/VNf4jD4VBiYqJy5syZ8c8FeKgyZcpo8ODBslqtevPNN/XEE09IktatW6cxY8YoKChI9913n+67778vtssWL16stWvX6tlnn1XXrl2v+Zgu33a5+Ws7t65+Pd9ASlqKunzbRYvbLs74BwHwCpxGBbihhg0b6pdfftHFixfTb1u5cqWqVq2qZs2a6eeff1ZaWtoV91ksFjVo0CD9thw5cmj06NHavn27/ve//yk5OVmdO3dWyZIl1aZnG33/x/f//wVJqqTjks7dWXdKWoq+7/m9Gj3RSN99951q166tnDlzauzYsdq7d68sFos+/fTTqz7OYrHogw8+uOK2gwcPqkOHDipcuLCyZ8+uqlWratKkSXcWCLiAfv36qUaNGurevbsSExOVmpqqrl276p577tHgwYOv+3FHjhxR27ZtVbx4cX3yySdX3HfixAlt27ZNG/7ccOVr+3b8IulTSXZJH0qKlVLWpOj7P77X78d/v+GHjhgxQlWrVlVAQIDy58+v2rVrX3UEh9c24Fk4sgG4oYYNG2rKlClas2ZN+gWYK1euVP369VW/fn2dOXNGW7ZsUY0aNdLvq1SpkgoWLHjF8zRt2lRt2rTR//73Px06dEhbtmzRvHnz9Nnvn135FphnJY3U5e90/vdUqVtlkX7d+qvatGmjLl26qFOnTqpYseItPcXRo0f14IMPymKxqEePHrrrrru0cOFCvfXWWzp79qz69Olzh5GAOX5+fho3bpzq16+vDz/8UHfffbc2btyoRYsWKSAg4Jofk5aWptdff10nT55UXFycChQocMX9sbGxGjJkiFp93Oqmb297+Qklnf/Pbdl0+QjmOkl3S6qoy9+y3CFpvuQjH41eP1oxzWKu+ZTjx49Xr1699OKLL6p3795KTEzUpk2btGbNGr366quSeG0DnoixAbihf1+30ahRI6WkpGjNmjVq166d7r33XhUuXFgrVqxQjRo1dO7cOW3evFkdOnS45nNFRkZq0aJFGjdunFq2bKlnn31W/WL63fyLkdvlkM4cOqNFixbpySefTL/5Vi4aHzhwoFJTU7V58+b0AdW1a1e1adNGH3zwgbp06cJpWXBr9erVU/fu3RUaGqrs2bOrTZs2V7xe/mvYsGFasmSJhgwZoocffvi6j1tzYI1SCmXgtb1bUuh/bntUUmNJ7XV5eKTHSpoipa1K08InF173KefPn6+qVavqyy+/vO5jeG0DnofTqAA3VLlyZRUsWDD9WozffvtN58+fT3+3qfr162vlypWSLl/LkZqaesX1Gv8WEBCQ/t3SJ554QucundMfp/+48kH5dfmdaO70qMY/8kkNGje46cOuxeFwaPbs2XrmmWfkcDh04sSJ9F9PPvmkzpw5o40bN2ZSKGDOsGHDVLBgQfn4+CgyMvK6j1u+fLmGDBmiRo0a6b33rv0OUx988IHOJp7V4UKHM/bJi0tq+59f/1zD8e+hkajLR0BKSzot7Tq0SwlJ175wPV++fDpw4IDWrVt3zft5bQOeiSMbgBuyWCyqX7++li1bprS0NK1cuVJ33323ypUrJ+ny2IiNjZWk9NFxvbExcOBAHTlyRJUrV9bgwYNVtXFVOeRw7h8gv7Tr1C7VLFLzlj/0+PHj+uuvvzRu3DiNGzfumo85duzYHQYC5uXJk0cVK1bUiRMnVLhw4Ws+5uTJk2rTpo3y58+vadOmycfn+t9D3H16d8Zf2wGS7r3OffskxUk6oKvfCjfx+q9tq9WqH374QXXr1lW5cuX0xBNP6NVXX02/lozXNuCZGBuAm2rYsKG++eYbbd68Of16jX/Ur19fwcHBOnjwoFasWKFixYqpbNmyVz3H+vXrNXLkSPXq1Uvt27fXAw88oIgPI6QKTo73ky6lXLriJovFcs2HpqamXvH7fy58f/3119WuXbtrfsw/16oAnszhcKhdu3Y6dOiQvvnmGxUrVuyGj//va+62nJI0WVIhXX5L6zy6/Ha4OyX9LMlx/c9TuXJlbd++Xd9++60WLVqk2bNna9SoURo0aJCGDBnCaxvwUIwNwE39+7qNlStXXnHh5AMPPKDs2bPrxx9/1Jo1a9S8efOrPj41NVWdO3dWsWLFNHToUOXOnVu9e/dWRESE1EFSSef2Z/fLfsXv8+fPL+nyDxX8tz///POK3991113KnTu3UlNT9fjjjzu1EXBlERERmj9/vvr27aunn376po//72vutmzX5XenayMp379u35uxz5MrVy61bt1arVu3VlJSkp5//nkNGzZMAwYM4LUNeCiu2QDcVO3atZUjRw5NmzZNBw8evOLIRvbs2XX//fdr5MiROn/+/DVPoYqJidEvv/yimJgY5c6dW5I0ZMgQFS9RXPpWl7+g+EcmvfXtv5UrUO6K3+fJk0eFChXSsmXLrrh91KhRV/ze19dXL7zwgmbPnq0tW7Zc9bzHjx/PvEjARa1bt04DBgzQAw88oI8//vimjz9x4oRSj6VKSXf4ia/1VUOiLr8d7t/++9r+x8mTJ6/4vb+/v6pUqSKHw6Hk5GRe24CH4sgG4Kb8/f1Vp04dLV++XNmzZ9cDDzxwxf3169dXeHi4pKuv19i/f78GDRqkZ5555oqfQp4rVy7FRMfo+eefl9ZI+me/ZOZb30oK8A9QoH/gVbd37NhRH3/8sTp27KjatWtr2bJl2rFjx1WP+/jjjxUXF6d69eqpU6dOqlKlik6dOqWNGzfqhx9+0KlTp+48EnBRFy5cUOvWrZWcnKwWLVpo5syZ13xc4cKF1bRpU0n//9a3xXoU06FCh27/k9+ry6dNfS6pti6Plw2ScklKkO7Jd881X9vS5TegKFKkiBo0aKDChQvr999/V2xsrJ5++un0b3jw2gY8D2MDcGMNGzbU8uXL00+b+rcGDRooPDxcuXPnvuqnDffs2VMOhyP9IvJ/a9WqlUrXLa29P+6VqkrKm8nRFunugLuvedegQYN0/PhxzZo1SzNnzlSzZs20cOFC3X33lY8vXLiw1q5dq6FDh2rOnDkaNWqUChYsqKpVq8pms2VyMOBajh07pj179ki6fDTyeh599NH0sfGPeiXq6ZtL39z+W1sXkvSypKWSFksK1OXRkUvSPKlR6UbX/dAuXbpo2rRpioiIUEJCgkqUKKFevXpd8Q5avLYBz2NxOBw3fWuKs2fPKm/evDpz5ozy5MmTFV0ADIo/Hq+qo6o67/m7x6vyXZWd9vwAro3XNoDMcCvbgGs2AFylyl1V1LRsU/n5ZO7BTz8fPzUt25QvRgBDeG0DyGqMDQDXNLbFWKd8QTK2xdhMfU4At4bXNoCsxNgAcE1l8pfRiGYjMvU5Y5vFqkz+Mpn6nABuDa9tAFmJsQHgujre31EfNf4oU55rWJNheuv+tzLluQDcGV7bALIKYwPADQ18ZKDGPzNeOfxy3PKpF34+fsrhl0MTnpmgdx9+10mFAG4Hr20AWYGxAeCmOt7fUb91/k0lUy7/WPGbfWHyz/2NSzdWfPd4vusJuKiO93dUfPd4NS7dWNLNX9u+Fl9JUqN7GvHaBpAh/JwNADd1/PhxvfjYi9qzeY8GRg3U2QpntXDXQu0+tVsO/f+7Z1tk0b0F7lWzcs3UrXY33pkGcANl8pfR4raLFX88XmPWj7nha7v4heL6KewnHS16VLmb5zZYDcBdMDYA3NCGDRv07LPP6tChyz91uHqR6mrdrLUkKSEpQR2COujLOV/q23nf6tHqj173pwcDcG1V7qqimGYxkqRN2zbpvsb36fmXntf7776vcgXKKdA/UDNmzNBPJ37S5hObdd999+mbb77R/fffb7gcgCvjNCoA1/XZZ5/poYce0pEjR9Jvu3TpUvp/9k311cJPFkoHpXlj5zE0AA8RExYjHZEWf7JYFfNWTH9t//v1f+TIET300EOaMmWKqUwAboCxAeCaBg4cqHbt2ik5OVlpaWnpt//7i42xY8cqISFBkjRp0iTt27cvyzsBZK4///xTn376qSQpISFBY8f+/8/P+PfrPy0tTUlJSXrjjTf03nvvZXUmADfB2ABwTdu2bZMk+fr6pt/m4+OT/sXGxYsXNWzYsCs+Zvjw4VkXCMAp/vu6HjZsmC5evCjp8tjw8fn/Lx3++f+H33//PesCAbgVxgaAa5o1a5Z++uknFS1aVNLlLyrS0tKUmJgo6fJRjZMnT6Y/PjU1VRMnTuToBuDG/vzzT02aNEmpqanpt508eTL96EZiYqLS0tLSR0axYsW0bNkyzZo1y0gvANfH2ABwTRaLRRUqVNDx48fVu3dvtWnTRr6+vsqTJ4+SkpI0bNgwORyOKz4mJSVFNpvNUDGAO2Wz2a4YGpLkcDg0bNgwJSUlKW/evPL19VWbNm3Uq1cvHTt2TOXLl5fFYjFUDMDV8W5UAK4rOjpa/v7++uCDD5QvXz6NGjVKAQEBSk1NVb169XTs2DGdOHFCe/bs0QMPPCAfHx+VKVPGdDaA23Tvvfeqbt26SktL0/r161W2bFkVKlRId999tyTprbfeUps2bZQ7d2799ddf+uSTTxQTE8MplACuy+L477cmr+Hs2bPKmzevzpw5ozx58mRFFwDDzp49q1KlSqlz586y2+3Xfdxnn32mdu3a6dKlS/L398/CQgDOkpSUpOzZs2vy5Ml64403rvu44OBgjR8/Xvv27ePrA8CL3Mo24DQqANc0duxYXbx4UX369DGdAsBF9enTRxcuXNC4ceNMpwBwUYwNAFe5dOmSIiMj1bZtWxUrVsx0DgAXVbx4cbVt21aRkZFXvC0uAPyDsQHgKlOnTtWRI0cUHBxsOgWAiwsODtahQ4c0bdo00ykAXBBjA8AV0tLSFBoaqueee04VK1Y0nQPAxVWqVEnPPfec7Hb7FT8AFAAkxgaA/5g3b562b98uq9VqOgWAm7Bardq+fbu+/vpr0ykAXAxjA0A6h8Mhm82mRx55RA8++KDpHABu4qGHHtLDDz8sm8121c/fAeDdGBsA0i1fvlxr1qzhqAaAW2a1WvXzzz9rxYoVplMAuBDGBoB0NptN1atXV7NmzUynAHAzzZs3V7Vq1WSz2UynAHAhjA0AkqTNmzdrwYIFCgkJkcViMZ0DwM1YLBaFhIRo/vz52rJli+kcAC6CsQFAkmS321WqVCm1bt3adAoAN/XKK6+oVKlSstvtplMAuAjGBgD9+eefmj59uvr3769s2bKZzgHgprJly6Z+/fpp+vTp2rdvn+kcAC6AsQFAERERypcvn9566y3TKQDcXMeOHZUnTx5FRESYTgHgAhgbgJc7efKkJkyYoB49eihXrlymcwC4uVy5cqlHjx4aP368Tp48aToHgGGMDcDLxcbGyuFwqEePHqZTAHiInj17yuFwaOTIkaZTABjG2AC82Pnz5zVixAh17NhRhQoVMp0DwEMUKlRIb731lkaMGKELFy6YzgFgEGMD8GKTJk3SX3/9pX79+plOAeBh+vXrp9OnT2vSpEmmUwAYxNgAvFRycrLCw8PVunVrlS5d2nQOAA9TpkwZvfzyywoPD1dKSorpHACGMDYALzVz5kz9+eefCgkJMZ0CwEOFhIRo7969mjlzpukUAIYwNgAv5HA4ZLfb9dRTT+m+++4znQPAQ9WsWVNPPvmk7Ha7HA6H6RwABjA2AC/03XffadOmTbJaraZTAHg4q9Wq3377TYsXLzadAsAAxgbghWw2m+rWratHH33UdAoAD9eoUSPVqVNHNpvNdAoAAxgbgJdZu3atfvzxR1mtVlksFtM5ADycxWKR1WpVXFyc1q1bZzoHQBZjbABexmazqUKFCnruuedMpwDwEi1btlT58uU5ugF4IcYG4EW2b9+ur776SsHBwfL19TWdA8BL+Pr6Kjg4WHPmzNGOHTtM5wDIQowNwIuEhYWpSJEiatu2rekUAF6mbdu2Kly4sMLCwkynAMhCjA3ASxw+fFifffaZ+vTpo+zZs5vOAeBlcuTIoT59+mjy5Mk6fPiw6RwAWYSxAXiJqKgo5ciRQ126dDGdAsBLde3aVdmzZ1d0dLTpFABZhLEBeIEzZ85ozJgx6tq1q/LmzWs6B4CXyps3r7p27arRo0frzJkzpnMAZAHGBuAFxowZo8TERPXp08d0CgAv16dPHyUmJmrs2LGmUwBkAcYG4OESExMVFRWlN954Q0WLFjWdA8DLFStWTG3btlVUVJQuXbpkOgeAkzE2AA83ZcoUHT16VMHBwaZTAECSFBwcrCNHjmjKlCmmUwA4GWMD8GCpqakKDQ1Vq1atVKFCBdM5ACBJqlixolq2bKnQ0FClpqaazgHgRIwNwIPNnTtXO3fulNVqNZ0CAFewWq3asWOH5s2bZzoFgBMxNgAP5XA4ZLPZ1KhRI9WtW9d0DgBcoV69enr00Udls9nkcDhM5wBwEsYG4KF++uknrVu3jqMaAFyW1WrV2rVrtWzZMtMpAJyEsQF4KJvNpvvuu09PPvmk6RQAuKannnpKNWrUkM1mM50CwEkYG4AH+u2337Ro0SKFhITIYrGYzgGAa7JYLAoJCdHChQu1adMm0zkAnICxAXggu92u0qVL6+WXXzadAgA31Lp1a91zzz2y2+2mUwA4AWMD8DB79+7VF198of79+8vPz890DgDckJ+fn/r3768ZM2Zo7969pnMAZDLGBuBhwsPDlS9fPrVv3950CgBkSIcOHZQvXz5FRESYTgGQyRgbgAc5fvy4Jk6cqJ49eypXrlymcwAgQ3LlyqUePXpowoQJOnHihOkcAJmIsQF4kNjYWFksFvXo0cN0CgDckn/+fys2NtZwCYDMxNgAPMT58+cVGxurjh07qmDBgqZzAOCWFCpUSB07dtSIESN0/vx50zkAMgljA/AQEyZM0JkzZ9SvXz/TKQBwW/r166czZ85o4sSJplMAZBLGBuABkpOTFRERoTZt2uiee+4xnQMAt6V06dJ65ZVXFB4eruTkZNM5ADIBYwPwADNmzNC+ffsUEhJiOgUA7khISIj27dunL774wnQKgEzA2ADcnMPhkN1uV/PmzVW9enXTOQBwR2rUqKFmzZrJbrfL4XCYzgFwhxgbgJtbuHChtmzZIqvVajoFADKF1WrV5s2btWjRItMpAO4QYwNwczabTQ8++KAefvhh0ykAkCkeeeQR1atXTzabzXQKgDvE2ADc2M8//6xly5bJarXKYrGYzgGATGGxWGS1WvXTTz9pzZo1pnMA3AHGBuDGbDabKlasqGeffdZ0CgBkqueee04VKlTg6Abg5hgbgJvatm2b5s2bp+DgYPn48FIG4Fl8fHwUHBysuXPnavv27aZzANwmvkIB3FRoaKiKFi2q119/3XQKADhF27ZtVaRIEYWGhppOAXCbGBuAGzp48KCmTJmiPn36KHv27KZzAMApsmfPrj59+mjKlCk6dOiQ6RwAt4GxAbihqKgoBQQEqEuXLqZTAMCpunTpohw5cigqKsp0CoDbwNgA3Mxff/2lsWPHqlu3bsqTJ4/pHABwqrx586pbt24aM2aM/vrrL9M5AG4RYwNwM6NHj1ZSUpJ69+5tOgUAskTv3r116dIljRkzxnQKgFvE2ADcSGJioqKjo9WuXTsVKVLEdA4AZImiRYuqXbt2ioqKUmJioukcALeAsQG4kcmTJ+vYsWMKCgoynQIAWSooKEjHjh3TZ599ZjoFwC1gbABuIjU1VWFhYXrhhRdUvnx50zkAkKUqVKig559/XmFhYUpNTTWdAyCDGBuAm/jqq6+0a9cuWa1W0ykAYITVatXOnTs1d+5c0ykAMoixAbgBh8Mhm82mJk2aqHbt2qZzAMCIOnXqqHHjxrLZbHI4HKZzAGQAYwNwA3FxcVq/fj1HNQB4PavVqnXr1unHH380nQIgAxgbgBuw2WyqWbOmmjZtajoFAIx64okndN9998lms5lOAZABjA3Axf3yyy9avHixQkJCZLFYTOcAgFEWi0UhISH67rvv9Ouvv5rOAXATjA3AxdntdpUpU0YvvfSS6RQAcAkvv/yySpcuLbvdbjoFwE0wNgAX9scff2jmzJnq37+//Pz8TOcAgEvw8/NT//799cUXX2jPnj2mcwDcAGMDcGHh4eEqUKCA2rdvbzoFAFxKhw4dVKBAAYWHh5tOAXADjA3ARR07dkyTJk1Sr169FBAQYDoHAFxKQECAevbsqUmTJun48eOmcwBcB2MDcFEjRoyQr6+v3n77bdMpAOCS3n77bVksFo0YMcJ0CoDrYGwALighIUEjR45Up06dVKBAAdM5AOCSChYsqE6dOik2NlYJCQmmcwBcA2MDcEHjx4/XuXPn1K9fP9MpAODS+vXrp3PnzmnChAmmUwBcA2MDcDHJycmKiIjQq6++qpIlS5rOAQCXVqpUKbVp00YRERFKTk42nQPgPxgbgIuZPn26Dhw4oJCQENMpAOAWQkJCtH//fs2YMcN0CoD/YGwALiQtLU12u10tWrRQ1apVTecAgFuoVq2ann76adntdjkcDtM5AP6FsQG4kAULFmjr1q2yWq2mUwDArVitVm3ZskULFiwwnQLgXxgbgAux2Wx66KGH1KBBA9MpAOBWGjZsqAcffFA2m810CoB/YWwALmLVqlVasWKFrFarLBaL6RwAcCsWi0VWq1XLly/X6tWrTecA+BtjA3ARNptNlStX1jPPPGM6BQDc0rPPPqtKlSpxdANwIYwNwAXEx8fr66+/VnBwsHx8eFkCwO3w8fFRcHCw5s2bp99//910DgAxNgCXEBoaquLFi+u1114znQIAbu21115TsWLFFBoaajoFgBgbgHEHDhzQtGnT1LdvX/n7+5vOAQC3lj17dvXt21dTp07VgQMHTOcAXo+xARgWGRmpXLlyqXPnzqZTAMAjdO7cWQEBAYqKijKdAng9xgZg0OnTpzVu3Dh1795duXPnNp0DAB4hT5486t69u8aOHavTp0+bzgG8GmMDMGjUqFFKTk5Wr169TKcAgEfp3bu3kpOTNXr0aNMpgFdjbACGXLx4UdHR0Wrfvr0KFy5sOgcAPErhwoX15ptvKjo6WhcvXjSdA3gtxgZgyOTJk3Xy5EkFBQWZTgEAjxQUFKQTJ07os88+M50CeC3GBmBAamqqwsLC9OKLL+ree+81nQMAHqlcuXJ64YUXFBYWptTUVNM5gFdibAAGzJ49W7t371ZISIjpFADwaCEhIdq1a5fmzJljOgXwSowNIIs5HA7ZbDY99thjeuCBB0znAIBHq127tpo0aSKbzSaHw2E6B/A6jA0giy1ZskQbN26U1Wo1nQIAXsFqtWrDhg1aunSp6RTA6zA2gCxms9lUq1YtPf7446ZTAMArNG3aVLVq1ZLNZjOdAngdxgaQhTZs2KAffvhBVqtVFovFdA4AeAWLxaKQkBB9//332rhxo+kcwKswNoAsZLfbVbZsWb3wwgumUwDAq7z44osqU6aM7Ha76RTAqzA2gCyye/duzZo1S0FBQfLz8zOdAwBexc/PT0FBQfryyy+1e/du0zmA12BsAFkkLCxMhQoV0ptvvmk6BQC8Uvv27VWwYEGFh4ebTgG8BmMDyAJHjx7VJ598ol69eilnzpymcwDAK+XMmVO9evXSJ598omPHjpnOAbwCYwPIAjExMcqWLZu6d+9uOgUAvFr37t3l6+urmJgY0ymAV2BsAE527tw5jRo1Sp07d1b+/PlN5wCAVytQoIA6d+6skSNH6ty5c6ZzAI/H2ACcbPz48UpISFDfvn1NpwAAJPXt21cJCQmaMGGC6RTA4zE2ACdKSkpSRESEXnvtNZUoUcJ0DgBAUsmSJfXqq68qIiJCSUlJpnMAj8bYAJzo888/18GDBxUSEmI6BQDwLyEhITpw4ICmT59uOgXwaIwNwEnS0tJkt9v1zDPPqEqVKqZzAAD/UrVqVbVo0UJ2u11paWmmcwCPxdgAnOTbb7/V77//LqvVajoFAHANVqtV8fHxmj9/vukUwGMxNgAnsdlsatCggRo0aGA6BQBwDQ0bNlT9+vVls9lMpwAei7EBOMGKFSu0atUqjmoAgIuzWq1auXKlVq5caToF8EiMDcAJbDabqlSpoqefftp0CgDgBlq0aKEqVapwdANwEsYGkMm2bNmib7/9ViEhIfLx4SUGAK7Mx8dHwcHB+uabb7R161bTOYDH4SshIJOFhoaqRIkSatOmjekUAEAGvPrqqypRooRCQ0NNpwAeh7EBZKJ9+/bp888/V79+/eTv7286BwCQAf7+/urbt6+mTZum/fv3m84BPApjA8hEkZGRyp07tzp16mQ6BQBwCzp16qTAwEBFRkaaTgE8CmMDyCSnTp3S+PHj9fbbbyswMNB0DgDgFuTOnVtvv/22xo0bp1OnTpnOATwGYwPIJKNGjVJqaqp69uxpOgUAcBt69uyplJQUjR492nQK4DEYG0AmuHjxomJiYtS+fXvdfffdpnMAALehcOHCat++vaKjo3Xx4kXTOYBHYGwAmeCTTz7RyZMnFRQUZDoFAHAHgoKCdPLkSX366aemUwCPwNgA7lBKSorCwsL00ksvqWzZsqZzAAB34N5779WLL76osLAwpaSkmM4B3B5jA7hDs2bN0p49e2S1Wk2nAAAygdVq1R9//KHZs2ebTgHcHmMDuAMOh0M2m01NmzZVrVq1TOcAADLB/fffr8cff1w2m00Oh8N0DuDWGBvAHfj+++/166+/clQDADyM1WrVL7/8oh9++MF0CuDWGBvAHbDZbHrggQfUpEkT0ykAgEz02GOP6f7775fNZjOdArg1xgZwm9avX6+lS5fKarXKYrGYzgEAZCKLxSKr1aolS5Zow4YNpnMAt8XYAG6TzWZTuXLl9Pzzz5tOAQA4wQsvvKB7772XoxvAHWBsALdh586dmj17toKCguTr62s6BwDgBL6+vgoKCtLs2bO1a9cu0zmAW2JsALchLCxMd911l9q1a2c6BQDgRO3atVOhQoUUFhZmOgVwS4wN4BYdOXJEkydPVu/evZUjRw7TOQAAJ8qZM6d69eqlTz/9VEeOHDGdA7gdxgZwi2JiYpQtWzZ169bNdAoAIAt0795d2bJl04gRI0ynAG6HsQHcgrNnz2rUqFHq0qWL8ufPbzoHAJAF8ufPr86dO2vUqFE6d+6c6RzArTA2gFswbtw4XbhwQX379jWdAgDIQn379tX58+c1btw40ymAW2FsABl06dIlRUZG6vXXX1fx4sVN5wAAslCJEiX02muvKTIyUklJSaZzALfB2AAyaNq0aTp06JCCg4NNpwAADAgJCdHBgwc1bdo00ymA22BsABmQlpYmu92u5557TpUrVzadAwAwoHLlynr22Wdlt9uVlpZmOgdwC4wNIAO+/vprbd++XVar1XQKAMAgq9Wqbdu26ZtvvjGdArgFxgZwEw6HQzabTQ8//LAeeugh0zkAAIPq16+vhg0bymazyeFwmM4BXB5jA7iJ5cuX6+eff+aoBgBA0uWjG6tXr9aKFStMpwAuj7EB3ITNZlO1atXUvHlz0ykAABfQvHlzVa1aVTabzXQK4PIYG8ANbN68WQsWLFBISIgsFovpHACAC/Dx8VFISIjmz5+vLVu2mM4BXBpjA7gBu92ukiVL6pVXXjGdAgBwIa+88opKlCghu91uOgVwaYwN4Dr+/PNPTZ8+Xf369VO2bNlM5wAAXIi/v7/69eun6dOna9++faZzAJfF2ACuIzIyUnny5FHHjh1NpwAAXFCnTp2UO3duRUZGmk4BXBZjA7iGkydPavz48erRo4cCAwNN5wAAXFBgYKDefvttjR8/XqdOnTKdA7gkxgZwDSNHjpTD4VDPnj1NpwAAXFjPnj2VmpqqkSNHmk4BXBJjA/iPCxcuaMSIEerQoYPuuusu0zkAABd29913q0OHDoqJidGFCxdM5wAuh7EB/MekSZN0+vRp9e/f33QKAMAN9O/fX6dOndInn3xiOgVwOYwN4F9SUlIUHh6ul19+WWXKlDGdAwBwA2XLltXLL7+ssLAwpaSkmM4BXApjA/iXmTNnau/evQoJCTGdAgBwIyEhIdq7d6++/PJL0ymAS2FsAH9zOByy2+168sknVbNmTdM5AAA3UqtWLT3xxBOy2WxyOBymcwCXwdgA/vbdd9/pt99+k9VqNZ0CAHBDVqtVv/32mxYvXmw6BXAZjA3gbzabTXXq1FGjRo1MpwAA3FDjxo1Vu3Zt2Ww20ymAy2BsAJLWrl2rH3/8USEhIbJYLKZzAABuyGKxKCQkRHFxcVq3bp3pHMAlMDYAXT6qUb58ebVq1cp0CgDAjT3//PMqV64cRzeAvzE24PW2b9+ur776SkFBQfL19TWdAwBwY76+vgoKCtKcOXO0Y8cO0zmAcYwNeL2wsDDdfffdeuONN0ynAAA8QLt27XT33XcrLCzMdApgHGMDXu3w4cP67LPP1KdPH+XIkcN0DgDAA+TIkUO9e/fW5MmTdeTIEdM5gFGMDXi16OhoZc+eXV27djWdAgDwIN26dVP27NkVHR1tOgUwirEBr3XmzBmNHj1aXbt2Vb58+UznAAA8SL58+dSlSxeNHj1aZ8+eNZ0DGMPYgNcaO3asEhMT1adPH9MpAAAP1KdPH124cEFjx441nQIYw9iAV7p06ZKioqLUtm1bFStWzHQOAMADFS9eXG3btlVkZKQuXbpkOgcwgrEBrzRlyhQdOXJEwcHBplMAAB4sODhYR44c0dSpU02nAEYwNuB1UlNTFRoaqpYtW6pixYqmcwAAHqxSpUp67rnnFBoaqrS0NNM5QJZjbMDrzJs3Tzt27JDVajWdAgDwAlarVdu3b9e8efNMpwBZjrEBr+JwOGSz2fTII4+oXr16pnMAAF7gwQcf1MMPPyybzSaHw2E6B8hSjA14lZ9++klr167lqAYAIEtZrVatWbNGy5YtM50CZCnGBryKzWZT9erV1axZM9MpAAAv0rx5c1WrVk02m810CpClGBvwGr/99psWLVqkkJAQWSwW0zkAAC9isVgUEhKihQsXatOmTaZzgCzD2IDXsNvtKlWqlFq3bm06BQDghV555RWVKlVKdrvddAqQZRgb8Ap79+7VF198of79+ytbtmymcwAAXihbtmzq16+fZsyYoT///NN0DpAlGBvwChEREcqXL5/eeust0ykAAC/WsWNH5c2bVxEREaZTgCzB2IDHO3HihCZMmKAePXooV65cpnMAAF4sV65c6tGjhyZMmKCTJ0+azgGcjrEBjxcbGytJ6tGjh+ESAACknj17yuFwpP/9BHgyxgY82vnz5zVixAh17NhRhQoVMp0DAIAKFSqkt956SyNGjND58+dN5wBOxdiAR5s4caLOnDmjfv36mU4BACBd//799ddff2nSpEmmUwCnYmzAYyUnJys8PFyvvPKKSpcubToHAIB0pUuXVuvWrRUeHq7k5GTTOYDTMDbgsb744gvt27dPwcHBplMAALhKcHCw/vzzT82cOdN0CuA0jA14JIfDIbvdrqeeekr33Xef6RwAAK5Ss2ZNPfnkk7Lb7XI4HKZzAKdgbMAjLVy4UJs3b5bVajWdAgDAdVmtVm3atEmLFi0ynQI4BWMDHslms6lu3bp69NFHTacAAHBdjRo1Up06dWSz2UynAE7B2IDH+fnnn7Vs2TJZrVZZLBbTOQAAXJfFYpHVatVPP/2kNWvWmM4BMh1jAx7HZrOpQoUKeu6550ynAABwUy1btlT58uU5ugGPxNiAR9m2bZvmzZun4OBg+fr6ms4BAOCmfH19FRwcrLlz52r79u2mc4BMxdiARwkLC1ORIkXUtm1b0ykAAGRY27ZtVbhwYYWFhZlOATIVYwMe49ChQ5oyZYr69Omj7Nmzm84BACDDcuTIoT59+uizzz7T4cOHTecAmYaxAY8RFRWlHDlyqEuXLqZTAAC4ZV27dlWOHDkUFRVlOgXINIwNeIS//vpLY8aMUbdu3ZQ3b17TOQAA3LK8efOqa9euGjNmjM6cOWM6B8gUjA14hDFjxujSpUvq3bu36RQAAG5b7969lZiYqDFjxphOATIFYwNuLzExUVFRUXrjjTdUtGhR0zkAANy2YsWKqW3btoqKilJiYqLpHOCOMTbg9j777DMdO3ZMwcHBplMAALhjwcHBOnr0qKZMmWI6BbhjjA24tdTUVIWGhqpVq1aqUKGC6RwAAO5YxYoV1bJlS4WGhio1NdV0DnBHGBtwa1999ZV27dolq9VqOgUAgExjtVq1c+dOzZ0713QKcEcYG3BbDodDNptNjRo1Ut26dU3nAACQaerVq6dHH31UNptNDofDdA5w2xgbcFtxcXFav349RzUAAB7JarVq3bp1+vHHH02nALeNsQG3ZbPZdN999+nJJ580nQIAQKZ76qmnVKNGDdlsNtMpwG1jbMAt/frrr1q8eLFCQkJksVhM5wAAkOksFotCQkL03Xff6bfffjOdA9wWxgbckt1uV+nSpfXyyy+bTgEAwGlat26te+65R3a73XQKcFsYG3A7e/bs0RdffKH+/fvLz8/PdA4AAE7j5+en/v3764svvtDevXtN5wC3jLEBtxMeHq4CBQqoQ4cOplMAAHC6Dh06KF++fAoPDzedAtwyxgbcyvHjxzVp0iT17NlTAQEBpnMAAHC6XLlyqWfPnpo4caKOHz9uOge4JYwNuJURI0bIYrHo7bffNp0CAECW6dGjhywWi2JjY02nALeEsQG3kZCQoNjYWHXs2FEFCxY0nQMAQJYpWLCg3nrrLcXGxur8+fOmc4AMY2zAbUyYMEFnz55Vv379TKcAAJDl+vXrpzNnzmjChAmmU4AMY2zALSQnJysiIkJt2rTRPffcYzoHAIAsV7p0ab3yyiuKiIhQcnKy6RwgQxgbcAvTp0/X/v37FRISYjoFAABjQkJCtG/fPs2YMcN0CpAhjA24vLS0NNntdjVv3lzVq1c3nQMAgDE1atRQs2bNZLfb5XA4TOcAN8XYgMtbsGCBtm7dKqvVajoFAADjrFartmzZogULFphOAW6KsQGXZ7PZ9OCDD+rhhx82nQIAgHGPPPKI6tWrJ5vNZjoFuCnGBlzaqlWrtGLFClmtVlksFtM5AAAYZ7FYZLVatXz5cq1evdp0DnBDjA24NLvdrkqVKunZZ581nQIAgMt47rnnVLFiRdntdtMpwA0xNuCyfv/9d82bN0/BwcHy8eFfVQAA/uHj46Pg4GDNmzdP27ZtM50DXBdfwcFlhYaGqlixYnrttddMpwAA4HJef/11FS1aVKGhoaZTgOtibMAlHThwQFOnTlXfvn2VPXt20zkAALic7Nmzq0+fPpoyZYoOHjxoOge4JsYGXFJUVJQCAgLUuXNn0ykAALisLl26KGfOnIqKijKdAlwTYwMu5/Tp0xo7dqy6deumPHnymM4BAMBl5cmTR926ddPYsWP1119/mc4BrsLYgMsZPXq0kpOT1bt3b9MpAAC4vN69e+vSpUsaPXq06RTgKowNuJSLFy8qOjpa7dq1U5EiRUznAADg8ooWLap27dopOjpaiYmJpnOAKzA24FImT56s48ePKygoyHQKAABuIygoSMeOHdPkyZNNpwBXYGzAZaSmpiosLEwvvPCCypcvbzoHAAC3UaFCBT3//PMKCwtTamqq6RwgHWMDLmP27NnavXu3rFar6RQAANyO1WrVrl27NGfOHNMpQDrGBlyCw+GQ3W5XkyZNVLt2bdM5AAC4nTp16qhx48ay2WxyOBymcwBJjA24iKVLl2rDhg0c1QAA4A5YrVZt2LBBcXFxplMASYwNuAibzaZatWqpadOmplMAAHBbTzzxhGrWrCmbzWY6BZDE2IAL2Lhxo77//nuFhITIYrGYzgEAwG1ZLBaFhIRo8eLF+uWXX0znAIwNmGe321WmTBm9+OKLplMAAHB7L730ksqUKSO73W46BWBswKzdu3fryy+/VFBQkPz8/EznAADg9vz8/NS/f3/NnDlTf/zxh+kceDnGBowKDw9XwYIF1b59e9MpAAB4jPbt26tAgQIKDw83nQIvx9iAMceOHdMnn3yinj17KmfOnKZzAADwGAEBAerZs6cmTZqkY8eOmc6BF2NswJiYmBj5+vrq7bffNp0CAIDHefvtt+Xj46MRI0aYToEXY2zAiHPnzmnkyJHq1KmTChQoYDoHAACPU7BgQXXq1EkjR45UQkKC6Rx4KcYGjBg/frwSEhLUr18/0ykAAHisfv366dy5cxo/frzpFHgpxgayXFJSkiIiIvTqq6+qZMmSpnMAAPBYpUqVUps2bRQREaGkpCTTOfBCjA1kuenTp+vgwYMKCQkxnQIAgMcLCQnRgQMHNGPGDNMp8EKMDWSptLQ02e12tWjRQlWrVjWdAwCAx6tWrZqefvpp2e12paWlmc6Bl2FsIEvNnz9f8fHxslqtplMAAPAaVqtVW7du1YIFC0ynwMswNpClbDab6tevr4YNG5pOAQDAazRs2FAPPfSQbDab6RR4GcYGsszKlSu1cuVKjmoAAJDFLBaLrFarVqxYoVWrVpnOgRdhbCDL2Gw2ValSRS1atDCdAgCA13nmmWdUuXJljm4gSzE2kCW2bt2qb775RsHBwfLx4V87AACymo+Pj4KDg/X1118rPj7edA68BF/1IUuEhoaqePHievXVV02nAADgtV577TUVK1ZMoaGhplPgJRgbcLr9+/dr2rRp6tu3r/z9/U3nAADgtfz9/dW3b19NmzZNBw4cMJ0DL8DYgNNFRkYqMDBQnTt3Np0CAIDX69y5swICAhQZGWk6BV6AsQGnOnXqlMaNG6fu3bsrd+7cpnMAAPB6efLkUffu3TVu3DidPn3adA48HGMDTjVq1CilpKSoV69eplMAAMDfevfureTkZI0aNcp0CjwcYwNOc/HiRcXExKh9+/YqXLiw6RwAAPC3woUL680331R0dLQuXrxoOgcejLEBp/n000918uRJBQUFmU4BAAD/ERQUpJMnT2ry5MmmU+DBGBtwipSUFIWFhenFF1/UvffeazoHAAD8R7ly5fTCCy8oLCxMqamppnPgoRgbcIrZs2frjz/+kNVqNZ0CAACuw2q1avfu3Zo9e7bpFHgoxgYyncPhkM1m0+OPP67777/fdA4AALiOBx54QI899phsNpscDofpHHggxgYy3Q8//KBffvmFoxoAALgBq9WqjRs3asmSJaZT4IEYG8h0NptN999/vx577DHTKQAA4CYef/xx1apVSzabzXQKPBBjA5lqw4YNWrJkiaxWqywWi+kcAABwExaLRVarVT/88IM2bNhgOgcehrGBTGWz2VS2bFm98MILplMAAEAGvfDCCypbtqzsdrvpFHgYxgYyza5duzR79mwFBQXJ19fXdA4AAMggPz8/9e/fX7NmzdLu3btN58CDMDaQacLCwlSoUCG9+eabplMAAMAtat++vQoWLKiwsDDTKfAgjA1kiiNHjujTTz9Vr169lDNnTtM5AADgFuXMmVO9evXSJ598oqNHj5rOgYdgbCBTxMTEKFu2bOrevbvpFAAAcJu6d+8uPz8/xcTEmE6Bh2Bs4I6dO3dOo0aNUufOnZU/f37TOQAA4DYVKFBAnTt31qhRo3Tu3DnTOfAAjA3csXHjxunChQvq27ev6RQAAHCH+vbtq4SEBI0fP950CjwAYwN3JCkpSZGRkXrttddUokQJ0zkAAOAOlSxZUq+99poiIiKUlJRkOgdujrGBOzJt2jQdPHhQISEhplMAAEAmCQkJ0cGDB/X555+bToGbY2zgtqWlpclut+vZZ59V5cqVTecAAIBMUqVKFT3zzDOy2+1KS0sznQM3xtjAbfvmm2+0bds2Wa1W0ykAACCTWa1W/f777/r2229Np8CNMTZwWxwOh2w2mxo2bKj69eubzgEAAJmsQYMGatCggWw2m+kUuDHGBm7LihUrtHr1ao5qAADgwaxWq1atWqUVK1aYToGbYmzgtthsNlWtWlXNmzc3nQIAAJzk6aefVpUqVTi6gdvG2MAt27Jli+bPn6/g4GD5+PCvEAAAnsrHx0fBwcH69ttvtWXLFtM5cEN8pYhbZrfbVaJECbVp08Z0CgAAcLJXX31VJUqUUGhoqOkUuCHGBm7Jvn37NH36dPXr10/+/v6mcwAAgJP5+/urb9+++vzzz7Vv3z7TOXAzjA3cksjISOXOnVudOnUynQIAALJIp06dFBgYqKioKNMpcDOMDWTYqVOnNH78eL399tsKDAw0nQMAALJI7ty59fbbb2vcuHE6deqU6Ry4EcYGMmzkyJFKTU1Vz549TacAAIAs1qtXL6WmpmrUqFGmU+BGGBvIkAsXLigmJkYdOnTQ3XffbToHAABksbvvvlvt27dXTEyMLl68aDoHboKxgQz55JNPdOrUKfXv3990CgAAMCQoKEgnT57UJ598YjoFboKxgZtKSUlRWFiYXn75ZZUtW9Z0DgAAMKRs2bJ66aWXFBYWppSUFNM5cAOMDdzUl19+qb179yokJMR0CgAAMMxqtWrPnj2aNWuW6RS4AcYGbsjhcMhms+mJJ55QrVq1TOcAAADDatWqpaZNm8pms8nhcJjOgYtjbOCGFi9erN9++01Wq9V0CgAAcBFWq1W//vqrvv/+e9MpcHGMDdyQzWZT7dq11bhxY9MpAADARTRp0kQPPPCAbDab6RS4OMYGrmvdunWKi4tTSEiILBaL6RwAAOAiLBaLQkJCtHTpUq1fv950DlwYYwPXZbPZVK5cOT3//POmUwAAgIt54YUXdO+993J0AzfE2MA17dixQ3PmzFFQUJB8fX1N5wAAABfj6+uroKAgzZ49Wzt37jSdAxfF2MA1hYeH6+6771a7du1MpwAAABfVrl073XXXXQoPDzedAhfF2MBVjhw5osmTJ6t3797KkSOH6RwAAOCicubMqd69e+vTTz/VkSNHTOfABTE2cJXo6Gj5+/urW7duplMAAICL69atm7Jly6aYmBjTKXBBjA1c4ezZsxo9erS6dOmifPnymc4BAAAuLn/+/OrSpYtGjRqls2fPms6Bi2Fs4Apjx47VhQsX1KdPH9MpAADATfTt21cXLlzQuHHjTKfAxTA2kO7SpUuKjIxU27ZtVbx4cdM5AADATRQvXlyvv/66IiMjdenSJdM5cCGMDaSbOnWqjhw5ouDgYNMpAADAzQQHB+vQoUOaNm2a6RS4EMYGJElpaWkKDQ3Vc889p0qVKpnOAQAAbqZy5cp67rnnZLfblZaWZjoHLoKxAUnSvHnztH37dlmtVtMpAADATVmtVm3fvl1ff/216RS4CMYG5HA4ZLPZ9Mgjj+jBBx80nQMAANzUQw89pIcfflg2m00Oh8N0DlwAYwNatmyZ1qxZw1ENAABwx6xWq37++WctX77cdApcAGMDstlsqlatmpo1a2Y6BQAAuLlmzZqpatWqstlsplPgAhgbXm7Tpk1auHChQkJCZLFYTOcAAAA35+Pjo5CQEC1YsECbN282nQPDGBteLjQ0VKVKldIrr7xiOgUAAHiINm3aqGTJkgoNDTWdAsMYG17szz//1PTp09WvXz9ly5bNdA4AAPAQ2bJlU79+/TR9+nTt27fPdA4MYmx4sYiICOXNm1cdO3Y0nQIAADxMx44dlTt3bkVERJhOgUGMDS918uRJTZgwQT169FCuXLlM5wAAAA8TGBioHj16aPz48Tp58qTpHBjC2PBSsbGxcjgc6tmzp+kUAADgoXr27CmHw6GRI0eaToEhjA0vdP78eY0YMUJvvfWWChUqZDoHAAB4qLvuuksdOnTQiBEjdOHCBdM5MICx4YUmTZqkv/76S/379zedAgAAPFz//v11+vRpTZo0yXQKDGBseJnk5GSFh4erdevWKl26tOkcAADg4cqUKaOXX35Z4eHhSklJMZ2DLMbY8DIzZ87Un3/+qZCQENMpAADAS4SEhGjv3r2aOXOm6RRkMcaGF3E4HLLb7Xrqqad03333mc4BAABeombNmnryySdlt9vlcDhM5yALMTa8yKJFi7Rp0yZZrVbTKQAAwMtYrVb99ttv+u6770ynIAsxNryI3W5XnTp19Oijj5pOAQAAXqZRo0aqXbu27Ha76RRkIcaGl1i7dq1+/PFHWa1WWSwW0zkAAMDLWCwWWa1WxcXFad26daZzkEUYG17CZrOpfPnyatmypekUAADgpVq1aqXy5cvLZrOZTkEWYWx4ge3bt+urr75ScHCwfH19TecAAAAv5evrq6CgIM2ZM0c7duwwnYMswNjwAmFhYSpcuLDatm1rOgUAAHi5N954Q3fffbfCwsJMpyALMDY83OHDh/XZZ5+pT58+ypEjh+kcAADg5XLkyKE+ffpo8uTJOnz4sOkcOBljw8NFRUUpR44c6tq1q+kUAAAASVLXrl2VPXt2RUdHm06BkzE2PNiZM2c0ZswYde3aVXnz5jWdAwAAIEnKly+funbtqtGjR+vMmTOmc+BEjA0PNmbMGCUmJqpPnz6mUwAAAK7Qp08fJSYmauzYsaZT4ESMDQ+VmJioqKgovfHGGypatKjpHAAAgCsUK1ZMbdu2VVRUlC5dumQ6B07C2PBQU6ZM0dGjRxUcHGw6BQAA4JqCg4N15MgRTZkyxXQKnISx4YFSU1MVGhqqVq1aqUKFCqZzAAAArqlixYpq2bKlQkNDlZqaajoHTsDY8EBz587Vzp07FRISYjoFAADghqxWq3bs2KF58+aZToETMDY8jMPhkM1m06OPPqp69eqZzgEAALihevXq6ZFHHpHNZpPD4TCdg0zG2PAwP/30k9atWyer1Wo6BQAAIEOsVqvWrl2rZcuWmU5BJmNseBibzaYaNWroqaeeMp0CAACQIc2aNVP16tVls9lMpyCTMTY8yG+//aZFixYpJCREFovFdA4AAECGWCwWhYSEaOHChdq0aZPpHGQixoYHsdvtuueee9S6dWvTKQAAALekdevWKlWqlOx2u+kUZCLGhofYu3evvvjiC/Xv319+fn6mcwAAAG5JtmzZ1L9/f82YMUN79+41nYNMwtjwEOHh4cqXL586dOhgOgUAAOC2vPXWW8qXL58iIiJMpyCTMDY8wPHjxzVx4kT17NlTuXLlMp0DAABwW3LlyqUePXpowoQJOnHihOkcZALGhgeIjY2VxWJRjx49TKcAAADckX++nomNjTVcgszA2HBz58+fV2xsrDp27KiCBQuazgEAALgjhQoVUseOHTVixAidP3/edA7uEGPDzU2YMEFnzpxRv379TKcAAABkin79+unMmTOaOHGi6RTcIcaGG0tOTlZERITatGmje+65x3QOAABApihdurReeeUVhYeHKzk52XQO7gBjw4198cUX2rdvn0JCQkynAAAAZKqQkBDt27dPM2fONJ2CO8DYcFMOh0N2u13NmjVT9erVTecAAABkqho1auipp56S3W6Xw+EwnYPbxNhwUwsXLtTmzZtltVpNpwAAADiF1WrVpk2btGjRItMpuE2MDTdls9lUr149PfLII6ZTAAAAnOLRRx9V3bp1ZbPZTKfgNjE23NDPP/+sZcuWyWq1ymKxmM4BAABwCovFIqvVqp9++klr1qwxnYPbwNhwQzabTRUrVtRzzz1nOgUAAMCpnnvuOVWoUIGjG26KseFmtm3bpnnz5ik4OFg+PvzPBwAAPJuvr6+Cg4M1d+5cbd++3XQObhFfrbqZ0NBQFS1aVK+//rrpFAAAgCzRtm1bFSlSRKGhoaZTcIsYG27k4MGDmjJlivr06aPs2bObzgEAAMgS2bNnV58+fTRlyhQdOnTIdA5uAWPDjURFRSkgIEBdunQxnQIAAJClunTpohw5cigqKsp0Cm4BY8NN/PXXXxo7dqy6deumPHnymM4BAADIUnnz5lW3bt00ZswY/fXXX6ZzkEGMDTcxevRoJSUlqXfv3qZTAAAAjOjdu7cuXbqkMWPGmE5BBjE23EBiYqKio6PVrl07FSlSxHQOAACAEUWLFlW7du0UHR2txMRE0znIAMaGG/jss8907NgxBQUFmU4BAAAwKigoSEePHtWUKVNMpyADGBsuLjU1VaGhoXr++edVvnx50zkAAABGVahQQa1atVJoaKhSU1NN5+AmGBsu7quvvtKuXbtktVpNpwAAALgEq9WqnTt3au7cuaZTcBOMDRfmcDhks9nUuHFj1alTx3QOAACAS6hbt64aNWokm80mh8NhOgc3wNhwYXFxcVq/fj1HNQAAAP7DarVq3bp1+vHHH02n4AYYGy7MZrOpZs2aeuKJJ0ynAAAAuJQnn3xS9913n2w2m+kU3ABjw0X98ssvWrx4sUJCQmSxWEznAAAAuBSLxaKQkBB99913+vXXX03n4DoYGy7KbrerTJkyeumll0ynAAAAuKSXX35ZpUuXlt1uN52C62BsuKA//vhDM2fOVP/+/eXn52c6BwDgZc4nn5elqEV/pvypX4/8qoSkBNNJwDX5+fmpf//++uKLL7Rnzx7TObgGxoYLCg8PV4ECBdS+fXvTKQAALxF/PF69FvZSuZhyKhhWUI4uDg3aP0i1xtZSnv/lUbmYcuq1sJfij8ebTgWu0KFDBxUoUEDh4eGmU3ANjA0Xc+zYMU2aNEm9evVSQECA6RwAgIfbc3qPnpjyhKqOqqrR60Zr9+ndcujKtxJ1yKHdp3dr9LrRqjqqqp6Y8oT2nOa7yHANAQEB6tmzpyZNmqTjx4+bzsF/MDZczIgRI+Tr66u3337bdAoAwMNN2DhBVUZVUdzeOElSiiPlho//5/64vXGqMqqKJmyc4PRGICPefvttWSwWjRgxwnQK/oOx4UISEhI0cuRIderUSQUKFDCdAwBwkkaNGslischisahFixZGGoYtG6ZO33RSYkqiUtJuPDL+KyUtRYkpier0TScNWzbMSYXO1adPn/T/DQIDA03n4A4VLFhQnTp1UmxsrBISuMbIlTA2XMiECRN07tw59e3b13QKAOA/mjVrpvz58+vo0aNX3XfmzBkVLVpU9erVU1paWoaer1KlSpoyZYqCgoIy3DBx4kRVrlxZOXLkUPny5W/7u7it3m6l9x59Txp5jTs/kfTBNX5NufZzvRf3niZunHjTz5mWlpb+Tos5cuRQjRo1NH369Az1fvrpp+nD4L+/jhw5csVjS5cufc3Hde3a9YrHtW3bVlOmTNHDDz+coQa4vn79+uns2bOaOPHm/z4i6/BWRy4iOTlZERERatOmjUqVKmU6BwDwH6NGjVK1atXUt29fff7551fc9+677+rEiRNatGiRfHwy9n28woUL6/XXX8/w5x87dqy6du2qF154Qf369dPy5cvVq1cvXbhwQVarNcPPs2rLKs0dP1fKdoMH5ZH02H9uy339h/dY2ENNyjRRmfxlrvuYgQMH6uOPP1anTp1Up04dzZs3T6+++qosFoteeeWVDLUPHTpUZcpc+Tny5ct31eNq1qyp/v37X3FbhQoVrvj9Aw88oAceeEA//PCDNm7cmKHPD9dWqlQptWnTRhEREerevbuyZbvRv+TIKowNFzF9+nTt379fISEhplMAANdQpkwZDR48WFarVW+++aaeeOIJSdK6des0ZswYBQUF6b777nPK57548aIGDhyop59+WrNmzZIkderUSWlpafrwww/VuXNn5c+fP0PP9XzH52UpYZEjzSFduM6Dsku6hT9KSlqKunzbRYvbLr7m/QcPHlR4eLjefvttxcbGSpI6duyoRx99VMHBwXrppZfk6+t708/TrFkz1a5d+6aPK168+C0NOXiOkJAQTZ06VTNmzFDbtm1N50CcRuUS/jm0/PTTT6tatWqmcwAA19GvXz/VqFFD3bt3V2JiolJTU9W1a1fdc889evfdd7Vt2zYdPnw40z9vXFycTp48qe7du19x+9tvv63z589r/vz5GXqez77+TEfXHpXjKcfNH5wq6VLG+lLSUvT9H9/r9+O/X/P+efPmKTk5+Yp+i8Wibt266cCBA1q9enXGPpGkc+fOKTU19aaPS0pK0vnz5zP8vPAM1atXV/PmzWW32+VwZODfczgdY8MFLFiwQFu3br2lw+AAgKzn5+encePGac+ePfrwww8VGxurjRs3avTo0Tp9+rQqV66sAQMGZPrn/eWXXyTpqu/qP/DAA/Lx8Um//0ZSU1PVr08/WR6wSIVv8uCTkoZL+p+kUElLdXl83ICfj59Grx993f5cuXKpcuXKV9xet27d9PszonHjxsqTJ48CAgL07LPPaufOndd83NKlSxUQEKDAwECVLl1a0dHRGXp+eAar1aotW7ZowYIFplMgTqNyCTabTQ899JAaNmxoOgUAcBP16tVT9+7dFRoaquzZs6tNmzZ68skntXfvXqd9zsOHD8vX11d33333Fbf7+/urYMGCOnTo0E2fY8yYMTp95LQcL97ku70FJJWRdLekZEnxkpbp8gB56foflpKWooW7Fl63v3DhwrJYLFfcXrRoUUm6aX9AQIDefPPN9LGxYcMGRUREqH79+tq4caNKliyZ/tgaNWqoYcOGqlixok6ePKlPP/1Uffr00aFDh2Sz2W78Z4dHePjhh/Xggw/KZrPp6aefNp3j9Rgbhq1atUorVqzQ3Llzr/o/YQCAaxo2bJhmzZqlCxcuKDIyUtLld0Fy1mkbFy9elL+//zXvy5Ejhy5evHjDjz958qTeH/S+0h5Ok3Ld5JM995/f3yfpa0kbJT0oqeRVH5Fu96ndSkhKUKD/lW8le/HiRWXPnv2a7f/cfyMvv/yyXn755fTft2zZUk8++aQeeeQRDRs2TGPGjEm/7+uvv77iY9u3b69mzZopIiJCPXv2VIkSJW74ueD+LBaLrFarWrVqpdWrV+uhhx4yneTVOI3KMJvNpsqVK+uZZ54xnQIAyKA8efKoYsWKKlmypAoXvtk5SRl3/PhxHTlyJP3XPz8vIGfOnEpKSrrmxyQmJipnzpw3fN733ntPgXkDpbq3GVb/73/+ceOHOc459PO2n9P7/xkROXPm1KVLV18AkpiYmH7/rWrYsKHq1aunH3744YaPs1gs6tu3r1JSUvTjjz/e8ueBe3r22WdVqVIljma5AMaGQfHx8fr6668VHByc4bdKBAB4rjp16qho0aLpv8LCwiRdPt0oNTVVx44du+LxSUlJOnnypIoVK3bd59y5c6fGjRunl9q/JJ2TdPrvXymS0v7+z9d7V6p/5Pn7nzc+ACGFS03va5re/8UXX6T3Hzly5KojP/9cTH+j/hspWbKkTp06laHHScrQY+EZfHx8FBwcrHnz5un336/9xgXIGpxGZVBoaKiKFy+u1157zXQKAMAFTJs27YpTisqWLSvp8s+NkKT169erefPm6fevX79eaWlp6fdfy8GDB5WWlqaIQRHXfkC0pHqSmt0g7PTf/7zZKVhtpdEtRqtcgXKSpKpVq6b3T5gwQb///ruqVKmS/vA1a9ak3387/vjjD911110ZepykDD0WnuO1117T+++/r9DQUE2aNMl0jtfi2+mGHDhwQNOmTVPfvn2vex4uAMB9JCcn3/Fb3zZo0ECPP/54+q9/xkaTJk1UoEABjR595bs9jR49WgEBAVdcBHvixAlt27ZNFy5cPlxRrVo1ffXVV/p85udSa/3/r7sk5f37P9//9wcn6vIRj39z6PIF4pJ07437Lfda9HrL19P7/7kA/LnnnlO2bNk0atSo/39ah0NjxoxR8eLFVb9+/fTbDx8+rG3btik5OTn9tuPHj1/1uRYsWKANGzboqaeeSr/t1KlTV70tbnJysj7++GP5+/urcePGN/4DwKNkz55dffv21dSpU3XgwAHTOV6LIxuGREVFKVeuXOrcubPpFABAJjh48KAqV66sdu3a6dNPP83U586ZM6c+/PBDvf3223rppZf05JNPavny5Zo6daqGDRumAgUKpD82NjZWQ4YMUVxcnBo1aqRChQqpZcuWkqT3D7+v3ad3X37gz39/wL/fjfawpNmSqunyu1KlSPpd0n5JD0i6ydlO9xa496qLwyWpRIkS6tOnj0JDQ5WcnKw6depo7ty5Wr58uaZNm3bFD/QbMGCAJk+erD179qh06dKSpPr166tWrVqqXbu28ubNq40bN2rSpEkqWbKk3n333fSP/frrr/XRRx/pxRdfVJkyZXTq1Cl9/vnn2rJli4YPH64iRYrc7L9qeJjOnTvro48+UnR0tEJDQ03neCXGhgGnT5/W2LFj1atXL+XOndt0DgDADXTv3l3ZsmVTeHi4vv76a5UsWVKRkZHq3bt3hp+jefnmGr1utFIc/z188bd8kkpJ2iYpQZJFUiFJLXR5bNyAn4+fmpW7/rlYH3/8sfLnz6+xY8fq008/Vfny5TV16lS9+uqrN+1u3bq15s+fr8WLF+vChQsqWrSoOnXqpMGDB19xgX716tVVpUoVTZ06VcePH5e/v79q1qypmTNn6qWXbvC+vfBYefLkUffu3RUbG6uBAwcqX758ppO8jsWRgffpO3v2rPLmzaszZ84oT548N3s4bmL48OEaOnSo/vzzz0x9FxPAhM8++0zt2rXTpUuXOCUQyKBGjRopOTlZ8+bNk7+/f5b93Rp/PF5VR1V13vN3j1fluyrf/IEu4Pz587p48aJ69uypb775Jv2dv+B5jh49qnvuuUeDBw92yg/d9Ea3sg24ZiOLXbx4UdHR0Wrfvj1DAwC82KpVq3TXXXdl6Dv7maXKXVXUtGxT+flk7okNfj5+alq2qdsMDUkaOHCg7rrrLs2YMcN0CpyscOHCevPNNxUdHZ3+dsvIOpxGlcUmT56sEydOqH///qZTAACGhIeH6/Tpy2/xlNXvkDS2xVhVGVVFKWnXOZXqNvj5+Glsi7GZ9nxZoXv37mrRooUkyc+PL4c8Xf/+/TVu3DhNnjxZXbp0MZ3jVTiNKgulpqaqYsWKuv/++zVz5kzTOcAdS0hKUOTkSA0aMkhrV69V5cKVr3lxKADXMmHjBHX6plPmPd8zE/TW/W9l2vMBzvDSSy/p119/1bZt2654UwLculvZBkz5LDR79mzt3r07/YccAe4o/ni8xqwfowU7F+iP03/IIYfUSao7qa4ssqhs/rJqXr65utbuqip3Vbn5EwLIch3v76ijCUf1Xtx7d/xcw5oMY2jALVitVtWpU0dz5szhDQOyEEc2sojD4VDt2rWVP39+/fDDD6ZzgFu25/Qedfm2i77/43v5Wfyu/242Uvr9Tcs21dgWY1Umf5ksLAWQURM2TlDPhT2VkpZyS6dV+fn4yc/HT7HNYhkacCuPPfaYzpw5o3Xr1slisZjOcVtcIO6ClixZoo0bN8pqtZpOAW7ZhI0TVGVUFcXtjZOkGw6Nf98ftzdOVUZV0YSNE5zeCODWdby/o+K7x6tx6cs/7O5mF47/c3/j0o0V3z2eoQG3Y7VatWHDBi1dutR0itdgbGQRm82mWrVq6fHHHzedAjf1448/ymKxpP9av359lnzeYcuGqdM3nZSYknjLF5SmpKUoMSVRnb7ppGHLhjmpMOv06dMn/b//wECuTYFnKJO/jBa3Xayt3beqW+1uKlegnCy68ju+FllUrkA5davdTfHd47W47WKOWMItNW3aVLVq1ZLNZjOd4jUYG1lgw4YN+uGHH2S1Wjlk5wVef/115ciRQzt27Ljqvo8//lgWi0XffvvtbT//u+++qylTpqhs2bI3fWxaWtr/tXfncVXV+R/HXxcumzsqGS4FiiiYS66FkNJo7mOLllYuk0uK4mDTz8bUh07qjEs6Wak4YWriklmuZY02WGpqqTSTUu5OopgoDqAi6/n9ceXG5bIJXEl9Px+P8+DB93zPOR/w4uP7Od+NOXPm4Ovri7u7Oy1atGDNmjUles6XX35JcN9gJj81GWYAbwGbgNSCHgR8BywGZgJzgWjg51+rTI6ZzNJDS0v07PKIH2D79u0EBwdTqVIlPD096devH2fOnLGr5+PjY5PI5R6jRo2yqTdo0CBWrlxJSEjILf0cIneCQK9A3u7xNsfDj5MyMYXYl2PZN2wfsS/HkjIxhePhx3m7x9t31PK2IvmZTCYmTJjA9u3bOXToUEWHc0/QnI3b4LnnnuPAgQMcPXpUy+vdAy5evEjTpk1p1aqVTTft6dOnadasGT179mT9+vW3fN+dO3cSGhpKTEwMnTt3LtE1EydOZNasWYwYMYJ27dqxadMmPv30U9asWcOAAQOKvLb5w8058t8jGIEG1ASuAN8CLsAooGqeyl8Ae4EWWHYfvgEcBJKBl4D6lmruZnfiwuJK/Ea0LPFv3bqVvn370rp1awYNGkRKSgoLFizAzc2N2NhYm+VGfXx88PT0tFuS2t/fn/bt29vde+jQoaxfv16bgImI3IGysrKs/79rn5XSuaXcwCiB5ORkAzCSk5NLUl3yOHHihOHk5GQsWrSookOR2+gf//iHARjLly+3lnXv3t2oVq2aER8fX6p7xsTEGIARExNTovrx8fGGi4uLMWbMGGtZTk6OERISYtSvX9/Iysoq8vq2r7c1nKc5G0zj12MoBmAQkqdsCgZmDAKxrfvHm3U7/FpmfsNsdP2g622JPzAw0PDz8zPS09OtZd9//73h5ORkvPLKKzZ1H3zwQaNXr14lisswDGPIkCFG5cqVS1xfRER+WxYuXGg4OTkZJ06cqOhQ7ki3khtoGJWDzZs3j9q1azN06NCKDkVuo+HDh9OxY0deffVVLl++zNq1a/n888+ZMWMG9erVs6mbkJDATz/9RGZmZrnGsGnTJjIzMwkLC7OWmUwmRo8eTXx8PHv37i302rjEOA64HiCbbNsTPoAHcClPWQ6QBVTOd5PKgAmbBbazcrLYfmo7Pyb+6ND4k5KSiIuL46mnnsLV1dVa3rJlSwICAgp9k5WRkcG1a9eKjU1ERO5sf/jDH6hVqxbz58+v6FDueko2HOjixYssW7aMcePG4eHhUdHhyG1kMplYsmQJycnJjB49mvHjx9O2bVvGjBljV3fixIkEBARw7ty5co0hNjaWypUrExBgO746d1hQbGxsoddGHojEbCpgyF86kAFUylPmAtQDvgf+A/wPuABsBNyBNra3MDuZWXxgsUPjT09PByjw765SpUqcP3+eCxcu2JT/61//olKlSlSpUgUfHx8WLFhQbIwiInJn8vDwYNy4cbz//vtcvHixosO5qynZcKC3334bs9ls82ZW7h3NmjXj1Vdf5aOPPiIxMZElS5bg5HT7/uQSEhKoU6eO3aIE3t7eAJw/f77Qaz87/lnBy9vuA7KBZvnKnwZqA59gmUgeCSQAw7DM98gjKyeLbSe2OTT+OnXqUKNGDfbs2WNTfvnyZeLi4gBskrsWLVowbdo0Pv74Y5YuXcoDDzxARESElqoWEbmLhYWF4ezszDvvvFPRodzVlGw4SGpqKgsXLmTkyJF4enpWdDhSQWrXrg1A3bp1eeihhwqss3z5cgzDwMfHp1yfnZaWhpubm125u7u79XxBUtNTOXXllP2JM8BXWBKN/AthuQFeQDvgOaAXluFVa4ECRiWdTDrJ1YyiJ1eXNn4AJycnXn75Zb788ksmTpzI8ePHOXjwIM8++ywZGRl212/evJkJEybQt29fXnrpJb766iu6devG/PnziY+PLzJOERG5M9WsWZORI0eycOFCLfjhQEo2HOS9997j6tWrREREVHQoUkHOnj3L1KlTeeihhzh79ixz5sy5rc/38PCwDifK68aNG9bzBTl55SQG+RapSwQ+BO4Dfp/vgmzgAyxDpnoBAViSjsFAEvCN/TMMDE4knQDgwoULNkduElDa+HO98cYbDBs2jDlz5uDv70/btm0xm80MG2bZhKyofTJMJhPjx48nKyuLnTt3FvkcERG5c40fP57U1FTee++9ig7lrqVkwwEyMjKYP38+L7zwAg0aNKjocKSCjB07FoBt27bRv39/Zs6cyalTBfQYOIi3tzcXLlzAyLe6dUJCAmDpbSlIela+Bn4ysBJL78ULN7/m9V/gItAkX3ktLL0dP1Og3Od4e3vbHB9++GGZ4s/l6upKVFQU58+f5+uvv+bo0aN88cUXJCcn4+TkhJ+fX5HX5/7tJiUlFVlPRETuXA0aNOD5559n/vz51p5vKV9KNhxg9erVnDt3jgkTJlR0KFJBNmzYwObNm5k+fTr169fnrbfewtXVtcAJ4o7SqlUrrl+/zo8/2q78tH//fuv5griZ82QT17EkGtnAIGz31siVO0wqp4Bz2YWU53nO9u3bbY5u3bqVKf786tSpQ0hICP7+/mRnZ7Nz5046dOhQ7A7guYlh3v04RETk7jNhwgTi4+NvadNYKTklG+Usd8fjPn36EBgYWNHhSAVITU1l3LhxPPzww4SHhwOWt/DTp0/n888/56OPPrKp76ilb/v27YuLiwuLFi2ylhmGQWRkJPXq1SMoKKjAGPxq+mHCZFl1ahWQgqVHo1YhD8otP5yv/DxwGfC2v8SECb+alp6FLl262By5E8BLG39R3nzzTRISEmw270tKSiI723aJ38zMTGbNmoWrqyuhoaFF3lNERO5szZo1o3fv3syZM4ecnELekEmpaTvrcrZ161Z+/PFHjf27h02ePJnz58/zySef4OzsbC0fM2YMK1asICIigu7du1O1qqWbYOLEiaxYsYLTp0+X6yTx+vXrExERwdy5c8nMzKRdu3Zs3LiRXbt2sWrVKpvY8sfQ0LMhJxedhHPAw1jmbCTmubkrlrkZAHWxTBj/N5alcRsBV4H9WP6HecQ+tkY1G1HFteiehbLEDxAdHc3HH3/MY489RpUqVdixYwfr1q1j+PDhPPPMM9ZrN2/ezIwZM+jXrx++vr4kJSWxevVqDh8+zF//+lfuv//+kvy6RUTkDvbaa68REhLCp59+Sp8+fSo6nLuKko1yNnv2bDp27EjHjh0rOhSpAAcPHmThwoWEhYXRrl07m3POzs5ERkbyyCOPMHny5Nuyj8OsWbPw9PRkyZIlLF++nMaNGxMdHc3zzz9f5HU9G/fknV9uLgUYe/PIqzq/JhsAA7FMBD8MnACcgQeBUCxL4uZhdjLTw6+HQ+MH8Pf3JykpienTp5OWlkaTJk2IjIxk5MiRNvWaN29OYGAg0dHRJCYm4urqSqtWrVi3bh39+/cvUZwiInJnCw4OJigoiNmzZyvZKGcmI//sywKkpKRQvXp1kpOTqVat2u2I6460e/duQkJC2Lx5sz6oUu527txJaGgoGzdupGPHjtSoUQOz2THvC+IS42i2KP9mGuV4/7A4ArwCiq/4G3Pt2jXS0tIIDw9ny5YtWipRROQusnnzZvr27cvu3bv10rgYt5IbaM5GOZo9ezaBgYH06tWrokORu9iTTz6Jl5cX33//vcOeEegVSNeGXTE7lW8yY3Yy07Vh1zsy0QCYNGkSXl5erF27tqJDERGRcta7d28CAwOZPXt2RYdyV9EwqnJy5MgRtm7dyvLly2/rLtFy72jZsiXbt2+3ft+kSf61ZsvXkt5LCFwUSFZOATuJl5LZycyS3kvK7X63W1hYGL179wZwWK+SiIhUDCcnJ/7v//6PP/zhD8TFxWmhn3KiYVS34GrGVU4knSA9Kx03sxt+Nf2sk1yHDh3Kl19+ycmTJ3F1da3gSEXKR9ShKEZsGVF+9+sTxbDWw8rtfiIiIuUpIyODRo0a0aVLF5YtW2YtL6oNeC+6ldxAr+aKEZcYR+SBSD47/hmnrpyy2VnZhImGng0J8Q4h+oto5k6Yq0RD7irDWw/nl6u/MDlmcpnvNfPxmUo0RETkN83V1ZXx48fz5z//mUGvDGJj/MYi24A9G/dkVNtRBHqpF6Qw6tkoxOkrp3l568tsP7Uds8lMllH4UBJnkzPZRjaPP/g4UX2j8PX0vY2Rijhe1KEowreFk5WTdUvDqsxOZsxOZt7t8a4SDRERuSP8EP8DHf/WkdT7UottA+ae79qwK0t6L7ln2oCaIF5GUYeiCFwUSMyZGIAiP2QA2YZlQ7Cvz35N4KJAog5FOTxGkdtpeOvhxIXFEepj2eCuuInjuedDfUKJC4tToiEiIneEqENRtF/RnrT704Di24C552POxKgNWIjffLJx5swZTCaT9Vi/fr1Dnzfz65mM2DKCG1k3bnlibFZOFjeybjBiywhmfj3TQRHefm+99ZbNv8GlS5cqOiSpAL6evvxz0D85EnaE0W1H/7rTeB65O4OPbjuauLA4/jnon/fMWx4RESm7nTt32rQ5Dhw4cNuerTagRUREhPX3X6VK2eellCrZWLduHSaTiQ0bNtida9myJSaTiZiYGLtzDzzwAEFBQaV5JCNHjmTlypW0b9++RPWXLl1KQEAA7u7uNG7cmHfeeafYa6IORTE5ejJ8CiwEZgLzgXVAYe3rw8B7wN+A2cAy4BhMjpnM0kNLSxRrecWf6+DBg3Tv3p1q1apRtWpVnnjiiQKXSe3cubPNH3Tu0b17d5t63bt3Z+XKlTz11FOl/nnk7hHoFcjbPd7mePhxUiamEPtyLPuG7SP25VhSJqZwPPw4b/d4+45d3lZERIpXUPsh/zFt2rRS3//1119n5cqVNGzYsNi6OTk5zJkzB19fX9zd3WnRogVr1qwp0XPytoUmd5oM0/j1eCNf5UxgF/AuMAOYh6WNeNG22q22AdPT03nttdeoW7cuHh4edOjQwWb1yeLs2LGD0NBQateuTY0aNWjfvj0rV660qZOWlsawYcN46KGHqF69OlWqVKFly5YsWLCAzMxMm7qDBg1i5cqVhISElDiGopRqgnhwcDBg2cQubwM0JSWFw4cPYzab2bNnD6GhodZzZ8+e5ezZswwYMKBUgT766KO8+OKLJaq7ZMkSRo0axTPPPMMrr7zCrl27GDduHNevX+e1114r8JrTV04Tvi0cdgNngUCgDnAV+BZYAgy/WZZrP7ANaAx0AbKA74HVwLMw1jyWx30fv+U3u6WJP9ehQ4cIDg6mQYMGTJ06lZycHBYtWkSnTp349ttv7ZZLrV+/Pn/7299syurWrWvzfdOmTWnatCknTpwoMMGUe1cV1yq0ur9VRYchIiK3Wf7GbF7Tpk3j5MmTdOjQodT379q1K507dy5R3UmTJjFr1ixGjBhBu3bt2LRpE88//zwmk6nYduekSZPoO7Avr+14jczsm43uTGAr0Chf5U+Ao0BrwBtIBb4DooAwoMavVcduK3kbcOjQoaxfv56IiAgaN27M8uXL6dmzJzExMdY2d2E2b97Mk08+yaOPPsq0adMwmUysW7eOwYMHc+nSJcaPHw9Yko0jR47Qs2dPfHx8cHJy4ptvvmH8+PHs37+f1atXW+/Zpk0b2rRpw44dOzh06FCx8RfLKIHk5GQDMJKTk61lvr6+Rvv27W3qff7554bJZDIGDhxodOvWzebc6tWrDcDYtGlTSR5pdfr0aQMwli1bVqL6169fN2rVqmX06tXLpvyFF14wKleubCQlJRV4XdcPuhrmN8wGL2EwGYNpeY5wDJwxaJ6vvCYGdTGYmqfszxi4YtAEw/yG2ej6Qddb+nlLG3+unj17Gp6ensalS5esZefPnzeqVKliPP300zZ1O3XqZDRr1qzEsU2dOtUAjMTExBJfIyIiIveO9957zwCM8PDwUl0fExNjAEZMTEyJ6sfHxxsuLi7GmDFjrGU5OTlGSEiIUb9+fSMrK6vYe1jbgLltuacwAIOn87TvXrlZFpSvLTjkZnk32/KStgH3799vAMbcuXOtZWlpaUajRo2MRx99tPjYu3Y16tata9y4ccNalpmZaTRq1Mho0aJFsdePHTvWAIyEhAS7c0OGDDEqV65c4HUF5QaFKfWcjeDgYGJjY0lLS7OW7dmzh2bNmtGjRw/27dtHTk6OzTmTyWTd/v3SpUv89NNPXL9+vbQhFCgmJobLly8TFhZmUz5mzBiuXbvGp59+andNXGIc209tt4zPewD7/p5awH3YD6VKByqDzbB1d8DVco+snCy2n9rOj4k/OjT+vHbt2kWXLl2oVauWtczb25tOnTqxdetWrl69andNVlZWgeUiIiIiJXXkyBHGjRvHww8/zNy5c23OJSQk8NNPP9kN2SmrTZs2kZmZadNuMplMjB49mvj4ePbu3Vvk9TZtwFw/AC5A0zwV029+rZzvBrlTGvK1HUvaBly/fj3Ozs6MHDnSWubu7s6wYcPYu3cvZ8+eLfL6lJQUPD09cXNzs5aZzWZq166Nh4dHkdcC+Pj4APC///2v2LqlVaZkIzMzk/3791vL9uzZQ1BQEEFBQSQnJ3P48GGbc02bNrU2gt99910CAgL49ttvyxC+vdjYWADatm1rU96mTRucnJys5/OKPBCJ2VTEiDIDy3CqSvnKfYATWIZTXQESscz3uAE8YqlidjKz+MBih8afV3p6eoEfrkqVKpGRkWHzbwJw7NgxKleuTNWqVbn//vuZMmVKuf9HICIiIne369ev8+yzz+Ls7MzatWttGr8AEydOJCAggHPnzpXrc2NjY6lcuTIBAbbzBHPn+BbXbrJrA14DTmFJNPJunVYTqAbsxTKUKhmIxzLcqgbwkP29S9IGjI2Nxd/f32752Nz4C5pzm1fnzp05cuQIU6ZM4cSJE5w8eZLp06dz4MABJkyYYFc/IyODS5cucfbsWTZs2MCbb77Jgw8+iJ+fX5HPKYtSb+qXd95G586dycrKYv/+/QwZMoRGjRpRp04ddu/eTYsWLUhNTeWHH37gpZdeKrfAC5OQkICzszP33XefTbmrqyu1atXi/Pnzdtd8dvyzopc2+w+WcXmh+cp7ANexzNvYdrOsEjAEaGD5Nisni20ntlFSpYk/ryZNmrBv3z6ys7NxdnYGLB+s3KQw7x95o0aNCA0NpXnz5ly7do3169czY8YMjh07xocffljimEVEROTeFh4eTlxcHCtWrMDf3/+2PTchIYE6depgMtmujujt7Q1QbLvJrg14GMgBWuSr6Aw8C3wM5J177g0MAwroRChJGzAhIcEaa2ninzJlCqdPn2bmzJnMmDEDsLxg/vjjj+nbt69d/U8++YSBAwdav2/bti3vv/8+ZrPj9vkudc9GQEAAtWrVYvfu3QD8+9//5tq1a9bVpoKCgtizZw8Ae/fuJTs722aSy7Rp0zAMo8STf0oqLS2t0F283d3dbYZ9AaSmp3LqyqnCb5gIfAbUB1rlO+cC1AZaAv2Bvli60z4ELv9a7WTSSa5mlGyY0q3Gn19YWBjHjh1j2LBhxMXFcfjwYQYPHkxCQoL1/rmWLl3K1KlTefrppxk0aBCbNm1ixIgRrFu3jn379pUoXhEREbm3rV69mvfff59BgwYxePDgAussX74cwzCsw3bKS1paml0vCljaTLnnC1NgG/AHLC+OC1oEywO4HwgGBgBPAP8DPsIyqbwAxbUByxI/gJubG/7+/vTr1481a9YQHR1N27ZtefHFFwtsy4WGhrJ9+3Y++ugjRo0ahYuLC9euXSvyGWVV6mTDZDIRFBRknZuxZ88e7rvvPms3TN5kI/drcTPqy4OHhwcZGRkFnrtx44bdEKOTV07abD9vIxXLylJuWLLZ/L+tj7B0oz0FNAMeBoYC2cC/fq1mYHAi6YTNpYmJiVy4cMF65M6ZuNX48xs1ahSvv/46q1evplmzZjRv3pyTJ09au9KKWy/5T3/6E2BZRk1ERESkKMePH2fUqFH4+/uzaNGi2/58Dw8P0tPT7cpv3LhhPV8YuzZgEpahUQ9h6cmwuSHwPpaRK12wDLMKAp4DfsayGmkBDAyOJh61afNduHDB2tYrS/wAY8eOZcuWLaxdu5YBAwbwwgsvsGPHDry9vfnjH/9oV79OnTp06dKFfv36sXjxYnr37k3Xrl25cOFCkc8pizJt6hccHExycjI//PCDdb5GrqCgIP773/9y7tw5du/eTd26dUu0VnJZeXt7k52dzcWLtoseZ2RkcPnyZbtlXdOz7P+BAcuHatXNry9iGaeXVxKW+RpN8pVXwjLJ/Gfb4vzPadeuHd7e3tbjzTffLFX8BZk5cya//PILu3bt4j//+Q/fffeddbJ+cV2bDRpYxn8lJSUV+xwRERG5d6Wnp/Pcc8+RkZHB2rVry2UDuFvl7e3NhQsXMAzbF8e5IzqKajfZtQF/uPm1eQGV47DM58jf7vPB8lL65/wX/Ornn3+2afN5e3vzzTffWOPPjfVW48/IyGDp0qX06tULJ6dfm/QuLi706NGDAwcOFPoCO1e/fv24evUqmzZtKrJeWZRpgFbeeRt79uwhIiLCeq5Nmza4ubmxc+dO9u/fT8+ePcsUaEm1atUKgAMHDtg888CBA+Tk5FjP53Iz23ddkYmlR+MyMBjLSlT55fY45RRwLtu+PP9zVq1aZdM1lpuI3Wr8hfH09LTpSdqxYwf169enadOmRVwFp05ZuhO9vLxK9BwRERG5N7366qvExsayYMECHn744QqJoVWrVkRFRfHjjz8SGBhoLc+dq1pUu8muDfgD4Il13q2Nwtp9xs2ygtqDN9X1rmu3SV/Lli2t8cXExJCSkmIzSbwk8V++fJmsrCyys7PtzmVmZpKTk1Pgubxy26LJyclF1iuLMvVstG3bFnd3d1atWsW5c+dsejbc3Nxo3bo1Cxcu5Nq1a3ZDqBy19O3jjz9OzZo1WbzYdvb/4sWLqVSpEr169bKJIftiNuRN+nKA9Vi60fpT8AcOLKsSmIAjYDMKKxlLdptnro8JE341bWf5d+zYkS5duliP3GTjVuMvye/www8/5LvvviMiIsKa+aakpNh12xmGYZ1c1K1btyLvKSIiIveuDRs28O677/L73/+ecePGFVvfUUvf9u3bFxcXF5shXIZhEBkZSb169Wzapvlj8Kvphyl3/4IELFscFNSrAZZtEMAygTyvo1heUtvP8QYsbcBmdZvZtPm6dOmCp6cnYOlZyM7O5h//+If1mvT0dJYtW0aHDh2sI07A0kPy008/Wb+/7777qFGjBhs2bLDpwbh69SpbtmyhadOm1mFYly5dsuv9AYiKigLsV0EtT2Xq2XB1daVdu3bs2rULNzc32rRpY3M+KCiIefPmAfbzNd59913+8pe/EBMTU66TxD08PJg+fTpjxoyhf//+dOvWjV27dhEdHc3MmTOpWbOmXQx1x9blfO2bs/2/wPLB8QfSgH/ne0DLm18rY5mjcQhYAQRgSVq+w/Khy/PjNqrZiCquJetaLE38eX+HX3/9NW+88QZPPPEEtWrVYt++fSxbtozu3bvbjN07dOgQAwcOZODAgfj5+ZGWlsaGDRvYs2cPI0eOpHXr1iWKV0RERO4tCQkJDBs2DGdnZ373u98RHR1dYL1GjRrx6KOPApalb1esWMHp06fLdZJ4/fr1iYiIYO7cuWRmZtKuXTs2btzIrl27WLVqlXVlzoJiqOJahYaeDTl55aRl5VGwX4Uqlz/gBXyF5cVyfSxD6r/FsjhQIR07xbUBO3ToQP/+/Zk4cSIXL17Ez8+PFStWcObMGZYuXWpTd/DgwXz11VfWpMHZ2ZlXX32VyZMn88gjjzB48GCys7NZunQp8fHxNv8u0dHRREZG8uSTT9KwYUNSU1P54osv2L59O3369OHxxx8vNMayKvM6V8HBwezatcs6bCqvjh07Mm/ePKpWrWrtLrodwsLCcHFxYd68eWzevJkGDRrw97//vcCJMgAd6ndgS/oWy9JnufNjjt088sv7Y/QC6gCxwJc3y+pimTDuY/nW7GSmh18Ph8afV7169XB2dmbu3Lmkpqbi6+vLjBkzeOWVV2yWNXvwwQcJCQlhw4YNXLhwAScnJwICAoiMjLTZWEZEREQkr6NHj3LlyhWAItsmQ4YMsSYbjjRr1iw8PT1ZsmQJy5cvp3HjxkRHR/P8888Xe23Pxj1ZtH8R2YezLb0TtQupaAZewpJsHMcy5MoNy0Tx32G/2R8lbwN+8MEHTJkyhZUrV3LlyhVatGjB1q1beeyxx4q9dtKkSfj6+rJgwQL+8pe/kJ6eTosWLVi/fj3PPPOMtV5wcDDffPMNa9as4ZdffsFsNtOkSRPmz59PeHh4sc8pC5NRUJ9KPikpKVSvXp3k5GS7TUcc7cyZM/j6+vLOO+8wYMAAqlWrVujSsKUVlxhHs0XNyvWeNvcPiyPAK6D4ir9RN27c4OrVq8yZM4e5c+eSmJhI7dqF/TWKiIiIlM7OnTsJDQ1l48aNdOzYkRo1ajh0Dwi1Ae1du3aNtLQ0wsPD2bJli3XF1LxuJTco05yN2yk8PBwvLy82b95c7vcO9Aqka8OumJ3K98NsdjLTtWHXO+5Dll9kZCReXl7MnTu3okMRERGRe8CTTz6Jl5dXsTtol5XagPYmTZqEl5cXa9euLZf7/eZ7Nm7cuGHdOBCgRYsWdrtrl4fTV04TuCiQG1k3yu2e7mZ34sLi8PX0Lbd7VoSzZ89y9OhR6/edOnXCxcWlAiMSERGRu9GVK1c4ePCg9fsOHTpQtWpVhz5TbUBbx44d4+efLWv5ms3mAudW30pu8JtPNm6nqENRjNgyovzu1yeKYa2Hldv9RERERKT8qQ14a+7KYVS3w/DWw5kROqNc7jXz8Zl39YdMRERE5G6hNqDjKNnIZ9Jjk3ivz3u4m91vefye2cmMu9mdqD5RvB7yuoMiFBEREZHypjagYyjZKMDw1sOJC4sj1CcUoNgPXO75UJ9Q4sLilM2KiIiI3IHUBix/mrNRjLjEOCIPRLLtxDZOJp3EyLNduAkTjWo2oodfD0a3HX1HrjggIiIiIvbUBiycJog7yNWMq5xIOkF6VjpuZjf8avqVeGdwEREREbkzqQ1o61ZyA8ftknIXquJahVb3t6roMERERETkNlIbsPQ0Z0NERERERBxCyYaIiIiIiDiEkg0REREREXEIJRsiIiIiIuIQSjZERERERMQhlGyIiIiIiIhDKNkQERERERGHULIhIiIiIiIOoWRDREREREQcQsmGiIiIiIg4hJINERERERFxCCUbIiIiIiLiEEo2RERERETEIZRsiIiIiIiIQyjZEBERERERh1CyISIiIiIiDqFkQ0REREREHELJhoiIiIiIOISSDRERERERcQglGyIiIiIi4hBKNkRERERExCGUbIiIiIiIiEMo2RAREREREYdQsiEiIiIiIg6hZENERERERBxCyYaIiIiIiDiEkg0REREREXEIJRsiIiIiIuIQSjZERERERMQhlGyIiIiIiIhDKNkQERERERGHULIhIiIiIiIOoWRDREREREQcQsmGiIiIiIg4hJINERERERFxCCUbIiIiIiLiEEo2RERERETEIZRsiIiIiIiIQyjZEBERERERh1CyISIiIiIiDqFkQ0REREREHELJhoiIiIiIOISSDRERERERcQglGyIiIiIi4hBKNkRERERExCGUbIiIiIiIiEMo2RAREREREYdQsiEiIiIiIg6hZENERERERBxCyYaIiIiIiDiEkg0REREREXEIc0kqGYYBQEpKikODERERERGR37bcnCA3RyhKiZKN1NRUABo0aFCGsERERERE5G6RmppK9erVi6xjMkqQkuTk5HD+/HmqVq2KyWQqtwBFREREROTOYhgGqamp1K1bFyenomdllCjZEBERERERuVWaIC4iIiIiIg6hZENERERERBxCyYaIiIiIiDiEkg0REREREXEIJRsiIiIiIuIQSjZERERERMQhlGyIiIiIiIhD/D+cTS+SSHjpOQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "setting = equality_model.run_forward(\n",
+    "    {\"W\": reps[0], \"X\": reps[0], \"Y\": reps[1], \"Z\": reps[3]}\n",
+    ")\n",
+    "print(\"No intervention:\\n\", setting, \"\\n\")\n",
+    "equality_model.print_setting(setting)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The algorithm with an intervention\n",
+    "\n",
+    "Let's now see the behavior of the algorithm when we provide the input with an intervention setting **WX** to **False**. First, a visual depiction:\n",
+    "\n",
+    "And then the same computation with `compute_A`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intervention setting WX to FALSE:\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    \"Intervention setting WX to FALSE:\\n\",\n",
+    ")\n",
+    "equality_model.print_setting(\n",
+    "    equality_model.run_forward(\n",
+    "        {\"W\": reps[0], \"X\": reps[0], \"Y\": reps[1], \"Z\": reps[3], \"WX\": False}\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that, in this example, even though the left two inputs are the same, the intervention has changed the intermediate prediction for those two inputs from **True** to **False**, and thus the algorithm outputs **True**, since **WX** and **YZ** are both **False**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The algorithm with an interchange intervention\n",
+    "\n",
+    "Finally, let's observe the behavior of the algorithm when we provide the base input `BBCD` with an intervention setting **WX** to be the value it would be for the source input `ABCC`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "base = {\"W\": reps[0], \"X\": reps[0], \"Y\": reps[1], \"Z\": reps[3]}\n",
+    "source = {\"W\": reps[0], \"X\": reps[1], \"Y\": reps[2], \"Z\": reps[2]}\n",
+    "setting = equality_model.run_interchange(base, {\"WX\": source})\n",
+    "equality_model.print_setting(setting)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hand Crafting an MLP to Solve Hierarchical Equality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we train a network to solve the hierarchical equality task, first consider an analytical solution where we define a neural network to have weights that are handcrafted to solve the task by implementing the algorithm $\\mathcal{A}$. The network is a two layer feedforward neural network that uses the ReLU function to compute the absolute difference between two vectors. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = MLPConfig(\n",
+    "    h_dim=embedding_dim * 4,\n",
+    "    activation_function=\"relu\",\n",
+    "    n_layer=2,\n",
+    "    num_classes=2,\n",
+    "    pdrop=0.0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, handcrafted = create_mlp_classifier(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer of our handcrafted model computes:\n",
+    "\n",
+    "$ReLU(W_1[\\mathbf{a}, \\mathbf{b}, \\mathbf{c}, \\mathbf{d}]) = [max(\\mathbf{a}-\\mathbf{b}, 0), max(\\mathbf{b}-\\mathbf{a}, 0), max(\\mathbf{c}-\\mathbf{d}, 0), max(\\mathbf{d}-\\mathbf{c}, 0)]$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W1 = [\n",
+    "    [1, 0, -1, 0, 0, 0, 0, 0],\n",
+    "    [0, 1, 0, -1, 0, 0, 0, 0],\n",
+    "    [-1, 0, 1, 0, 0, 0, 0, 0],\n",
+    "    [0, -1, 0, 1, 0, 0, 0, 0],\n",
+    "    [0, 0, 0, 0, 1, 0, -1, 0],\n",
+    "    [0, 0, 0, 0, 0, 1, 0, -1],\n",
+    "    [0, 0, 0, 0, -1, 0, 1, 0],\n",
+    "    [0, 0, 0, 0, 0, -1, 0, 1],\n",
+    "]\n",
+    "handcrafted.mlp.h[0].ff1.weight = torch.nn.Parameter(torch.FloatTensor(W1))\n",
+    "handcrafted.mlp.h[0].ff1.bias = torch.nn.Parameter(\n",
+    "    torch.FloatTensor([0, 0, 0, 0, 0, 0, 0, 0])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer of our handcrafted model computes:\n",
+    "\n",
+    "$ReLU(W_2ReLU(W_1[\\mathbf{a}, \\mathbf{b}, \\mathbf{c}, \\mathbf{d}])) = [|\\mathbf{a}-\\mathbf{b}| - |\\mathbf{c}-\\mathbf{d}|, |\\mathbf{c}-\\mathbf{d}|-|\\mathbf{a}-\\mathbf{b}|, |\\mathbf{a}-\\mathbf{b}|, |\\mathbf{c}-\\mathbf{d}|,0,0,0,0]$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W2 = [\n",
+    "    [1, -1, 0, 1, 0, 0, 0, 0],\n",
+    "    [1, -1, 0, 1, 0, 0, 0, 0],\n",
+    "    [1, -1, 0, 1, 0, 0, 0, 0],\n",
+    "    [1, -1, 0, 1, 0, 0, 0, 0],\n",
+    "    [-1, 1, 1, 0, 0, 0, 0, 0],\n",
+    "    [-1, 1, 1, 0, 0, 0, 0, 0],\n",
+    "    [-1, 1, 1, 0, 0, 0, 0, 0],\n",
+    "    [-1, 1, 1, 0, 0, 0, 0, 0],\n",
+    "]\n",
+    "handcrafted.mlp.h[1].ff1.weight = torch.nn.Parameter(\n",
+    "    torch.FloatTensor(W2).transpose(0, 1)\n",
+    ")\n",
+    "handcrafted.mlp.h[1].ff1.bias = torch.nn.Parameter(\n",
+    "    torch.FloatTensor([0, 0, 0, 0, 0, 0, 0, 0])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The third layer of our handcrafted model computes the logits:\n",
+    "\n",
+    "$W_3 ReLU(W_2ReLU(W_1[\\mathbf{a}, \\mathbf{b}, \\mathbf{c}, \\mathbf{d}])) = [||\\mathbf{a}-\\mathbf{b}| - |\\mathbf{c}-\\mathbf{d}|| -0.999999|\\mathbf{a}-\\mathbf{b}|-0.999999|\\mathbf{c}-\\mathbf{d}|, 0]$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W3 = [[1, 0], [1, 0], [-0.999999, 0], [-0.999999, 0], [0, 0], [0, 0], [0, 0], [0, 0]]\n",
+    "handcrafted.score.weight = torch.nn.Parameter(torch.FloatTensor(W3).transpose(0, 1))\n",
+    "handcrafted.score.bias = torch.nn.Parameter(torch.FloatTensor([0, 0.00000000000001]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now use the causal model of $\\mathcal{A}$ that we created to generate a labeled dataset for the hierarchical equality task and show that our handcrafted network solves the task with perfect accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_examples = 100000\n",
+    "\n",
+    "examples = equality_model.generate_factual_dataset(\n",
+    "    n_examples, equality_model.sample_input_tree_balanced\n",
+    ")\n",
+    "\n",
+    "X = torch.stack([example['input_ids'] for example in examples])\n",
+    "y = torch.stack([example['labels'] for example in examples])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Results\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       1.00      1.00      1.00     50050\n",
+      "         1.0       1.00      1.00      1.00     49950\n",
+      "\n",
+      "    accuracy                           1.00    100000\n",
+      "   macro avg       1.00      1.00      1.00    100000\n",
+      "weighted avg       1.00      1.00      1.00    100000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "preds = handcrafted.forward(inputs_embeds=X)\n",
+    "\n",
+    "print(\"Train Results\")\n",
+    "print(classification_report(y, preds[0].argmax(1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal abstraction\n",
+    "\n",
+    "The theory of **causal abstraction** describes the conditions that must hold for the high-level tree structured algorithm to be a **simplified and faithful description** of the neural network. To perform causal abstraction analysis, we need to align high-level variables in our hypothesized algorithm $\\mathcal{A}$ with sets of low-level variables in the low-level neural network $\\mathcal{N}$. \n",
+    "\n",
+    "In essence: $\\mathcal{A}$ is a causal abstraction of a $\\mathcal{N}$ if and only if $\\mathcal{A}$ and $\\mathcal{N}$ provides the same output for all interchange interventions that target aligned variables.\n",
+    "\n",
+    "For our handcrafted network, we align the first four neurons in the first feed-forward layer with the high-level variable 'WX' and align the other four neurons in that layer with 'YZ'. Below, we create an IntervenableConfig that allows us to taget the first four and last four neurons of the first layer for an interchange intervention. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = IntervenableConfig(\n",
+    "    model_type=type(handcrafted),\n",
+    "    representations=[\n",
+    "        RepresentationConfig(\n",
+    "            0,  # layer\n",
+    "            \"block_output\",  # intervention type\n",
+    "            subspace_partition=[[0, 4], [4, 8]],\n",
+    "        ),\n",
+    "        RepresentationConfig(\n",
+    "            0,  # layer\n",
+    "            \"block_output\",  # intervention type\n",
+    "            subspace_partition=[[0, 4], [4, 8]],\n",
+    "        ),\n",
+    "    ],\n",
+    "    intervention_types=VanillaIntervention,\n",
+    ")\n",
+    "handcrafted = IntervenableModel(config, handcrafted)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we create a counterfactual equality dataset that includes interchange intervention examples. We first define a function that create an id for the three possible high-level interventions, namely targetting 'WX', targetting 'YZ', and targetting them both."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def intervention_id(intervention):\n",
+    "    if \"WX\" in intervention and \"YZ\" in intervention:\n",
+    "        return 2\n",
+    "    if \"WX\" in intervention:\n",
+    "        return 0\n",
+    "    if \"YZ\" in intervention:\n",
+    "        return 1\n",
+    "\n",
+    "\n",
+    "data_size = 2048\n",
+    "batch_size = 16\n",
+    "dataset = equality_model.generate_counterfactual_dataset(\n",
+    "    data_size,\n",
+    "    intervention_id,\n",
+    "    batch_size,\n",
+    "    device=\"cuda:0\",\n",
+    "    sampler=equality_model.sample_input_tree_balanced,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This dataset has the following components:\n",
+    "\n",
+    "* `input_ids`: a regular set of train examples\n",
+    "* `base_labels`: a regular set of train labels\n",
+    "* `source_input_ids`: sets additional training inputs sets (here, two sets) for interchange interventions\n",
+    "* `labels`: a list of labels if interchange interventions are performed with 'source_input_ids'\n",
+    "* `intervention_id`: a list of intervention sites (here, all `0` corresponding to our key for \"V1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([ 0.4700,  0.3500,  0.4700,  0.3500,  0.7800, -0.8300, -0.5600,  0.1800],\n",
+      "       device='cuda:0')\n",
+      "tensor([[-0.1600, -0.9400,  0.6600,  0.2400,  0.0700,  0.9500,  0.0700,  0.9500],\n",
+      "        [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000]],\n",
+      "       device='cuda:0')\n",
+      "tensor([0.], device='cuda:0')\n",
+      "tensor([1.], device='cuda:0')\n",
+      "tensor([0], device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset[0][\"input_ids\"])\n",
+    "print(dataset[0][\"source_input_ids\"])\n",
+    "print(dataset[0][\"base_labels\"])\n",
+    "print(dataset[0][\"labels\"])\n",
+    "print(dataset[0][\"intervention_id\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To evaluate the model on this dataset, we loop through batches and peform interchange interventions based on the intervention_id. \n",
+    "* When the id is 0, the first four neurons in the first layer are targetted ('WX' is targetted at the high-level)\n",
+    "* When the id is 1, the last four neurons in the first layer are targetted ('YZ' is targetted at the high-level)\n",
+    "* When the id is 2, all of the neurons in the first layer are targetted ('WX' and 'YZ' are both targetted at the high-level) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "handcrafted.to(\"cuda:0\")\n",
+    "for parameter in handcrafted.get_trainable_parameters():\n",
+    "    parameter.to(\"cuda:0\")\n",
+    "preds = []\n",
+    "for batch in DataLoader(dataset, batch_size):\n",
+    "    batch[\"input_ids\"] = batch[\"input_ids\"].unsqueeze(1)\n",
+    "    batch[\"source_input_ids\"] = batch[\"source_input_ids\"].unsqueeze(2)\n",
+    "    if batch[\"intervention_id\"][0] == 2:  # Intervention on both high-level variables\n",
+    "        _, counterfactual_outputs = handcrafted(\n",
+    "            {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "            [\n",
+    "                {\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]},\n",
+    "                {\"inputs_embeds\": batch[\"source_input_ids\"][:, 1]},\n",
+    "            ],\n",
+    "            {\n",
+    "                \"sources->base\": (\n",
+    "                    [[[0]] * batch_size, [[0]] * batch_size],\n",
+    "                    [[[0]] * batch_size, [[0]] * batch_size],\n",
+    "                )\n",
+    "            },\n",
+    "            subspaces=[[[0]] * batch_size, [[1]] * batch_size],\n",
+    "        )\n",
+    "    elif (\n",
+    "        batch[\"intervention_id\"][0] == 0\n",
+    "    ):  # Intervention on just the high-level variable 'WX'\n",
+    "        _, counterfactual_outputs = handcrafted(\n",
+    "            {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "            [{\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]}, None],\n",
+    "            {\"sources->base\": ([[[0]] * batch_size, None], [[[0]] * batch_size, None])},\n",
+    "            subspaces=[[[0]] * batch_size, None],\n",
+    "        )\n",
+    "    elif (\n",
+    "        batch[\"intervention_id\"][0] == 1\n",
+    "    ):  # Intervention on just the high-level variable 'YZ'\n",
+    "        _, counterfactual_outputs = handcrafted(\n",
+    "            {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "            [None, {\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]}],\n",
+    "            {\"sources->base\": ([None, [[0]] * batch_size], [None, [[0]] * batch_size])},\n",
+    "            subspaces=[None, [[1]] * batch_size],\n",
+    "        )\n",
+    "    preds.append(counterfactual_outputs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = torch.cat(preds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, we can see that our handcrafted neural network is a perfect implementation of the high-level algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       1.00      1.00      1.00       997\n",
+      "         1.0       1.00      1.00      1.00      1051\n",
+      "\n",
+      "    accuracy                           1.00      2048\n",
+      "   macro avg       1.00      1.00      1.00      2048\n",
+      "weighted avg       1.00      1.00      1.00      2048\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    classification_report(\n",
+    "        torch.tensor([x[\"labels\"] for x in dataset]).cpu(), preds.argmax(1).cpu()\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training an MLP to Solve Hierarchical Equality\n",
+    "\n",
+    "We've now seen how to perform causal abstraction analysis on a simple handcrafted neural networks. We turn now to training a neural network to perform the hierarchical equality task with a 4 dimensional vector embedding for each object. We define an input sampler to provide an infinite stream of new entities, rather than relying on a fixed set of vector representations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_dim = 4\n",
+    "\n",
+    "\n",
+    "def input_sampler():\n",
+    "    A = randvec(4)\n",
+    "    B = randvec(4)\n",
+    "    C = randvec(4)\n",
+    "    D = randvec(4)\n",
+    "    x = random.randint(1, 4)\n",
+    "    if x == 1:\n",
+    "        return {\"W\": A, \"X\": B, \"Y\": C, \"Z\": D}\n",
+    "    elif x == 2:\n",
+    "        return {\"W\": A, \"X\": A, \"Y\": B, \"Z\": B}\n",
+    "    elif x == 3:\n",
+    "        return {\"W\": A, \"X\": A, \"Y\": C, \"Z\": D}\n",
+    "    elif x == 4:\n",
+    "        return {\"W\": A, \"X\": B, \"Y\": C, \"Z\": C}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_examples = 1048576\n",
+    "batch_size = 1024\n",
+    "\n",
+    "examples = equality_model.generate_factual_dataset(n_examples, input_sampler)\n",
+    "\n",
+    "X = torch.stack([example['input_ids'] for example in examples])\n",
+    "y = torch.stack([example['labels'] for example in examples])\n",
+    "\n",
+    "# X = X.unsqueeze(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The examples in this dataset are 8-dimensional vectors: the concatenation of 4 2-dimensional vectors. Here's the first example with its label:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([-0.7200,  0.6300,  1.0000,  0.6900, -0.7200,  0.6300,  1.0000,  0.6900,\n",
+       "          0.0800, -0.8800, -0.0400, -0.0400, -0.5200, -0.8500, -0.6400,  0.6400]),\n",
+       " tensor([0.]))"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[0], y[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The label for this example is determined by whether the equality value for the first two inputs matches the equality value for the second two inputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "left = torch.equal(X[0][:embedding_dim], X[0][embedding_dim : embedding_dim * 2])\n",
+    "\n",
+    "left"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "right = torch.equal(\n",
+    "    X[0][embedding_dim * 2 : embedding_dim * 3], X[0][embedding_dim * 3 :]\n",
+    ")\n",
+    "\n",
+    "right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(left == right)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a three layer neural network with a ReLU activation function this task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "MLPForClassification(\n",
+       "  (mlp): MLPModel(\n",
+       "    (dropout): Dropout(p=0.0, inplace=False)\n",
+       "    (h): ModuleList(\n",
+       "      (0-2): 3 x MLPBlock(\n",
+       "        (ff1): Linear(in_features=16, out_features=16, bias=True)\n",
+       "        (act): ReLU()\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (score): Linear(in_features=16, out_features=2, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "config = MLPConfig(\n",
+    "    h_dim=embedding_dim * 4,\n",
+    "    activation_function=\"relu\",\n",
+    "    n_layer=3,\n",
+    "    num_classes=2,\n",
+    "    pdrop=0.0,\n",
+    ")\n",
+    "config, tokenizer, trained = create_mlp_classifier(config)\n",
+    "trained.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_ds = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"labels\": [\n",
+    "            torch.FloatTensor([0, 1]) if i == 1 else torch.FloatTensor([1, 0])\n",
+    "            for i in y\n",
+    "        ],\n",
+    "        \"inputs_embeds\": X,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import TrainingArguments, Trainer\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"test_trainer\",\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    learning_rate=0.001,\n",
+    "    num_train_epochs=3,\n",
+    "    per_device_train_batch_size=batch_size,\n",
+    "    per_device_eval_batch_size=batch_size,\n",
+    "    report_to=\"none\",\n",
+    ")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=trained,\n",
+    "    args=training_args,\n",
+    "    train_dataset=train_ds,\n",
+    "    eval_dataset=train_ds,\n",
+    "    compute_metrics=lambda x: {\n",
+    "        \"accuracy\": classification_report(\n",
+    "            x[0].argmax(1), x[1].argmax(1), output_dict=True\n",
+    "        )[\"accuracy\"]\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This neural network achieves perfect performance on its train set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "267ad9a4be754be89e338aa6240214e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3072 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n",
+      "Checkpoint destination directory test_trainer\\checkpoint-500 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 0.6401, 'grad_norm': 0.23116283118724823, 'learning_rate': 0.0008372395833333334, 'epoch': 0.49}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Checkpoint destination directory test_trainer\\checkpoint-1000 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 0.195, 'grad_norm': 0.18332688510417938, 'learning_rate': 0.0006744791666666667, 'epoch': 0.98}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6718015efcb34628b758ff359877ff36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1024 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 0.07157168537378311, 'eval_accuracy': 0.9790763854980469, 'eval_runtime': 37.061, 'eval_samples_per_second': 28293.236, 'eval_steps_per_second': 27.63, 'epoch': 1.0}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Checkpoint destination directory test_trainer\\checkpoint-1500 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 0.0472, 'grad_norm': 0.15438705682754517, 'learning_rate': 0.00051171875, 'epoch': 1.46}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Checkpoint destination directory test_trainer\\checkpoint-2000 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 0.025, 'grad_norm': 0.1841142624616623, 'learning_rate': 0.00034895833333333334, 'epoch': 1.95}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ade15e1911a840ceb47b8c372e241568",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1024 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 0.019501574337482452, 'eval_accuracy': 0.9946937561035156, 'eval_runtime': 37.7091, 'eval_samples_per_second': 27806.989, 'eval_steps_per_second': 27.155, 'epoch': 2.0}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Checkpoint destination directory test_trainer\\checkpoint-2500 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 0.0177, 'grad_norm': 0.18471458554267883, 'learning_rate': 0.00018619791666666665, 'epoch': 2.44}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Checkpoint destination directory test_trainer\\checkpoint-3000 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'loss': 0.0149, 'grad_norm': 0.15878142416477203, 'learning_rate': 2.34375e-05, 'epoch': 2.93}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6611af998ca848efbcb7ad9afb3bc344",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1024 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 0.014224954880774021, 'eval_accuracy': 0.9962148666381836, 'eval_runtime': 42.0755, 'eval_samples_per_second': 24921.316, 'eval_steps_per_second': 24.337, 'epoch': 3.0}\n",
+      "{'train_runtime': 257.663, 'train_samples_per_second': 12208.692, 'train_steps_per_second': 11.923, 'train_loss': 0.15329135081265122, 'epoch': 3.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we create a separate causal model with vector representations distinct from those used in training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hi!\n"
+     ]
+    }
+   ],
+   "source": [
+    "variables = [\"W\", \"X\", \"Y\", \"Z\", \"WX\", \"YZ\", \"O\"]\n",
+    "\n",
+    "number_of_test_entities = 100\n",
+    "\n",
+    "reps = [randvec(embedding_dim) for _ in range(number_of_test_entities)]\n",
+    "values = {variable: reps for variable in [\"W\", \"X\", \"Y\", \"Z\"]}\n",
+    "values[\"WX\"] = [True, False]\n",
+    "values[\"YZ\"] = [True, False]\n",
+    "values[\"O\"] = [True, False]\n",
+    "\n",
+    "parents = {\n",
+    "    \"W\": [],\n",
+    "    \"X\": [],\n",
+    "    \"Y\": [],\n",
+    "    \"Z\": [],\n",
+    "    \"WX\": [\"W\", \"X\"],\n",
+    "    \"YZ\": [\"Y\", \"Z\"],\n",
+    "    \"O\": [\"WX\", \"YZ\"],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def FILLER():\n",
+    "    return reps[0]\n",
+    "\n",
+    "\n",
+    "functions = {\n",
+    "    \"W\": FILLER,\n",
+    "    \"X\": FILLER,\n",
+    "    \"Y\": FILLER,\n",
+    "    \"Z\": FILLER,\n",
+    "    \"WX\": lambda x, y: np.array_equal(x, y),\n",
+    "    \"YZ\": lambda x, y: np.array_equal(x, y),\n",
+    "    \"O\": lambda x, y: x == y,\n",
+    "}\n",
+    "\n",
+    "pos = {\n",
+    "    \"W\": (0, 0),\n",
+    "    \"X\": (1, 0.1),\n",
+    "    \"Y\": (2, 0.2),\n",
+    "    \"Z\": (3, 0),\n",
+    "    \"WX\": (1, 2),\n",
+    "    \"YZ\": (2, 2),\n",
+    "    \"O\": (1.5, 3),\n",
+    "}\n",
+    "\n",
+    "\n",
+    "test_equality_model = CausalModel(variables, values, parents, functions, pos=pos)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our trained model generalizes perfectly this test set consisting of distinct vectors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Results\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "171e7809005a4020ab22e408905323ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.99      1.00      1.00      5026\n",
+      "         1.0       1.00      0.99      1.00      4974\n",
+      "\n",
+      "    accuracy                           1.00     10000\n",
+      "   macro avg       1.00      1.00      1.00     10000\n",
+      "weighted avg       1.00      1.00      1.00     10000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "examples = test_equality_model.generate_factual_dataset(10000, input_sampler)\n",
+    "print(\"Test Results\")\n",
+    "\n",
+    "test_ds = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"labels\": [\n",
+    "            torch.FloatTensor([0, 1]) if example['labels'].item() == 1 else torch.FloatTensor([1, 0])\n",
+    "            for example in examples\n",
+    "        ],\n",
+    "        \"inputs_embeds\": torch.stack([example['input_ids'] for example in examples]),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "test_preds = trainer.predict(test_ds)\n",
+    "y_test = [example['labels'].item() for example in examples]\n",
+    "\n",
+    "print(classification_report(y_test, test_preds[0].argmax(1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Does it implement our high-level model of the problem, though?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distributed Alignment Search\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We previously handcrafted the weights of a network so the two high-level variables are perfectly stored in two non-overlapping sets of neurons in the first layer of the network. However, the trained network won't have axis aligned representations of high-level concepts. Rather, the two high-level variables will be encoded in multidimensional linear subspaces of the first layer in the network.  \n",
+    "\n",
+    "To learn these subspaces, we define an IntervenableConfig that allows us to target the first layer of in the network after it has been rotated by an orthogonal matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "config = IntervenableConfig(\n",
+    "    model_type=type(trained),\n",
+    "    representations=[\n",
+    "        RepresentationConfig(\n",
+    "            0,  # layer\n",
+    "            \"block_output\",  # intervention type\n",
+    "            \"pos\",  # intervention unit is now aligne with tokens\n",
+    "            1,  # max number of unit\n",
+    "            subspace_partition=None,  # binary partition with equal sizes\n",
+    "            intervention_link_key=0,\n",
+    "        ),\n",
+    "        RepresentationConfig(\n",
+    "            0,  # layer\n",
+    "            \"block_output\",  # intervention type\n",
+    "            \"pos\",  # intervention unit is now aligne with tokens\n",
+    "            1,  # max number of unit\n",
+    "            subspace_partition=None,  # binary partition with equal sizes,\n",
+    "            intervention_link_key=0,\n",
+    "        ),\n",
+    "    ],\n",
+    "    intervention_types=RotatedSpaceIntervention,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.\n",
+      "\n",
+      "In case multiple location tags are passed only the first one will be considered\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable = IntervenableModel(config, trained, use_fast=True)\n",
+    "intervenable.set_device(\"cuda\")\n",
+    "intervenable.disable_model_gradients()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epochs = 10\n",
+    "gradient_accumulation_steps = 1\n",
+    "total_step = 0\n",
+    "target_total_step = len(dataset) * epochs\n",
+    "\n",
+    "t_total = int(len(dataset) * epochs)\n",
+    "optimizer_params = []\n",
+    "for k, v in intervenable.interventions.items():\n",
+    "    optimizer_params += [{\"params\": v.rotate_layer.parameters()}]\n",
+    "    break\n",
+    "optimizer = torch.optim.Adam(optimizer_params, lr=0.001)\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_preds, eval_labels):\n",
+    "    total_count = 0\n",
+    "    correct_count = 0\n",
+    "    for eval_pred, eval_label in zip(eval_preds, eval_labels):\n",
+    "        total_count += 1\n",
+    "        correct_count += eval_pred == eval_label\n",
+    "    accuracy = float(correct_count) / float(total_count)\n",
+    "    return {\"accuracy\": accuracy}\n",
+    "\n",
+    "\n",
+    "def compute_loss(outputs, labels):\n",
+    "    CE = torch.nn.CrossEntropyLoss()\n",
+    "    return CE(outputs, labels)\n",
+    "\n",
+    "\n",
+    "def batched_random_sampler(data):\n",
+    "    batch_indices = [_ for _ in range(int(len(data) / batch_size))]\n",
+    "    random.shuffle(batch_indices)\n",
+    "    for b_i in batch_indices:\n",
+    "        for i in range(b_i * batch_size, (b_i + 1) * batch_size):\n",
+    "            yield i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def input_sampler(*args, **kwargs):\n",
+    "    A = randvec(4)\n",
+    "    B = randvec(4)\n",
+    "    C = randvec(4)\n",
+    "    D = randvec(4)\n",
+    "    if kwargs.get('output_var', None) is None:\n",
+    "        return random.choice([\n",
+    "            {\"W\": A, \"X\": B, \"Y\": C, \"Z\": D},\n",
+    "            {\"W\": A, \"X\": A, \"Y\": B, \"Z\": B},\n",
+    "            {\"W\": A, \"X\": A, \"Y\": C, \"Z\": D},\n",
+    "            {\"W\": A, \"X\": B, \"Y\": C, \"Z\": C}\n",
+    "        ])\n",
+    "    elif kwargs['output_var'] == 'WX' and kwargs['output_var_value']:\n",
+    "        return random.choice([\n",
+    "            {\"W\": A, \"X\": A, \"Y\": C, \"Z\": D},\n",
+    "            {\"W\": A, \"X\": A, \"Y\": C, \"Z\": C}\n",
+    "        ])\n",
+    "    elif kwargs['output_var'] == 'WX' and not kwargs['output_var_value']:\n",
+    "        return random.choice([\n",
+    "            {\"W\": A, \"X\": B, \"Y\": C, \"Z\": D},\n",
+    "            {\"W\": A, \"X\": B, \"Y\": C, \"Z\": C}\n",
+    "        ])\n",
+    "    elif kwargs['output_var'] == 'YZ' and kwargs['output_var_value']:\n",
+    "        return random.choice([\n",
+    "            {\"W\": A, \"X\": B, \"Y\": C, \"Z\": C},\n",
+    "            {\"W\": A, \"X\": A, \"Y\": C, \"Z\": C}\n",
+    "        ])\n",
+    "    else:\n",
+    "        return random.choice([\n",
+    "            {\"W\": A, \"X\": B, \"Y\": C, \"Z\": D},\n",
+    "            {\"W\": A, \"X\": A, \"Y\": C, \"Z\": D}\n",
+    "        ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We again generate a counterfactual dataset using our high-level causal model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_examples = 1280000\n",
+    "batch_size = 6400\n",
+    "train_dataset = equality_model.generate_counterfactual_dataset(\n",
+    "    n_examples, intervention_id, batch_size, sampler=input_sampler\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we train the orthgonal matrix to be such that the first four dimensions in the rotated space encode the high-level variable 'WX' and the second four dimensions encode the high-level variable 'YZ'. \n",
+    "\n",
+    "Again, we check the intervention_id for each batch of training data in order to determine whether to intervene of the first four rotated dimensions ('WX' is targetted at the high-level), the last four rotated dimensions ('YZ' is targetted at the high-level), or all of the dimensions ('WX' and 'YZ' are both targetted at the high-level). \n",
+    "\n",
+    "We can train the rotation matrix such that we get perfect interchange intervention accuracy, meaning the trained network perfectly implements the high-level algorithm on the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "intervention trainable parameters:  256\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0: 0it [00:00, ?it/s] [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0: 200it [01:17,  2.59it/s, loss=tensor(0.4580, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.872]\n",
+      "Epoch: 1: 200it [00:54,  3.69it/s, loss=tensor(0.4646, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.89] \n",
+      "Epoch: 2: 200it [00:53,  3.71it/s, loss=tensor(0.1925, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.962]\n",
+      "Epoch: 3: 200it [00:53,  3.71it/s, loss=tensor(0.5047, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.837]\n",
+      "Epoch: 4: 200it [00:54,  3.70it/s, loss=tensor(0.1448, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.969]\n",
+      "Epoch: 5: 200it [00:52,  3.81it/s, loss=tensor(0.1444, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.967]\n",
+      "Epoch: 6: 200it [00:58,  3.40it/s, loss=tensor(0.1562, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.97] \n",
+      "Epoch: 7: 200it [01:04,  3.09it/s, loss=tensor(0.1703, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.958]\n",
+      "Epoch: 8: 200it [01:05,  3.07it/s, loss=tensor(0.1553, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.959]\n",
+      "Epoch: 9: 200it [01:04,  3.10it/s, loss=tensor(0.1505, device='cuda:0', grad_fn=<NllLossBackward0>), acc=0.967]\n",
+      "Epoch: 100%|██████████| 10/10 [09:59<00:00, 59.91s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable.model.train()  # train enables drop-off but no grads\n",
+    "print(\"intervention trainable parameters: \", intervenable.count_parameters())\n",
+    "train_iterator = trange(0, int(epochs), desc=\"Epoch\")\n",
+    "\n",
+    "for epoch in train_iterator:\n",
+    "    epoch_iterator = tqdm(\n",
+    "        DataLoader(\n",
+    "            train_dataset,\n",
+    "            batch_size=batch_size,\n",
+    "            sampler=batched_random_sampler(train_dataset),\n",
+    "        ),\n",
+    "        desc=f\"Epoch: {epoch}\",\n",
+    "        position=0,\n",
+    "        leave=True,\n",
+    "    )\n",
+    "    for batch in epoch_iterator:\n",
+    "        batch[\"input_ids\"] = batch[\"input_ids\"].unsqueeze(1)\n",
+    "        batch[\"source_input_ids\"] = batch[\"source_input_ids\"].unsqueeze(2)\n",
+    "        batch_size = batch[\"input_ids\"].shape[0]\n",
+    "        for k, v in batch.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                batch[k] = v.to(\"cuda\")\n",
+    "\n",
+    "        if batch[\"intervention_id\"][0] == 2:\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "                [\n",
+    "                    {\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]},\n",
+    "                    {\"inputs_embeds\": batch[\"source_input_ids\"][:, 1]},\n",
+    "                ],\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [[[0]] * batch_size, [[0]] * batch_size],\n",
+    "                        [[[0]] * batch_size, [[0]] * batch_size],\n",
+    "                    )\n",
+    "                },\n",
+    "                subspaces=[\n",
+    "                    [[_ for _ in range(0, embedding_dim * 2)]] * batch_size,\n",
+    "                    [[_ for _ in range(embedding_dim * 2, embedding_dim * 4)]]\n",
+    "                    * batch_size,\n",
+    "                ],\n",
+    "            )\n",
+    "        elif batch[\"intervention_id\"][0] == 0:\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "                [{\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]}, None],\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [[[0]] * batch_size, None],\n",
+    "                        [[[0]] * batch_size, None],\n",
+    "                    )\n",
+    "                },\n",
+    "                subspaces=[\n",
+    "                    [[_ for _ in range(0, embedding_dim * 2)]] * batch_size,\n",
+    "                    None,\n",
+    "                ],\n",
+    "            )\n",
+    "        elif batch[\"intervention_id\"][0] == 1:\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "                [None, {\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]}],\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [None, [[0]] * batch_size],\n",
+    "                        [None, [[0]] * batch_size],\n",
+    "                    )\n",
+    "                },\n",
+    "                subspaces=[\n",
+    "                    None,\n",
+    "                    [[_ for _ in range(embedding_dim * 2, embedding_dim * 4)]]\n",
+    "                    * batch_size,\n",
+    "                ],\n",
+    "            )\n",
+    "        eval_metrics = compute_metrics(\n",
+    "            counterfactual_outputs[0].argmax(1), batch[\"labels\"].squeeze()\n",
+    "        )\n",
+    "\n",
+    "        # loss and backprop\n",
+    "        loss = compute_loss(\n",
+    "            counterfactual_outputs[0], batch[\"labels\"].squeeze().to(torch.long)\n",
+    "        )\n",
+    "\n",
+    "        epoch_iterator.set_postfix({\"loss\": loss, \"acc\": eval_metrics[\"accuracy\"]})\n",
+    "\n",
+    "        if gradient_accumulation_steps > 1:\n",
+    "            loss = loss / gradient_accumulation_steps\n",
+    "        loss.backward()\n",
+    "        if total_step % gradient_accumulation_steps == 0:\n",
+    "            optimizer.step()\n",
+    "            intervenable.set_zero_grad()\n",
+    "        total_step += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What's more, is it generalizes unseen test data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_dataset = test_equality_model.generate_counterfactual_dataset(\n",
+    "    10000, intervention_id, batch_size, device=\"cuda:0\", sampler=input_sampler\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test:   0%|          | 0/2 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████| 2/2 [00:00<00:00, 14.01it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.98      0.96      0.97      6407\n",
+      "         1.0       0.96      0.98      0.97      6393\n",
+      "\n",
+      "    accuracy                           0.97     12800\n",
+      "   macro avg       0.97      0.97      0.97     12800\n",
+      "weighted avg       0.97      0.97      0.97     12800\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_labels = []\n",
+    "eval_preds = []\n",
+    "with torch.no_grad():\n",
+    "    epoch_iterator = tqdm(DataLoader(test_dataset, batch_size), desc=f\"Test\")\n",
+    "    for step, batch in enumerate(epoch_iterator):\n",
+    "        for k, v in batch.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                batch[k] = v.to(\"cuda\")\n",
+    "        batch[\"input_ids\"] = batch[\"input_ids\"].unsqueeze(1)\n",
+    "        batch[\"source_input_ids\"] = batch[\"source_input_ids\"].unsqueeze(2)\n",
+    "        if batch[\"intervention_id\"][0] == 2:\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "                [\n",
+    "                    {\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]},\n",
+    "                    {\"inputs_embeds\": batch[\"source_input_ids\"][:, 1]},\n",
+    "                ],\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [[[0]] * batch_size, [[0]] * batch_size],\n",
+    "                        [[[0]] * batch_size, [[0]] * batch_size],\n",
+    "                    )\n",
+    "                },\n",
+    "                subspaces=[\n",
+    "                    [[_ for _ in range(0, embedding_dim * 2)]] * batch_size,\n",
+    "                    [[_ for _ in range(embedding_dim * 2, embedding_dim * 4)]]\n",
+    "                    * batch_size,\n",
+    "                ],\n",
+    "            )\n",
+    "        elif batch[\"intervention_id\"][0] == 0:\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "                [{\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]}, None],\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [[[0]] * batch_size, None],\n",
+    "                        [[[0]] * batch_size, None],\n",
+    "                    )\n",
+    "                },\n",
+    "                subspaces=[\n",
+    "                    [[_ for _ in range(0, embedding_dim * 2)]] * batch_size,\n",
+    "                    None,\n",
+    "                ],\n",
+    "            )\n",
+    "        elif batch[\"intervention_id\"][0] == 1:\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                {\"inputs_embeds\": batch[\"input_ids\"]},\n",
+    "                [None, {\"inputs_embeds\": batch[\"source_input_ids\"][:, 0]}],\n",
+    "                {\n",
+    "                    \"sources->base\": (\n",
+    "                        [None, [[0]] * batch_size],\n",
+    "                        [None, [[0]] * batch_size],\n",
+    "                    )\n",
+    "                },\n",
+    "                subspaces=[\n",
+    "                    None,\n",
+    "                    [[_ for _ in range(embedding_dim * 2, embedding_dim * 4)]]\n",
+    "                    * batch_size,\n",
+    "                ],\n",
+    "            )\n",
+    "        eval_labels += [batch[\"labels\"]]\n",
+    "        eval_preds += [torch.argmax(counterfactual_outputs[0], dim=1)]\n",
+    "print(classification_report(torch.cat(eval_labels).cpu(), torch.cat(eval_preds).cpu()))"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "933b0a94e0d88ac80a17cb26ca3d8d36930c12815b02a2885c1925c2b1ae3c33"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/tutorials/advanced_tutorials/IOI_Replication.ipynb b/_sources/tutorials/advanced_tutorials/IOI_Replication.ipynb
new file mode 100644
index 00000000..90df0e3a
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/IOI_Replication.ipynb
@@ -0,0 +1,4512 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Replicating the IOI paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Aryaman Arora\"\n",
+    "__version__ = \"1/24/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "We're going to try to replicate some results of the original IOI paper ([Wang et al., 2022](https://arxiv.org/abs/2211.00593)) using `pyvene`, as a demonstration of path patching and verification of their results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/frankaging/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [58], line 27\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m     25\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 27\u001b[0m config, tokenizer, gpt2 \u001b[38;5;241m=\u001b[39m \u001b[43mpv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_gpt2_lm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/Users/aryamanarora/.cache/huggingface/hub/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m _ \u001b[38;5;241m=\u001b[39m gpt2\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m     30\u001b[0m titles\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblock_output\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle restored layer in GPT2-XL\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmlp_activation\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter of interval of 10 patched mlp layer\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattention_output\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter of interval of 10 patched attn layer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     34\u001b[0m }\n",
+      "File \u001b[0;32m~/Documents/Code/pyvene/pyvene/models/gpt2/modelings_intervenable_gpt2.py:84\u001b[0m, in \u001b[0;36mcreate_gpt2_lm\u001b[0;34m(name, config, cache_dir)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;124;03m\"\"\"Creates a GPT2 LM, config, and tokenizer from the given name and revision\"\"\"\u001b[39;00m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n\u001b[0;32m---> 84\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m \u001b[43mGPT2Tokenizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgpt2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     86\u001b[0m     config \u001b[38;5;241m=\u001b[39m GPT2Config\u001b[38;5;241m.\u001b[39mfrom_pretrained(name)\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1968\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, *init_inputs, **kwargs)\u001b[0m\n\u001b[1;32m   1965\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenizer_file\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m vocab_files:\n\u001b[1;32m   1966\u001b[0m     \u001b[38;5;66;03m# Try to get the tokenizer config to see if there are versioned tokenizer files.\u001b[39;00m\n\u001b[1;32m   1967\u001b[0m     fast_tokenizer_file \u001b[38;5;241m=\u001b[39m FULL_TOKENIZER_FILE\n\u001b[0;32m-> 1968\u001b[0m     resolved_config_file \u001b[38;5;241m=\u001b[39m \u001b[43mcached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1969\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1970\u001b[0m \u001b[43m        \u001b[49m\u001b[43mTOKENIZER_CONFIG_FILE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1971\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1972\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1973\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1974\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1975\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1976\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1977\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1978\u001b[0m \u001b[43m        \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1979\u001b[0m \u001b[43m        \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1980\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_raise_exceptions_for_missing_entries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1981\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_raise_exceptions_for_connection_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1982\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1983\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1984\u001b[0m     commit_hash \u001b[38;5;241m=\u001b[39m extract_commit_hash(resolved_config_file, commit_hash)\n\u001b[1;32m   1985\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m resolved_config_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/transformers/utils/hub.py:429\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    426\u001b[0m user_agent \u001b[38;5;241m=\u001b[39m http_user_agent(user_agent)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    428\u001b[0m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[0;32m--> 429\u001b[0m     resolved_file \u001b[38;5;241m=\u001b[39m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m        \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    436\u001b[0m \u001b[43m        \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    437\u001b[0m \u001b[43m        \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    438\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    440\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    442\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m GatedRepoError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    444\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m    445\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are trying to access a gated repo.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMake sure to request access at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    446\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://huggingface.co/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and pass a token having permission to this repo either \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    447\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby logging in with `huggingface-cli login` or by passing `token=<your_token>`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    448\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:1232\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, endpoint, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)\u001b[0m\n\u001b[1;32m   1230\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1231\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1232\u001b[0m         metadata \u001b[38;5;241m=\u001b[39m \u001b[43mget_hf_file_metadata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1233\u001b[0m \u001b[43m            \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1234\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1235\u001b[0m \u001b[43m            \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1236\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43metag_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1237\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1238\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m http_error:\n\u001b[1;32m   1239\u001b[0m         \u001b[38;5;66;03m# Cache the non-existence of the file and raise\u001b[39;00m\n\u001b[1;32m   1240\u001b[0m         commit_hash \u001b[38;5;241m=\u001b[39m http_error\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(HUGGINGFACE_HEADER_X_REPO_COMMIT)\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:1599\u001b[0m, in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout)\u001b[0m\n\u001b[1;32m   1596\u001b[0m headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccept-Encoding\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midentity\u001b[39m\u001b[38;5;124m\"\u001b[39m  \u001b[38;5;66;03m# prevent any compression => we want to know the real size of the file\u001b[39;00m\n\u001b[1;32m   1598\u001b[0m \u001b[38;5;66;03m# Retrieve metadata\u001b[39;00m\n\u001b[0;32m-> 1599\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43m_request_wrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1600\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHEAD\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1601\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1602\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1603\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1604\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfollow_relative_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1605\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1606\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1607\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1608\u001b[0m hf_raise_for_status(r)\n\u001b[1;32m   1610\u001b[0m \u001b[38;5;66;03m# Return\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:417\u001b[0m, in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, max_retries, base_wait_time, max_wait_time, timeout, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    415\u001b[0m \u001b[38;5;66;03m# 2. Force relative redirection\u001b[39;00m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m follow_relative_redirects:\n\u001b[0;32m--> 417\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43m_request_wrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    419\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    420\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    421\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbase_wait_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_wait_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    422\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_wait_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_wait_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_relative_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    428\u001b[0m     \u001b[38;5;66;03m# If redirection, we redirect only relative paths.\u001b[39;00m\n\u001b[1;32m    429\u001b[0m     \u001b[38;5;66;03m# This is useful in case of a renamed repository.\u001b[39;00m\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m300\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m399\u001b[39m:\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:452\u001b[0m, in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, max_retries, base_wait_time, max_wait_time, timeout, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    449\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n\u001b[1;32m    451\u001b[0m \u001b[38;5;66;03m# 3. Exponential backoff\u001b[39;00m\n\u001b[0;32m--> 452\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhttp_backoff\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    453\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    454\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    456\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbase_wait_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_wait_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_wait_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_wait_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    458\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry_on_exceptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mTimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mProxyError\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    459\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry_on_status_codes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    461\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    462\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_http.py:258\u001b[0m, in \u001b[0;36mhttp_backoff\u001b[0;34m(method, url, max_retries, base_wait_time, max_wait_time, retry_on_exceptions, retry_on_status_codes, **kwargs)\u001b[0m\n\u001b[1;32m    255\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mseek(io_obj_initial_pos)\n\u001b[1;32m    257\u001b[0m \u001b[38;5;66;03m# Perform request and return if status_code is not in the retry list.\u001b[39;00m\n\u001b[0;32m--> 258\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m retry_on_status_codes:\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_http.py:63\u001b[0m, in \u001b[0;36mUniqueRequestIdAdapter.send\u001b[0;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03m\"\"\"Catch any RequestException to append request id to the error message for debugging.\"\"\"\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 63\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mRequestException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     65\u001b[0m     request_id \u001b[38;5;241m=\u001b[39m request\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(X_AMZN_TRACE_ID)\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    483\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    704\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    706\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    711\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    713\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    715\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    716\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n\u001b[1;32m    717\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connectionpool.py:386\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;66;03m# Trigger any extra validation we need to do.\u001b[39;00m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 386\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mconn\u001b[38;5;241m.\u001b[39mtimeout)\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connectionpool.py:1042\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1040\u001b[0m \u001b[38;5;66;03m# Force connect early to allow us to validate the connection.\u001b[39;00m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[0;32m-> 1042\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n\u001b[1;32m   1045\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m   1046\u001b[0m         (\n\u001b[1;32m   1047\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnverified HTTPS request is being made to host \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1052\u001b[0m         InsecureRequestWarning,\n\u001b[1;32m   1053\u001b[0m     )\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connection.py:358\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    357\u001b[0m     \u001b[38;5;66;03m# Add certificate verification\u001b[39;00m\n\u001b[0;32m--> 358\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    359\u001b[0m     hostname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n\u001b[1;32m    360\u001b[0m     tls_in_tls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connection.py:174\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    171\u001b[0m     extra_kw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msocket_options\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msocket_options\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 174\u001b[0m     conn \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mextra_kw\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n\u001b[1;32m    179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[1;32m    180\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    181\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    182\u001b[0m         \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout),\n\u001b[1;32m    183\u001b[0m     )\n",
+      "File \u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m source_address:\n\u001b[1;32m     84\u001b[0m         sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 85\u001b[0m     \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m sock\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39merror \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "from tutorial_ioi_utils import *\n",
+    "import pyvene as pv\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    scale_y_reverse,\n",
+    "    scale_fill_cmap,\n",
+    "    geom_text,\n",
+    "    theme_bw,\n",
+    "    xlim,\n",
+    "    ylim,\n",
+    "    scale_x_continuous\n",
+    ")\n",
+    "\n",
+    "# please try not to do this, the plot somehow throw warnings though :(\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "config, tokenizer, gpt2 = pv.create_gpt2_lm(cache_dir=\"/Users/aryamanarora/.cache/huggingface/hub/\")\n",
+    "_ = gpt2.eval()\n",
+    "\n",
+    "titles={\n",
+    "    \"block_output\": \"single restored layer in GPT2-XL\",\n",
+    "    \"mlp_activation\": \"center of interval of 10 patched mlp layer\",\n",
+    "    \"attention_output\": \"center of interval of 10 patched attn layer\"\n",
+    "}\n",
+    "\n",
+    "colors={\n",
+    "    \"block_output\": \"Purples\",\n",
+    "    \"mlp_activation\": \"Greens\",\n",
+    "    \"attention_output\": \"Reds\"\n",
+    "} "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Path patching config\n",
+    "\n",
+    "This is taken from the `pyvene` 101 tutorial. Basically, we'll intervene at all positions for a single attention head, and restore the base input for all upstream model components. This will get the direct effect of the intervention on the logits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def path_patching_config(\n",
+    "    layer, last_layer, \n",
+    "    component=\"head_attention_value_output\", unit=\"h.pos\"\n",
+    "):\n",
+    "    intervening_component = [\n",
+    "        {\"layer\": layer, \"component\": component, \"unit\": unit, \"group_key\": 0}]\n",
+    "    restoring_components = []\n",
+    "    if not component.startswith(\"mlp_\"):\n",
+    "        restoring_components += [\n",
+    "            {\"layer\": layer, \"component\": \"mlp_output\", \"group_key\": 1}]\n",
+    "    for i in range(layer+1, last_layer):\n",
+    "        restoring_components += [\n",
+    "            {\"layer\": i, \"component\": \"attention_output\", \"group_key\": 1},\n",
+    "            {\"layer\": i, \"component\": \"mlp_output\", \"group_key\": 1}\n",
+    "        ]\n",
+    "    intervenable_config = pv.IntervenableConfig(\n",
+    "        intervening_component + restoring_components)\n",
+    "    return intervenable_config, len(restoring_components)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataset + Utils\n",
+    "\n",
+    "Just sampling prompts for the IOI task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_distribution = PromptDistribution(\n",
+    "    names=NAMES,\n",
+    "    objects=OBJECTS,\n",
+    "    places=PLACES,\n",
+    "    templates=TEMPLATES,\n",
+    ")\n",
+    "\n",
+    "D_test = test_distribution.sample_das(\n",
+    "    tokenizer=tokenizer,\n",
+    "    base_patterns=[\n",
+    "        \"ABB\",\n",
+    "    ],\n",
+    "    source_patterns=[\"DCE\"],\n",
+    "    labels=\"name\",\n",
+    "    samples_per_combination=25,\n",
+    ") + test_distribution.sample_das(\n",
+    "    tokenizer=tokenizer,\n",
+    "    base_patterns=[\n",
+    "        \"BAB\",\n",
+    "    ],\n",
+    "    source_patterns=[\"DCE\"],\n",
+    "    labels=\"name\",\n",
+    "    samples_per_combination=25,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "\n",
+    "def get_last_token(logits, attention_mask):\n",
+    "    last_token_indices = attention_mask.sum(1) - 1\n",
+    "    batch_indices = torch.arange(logits.size(0)).unsqueeze(1)\n",
+    "    return logits[batch_indices, last_token_indices.unsqueeze(1)].squeeze(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[<===PROMPT=== Then, Justin and Bryan went to the hospital. Justin gave a necklace to>] torch.Size([1, 15])  Bryan\n",
+      "[<===PROMPT=== Then, Courtney and Thomas went to the hospital. Ashley gave a necklace to>] torch.Size([1, 15])  None\n"
+     ]
+    }
+   ],
+   "source": [
+    "for batch in D_test.batches(batch_size=1):\n",
+    "    print(batch.base, batch.base.tokens['input_ids'].shape, tokenizer.decode(batch.patched_answer_tokens[0][1]))\n",
+    "    print(batch.source, batch.source.tokens['input_ids'].shape, tokenizer.decode(batch.patched_answer_tokens[0][0]))\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're also implementing the logit diff metric, which checks the difference in logits between the two names in the sentence. Positive logit diff means a correct prediction is more likely (the IO, i.e. non-subject name)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_logit_diff(logits: torch.tensor, batch):\n",
+    "    base_logit = get_last_token(logits, batch.base.tokens['attention_mask'])\n",
+    "    base_label = batch.patched_answer_tokens[:, 1].to(gpt2.device)\n",
+    "    logit_diffs = []\n",
+    "    for batch_i in range(base_logit.size(0)):\n",
+    "        correct_name = base_label[batch_i]\n",
+    "        other_name = tokenizer.encode(' ' + batch.base.prompts[batch_i].s_name)[0]\n",
+    "        logit_diffs.append(base_logit[batch_i, correct_name] - base_logit[batch_i, other_name])\n",
+    "    return logit_diffs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They reported a baseline logit difference of 3.56 and a task accuracy of 99.3% for GPT-2 in the paper. Let's check if this holds on our dataset (it pretty much does):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:02<00:00,  4.54it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "avg logit diff: 3.7562549114227295\n",
+      "argmax acc: 0.94\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with torch.no_grad():\n",
+    "    logit_diffs = []\n",
+    "    argmax_acc = 0\n",
+    "    for batch in tqdm(D_test.batches(batch_size=5), total=10):\n",
+    "        base_label = batch.patched_answer_tokens[:, 1].to(gpt2.device)\n",
+    "        base_logit = get_last_token(gpt2(**batch.base.tokens).logits, batch.base.tokens['attention_mask'])\n",
+    "        src_logit = get_last_token(gpt2(**batch.source.tokens).logits, batch.source.tokens['attention_mask'])\n",
+    "        for batch_i in range(5):\n",
+    "            other_name = tokenizer.encode(' ' + batch.base.prompts[batch_i].s_name)[0]\n",
+    "            correct_name = base_label[batch_i]\n",
+    "\n",
+    "            # logit diff\n",
+    "            logit_diffs.append(\n",
+    "                base_logit[batch_i, base_label[batch_i]] - base_logit[batch_i, other_name].item())\n",
+    "\n",
+    "            # baseline accuracy\n",
+    "            argmax = base_logit[batch_i].argmax()\n",
+    "            if argmax == base_label[batch_i]:\n",
+    "                argmax_acc += 1\n",
+    "\n",
+    "    logit_diff = (sum(logit_diffs) / len(logit_diffs)).item()\n",
+    "    print(\"avg logit diff:\", logit_diff)\n",
+    "    print(\"argmax acc:\", argmax_acc / 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Name mover heads\n",
+    "\n",
+    "We will replicate figure 3, which identifies heads which directly affect the logits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.\n",
+      "\n",
+      "In case multiple location tags are passed only the first one will be considered\n",
+      "100%|██████████| 50/50 [00:07<00:00,  6.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 0, 'logit_diff': tensor(3.7504), 'accuracy': 0.94, 'kl_div': tensor(-74.3469), 'label_logit': -74.34694038391113, 'label_prob': 0.5380359962582588}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 1, 'logit_diff': tensor(3.7568), 'accuracy': 0.94, 'kl_div': tensor(-74.4057), 'label_logit': -74.40572700500488, 'label_prob': 0.5395114549994469}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 2, 'logit_diff': tensor(3.8010), 'accuracy': 0.94, 'kl_div': tensor(-74.4281), 'label_logit': -74.42814888000488, 'label_prob': 0.5384641465544701}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.38it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 3, 'logit_diff': tensor(3.7179), 'accuracy': 0.92, 'kl_div': tensor(-74.5966), 'label_logit': -74.59663963317871, 'label_prob': 0.5215314196050167}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:08<00:00,  5.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 4, 'logit_diff': tensor(3.7548), 'accuracy': 0.94, 'kl_div': tensor(-74.4089), 'label_logit': -74.40891792297363, 'label_prob': 0.5398365586996079}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 5, 'logit_diff': tensor(3.7737), 'accuracy': 0.94, 'kl_div': tensor(-74.4948), 'label_logit': -74.49480590820312, 'label_prob': 0.5443936404585838}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:08<00:00,  6.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 6, 'logit_diff': tensor(3.8182), 'accuracy': 0.94, 'kl_div': tensor(-74.4737), 'label_logit': -74.47373725891113, 'label_prob': 0.5551303905248642}\n"
+     ]
+    },
+    {
+     "name": "stderr",
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+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.79it/s]\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  7.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 8, 'logit_diff': tensor(3.7844), 'accuracy': 0.94, 'kl_div': tensor(-74.4013), 'label_logit': -74.40127014160156, 'label_prob': 0.536662351489067}\n"
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+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  7.08it/s]\n"
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+    {
+     "name": "stdout",
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+     "text": [
+      "{'layer': 8, 'head': 9, 'logit_diff': tensor(3.7546), 'accuracy': 0.94, 'kl_div': tensor(-74.4106), 'label_logit': -74.41055702209472, 'label_prob': 0.5401303231716156}\n"
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+     "name": "stderr",
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+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 8, 'head': 10, 'logit_diff': tensor(3.5075), 'accuracy': 0.94, 'kl_div': tensor(-74.2388), 'label_logit': -74.23884353637695, 'label_prob': 0.5428883665800095}\n"
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+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.37it/s]\n",
+      "WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.\n",
+      "\n",
+      "In case multiple location tags are passed only the first one will be considered\n"
+     ]
+    },
+    {
+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:06<00:00,  7.30it/s]\n"
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+    {
+     "name": "stdout",
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+     "text": [
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+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.15it/s]\n"
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+    },
+    {
+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:06<00:00,  7.28it/s]\n"
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+      "100%|██████████| 50/50 [00:08<00:00,  6.16it/s]\n"
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+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:07<00:00,  6.86it/s]\n"
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+     "name": "stdout",
+     "output_type": "stream",
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+      "100%|██████████| 50/50 [00:08<00:00,  5.85it/s]\n"
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+     "text": [
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+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  7.05it/s]\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "100%|██████████| 50/50 [00:07<00:00,  7.05it/s]\n"
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+     "name": "stdout",
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+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.61it/s]\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.70it/s]\n"
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+    {
+     "name": "stdout",
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+     "text": [
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+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.31it/s]\n",
+      "WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.\n",
+      "\n",
+      "In case multiple location tags are passed only the first one will be considered\n"
+     ]
+    },
+    {
+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:07<00:00,  6.85it/s]\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 10, 'head': 0, 'logit_diff': tensor(3.1255), 'accuracy': 0.66, 'kl_div': tensor(-75.9131), 'label_logit': -75.91307647705078, 'label_prob': 0.3100175105780363}\n"
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+      "100%|██████████| 50/50 [00:06<00:00,  7.76it/s]\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.71it/s]\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "100%|██████████| 50/50 [00:06<00:00,  7.57it/s]\n"
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+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:06<00:00,  7.59it/s]\n"
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+     "text": [
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+      "100%|██████████| 50/50 [00:06<00:00,  7.19it/s]\n"
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+     "text": [
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+      "100%|██████████| 50/50 [00:07<00:00,  7.00it/s]\n"
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+     "name": "stdout",
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+     "text": [
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+      "100%|██████████| 50/50 [00:07<00:00,  6.58it/s]\n"
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+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 10, 'head': 7, 'logit_diff': tensor(5.1090), 'accuracy': 0.98, 'kl_div': tensor(-72.2116), 'label_logit': -72.21165046691894, 'label_prob': 0.8099988362193108}\n"
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+      "100%|██████████| 50/50 [00:07<00:00,  6.89it/s]\n"
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+    {
+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:06<00:00,  7.34it/s]\n"
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+    {
+     "name": "stdout",
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+     "text": [
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.46it/s]\n"
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+     "name": "stdout",
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+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:06<00:00,  7.67it/s]\n",
+      "WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.\n",
+      "\n",
+      "In case multiple location tags are passed only the first one will be considered\n"
+     ]
+    },
+    {
+     "name": "stdout",
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+     "text": [
+      "{'layer': 10, 'head': 11, 'logit_diff': tensor(3.7515), 'accuracy': 0.94, 'kl_div': tensor(-74.4513), 'label_logit': -74.4512720489502, 'label_prob': 0.5345661920309067}\n"
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+      "100%|██████████| 50/50 [00:06<00:00,  7.49it/s]\n"
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+     "text": [
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+      "100%|██████████| 50/50 [00:06<00:00,  7.53it/s]\n"
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+      "100%|██████████| 50/50 [00:06<00:00,  7.46it/s]\n"
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+      "100%|██████████| 50/50 [00:06<00:00,  7.71it/s]\n"
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+      "100%|██████████| 50/50 [00:06<00:00,  7.60it/s]\n"
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+      "100%|██████████| 50/50 [00:06<00:00,  7.54it/s]\n"
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+     "text": [
+      "{'layer': 11, 'head': 5, 'logit_diff': tensor(3.7504), 'accuracy': 0.94, 'kl_div': tensor(-74.3879), 'label_logit': -74.38786392211914, 'label_prob': 0.5406103874742985}\n"
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+      "100%|██████████| 50/50 [00:07<00:00,  6.85it/s]\n"
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+     "name": "stdout",
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+      "100%|██████████| 50/50 [00:07<00:00,  6.28it/s]\n"
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+     "output_type": "stream",
+     "text": [
+      "{'layer': 11, 'head': 9, 'logit_diff': tensor(3.6901), 'accuracy': 0.94, 'kl_div': tensor(-74.6614), 'label_logit': -74.66142028808594, 'label_prob': 0.4914996309578419}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 11, 'head': 10, 'logit_diff': tensor(4.5615), 'accuracy': 0.96, 'kl_div': tensor(-72.9968), 'label_logit': -72.99680526733398, 'label_prob': 0.7122889611124993}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50/50 [00:07<00:00,  6.59it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'layer': 11, 'head': 11, 'logit_diff': tensor(3.7668), 'accuracy': 0.94, 'kl_div': tensor(-76.5733), 'label_logit': -76.57328651428223, 'label_prob': 0.507421883046627}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = []\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    for layer in range(8, 12):\n",
+    "        intervenable_config, num_restores = path_patching_config(layer, 12)\n",
+    "        intervenable = IntervenableModel(intervenable_config, gpt2, use_fast=True)\n",
+    "\n",
+    "        for head in range(gpt2.config.n_head):\n",
+    "            eval_labels, eval_preds, logit_diffs = [], [], []\n",
+    "            for batch_dataset in tqdm(D_test.batches(batch_size=1), total=50):\n",
+    "                # prepare\n",
+    "                base_inputs = batch_dataset.base.tokens\n",
+    "                source_inputs = batch_dataset.source.tokens\n",
+    "                labels = batch_dataset.patched_answer_tokens[:, 1].to(gpt2.device)\n",
+    "                pos = list(range(base_inputs[\"input_ids\"].shape[-1]))\n",
+    "\n",
+    "                # inference\n",
+    "                _, counterfactual_outputs = intervenable(\n",
+    "                    {\"input_ids\": base_inputs[\"input_ids\"]}, \n",
+    "                    [{\"input_ids\": source_inputs[\"input_ids\"]}, {\"input_ids\": base_inputs[\"input_ids\"]}],\n",
+    "                    {\"sources->base\": ((\n",
+    "                        [[[[head]], [pos]]]+[[pos]]*num_restores, \n",
+    "                        [[[[head]], [pos]]]+[[pos]]*num_restores\n",
+    "                    ))}\n",
+    "                )\n",
+    "                logit_diffs.extend(compute_logit_diff(counterfactual_outputs.logits, batch_dataset))\n",
+    "                eval_labels += [labels]\n",
+    "                last_token_logits = get_last_token(counterfactual_outputs.logits, batch_dataset.base.tokens['attention_mask']).unsqueeze(1)\n",
+    "                eval_preds += [last_token_logits]\n",
+    "            \n",
+    "            # metrics\n",
+    "            eval_metrics = compute_metrics(\n",
+    "                eval_preds, eval_labels,\n",
+    "            )\n",
+    "            mean_logit_diff = sum(logit_diffs) / len(logit_diffs)\n",
+    "            data.append({\"layer\": layer, \"head\": head, \"logit_diff\": mean_logit_diff, **eval_metrics})\n",
+    "            print(data[-1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data)\n",
+    "df[\"logit_diff\"] = df[\"logit_diff\"].apply(lambda x: x.item())\n",
+    "df[\"logit_diff_relative\"] = (df[\"logit_diff\"] - logit_diff) / logit_diff\n",
+    "lim = df[\"logit_diff_relative\"].abs().max()\n",
+    "df[\"formatted\"] = df[\"logit_diff_relative\"].apply(lambda x: f\"{x:.2f}\")\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"head\", y=\"layer\", fill=\"logit_diff_relative\")) + geom_tile()\n",
+    "    + scale_fill_cmap(\"RdBu\", limits=(-lim, lim)) + scale_y_reverse(expand=[0, 0])\n",
+    "    + geom_text(aes(label=\"formatted\"), size=8, color=\"black\")\n",
+    "    + theme_bw() + scale_x_continuous(expand=[0, 0])\n",
+    ")\n",
+    "print(plot)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/tutorials/advanced_tutorials/IOI_with_DAS.ipynb b/_sources/tutorials/advanced_tutorials/IOI_with_DAS.ipynb
new file mode 100644
index 00000000..f9e29ec8
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/IOI_with_DAS.ipynb
@@ -0,0 +1,23409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "551d125e",
+   "metadata": {},
+   "source": [
+    "## IOI with DAS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "303cfd3b",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/Indirect%20object%20identification%20(IOI)%20circuit%20with%20DAS.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a8810845",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"12/31/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "862b5e8d",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "This tutorial aims to replicate key findings from previous mechanistic interpretability papers that examine the IOI task using this library. It focuses on identifying alignments for the name position information and the output IO name at various locations within the neural networks.\n",
+    "\n",
+    "Additionally, the tutorial seeks to provide new insights into GPT-2's causal mechanisms in solving this task by analyzing how information is stored and processed across different attention heads."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a1a307",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f6d2cbb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6ca42bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "from tutorial_ioi_utils import *\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token, sigmoid_boundary\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    "    LowRankRotatedSpaceIntervention,\n",
+    "    SkipIntervention,\n",
+    "    VanillaIntervention,\n",
+    "    BoundlessRotatedSpaceIntervention,\n",
+    ")\n",
+    "from pyvene import create_gpt2_lm\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    "    scale_y_reverse,\n",
+    "    scale_fill_cmap,\n",
+    "    geom_text,\n",
+    "    scale_fill_gradient,\n",
+    "    geom_point,\n",
+    "    geom_line,\n",
+    "    theme_minimal,\n",
+    "    ylim,\n",
+    "    ggtitle,\n",
+    "    ggsave,\n",
+    "    labs,\n",
+    "    scale_x_discrete,\n",
+    "    geom_histogram,\n",
+    "    scale_fill_manual,\n",
+    ")\n",
+    "\n",
+    "# please try not to do this, the plot somehow throw warnings though :(\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "config, tokenizer, gpt2 = create_gpt2_lm()\n",
+    "_ = gpt2.eval().to(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8dfa3c",
+   "metadata": {},
+   "source": [
+    "### Factual recall\n",
+    "We first check IOI task performance of GPT-2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67c757c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gpt-2 IOI task accuracy: 0.91\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformers.set_seed(42)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "\n",
+    "def get_last_token(logits, attention_mask):\n",
+    "    last_token_indices = attention_mask.sum(1) - 1\n",
+    "    batch_indices = torch.arange(logits.size(0)).unsqueeze(1)\n",
+    "    return logits[batch_indices, last_token_indices.unsqueeze(1)].squeeze(1)\n",
+    "\n",
+    "train_distribution = PromptDistribution(\n",
+    "    names=NAMES[: len(NAMES) // 2],\n",
+    "    objects=OBJECTS[: len(OBJECTS) // 2],\n",
+    "    places=PLACES[: len(PLACES) // 2],\n",
+    "    templates=TEMPLATES_PATH,\n",
+    ")\n",
+    "\n",
+    "D_train = train_distribution.sample_das(\n",
+    "    tokenizer=tokenizer,\n",
+    "    base_patterns=[\"ABB\", \"BAB\"],\n",
+    "    source_patterns=[\"ABB\", \"BAB\"],\n",
+    "    labels=\"position\",\n",
+    "    samples_per_combination=50,\n",
+    ")\n",
+    "\n",
+    "total_count = 0\n",
+    "correct_count = 0\n",
+    "with torch.no_grad():\n",
+    "    for batch_dataset in D_train.batches(batch_size=30):\n",
+    "        inputs = batch_dataset.base.tokens\n",
+    "\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(gpt2.device)\n",
+    "        labels = batch_dataset.base.answer_tokens[:, 0].to(gpt2.device)\n",
+    "\n",
+    "        outputs = gpt2(**inputs)\n",
+    "        logits = get_last_token(outputs.logits, inputs[\"attention_mask\"]).unsqueeze(1)\n",
+    "\n",
+    "        pred_labels = logits.argmax(dim=-1)[:, -1]\n",
+    "        correct_labels = labels == pred_labels\n",
+    "\n",
+    "        total_count += len(correct_labels)\n",
+    "        correct_count += correct_labels.sum().tolist()\n",
+    "current_acc = round(correct_count / total_count, 2)\n",
+    "print(f\"gpt-2 IOI task accuracy: {current_acc}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a89af96",
+   "metadata": {},
+   "source": [
+    "### Localizing the name position information in the main residual stream and MLP activations\n",
+    "This section reproduces main results in [this paper](https://arxiv.org/pdf/2311.17030.pdf). To address the IOI task, previous studies have shown that GPT-2 creates representations for a causal variable representing the correct name position (either the first or second name mentioned in a sentence). Our objective is to utilize our library to pinpoint the location of name position information at each layer and across several positions, not just at the last token position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f07658ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finding name position at: pos->13, layers->0, stream->block_output\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m block_out_data \u001b[38;5;241m=\u001b[39m \u001b[43mfind_variable_at\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgpt2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtokenizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpositions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m14\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m15\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m17\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlayers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblock_output\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43maligning_variable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mposition\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/Code/pyvene/tutorials/advanced_tutorials/tutorial_ioi_utils.py:704\u001b[0m, in \u001b[0;36mfind_variable_at\u001b[0;34m(gpt2, tokenizer, positions, layers, stream, heads, low_rank_dimension, aligning_variable, do_vanilla_intervention, do_boundless_das, seed, return_intervenable, debug)\u001b[0m\n\u001b[1;32m    699\u001b[0m \u001b[38;5;66;03m# prepare label\u001b[39;00m\n\u001b[1;32m    700\u001b[0m labels \u001b[38;5;241m=\u001b[39m batch_dataset\u001b[38;5;241m.\u001b[39mpatched_answer_tokens[:, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mto(\n\u001b[1;32m    701\u001b[0m     gpt2\u001b[38;5;241m.\u001b[39mdevice\n\u001b[1;32m    702\u001b[0m )\n\u001b[0;32m--> 704\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mall\u001b[39m(x \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m18\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m batch_dataset\u001b[38;5;241m.\u001b[39mbase\u001b[38;5;241m.\u001b[39mlengths)\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mall\u001b[39m(x \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m18\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m batch_dataset\u001b[38;5;241m.\u001b[39msource\u001b[38;5;241m.\u001b[39mlengths)\n\u001b[1;32m    707\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m heads \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "block_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    positions=[13, 14, 15, 16, 17],\n",
+    "    layers=[i for i in range(12)],\n",
+    "    stream=\"block_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e1cb1c8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (640 x 480)>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# these lines are comment out since we have run the experiments.\n",
+    "# df = pd.DataFrame(block_out_data)\n",
+    "# df.to_csv(\"./tutorial_data/block_out_df.csv\")\n",
+    "df = pd.read_csv(\"./tutorial_data/block_out_df.csv\")\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"token position\"] = df[\"pos\"].astype(int)\n",
+    "df[\"IIA\"] = df[\"acc\"].astype(float)\n",
+    "df[\"token position\"] = df[\"token position\"].astype(\"category\")\n",
+    "\n",
+    "# Use format string to keep two decimal places\n",
+    "df[\"IIA_label\"] = df[\"IIA\"].apply(lambda x: f\"{x:.2f}\")\n",
+    "\n",
+    "block_out_plot = (\n",
+    "    ggplot(df, aes(x=\"token position\", y=\"layer\"))\n",
+    "    + geom_tile(aes(fill=\"IIA\"))\n",
+    "    + scale_fill_cmap(\"Purples\", limits=[0, 1])\n",
+    "    + geom_text(aes(label=\"IIA_label\"), color=\"black\", size=10)\n",
+    "    + ggtitle(\"Main Residual Streams\")\n",
+    ")\n",
+    "ggsave(block_out_plot, filename=\"./tutorial_data/block_out_plot.pdf\", dpi=200)\n",
+    "block_out_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "670a1917",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mlp_activation_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    positions=[13, 14, 15, 16, 17],\n",
+    "    layers=[i for i in range(12)],\n",
+    "    stream=\"mlp_activation\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fa8f8add",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (640 x 480)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df = pd.DataFrame(mlp_activation_data)\n",
+    "# df.to_csv(\"./tutorial_data/mlp_activation_df.csv\")\n",
+    "df = pd.read_csv(\"./tutorial_data/mlp_activation_df.csv\")\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"token position\"] = df[\"pos\"].astype(int)\n",
+    "df[\"IIA\"] = df[\"acc\"].astype(float)\n",
+    "df[\"token position\"] = df[\"token position\"].astype(\"category\")\n",
+    "\n",
+    "# Use format string to keep two decimal places\n",
+    "df[\"IIA_label\"] = df[\"IIA\"].apply(lambda x: f\"{x:.2f}\")\n",
+    "\n",
+    "mlp_activaiton_plot = (\n",
+    "    ggplot(df, aes(x=\"token position\", y=\"layer\"))\n",
+    "    + geom_tile(aes(fill=\"IIA\"))\n",
+    "    + scale_fill_cmap(\"Greens\", limits=[0, 1])\n",
+    "    + geom_text(aes(label=\"IIA_label\"), color=\"black\", size=10)\n",
+    "    + ggtitle(\"MLP Activations\")\n",
+    ")\n",
+    "ggsave(mlp_activaiton_plot, filename=\"./tutorial_data/mlp_activaiton_plot.pdf\", dpi=200)\n",
+    "mlp_activaiton_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e131c4f4",
+   "metadata": {},
+   "source": [
+    "### Localizing in across multiple tokens at layer 7, one layer earlier than layer 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b442fe48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block_out_across_tokens_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]],\n",
+    "    [7],\n",
+    "    \"block_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "617bce83",
+   "metadata": {},
+   "source": [
+    "### Localizing in different streams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b106423",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attn_input_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"attention_input\", debug=True\n",
+    ")\n",
+    "block_input_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"block_input\", debug=True\n",
+    ")\n",
+    "mlp_input_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"mlp_input\", debug=True\n",
+    ")\n",
+    "mlp_act_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"mlp_activation\", debug=True\n",
+    ")\n",
+    "attn_out_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"attention_output\", debug=True\n",
+    ")\n",
+    "mlp_out_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"mlp_output\", debug=True\n",
+    ")\n",
+    "attn_value_out_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"attention_value_output\", debug=True\n",
+    ")\n",
+    "block_output_data = find_variable_at(\n",
+    "    gpt2, tokenizer, [17], [i for i in range(12)], \"block_output\", debug=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "723d6862",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_streams_df = pd.read_csv(\"./tutorial_data/all_streams_df.csv\")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin(\n",
+    "        {\n",
+    "            \"block_output\",\n",
+    "            \"attention_input\",\n",
+    "            \"attention_output\",\n",
+    "            \"attention_value_output\",\n",
+    "        }\n",
+    "    )\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"block_output\": \"Block Output\",\n",
+    "    \"attention_input\": \"Attention Input\",\n",
+    "    \"attention_output\": \"Attention Output\\n(After Head Mixing)\",\n",
+    "    \"attention_value_output\": \"Attention Value Output\\n(Before Head Mixing)\",\n",
+    "}\n",
+    "df.loc[:, \"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df.loc[:, \"IIA_formatted\"] = df[\"IIA\"].map(custom_format)\n",
+    "other_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Name Position w/ A Single DAS Direction\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    other_locations_plot, filename=\"./tutorial_data/other_locations_plot.pdf\", dpi=200\n",
+    ")\n",
+    "other_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0660c1d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all_streams_df = pd.DataFrame(\n",
+    "#     block_output_data+attn_out_data+\n",
+    "#     mlp_out_data+attn_value_out_data+\n",
+    "#     mlp_act_data+mlp_input_data+\n",
+    "#     block_input_data+attn_input_data\n",
+    "# )\n",
+    "# all_streams_df.to_csv(\"./tutorial_data/all_streams_df.csv\")\n",
+    "all_streams_df = pd.read_csv(\"./tutorial_data/all_streams_df.csv\")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin({\"mlp_output\", \"mlp_input\", \"mlp_activation\"})\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"mlp_output\": \"MLP Output\",\n",
+    "    \"mlp_input\": \"MLP Input\",\n",
+    "    \"mlp_activation\": \"MLP Activations\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "all_mlp_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Name Position w/ A Single DAS Direction\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    all_mlp_locations_plot,\n",
+    "    filename=\"./tutorial_data/all_mlp_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "all_mlp_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec1e2720",
+   "metadata": {},
+   "source": [
+    "### Localizing in each individual head at layer 7 and 8\n",
+    "We further investigate whether any heads in the 7th and 8th layers specialize in name position information. Note that we consider each head individually in this analysis. Therefore, if the name position information is distributed across tokens, it would not be detectable in this context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed8bc7ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_attn_value_out_data = []\n",
+    "for h in range(12):\n",
+    "    _head_attn_value_out_data = find_variable_at(\n",
+    "        gpt2, tokenizer, [17], [7, 8], \"head_attention_value_output\", [h]\n",
+    "    )\n",
+    "    head_attn_value_out_data.extend(_head_attn_value_out_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ff1ce53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(head_attn_value_out_data)\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"acc\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 1)\n",
+    ")\n",
+    "\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0484e997",
+   "metadata": {},
+   "source": [
+    "### Localizing with group of heads using rankings of missing out head IIA\n",
+    "When examining each individual head, localizing the name position information was not possible. Why is this? The information is likely distributed across multiple heads. To further investigate, let's perform an additional check by localizing in the concatenated head representations, leaving only one head out at a time. We leave one head out to determine if omitting specific heads results in significant drops in IIA."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efae847a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = 7\n",
+    "\n",
+    "head_attn_value_out_mo_data = []\n",
+    "for i in range(12):\n",
+    "    print(\"evaluating grouped IIA without head\", i)\n",
+    "    _head_attn_value_out_mo_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_attention_value_output\",\n",
+    "        heads=list(set([i for i in range(12)]) - {i}),\n",
+    "    )[0]\n",
+    "    _head_attn_value_out_mo_data[\"mo_head\"] = i\n",
+    "    head_attn_value_out_mo_data += [_head_attn_value_out_mo_data]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "565aed2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_rank_list = []\n",
+    "anchor_acc = 0.48\n",
+    "for data in head_attn_value_out_mo_data:\n",
+    "    head_rank_list += [(data[\"mo_head\"], anchor_acc - data[\"acc\"])]\n",
+    "head_rank_list = sorted(head_rank_list, key=lambda x: x[1], reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd2ec29b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(head_attn_value_out_mo_data)\n",
+    "df[\"mo_head_cat\"] = pd.Categorical(\n",
+    "    df[\"mo_head\"], categories=df[\"mo_head\"].unique(), ordered=True\n",
+    ")\n",
+    "head_drop_plot = (\n",
+    "    ggplot(df, aes(x=\"mo_head_cat\", y=\"acc\", fill=\"mo_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Leave-One-Out (LOO) Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"head {i}\" for i in df[\"mo_head\"]])\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_drop_plot,\n",
+    "    filename=f\"./tutorial_data/layer_{layer}_head_drop_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_drop_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df643ee6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_attn_value_out_cumulative_data = []\n",
+    "current_heads = []\n",
+    "for i in head_rank_list:\n",
+    "    current_heads += [i[0]]\n",
+    "    print(\"evaluating grouped IIA adding head\", i[0])\n",
+    "    _head_attn_value_out_cumulative_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_attention_value_output\",\n",
+    "        heads=current_heads,\n",
+    "    )[0]\n",
+    "    _head_attn_value_out_cumulative_data[\"adding_head\"] = i[0]\n",
+    "    head_attn_value_out_cumulative_data += [_head_attn_value_out_cumulative_data]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "649efc83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(head_attn_value_out_cumulative_data)\n",
+    "df[\"adding_head_cat\"] = pd.Categorical(\n",
+    "    df[\"adding_head\"], categories=df[\"adding_head\"].unique(), ordered=True\n",
+    ")\n",
+    "\n",
+    "head_acc_plot = (\n",
+    "    ggplot(df, aes(x=\"adding_head_cat\", y=\"acc\", fill=\"adding_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Cumulative Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"+ head {i}\" for i in df[\"adding_head\"]])\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_acc_plot, filename=f\"./tutorial_data/layer_{layer}_head_acc_plot.pdf\", dpi=200\n",
+    ")\n",
+    "head_acc_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a30c7613",
+   "metadata": {},
+   "source": [
+    "### Localizing the correct IO name with vanilla causal abstraction (no training)\n",
+    "We can further localize the correct IO name in the output streams. We use vanilla interchange intervention, so there is no training just activation swap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b2cf193e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attn_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_input\",\n",
+    "    aligning_variable=\"name\",  # now we are localizing the IO name\n",
+    "    do_vanilla_intervention=True,  # we avoid learning DAS for this since outspace is large here.\n",
+    "    debug=False,\n",
+    ")\n",
+    "block_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_input\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")\n",
+    "mlp_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_input\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")\n",
+    "mlp_act_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_activation\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")\n",
+    "attn_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")\n",
+    "mlp_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")\n",
+    "attn_value_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_value_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")\n",
+    "block_output_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_vanilla_intervention=True,\n",
+    "    debug=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ebe8298c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_streams_df = pd.DataFrame(\n",
+    "    block_output_data\n",
+    "    + attn_out_data\n",
+    "    + mlp_out_data\n",
+    "    + attn_value_out_data\n",
+    "    + mlp_act_data\n",
+    "    + mlp_input_data\n",
+    "    + block_input_data\n",
+    "    + attn_input_data\n",
+    ")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin(\n",
+    "        {\n",
+    "            \"block_output\",\n",
+    "            \"attention_input\",\n",
+    "            \"attention_output\",\n",
+    "            \"attention_value_output\",\n",
+    "        }\n",
+    "    )\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"block_output\": \"Block Output\",\n",
+    "    \"attention_input\": \"Attention Input\",\n",
+    "    \"attention_output\": \"Attention Output\\n(After Head Mixing)\",\n",
+    "    \"attention_value_output\": \"Attention Value Output\\n(Before Head Mixing)\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "other_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 1)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Correct IO Name (20) w/ Interchange Intervention (Original Basis)\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    other_locations_plot,\n",
+    "    filename=\"./tutorial_data/IO_name_other_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "other_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c4a2be4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin({\"mlp_output\", \"mlp_input\", \"mlp_activation\"})\n",
+    "]\n",
+    "stream_labels = {\n",
+    "    \"mlp_output\": \"MLP Output\",\n",
+    "    \"mlp_input\": \"MLP Input\",\n",
+    "    \"mlp_activation\": \"MLP Activations\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "all_mlp_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Correct IO Name (20) w/ Interchange Intervention (Original Basis)\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    all_mlp_locations_plot,\n",
+    "    filename=\"./tutorial_data/IO_name_all_mlp_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "all_mlp_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bb2269d",
+   "metadata": {},
+   "source": [
+    "### Localizing the correct IO name with DAS (20 names only with 20 dimension DAS)\n",
+    "We now localize the IO name with DAS. To make things easier, we restrict the dataset to contain 20 distinct names. Training and evaluation share the same set of names. We use a fixed dimension size of 20 for DAS training. Everything else is kept the same as previous experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a681eed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attn_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_input\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",  # now we are localizing the IO name\n",
+    "    debug=False,\n",
+    ")\n",
+    "block_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_input\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")\n",
+    "mlp_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_input\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")\n",
+    "mlp_act_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_activation\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")\n",
+    "attn_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_output\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")\n",
+    "mlp_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_output\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")\n",
+    "attn_value_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_value_output\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")\n",
+    "block_output_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_output\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    debug=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6c1128f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZEe/vZNwuPbm0/s+U0pXTcAzImcXqNm7VqMhwd5/Srl92OH653X07PL8sL63vHBF7dnisTsdcRC7V2crShu355Gw7rexLDtiDHIDwu3YTKc4B9QxPidmTDeUYcnaXuhdGxLYd9l1WWv9mSikV698o7R8psgxNpvFzuH7TVjOr/H08JXWQ+Wa6RMSWwB+ALzLxO9loCBiNiGqRHbCdQ8mBvI9jPGtj08MDjyWfg4/oeNL1zj38hin1fQg56PU5NM+yG+QsZb/r9HNbZEQ9nxwgvmWrZsBjgFMj4hVdzPUcctnsZp/5DYCXAKdFRFfvwwwpf15unaTth4D3kM+p7b7f6wGHAX8tSjy3VXoPX0T7zyTk3yXPYTwLaE8iYlfgTOA1tP79X7ch8BTaXxvqTiEHDdb7HdjbDCVJkiRJkiRJ6zJLEKpfDiTfsKj70nQdqCjlczLjJbkA/gl8l5xJYBH55uOTyDea5gH/GxGrUkrHdniY3cjBTpuSg3O+T77hcyv5huRBbfr+H+M3gv9CvnF/GbAJ8BByloDy6/kwE8uE3VX0OYVcnm0L8r/Mr5eO2hn4TUTsndqUGYqI+5GDDLYq7b6efGP1rGLszcgljJYWj42uImc5WgDsUtp/MTnjTKPLWs2nnyJia2Cf0q6TUo8lFFNKt0XEt4H/KnZtTw7a6aqcVhfeynigyY7k8l8f6fMxVpL/9mcAV5M/U9sA+zGxrNobyH/Lj3cx9ksZ/7z+BTiRXGJoMTn4q37TdkPgGxGxO7k83cuL/acXfS4jf1eHS302A46LiIekNqUZi0Cj35EzrdSdT87+9S9yQN4ewPNKbY6MiFtSSuUyqVNVPg/8vo/jNrpXaf2qNu0ab2y3LAXZ4I8N2w8kv48tpZRqEXEl4+/vQcC5HR6v1ZhXRsQ/ySWsIJ+XWn0Pl7bY96MO2p+ZUrpxkuk8mvEyWqenKZRN66eU0qqI+Aw5mALyuflAcraulopgyPuWdn29Yf1dxfoO5L/lzyeZynXkwLR6AOdBERGloK5BmKnvY1sRsUVx/PuVdl8GnACcR84yNEQORqkHsb6I/Nvgvzs8zIriGGeRyz2vIV+7ljIeODIP+GhE/DulNNbFS+jqN0zJNuRz8Lbk300/IF+DbiWfw15IzqgI+fV/BnhGu4lExL7kANFNSrv/CvwEuIT8+d+LHNS1mPyaPx0RN6U2ZaIjYifgZ0z8HXk+OUPTxcVY9d9d9yUHKXYdrDxdSqUY687povvfyef8f5B/E65H/hw+hXzdhPy+/DAiHpRSuqTFHDYi/43L7+Fvyb9vLiMHM21G/lvvU8x3Sv/wpwgYP56Jv0dPJ39GLiJnOdyseP6h5PLkHf23bvFb8AzgEcWug2h9PZEkSZIkSZIkqblBp+BymRsL8DEmlv3oqgxMF8eZR84qUT7Wu4B5Tdo+mnxjst7uDuABbcZezj3Ll/wC2HqSOS1t0u8O4PkdvJ7hhn6/o0XpF/LNwHK5ldE2465PvklZHvvTFKV0WvR5CPAdYOcmz+3aMNau0/hZKh/nuBZtntHQ7o1TPOZzGsY7ok+v5aLSmJ8t7f9Zaf81wKYt+je+7+1KED61+G4M06b0Fvlm8lmlMW8DtmjTvnEOa8g3Vpe1aP/WhvafIAfr3QW8uEWfxjKQh7SZzwLgz6W2d5KDFpqdAzYFRktt7wIe2Ke/bTR8H187Td+HHZhYMurbbdoe2fA+7tLhMRr/xm/tsN/PS31ano+6fL2fLY35ozbtzivalN+bv7Rpv7zU7sMdzOP/Su3f2afXtqzhfV7a4zj7Nozz2Q76fKHU/qwmz/+x278lObimPI+PAgv68V718J5s0TCX4S76XlTqt7wPc/leabw1wDuB9Zq0W694z8rzflKbcV9FDjw6sNn5rtTuEeQgovqYl7X7uzC13zDl71X9u/gTYKsmbTcgB9+Wj9PyfEwOpLmw1PZG4Bkt2m5NDsyvt10JbNdm7BMa5vFBmpQ8JQdg3dzw+hIDLkEIvKlh/q+YpP03gE/R5jdw0e7FTCzV2u5685JSu9uAx00y9rbkMogHtHh+WWm8i1q0eWzDd+vQSY65mJwt9Tkdvq+fK43/p379jV1cXFxcXFxcXFxcXFxcXFxcXFxcXNadxRKE6pdyNqK76O5f43fjEHIWn7pjU0rvTimtaWyYUvpd0b7+3PpAN2XI/ka+iXt1D/P8r5TSZBlJFpBviNX9BXh8SuniZu1TSieRA4XqnhcRD2gx/OHkLE51H0wpHZ5SuqlFe1JKZ6SUnpNaZDuYZR7esH36FMdr7L9P01b9Uw+UgVxS6f+1adupk1JKj0opjaU2pbdSSueTbypfU+zakImlySYT5GCj41o8fww5+K/u1eSMW69OKX21RZ+jyRnm6p7X5vgvY+Lf/wUppc+1OAfcTM66Ui83t5AcDNEP92Zi+aO/92ncRq9nYtaQ49u0LWfKWkPOTNaJyxk/T0Lr0pKN/lZaf0iHfSazvLS+f7OybEXpsnp2oTPJmVwA9o6IRU3ab0AOWmp2jFaeVlo/sYP2M+mv5OtsXduynUWmmueWdn29SbOvldafHhGLO5jHVxq2jwBqEfGeiHhkRMxkWcLG92C6vo9tFaWLn1na9caU0ntSSnc1tk0p3ZVSej25nGtdu1K6X0opPTmldHKz811p3D+TM4DWj7kD8PSOX0Q26W+YJuYBpwFPTyld0/hkSukOcoBPOfvcC9qMdyTjGbPuIgen/aBZw+J32lOBWrFrc3KGx3sosmqV34+vp5TenJpkXUwp/YJc+hGmmL1pKiJiQURsExEHR8R3yAFjdb8jl7ps56UppVellP7WrlFxjT68tOsZbUpFLi2tfzal9KtJxr4ypfTelNJvJplrO+VjnpBSanYuKx/zhpTSx1NK3+lw/PL7s1cHZUElSZIkSZIkSZrAACz1yzal9cub3Wzsk1eU1q8m/2v6llJKvweOK+16akTs2OGx3lTcMOzWaSmlr03ejGeTS9BBDsY5LKV0Z7sOxc3AcjDAyxvbFDeMXlfadQ7w9g7mszbZumG71rRV5y5kYgDKNq0a9kNK6Qxyycy6N0TEllMcs+PPakrpKuCTpV1P7OJQ56SUPtNm7ETOtlF2dkrp8130aQywA/5Tfqj82f5OSul77SZb3FQ/orTr6UUJy6m6d8N2TyUw24mIhzLx9Z5NLofaSjkg7OaUUrMyofdQBOzdWtq1aYdTLL/mXYuyWFO1vLS+iObBRUsb2tf7zCdnPmy0LzkAF3IWm981afMfRfnW+xSbl6WU/tqu/UwrrhPlkohbTNLlWYx/NtYwMeCn7tvkzHaQMxVVOpjK57hnmctdyNflU4CbIuLUiPhURDw/IrbrYMxeNX4fZ6QcbhNHlNZPSyl1Ul72DYy/9w+LiKYBdV2e4//BxHNqN+f4Tn/DNPOaSQKAV5IzhNW1OtdvxMTfNx9LKTWWSm0c+1Zyudu6ZRGxsEnTl5TWbwPeOMm4PyRnQ50JB0REalzIn48rydnFnl20vY2cffbgyc71Xf6W/QrjJWgXkrNONVMOzLqgi/GnYrqPWb6mbUAu6ylJkiRJkiRJUscMwFK/LCmt39iy1RRExIbk0jt130wp3dJB13KwyHw6uxF5Dbm8Vi86vXFZvsH9q5TSuT2M/7gmzz+MfBO87thOAzHWIksatqf0mSsCgMrZwRrHnw7vIAeDQA54eesMHLOsnK3ioV30a5txotAYsNJJnzNK6/dqceN8L+D+pe2PdTBuPeCtnhFnIfCYTvpNYqeG7SuatupRkYHo2+SSi5A/K68qPqutbFJa7zZ49PYW47SzorS+EJhygE1K6Urgn6VdBzZptrS0fjITg7Yma39mSmmy80U5+9WPJmk7KDeU1ic7Xy0rrS9PKd0jM1pK6Trgp6Vdh002gSLQ+ikN/crWI2cTPJwc9HV5RPwqIg6ZbOwelL+P1/cYPD0lEbE5ObtgXafnp6uAX5Z2Nbuu96LXc3yvwVfnFdm3JlMO2rtfizZPIGexqvt4h3P4IePX8i3J14xGTy2t/6h4/yfzhQ6PP1OuJ2d0fVcReNY3xTXm5NKuVp+d20rr+7Zo02/TfcwVDds7T8MxJEmSJEmSJElzmAFY6pdyxpROgqJ68RDGgxEAftZJp5TSXxgvtwYtMi40OLVZOZoOtc3SUFIupdjRaymcWlrfPSIagyX2b9g+oYux1xaNGXr6cQOylwxAPUsp/ZOJ2dkO7yI7Wz+UbzovKcq0deLUyZvQeEO72z5Bzn7U6FGl9Rvp/LvWOId+lJhc3LDdt/NeEXz2HSZm9flAkdGvnfLfsNsshOXsext22KfxNTf7m/VieWl9aZPn6/vq2aw6bd84ditrQwBW+b1veb6KiF2ZGJTWLhiyHHjz8IjYfbJJpJRWppSeTC7p9lvGS6s2nQ45m84PIuKkqWb9a7C4tD5dv0Emsx/5Ndb1el3vVwnc8jl1hy76dXNeLesk+AomBrksbtGmfK4/N6XUUUaz4ndbOZh3wnsZETswMVC0HPjWTqftpuoOcvapxuXf5N+x9e/X5uQArIsiol3J3l518tk5s7Q+EhFHFv9QYjqVj/mYiPh4h+VSOzVd1zRJkiRJkiRJ0jrCACz1y82l9Y2n6Rj3adg+p4u+Z7cZp5l/Td6k975FKaatSrv+0cX45Rtj87hn1pnyTfOLipI/c83NDdv9+MyVx2gcf7q8m/FMRRsA75rqgBExLyIeGxGfiIjfR8SKiLi1SUmjxvI9nd5ovLKDNo0BcZ1kGGns0+xv+qDS+vkppTVN2rRSnkM/At3KN5pXdTmXlooyi18BHl/aPQYc1UH3ctaf9bo89Pql9dtbtpqosd1GXR6zleWl9f2LsqoARMS2jGfNOTOldGNK6WrGz6F7R8SiUvsNmJgppTz2PRRZjOrBsbcxMYvQbFIOurqpZSt4MeNBQbczsfxboxOZmE1w0ixYdSmlsZTSAcCuwH8BVeBvjGf5a/Q44A/lv9UUlb+PM579qlA+P11TZBXrVMfnp4hYGBFPi4gvFCUer4qI25uc48tBQ928z73+/unk2gATz/Wtrt3l97Kb30fQ/r28b8P23zoZsMia1/cys038OaU01GS5d0ppa3K2u+cz/p4sBr4VEYd2MnhELI6Il0XEtyLi3Ii4NiLuavLZeVupW6vPznFMDPR9P3BFRHwjIl4SEUNdvO5OfQ8of69eA6yIiO9HxOER8YDiGtqr6bqmSZIkSZIkSZLWEQZgqV/KQT6Lp+kYmzdsX9O0VXPlto3jNDOVAJxO+m7RsP3jxhtgrRbueYNocZuxO70hurZpDCpbPJXBiht2m7UZf1qklC4FPl3atSwiGm8QdywiHk7O/vEr4NXkLCLb0dlNxE4zYPUS3NBLn2Y3Ucuf7X06/c4U35s3l/ou7mE+jVaV1ue3bNW9jwMvLG0vB57XYUa+cvaOTv+edeUAlk4zCDWWiVzVtFX3lpfWNyNnP6xbWlovl8mq95nHxBKT+zIeXFbPmNXOwYxnWjxpEKXsOrS4tN70fFWc115c2nViSqllsFZK6U5y5rW6Q8vBb51IKV2SUvpiSunFKaU9yYFijyOX42uc533pvLTcZMqfvQUtW02v8vlpqy7PT+VSyYtbHSAinkQu0TkGvIyc4WlrJv++d3M+6PX3Tz+/K+X38jldvpfljFCLG8Zt3O71d+RApJRuSCmNkktNn1bsDuAzEbF9q36RvYEcRPYFcgnsB5Df52blfsuafnZSSheRgy3LJa4XAS8AvgRcEBGXRsQXI2LpJMfoSErpZnIAWrkU4YbAM4BPAecCVxVBYE/t9vzF9F3TJEmSJEmSJEnrCAOw1C/ljAPbFyW0+q2cKeHulFI3N0bKGRcaS/Y1c/fkTZpLKXXSt59lTRqDa2aiHOSgXd2wfe+mrTp3LyaeDzvJ2NQvH2D8hvcC4L29DFLc4FwO7NXk6ZvJZZ/+zXhJo4sbh+jluDOsX9+bfmS1KJ9T5kVEtxmn7iEi3k8OnKs7DRjuIgioHFyzSUR0FIhStOslA1xjual+lAIlpXQlOcikbmmL9eUt1lu1P7PIZNNOufzgiZO0HYiIWB/YtrTr2hZNDyCf2+ralR+sK5ch3A54UnezmyildHtK6dcppSOA3YBvNTR5YUTsPJVjFMqfvW6DD/tlWs9PEfF84MdM/JvW3QhczsSydSuatJtUh79hptt0vZeNGbc6zfYHE4N+BiqldBsTrxWbAIe36fIp4P+45+/fRD5/XMrEz871pTYtfxuklKrkste/adFkR+ClwMkR8aeI2LPNHDuSUvol8FByEGKzzJNbkYPATgTOiYjHNGnTyrRc0yRJkiRJkiRJ645BZQnQ3HMa8MhifX3ggeRMPP1UDiZaEBELuwjCKt90mw1BSY038i6n9+wRjf3KwROdBJutjU5r2H4oE7PhdOuhk4w/bVJK10bE/zFeYu45EXFMSumvnY4RERsCX2X85uFdwOeA7wNnNMt6ExH3IgdkrU3K35vb6THAYAr9yhqzy21F/h73JCLeAhxZ2nUu8KQi40enLiytzwd24J6Bds3syMQAxE4/F1s1bPcz495yxksNHgh8qFhfWjw2ZrNaXlo/sLS+tEWbeygC0eoBR4kc7DIbPZSJJSZbXWsbSwiO9VCdaxl9eh9SSjcWpdJ2BB5d7J4PPAH44hSHL3/2toiISCmlKY7ZrfL5aRVwSY/jXNa4oyi9+TnGv6c3AZ8kB5mcXQTkNPY5EPh1j3MYtPLruYnes081Bms3BtQ0Bty0M6vK0aWUTo2Iq4Btil1PBN7e2C4ingK8srTr3+SMdCcBFzT7HR0R7wbe2eE8/gwsLbJ3Ppl8/n0U98z0+gjgTxFxQErp9E7GbnPM84CnR8SOwFOLYz6a5iW5fxURh6SUOjmPTec1TZIkSZIkSZK0DjAAS/3yW+C1pe0D6X8A1vUN21vReSBF+aZK4ziDcF3D9otTSr+ahrG3bdlq7fb7hu0nAB+ewngHNWz/YQpj9eIj5GwWW5KzTRxNLoXWqUOAehaZNcCTO/g8Le5uirNC+bN9ekrp0S1bTr/GwKYd6DEAKyJeQ86EVncBcFBKqdtSmOc1bN+bzgKwGjPINY7TSrnk1cqUUj+DW5cD/12s71+UktqK8aCsCYGFKaWrI+LvwB7AXhGxmBycum/DmO3sz/j34vSU0hVTmP90ekLD9j2yz0TEpsCz+3Cs4YhY0sNnsamU0pqIOJbxACyA+/dh6PLnfD3yZ6Ux+Ga6lc9PV6WUhvo49mGMZ7e8DXhUSuncSfos7uPxZ1r5vRxLKb2oT+Pe0LC9FVDrsG9jcM5scDHjAVityheXf5ufS/7stCxFWljc7URSSucD5wPHFuVPH0wuDfhSxgOjNiaXQHxI00G6P+ZlwGeLhYjYHXg6uTxn/bq2APhCROzWQTbJxjKOnVw/JUmSJEmSJEn6D0sQql9OZmIpl5dOwzEab5I9qIu+5bYX9GEuU7WCiZm4dujj2H8vre8aEUv6OPaskFK6GihnUHhcROzUy1gRsRHwvNKuK4COs0/1Q5Hl6OjSrid1WTbnsaX1X3QYzNesjNVsVy5L18/vTC/+zsTyR/fpZZCIOIycjaTuEuDxRRm+bp3dsP3Ipq3uqbHdOR32K7/myYJBurW8tL4pOevT0hbPN+6bBzyGHHy1frGvMWNWM2tD+cH1GA9Mg5xpaXmTps9lYsaef3W5rC76rUcu59VPf2/Y3qwPYzZ+/nr6Pk5R+fy0VZ9LMZfP8dUOgq9g7TzH103Xuf78hu0HdNIpIhYBPf3GmGbllHb3KL0ZEfOYeN58XwfBVzDFz07KzkgpvYP8XVxeevrBRaBU36WU/pFSOoac+eqbpae2Y2JmxFbK541LusxAKUmSJEmSJEmSAVjqjyI7xldLu3aPiH5k3yg7A7i7tP3ETjpFxEOZmLngz/2cVC9SSnczMcvSAX0cvjHI4JA+jNlYomY2nDs+XlqfB7yjx3Fez8QAgE8Vf5+Z9mng0tL20a0aNlHO2nBWh306uRk525Qz/dyr16C7fiiyPZVv5u/Z7RgR8Vxy6bX6TfQrycFXPZUuSynVmBio2pjZrZVyuwuKcTpRfs1TKinVqAhAKwdhLKXzAKxm7c9MKd04yWFnfQAW8GYmZjasFgGpjZaV1s9MKQ11s5DLkzUbqx/Wb9juR3at85hYXq7r72MflM9P6zMx+9pUrSvn+Lrye/mIiGj8zPQkpXQ5Oci6rpdz5GyyS2n92ibPb8HEcqWTfnaKIM9HTXFe/5FSuhU4omH3tARglY65CngVuZRsN8ectmuaJEmSJEmSJGndMBuCKDR3/B8TA3U+HRHbtGrcTkRs2RjAlVK6Hfh1adcLImKTDoZ7RWl9NfDzXuY0Db5TWn9eRGzZp3FPB/5d2j4iIqZabrSxtFg/MpZM1SgTA5ZeFhFLuxkgIu4PvK2062bgc1OeWQ9SSncC7y7tehTwlA67t82CcY/GOZtHv0o6zaTTgItK268e0Dzqfltaf1g3HSPiKcDXGb8OX0cOvppqhr4TSuuPiYi2mYCK58vZ1n7YyUGKzHF7lHbdowxeHywvrS9lPKDqbppns2rVvvG5e4iI+zGe/eSylNKMZsHrREQ8HTiqtOtuJpaurLe7D7mcYt23ezhcuc9DI+KBPYzRSmNwx7+mOmBKaQ0Tg5q7+j72QxE0WC6P28/zU7fn+N3oroztbPMz8vUYcia3l/Rx7B+V1p8aEVt30Gc6srpOSUQ8EijPvTEDIkz83EAHnx3g+UC/M6c2Zh6b6m/SSaWUbgCu6fKY+5TWp+OaJkmSJEmSJEma4wzAUt8UWVPeWtq1FfCbiNi5m3GKm0qnM/EGcl05OGZr4L0djFW+cXdikQFhNvgaUJ/LxsBXI2J+NwNExD1uphU3osslzR7IJO/TZIrMMTeUdg0iu8gEKaW7yJlZ6hkOAjghIh7SSf+I2AX4BbBhaferU0rNskjMlOOYmPXn7R32KweiPakoO9TOp4BFXcxrVkgprQY+XNp1RER0lT2u2XdmCn5WWt+v05JjRaDgd4F6+5uAJ6aU/taHOX2W8UDYYOL71cz/ldZXAZ/p8Dj7MT7/VUwMju2X5aX1pcD9ivUzmpWGSildw3h5u72YmIFoeWP7BuXsVz/uZpLTLSI2iYj3Az9g4u+216SUmgUvLWvY7iUA6wfAXa3GjIj5EfGViOiqVFlEbA68pWH3T3uYXzPl7+PSPo3ZrWNK68+NiOd307l4X5sFipTP8W0Dc4vz0JeBrn5PzCZF8Ez5997R3Zata3Ou/3JpfSPgfycZ56nAk7o59nSLiI2BTzTs/kGTptcBt5W2J/vsbA98qMM57DJ5q/9oDOC8uIu+PR0zIrYjZwDr6JgRMcTETHOz5R9rSJIkSZIkSZLWIgZgqd8+wsQMLPcD/hoRr5+shExE7BUR3wFOAVoFbZ1QPF93RES8o1nASUQ8ipzNpf7cnXQe0DLtigCiVzIeQPRk4FcRcd92/SJig4h4ekT8nFyOqpnPkks21r0lIj4VES0zVxXv/7fbBMydWlr/n4jYtd08Z0JK6ddMzACzCPhdRLyxVTBMZC8iB/mVS9h9I6VUnb7ZTq4IMCqXUty2VdsGvyyt3w/4SLNgvojYLCK+ArwQWNPzRAfr88CfivX1gJ9GxKsmC36KiPtExFFAT+X9WvgF42XPNqJ50GjjPPYBxhjPRHIr8OSUUl/KHRUBOeUAg+GI+GBETMiEUnwP/peJgUdfSimVs+e1Uy7J9atmAVF9sLy0vmGL/a36zGO81N1qmmfMKntqaX3g5QcjYpuIGI6IY4HLgCOZmM3mQymlzzbpNw8YKe06NaV0YbfHLwJgygEIhzYEBgU5KOufEfHNiHjCZIGfRTng3zLxvPutlNJl3c6vhRNK60PdBof1Q0rpx8D3Sru+FhHvKgJmWoqIHSPi/5Gzge3YpEn5HP/4om2zcbYh/+45gLX3HF/3Psazoy0Gfh8Rz+vgc7ZXRHyEib9Z/iOl9CcmZvp7cUQc3eKa+XjgG8XmwN/PiFhcBPWdDjy09NT5wFca2xe/KU4u7Xprq6DliNib/P3cis5e68kR8YOIeGK7fzwQETuQr9t1V5CzWfbiuIj4dUQ8s91/U0TEYvI/cqjP607y9bqd8jXtvJTSeT3OUZIkSZIkSZK0Dpv2EhBat6SUUkQ8l3yzZVmxewk5MOu9EfFr8o2ja8g3RLYhB1s9AZj0ZmlKaU1EHEYOwNi82P0e4PkR8V3yv3BfBBxILr9Tvin0lj5lmOmblNKJRVBIvfTcAcB5EfEbcimjy4E7yK9pR2Bv4JHkjFkAf2wx7l0RUSnGqJeoORyoRMSPgDOB68mlBO9bHLee1apVUNdx5L8TwIOAf0fEpeTMWPUgshUppSdP+sL7KKX0tiK25Mhi10bkDA5HRsQY8A/y521z4N7A05mY5QCgyuwpMfRdcvBcR5m8CieQb8DWg/deBxxUfCcuIr8nDwSeBdRLXR5F/u6sVVJKqyLiOeRyYzuTA3M+CbwtIn4GnEP+bK9PPvfsQS4rdL/mI05pLrdGxA+BFxS7DmHize5mjgE2LQ9Dzn7XzaFfmFL6c5vn3wQ8mvESgW8il9r6NrAC2AGoAPcv9fkbrb/7zTyztP71Lvp1LKV0ZUT8k3v+7dq9x8vJ57qyM4ssfk0VWZnqZfFuA37V5VR79Y2IuL08FfJnYxE5uLCZm4D/SSl9vsXzj2diAE8v2a/KfesBeluTg4THGtosJJcsez6wMiL+RD5/XV3MdSNgN3KZy4c39L0IaBpI1IuU0oURcQo5Oxvk7+NHuxzmERFR67LPn1NKLyxtvwQYImdhm08+176uOD+dAaws9m9O/mw/tGjbzhfJGUbrpeE+XPzWGiP/TtisGOcZ5M/QanIA07u6fC2zRkrpxoh4Fvn7vjn5tY+Ss2H9gnxtv4l8DdiS/BvmEUA9S1K7jEevIX9Otiq23wo8MyKOL/otJn+Xnkj+Xv6GHNT56D69vFZaff7q54YtuWdZwauBp6eUVt2jV/a/jGe+2hj4dUScSD5X3kB+Dw4kv9Z55GvEGBPLdzczj/wdOwS4NiL+wPh3/3Zy9qmHkX9zbVTq9+YiU2svgpzd7kDgpuKYp5ODum4l/90eRP4ebF7q9/6U0vWTjD3t1zRJkiRJkiRJ0txnAJb6rrgJdFhE/IV847Ee8LEx+Wbu01p0rVtD/pf8H2wx/vkR8VhyuaFtit27MzFz0IQu5Bs+x3b4EmZUSuk9EXEFuZzM+ozfYFraQffVbca9ICL2A37CeGDOEnJ2lJFW/dqM962IeDJwaLEryAEw5YxZi7sdtx+KIKwLyDca6zdUNwdePEnXW8g3qf83pZQmaTsjiiDGI5lYTmuyPncXQUm/YfxvsAfwzmbNya/5a6yFAVgAKaXLIuLh5JJLjyx2bwcc1kH3fmcx+RLjAVjPjog3FFlHWmnMFLJJsXRjw3ZPppRuioinkEu71YOs9mA80LPRecBTU0o3dXLwyGU+h4rN62le+qpfljMxAOtucmBpu/ad7Cs7mPHfQyellO7ocG5T1RgI2s5V5Mxmx6aUrm7TrvwdSMDxvUys8ENyIEX983YY4wFYiRwcXC7ztoQcpNVJEO6pwHNTSldMYX7NfInxAKzn0X0A1gbkQN1uTMjgVXz/9idnThoudm/OeKDaZO5xjkop3VAEVZ/IeGa3h3PPoDbIJUFfTQ7KXWsDsABSSmdFxL7kz139PLAbkwcHQfvfR5dGxMHASYxfM+9H89+R/yT/7pmJoJxuP38/AV6ZUmqZ2TGl9NuIeBfj5/955KCopzdpfg05EOngLuYA+Xd+qzH/MxXgbSmlr3U5diubkec52Vw/Tf7N01JEbM34b+67ga9OdXKSJEmSJEmSpHWTJQg1bVJKnyLfKHsb8FfGsyS1cik56Gr3lNLL2t2YTSmdSQ66+jjQqvTVGnLmhEeklD7U3exnVkrpC+Qgqc8DLTO1FK4FvkW+sfuBdg2LcmQPAv6H/P62cw45G8mKNuO9iBxANwpcQA5gmi2BS8eRg0LeQ75h2s4lwMeA+6SUPjhbgq/qUko/JwdTddPnbHKmp1+2aXYWOdCmWWDWWiWldBW55N8LyOeXdtaQSx69gw4y7XU5j1+TvzuQA2q6vXE9LVJKF5GzqP0fOUiqmeuL5x9StO9UOVvcF1JKt/Uyxw4tb9g+PaV0S6vGKaVryNm82o3RqBwU/KOOZ9Z/a8gBT1eQM8kcT75+7g9sn1I6sl3wVVF265DSrj9Mpbxf8T7/pLTrKRGxVfHcanKw6/PIwZydlPZM5PPaCPDIlFK7DEW9+iY5iARyNqE92zWeLimlW1JKTycHo/2OyQM/zyVnx9u9VTBNSumX5M9C09J6hT8Aj26TIW2tk1I6n/w75nByUFk7q8hl9N4A7DvJuKeTM0P+kOZ/nzvIQY8P72OZzKm4lZzt7FfA0cADU0pPaRd8VZdSeg85iKzV78A7yRnv9poku2LZq8iBSpdP0m4NuZzpfimltr9ZO/AO4DNAJ6VyTwGeklJ6VQe/8V7MeBDu92fJ31uSJEmSJEmStBaKWRZ3oDmsuHG7D7mU0Zbkmx03kG82n97rDY+IWI9cFma3YtxbizF/M0mmkFkpIhYwXjJtS3IpqpvJWTb+Dpzfa8BQRDyQXMZwa3KmhZuAC4EzUkotA6/WRhExRL65ug05M8uN5NI4/yyCleasiNiNfKN+O/IN6SuAs1JKfx/oxKZRRGxLznyzLTnbzJ3kUl8XAOeklG6YxmO/iFzGEuDHKaWnTtexelGcIw8AdiWfU64ll3/7TUrpri7H2oR8E38x+T0eWptvVhfn22vIrycBO0xDVqZ1QkRsT864thv5/dyAXNLxRvL38MxOs6xNcR7vYDy736dSSq+e7mNOpihzuT85SHMLcpadG4Aa+fx0TeveTcd7ADnAaGvye3wFcGqXgZRrpYjYlZz5a2vy5+w24Dpy4PU5KaVbexhzB3IGpB3IQZCXks+Pk5WtW6sU57t9ySUvF5GDcC8HfjuVa2RE7ELOsrgr+W8S5N+X/wJOSyldO5V5tzjmtuTfePciX/MXkH8rXwz8JaU0WWBYfZx55CyQ9yl2PTyldFq/5ytJkiRJkiRJWjcYgCVJ0hRExHxycOR9yUE8D0gp/WOws5oeEfEGctYsgE+mlF4zyPlMVUQsJWdKhHzTfp/BzUb9EBGbkQMMNycH5+ycUrpuoJOSNCtFxDOB7xWbP0opTVYmXZIkSZIkSZKklixBKEnSFBTl2N5RbAbwlgFOZ9pExPrA64vNW8hlsNZ25ZvtJw5sFuqbIsvWMcXmRsBrBzgdSbNb/Xq9Bnj7ICciSZIkSZIkSVr7GYAlSdIUpZSOB35bbB4aEXsMcj7T5HBgx2L9/XOkVF85AOtHA5uF+u1Ycnk/gNcXJZAl6T8i4hnkkt8AX0gpnTXI+UiSJEmSJEmS1n4LBj0BSZLmiP8GKsX6TuSyhHPJXcC7gdXARwc8l75IKd130HNQ/6WU7oqIFwFPKnbtBlwzwClJmn02Il/TAD45yIlIkiRJkiRJkuaGSCkNeg6SJEmSJEmSJEmSJEmStFayBKEkSZIkSZIkSZIkSZIk9cgALEmSJEmSJEmSJEmSJEnqkQFYkiRJkiRJkiRJkiRJktQjA7AkSZIkSZIkSZIkSZIkqUcGYEmSJEmSJEmSJEmSJElSjwzAkiRJkiRJkiRJkiRJkqQeGYAlSZIkSZIkSZIkSZIkST0yAEuSJEmSJEmSJEmSJEmSemQAliRJkiRJkiRJkiRJkiT1yAAsSZIkSZIkSZIkSZIkSeqRAViSJEmSJEmSJEmSJEmS1CMDsCRJkiRJkiRJkiRJkiSpRwZgSZIkSZIkSZIkSZIkSVKPDMCSJEmSJEmSJEmSJEmSpB4ZgCVJkiRJkiRJkiRJkiRJPTIAS5IkSZIkSZIkSZIkSZJ6ZACWJEmSJEmSJEmSJEmSJPXIACxJkiRJkiRJkiRJkiRJ6pEBWJIkSZIkSZIkSZIkSZLUIwOwJEmSJEmSJEmSJEmSJKlHBmBJkiRJkiRJkiRJkiRJUo8MwJIkSZIkSZIkSZIkSZKkHhmAJUmSJEmSJEmSJEmSJEk9MgBLkiRJkiRJkiRJkiRJknpkAJYkSZIkSZIkSZIkSZIk9cgALEmSJEmSJEmSJEmSJEnqkQFYkiRJkiRJkiRJkiRJktQjA7AkSZIkSZIkSZIkSZIkqUcGYEmSJEmSJEmSJEmSJElSjwzAkiRJkiRJkiRJkiRJkqQeGYAlSZIkSZIkSZIkSZIkST0yAEuSJEmSJEmSJEmSJEmSemQAliRJkiRJkiRJkiRJkiT1yAAsSZIkSZIkSZIkSZIkSeqRAViSJEmSJEmSJEmSJEmS1CMDsCRJkiRJkiRJkiRJkiSpRwZgSZIkSZIkSZIkSZIkSVKPDMCSJEmSJEmSJEmSJEmSpB4ZgCVJkiRJkiRJkiRJkiRJPTIAS5IkSZIkSZIkSZIkSZJ6ZACWJEmSJEmSJEmSJEmSJPXIACxJkiRJkiRJkiRJkiRJ6pEBWJIkSZIkSZIkSZIkSZLUIwOwJEmSJEmSJEmSJEmSJKlHBmBJkiRJkiRJkiRJkiRJUo8MwJIkSZIkSZIkSZIkSZKkHhmAJUmSJEmSJEmSJEmSJEk9MgBLkiRJkiRJkiRJkiRJknpkAJYkSZIkSZIkSZIkSZIk9WjBoCcgretWrFixI3BpsbnT9ttvf9kg5yOty1atWvUGYDPgpoULF35k0POR1jVeE6XZweuhNFheD6XZw2uiNFheE6XZweuhJElSZwzAkiRp3BuAHYDLAf9ngiRpXeX1UJKkzGuiJEleDyVJkjpiCUJJkiRJkiRJkiRJkiRJ6pEBWJIkSZIkSZIkSZIkSZLUIwOwJEmSJEmSJEmSJEmSJKlHCwY9AU2P4eHhzYEDgIeWlq2Lpw8cGxtbPqCpSZIkSZIkSZIkSZIkSXOGAVhz19OBrwx6EpIkSZIkSZIkSZIkSdJcZgDW3HYlcHqxnA98fbDTkSRJkiRJkiRJkiRJkuYWA7Dmrq+NjY0dV98YHh5ePLipSJIkSZIkSZIkSZIkSXPTvEFPQNNjbGxs9aDnIEmSJEmSJEmSJEmSJM11BmBJkiRJkiRJkiRJkiRJUo8MwJIkSZIkSZIkSZIkSZKkHhmAJUmSJEmSJEmSJEmSJEk9MgBLkiRJkiRJkiRJkiRJknq0YNAT0NptxYoVOw56DnPAtuX1FStWDGwi0rpuyZIl84vV+ddcc43nN2nmeU2UZgGvh9LAeT2UZgmvidLAeU2UZgGvhxqk7bff/rJBz0GSpE4ZgKWpunTQE5hjThv0BKR12cqVK+ur2+L5TRo0r4nSgHg9lGYVr4fSAHlNlGYVr4nSgHg91IDFoCcgSVKnLEEoSZIkSZIkSZIkSZIkST0yA5amaqdBT2AO2Jbxf8G1D3DlAOcirdOWLFlyGvk7eeXKlSv3GfR8pHWQ10RpFvB6KA2c10NplvCaKA2c10RpFvB6KEmS1BkDsDQl1l6euhUrVpQ3r/Q9lQZn1apVq4vV1X4XpZnnNVGaHbweSoPl9VCaPbwmSoPlNVGaHbweSpIkdcYShJIkSZIkSZIkSZIkSZLUIzNgzWHDw8NbljY3K60vanjuxrGxsVUzNC1JkiRJkiRJkiRJkiRpzjAAa267psX+Exq2DwSWT+tMJEmSJEmSJEmSJEmSpDnIEoSSJEmSJEmSJEmSJEmS1CMzYM1hY2NjMeg5SJIkSZIkSZIkSZIkSXOZGbAkSZIkSZIkSZIkSZIkqUcGYEmSJEmSJEmSJEmSJElSjwzAkiRJkiRJkiRJkiRJkqQeGYAlSZIkSZIkSZIkSZIkST0yAEuSJEmSJEmSJEmSJEmSemQAliRJkiRJkiRJkiRJkiT1yAAsSZIkSZIkSZIkSZIkSeqRAViSJEmSJEmSJEmSJEmS1CMDsCRJkiRJkiRJkiRJkiSpRwZgSZIkSZIkSZIkSZIkSVKPDMCSJEmSJEmSJEmSJEmSpB4ZgCVJkiRJkiRJkiRJkiRJPTIAS5IkSZIkSZIkSZIkSZJ6ZACWJEmSJEmSJEmSJEmSJPXIACxJkiRJkiRJkiRJkiRJ6pEBWJIkSZIkSZIkSZIkSZLUIwOwJEmSJEmSJEmSJEmSJKlHBmBJkiRJkiRJkiRJkiRJUo8MwJIkSZIkSZIkSZIkSZKkHhmAJUmSJEmSJEmSJEmSJEk9MgBLkiRJkiRJkiRJkiRJknpkAJYkSZIkSZIkSZIkSZIk9cgALEmSJEmSJEmSJEmSJEnq0YJBT0CSJEmSJEmSJElqp1qpPQd4FbAXsB5QA74BfHRkdGhVl2NtDLwWeBZwX2BD4DrgL8DnR0aHxpr02QN4OfAQYFdgSyCAy4HfAMeOjA6d08trkyRJ0trPDFiSJEmSJEmSJEmataqV2rHA8cCjgFOBnwE7Ax8Efl2t1DbsYqwtijGOBu4H/BH4PjmQ6inAD6uV2seadN0PeB05YOtC4ETgJHKyg5cAZ1Qrtef38PIkSZI0B5gBS5IkSZIkSZIkSbNStVI7hBz4dAtwwMjo0BnF/i2BXwP7A+8F3tjhkO8E9gBOB54wMjq0snSsJwM/BF5brdS+NTI69KdSv5OA3UdGh85rmN884A3Ah4AvViu1n42MDl3f9QuVJEnSWs0MWJIkSZIkSZIkSZqtjiwej6kHXwGMjA5dCxxebL66Wqkt6nC8xxaPHywHXxVj/gQ4udh8ZMNzFzUGXxX714yMDn0Y+DewETkgTJIkSesYA7AkSZIkSZIkSZI061QrtR2AfYrNbzY+PzI69HvgUmB94MkdDntHh+2u7bBd3d3F451d9pMkSdIcYACWJEmSJEmSJEmSZqMHF48rR0aHLmzR5i8NbSfz0+LxzdVKbUn5iaIE4YHAlcBYp5OsVmovB+4LXA38aZLmkiRJmoMWDHoCkiRJkiRJkiRJUhP3Kh4vadPm0oa2k/kg8HDgicDF1UrtD8ANwBDwUOAPwEtHRodubNa5WqltBHy62FwE7Fn0vQp4zsjo0E0dzkOSJElziAFYkiRJkiRJkiRJmo02LR5vbdPmluJxs04GHBkdurVaqT0NOBr4f+RArLrrgJOAy9sMsR7w4oZ9/yYHbf2ukzlIkiRp7omU0qDnoLXYqlWr3gC8YdDzWJullOavWbNmW4B58+ZdGRGrBz0naR22LTAfWE1OMy5pBnlNlGYNr4fSAHk9lGYVr4nSAHlNFMA537th03NPuHGzLXZb764nvHu7a5q1+es3r9/svJ/etOnWu69/5+OO3Pbayca8beXd8377kau3vPHyVQv3eOqim3Z91Ma3bbB4/pobLr1rwdnfuWHR1f+4c/1FOyxc9fh3bnvNehvNa3sT7fbr75533b/vWnjuCTdudv1Fd61334M2veWhI0uaZs5ai3k91MAsXLhwx0HPQZKkTpkBS1O1GbDDoCexNosI5s+fX9/cdpBzkfQf8/HcJs04r4nSrOP1UBoAr4fSrOQ1URoAr4kCWH/TeQCsXp3Wo8W5eM3qHCO1/ibz12/VpuzPX7iO6y9exd6Vxez+lEWbUWTO2uo+G3Dgm7bhZ++8ghsvXbXwnz+9afsHPmtx27E23HwBOz50AdvttSG/OOoKzv/lzZts+8ANNtnhwRt18SrXGl4PJUmS2jAAS1N1E+1T8WoS/ksuaVbxX3NJA+Q1UZo1vB5KA+T1UJpVvCZKA+Q1UQAbLp6/AbDFbdeuXgNc0azNLVffvQWwwYaL598CtM0+deu1d8+78tw7tgPY+REbX0k+x//HvAXBDg/ecNMbL1212Yqzb7/rgc9a3DTrVqP5C4Kd99l4kxsuvmHRJX+67bYdHrzR9Z30W0t4PZQkSeqAJQilAVuxYsWOwKXF5k7bb7/9ZYOcj7QuW7Vq1WXkf8V1uamNpZnnNVGaHbweSoPl9VCaPbwmSoPlNVEA1Uqt/DnYbWR06MImbS4BdgJeMDI69K1JxnskcEqxuWhkdOimJm1eBxwLnDcyOrR7F3N9JfBp4Ecjo0NP67TfbOf1UJIkqTPzBj0BSZIkSZIkSZIkqdHI6NBlwGnF5gsan69WavuTg6/uBH7SwZDlih6PaNFm3+LxHsFek3hc8Xh+l/0kSZI0BxiAJUmSJEmSJEmSpNnq6OLxLdVK7SH1ndVKbQtyximAT46MDt1Yeu4Z1UrtvGql9qvyQCOjQ5cwHtD1sWqltmv5+WqldijwvGLzmw3PHVGt1HZqnFy1UtuoWqm9HXgWcDfwlS5fnyRJkuaABYOegCRJkiRJkiRJktTMyOjQCdVK7ePAa4E/FUFVt5IzTi0G/gC8o6HbIuB+wAZNhnwJcDKwO/CPaqX2J+DaYvsBRZuvA99o6HcE8JFqpfYP4J/AHcB2wF7A5uQsXP81Mjp0bq+vVZIGISJ2BZYVm8tTSssHNhlJWosZgCVJkiRJkiRJkqRZa2R06HXVSu0PwKuA/YCFwL+AY4CPjowO3dXFWOdWK7U9gdcDBwP7AOsD1wM/B748Mjp0fJOuRwJPAB4GPIYc/HVrMY8vAZ8ZGR36d08vUJIGa1fgXaXt5YOZhiSt3QzAkiRJkiRJkiRJ0qxWBEU1C4xq1vY44LjyvmqltgHwXHKJwe3I5QLPJQdinTwyOpQmGfObNJQllCRJkurmDXoCkiRJkiRJkiRJ0nSpVmr/BVwOfBV4MvBgcuarFwC/Av5WrdT2HdwMJUmStLYzAEuSJEmSJEmSJElzUrVSezvweWBJm2a7AydXK7XHzsysJEmSNNcYgCVJkiRJkiRJkqQ5p1qpDQPv7bD5BsAPqpXa9tM4JUmaNhExLyJeEBEnRMTFEXF7RNwREZdHxFkR8Z2IODwitijaL42IBJxcGuZdEZGaLLuWjrOstH9Zse8hEfHZiDg/Im4uP9cwx0UR8f8i4qSIWBERd0bEyog4PSI+EBE7dPA6dyxex2hE/L043qqIuDYi/lyMs1MH41xUzPOiYnu9iHh1RJwSEddExC3F+/amiNi4oe82EXFU8fyNxRz+FBEvi4iY7NiS5qYFg56AJEmSJEmSJEmSNA3e0mX7zYBXAO+chrloFqpWas8BXgXsBawH1IBvAB8dGR1a1cU4FwG7dND0XSOjQ+8p9VsOHNBBv6+MjA69pNP5aN1TBFX9CGhWTnX7YnkQ8GxgI+DDfTz2m4CjgfmTtHsO8FnumZFwPWBz4CHAERHxipTSV1uMsRT4NdAsyGmLYnk48IaIODyl9KUOX8O25PfvoQ1PPahYnh0RT0gp3RAR+wInANs0tH1EsTw2Il6YUkqdHFvS3GEAliRJkiRJkiRJkuaUaqW2N/DIHrq+vFqpvbeb4ButnaqV2rHA64C7yQEdtwCPBT4IPK1aqT1hZHTo9g6H+y6wZYvnlgBPK9ZPbnjuZ8BFLfqtBzy/RT+p0RcYD766FBgFLgCuBzYG7kM+Jz661Odc4BnAnoxnC/x20bfR1S2O+1zgYPL3pwqcCqwC9gCurDeKiP8CPkcOnLoL+CHwW+AqYBNgf+AF5GyEx0XEXSmlbzU53gbFGP8kfy/+DlxL/h5vCzwGOIT8/flCRFyVUvpRi7nXLQS+Rw6++iU5uOpaYDdygOaOwD7AsRFxFPBzYEPguOI13F48/8pi//OBk4AvT3JcSXOMAViSJEmSJEmSJEmaax7bY79tyIEDZ/VxLpplqpXaIeTgq1uAA0ZGh84o9m9JDsbanxyQ8sZOxhsZHWrZrlqpvYkcgHX+yOjQ7xr6HdOm33PJgRw3kgO8pKYiYmvg6cXmKcDjUkp3tGi7FUWwYErpWuCEiLih1OS8lNIJXRz+YOB84KCU0iUtjvkg4JPkwKkLgOGU0nkNzb4SER8mBy5tD3wuIn6eUlrZ0O4fwN4ppVbn6E9ExN7kIKmtgY9ExI8nyUa1PbAd8NKU0oSgqYj4CnAmObjrUHK2vFXA0pTSX0tNRyPix8Cviu03YACWtM6ZN+gJSJIkSZIkSZIkSX22aAp9P1ut1D5VrdTeWK3Unl2t1B5ardSWVCu1ZiWvtHY6sng8ph58BTAyOnQtcHix+epqpTaVz1FdvXRgt8EYLy0ev9VFJi6tm3Zj/L7/N1oFXwGklK5JKf2jj8dOQKVV8FXhKHJGqjuAJzcJvqrP7R/AsmJzU+C/mrS5uE3wVb3NmYx/x+8D7NeufeGLjcFXxVhXkYPHIJdY3Bt4VUPwVb3trxkPwHpAROzUwXElzSFmwJIkSZIkSZIkSdJcc8sU+u7LeCmvspurldqFwIXksnET1kdGh26ewjE1Q6qV2g7kcmEA32x8fmR06PfVSu1SYCfgyUCzMmidHutRwP3I5dG+2kW/nYDHF5tf6vX4WmfcWlp/6Awf+/fNgpHqImIx49m5fpBSqrUbLKX0y4i4gpyR6onkkqA9zau0vi/wh0naf6LDsa4CvtOm7e+AxxXre5DLQUpaRxiAJUmSJEmSJEmSpLnmjz32WwX8FdiZXHKqbFPgQcVyD9VK7TqaBGYVy8VmMZo1Hlw8rhwZHbqwRZu/kAOwHswUArAYz371k5HRoSu76LeMnNHo7JHRob9M4fhaN/wduBzYAXhJRMwHvgD8KaW0epqP/btJnn8U49m57oyIQzoY82ZyANYerRoUZQYPBR5JznK1GbB+i+Y7TnK8W4Fz2zxf/u6enlJa02HbzSc5rqQ5xgAsSZIkSZIkSZIkzTV/IN9Q37PLfp8aGR16PUC1UtsQ2LVY7lUs5fUlDX23KJamGWiqldqVtMieBVwyMjq0qsu5qjf3Kh7blUyrZ625V5s2bVUrtY2B5xabHWexKkpdLuu2n9ZdKaXVEfFy4PvkIKQXF8tNEfFn8vnwJOCUlFLq8+Evm+T5XUvryxj/bHei8RxLRCwAPkUuT9hpWdjNJnl+5STvy52l9esmGavcdoNJ2kqaYwzAkiRJkiRJkiRJ0pwyMjqUqpXa/wFf6aLbXeQb+/Uxbgf+USz3UK3UNmM8IKvx8V7kjFll2xbLI5sMt6ZaqV1O8+xZFwGXj4wOTXcmm3VF/e9ya5s29RKWkwVutPNcYBNyRpyfdNFvKbAbOZDj61M4vtYhKaWfRMTDgHcBw8B65M/vQcVyFHBhRLwzpdTPz9Vkmf0WT2HshU32fQx4ebG+CvgZcCo5EOzWYh/A1sDnivX5kxynXUarqbSVtI4xAEuSJEmSJEmSJElzUTc3yhOwbGR0qNZph5HRoZuAs4tlgiKL0eY0z5xVXy9nR5lHLnm3E/CYJodbVa3ULqF1icOrRkaH+p3ZRlPz0uKxOjI6dHcP/X44Mjq0ss9z0hyWUjoXeE5EbEwu/bcv8OhiWZ983vlaRNw7pfTuGZrWLaX116aUPtHrQBGxE/CKYvNy4MCU0gUt2j6g1+NIUq8MwJIkSZIkSZIkSdKcUq3UhoEvF5u3kbPBtLovthJ46cjo0An9On4RDLWyWE5vMr8AtqF1ecOdmZj9ZSFw72Jp5o5qpXYRrUscrjRA6z9uLh43btNmk+Lxpl4OUK3U7ksOgIHxz2En/RYBzyw2LT+onqSUbgV+USxExKbA64D3Fk3eFhGfSyldOQPTKZco3GmKYz2eHKwKcEyr4KtCz+VDJalXBmBJkiRJkiRJkiRpzqhWagcAx5PLTt1CLum2AngZ8DxgO3KZqho5yOXbI6NDt83kHItgqCuL5U+Nz1crtfnA9rTOnrUj44EIkLNp3b9Ymrm5FKB1jyCtIpvXuuKi4rFdMEj9uYvatGnnJcXj70dGh/7ZRb/nAxsClwAn9XhsaYKU0s3A+4oShU8nB3TuC5xQNClnC4w+H/535AyDATwJeNMUxtq2tD5ZtsKDp3AcSeqJAViSJEmSJEmSJEmaE6qV2oOBMXK5rbuA4ZHRoXoGqvcyngFmVhsZHVoNXFosv218vlqpLSQHCbUqcbhdQ5dNgQcWyz1UK7WVNMmcdfDR2y3YZJsFLFhvXrNua6u/Fo9bVCu1e42MDl3YpM3Disczuh28CJ4bKTa7zWJVD9z6ysjoUDclNKVOlD/r5TiBcpnAdpnhupZSujoifgo8GXhgRDw/pfStHoe7tbQ+1KpRROwGvLjHY0hSzwzAkiRJkiRJkiRJ0lqvWqndB/gZsBk5o0tlZHTo5MHOanqMjA6tAv5dLPdQrdQ2BHahdYnDLRq6LCmWh5R3/vTIKwBYf9N5291585o/0ry84SUjo0N3TfU1zZSR0aHLqpXaacA+wAuA95efr1Zq+5OD2+4EftLDIZ5MDoC7GfhOp52qldqexZwS8JUejqt1VEQ8EdgDOC6ldH2LNlsDzyrtOqu0Xg7MmnAO6JO3kcsHrgd8MSJoF4QVEUuAZcDZKaVyJrjTSutvjIhvpZSua+i7M3AifQ4kk6ROGIAlSZIkSZIkSZKktVq1UtsB+CWwdbHrv0ZGh34wwCkN1Mjo0O3AecVyD9VKbVPGA7Lqj+X1zcrt77x5zTxyybJ9mwyXqpXa5bQobwhcVmT0mk2OBn4AvKVaqf10ZHToDIBqpbYF8OmizSdHRodurHeoVmrPAD4AXD4yOvS4NmPXs1iNjowO3dqmXaOXFo8njYwOXdxFP2k74CPAByNiObms6b/Jma22AB5ELm+5edH++JTSBfXOKaXrI+IMcvDVgRHxOXIJzJtLx/hNSun2XiaXUjozIv6bnBFuI+CbEfEmcqDUBcDtwCJyVquHA48hxzG8qGGcP0bEn4FHkANMz4uIzwP/IJec3bfoszFwHDmIS5JmjAFYkiRJkiRJkiRJWmtVK7UlwM/JN+QB3jQyOvTlAU5p1hsZHboZOKdYJqhWakEO1Nj1ka/c8pe3X3/3kpuvvPvWfy2/5TeMB2htWOoSwI7F8ugmh7u7WqldQvPsWRcCV810ub2R0aETqpXax4HXAn+qVmq/Ipc3exywGPgD8I6GbouA+wEbtBq3WqltDTyl2Oy4/GBRUvLQbvtJhVQ8LgQOKpZWvgsc1mT/kcCPyPEDLy+WsnuRv7e9TTCl4yLiCuDLwPbA3sXSyp3AtU32V4BfF/PZsph3o08AH8UALEkzzAAsSZIkSZIkSZIkrZWqldrGwI+BBxS7PjgyOvShAU5prTcyOpSAlcDKVatW1TPe3PCoV2z7FPhPgNbWtC5vuAs5EKRuAbBbsTRzR7VSu5jm2bMuBK4r5tRXI6NDr6tWan8AXgXsV8z5X8AxwEe7KatYrdQeDDyNXGZtIXAZE0u8TWaYHEyyEjihi34SQBX4O/nz9whgd3KQ04bAbcAl5KxYX0sp/abZACmln0fEI8lBifuRs2pt1M9JFsfYjVz688nAQ4GtyEGNN5O/82eRA6xOTCnd0GSMiyLiwcARwDPJWbMArgROAb6UUloeEbv2c+6S1IlIqe+/VyR1YcWKFTsClxabO22//faXDXI+0rps1apVlwE7AJcvXLhwx0HPR1rXeE2UZgevh9JgeT2UZg+vidJgeU1UJ6qV2nrAGPDEYteXyKUHvfnVJ71cD6uV2jxy8Eer8oY7AfO6mMYttM6edVG5TOBMq1ZqTwSOonlpxuuAzwPvLUpCSpKkOcwMWJIkSZIkSZIkSVqrVCu1+eSsL/Xgq+8DrzD4avCKcoKXFcvvGp8vyu3tSPPsWbuSg7fKNgH2LJZ7qFZq19M6e9ZFI6NDt03pBbVQrdReAXyaXIKxmS2AtwIHVCu1g0dGh26ajnlIkqTZwQCsPhkeHt4KeAs5ReiO5DrRZwCfHhsbO6GH8ZYDB3TY/LixsbEJtXqHh4ePA148Sb+/jY2NNf2xKkmSJEmSJEmSNBsVJfA+Djyv2PVr4IUjo0N3D25W6tTI6NAqxgOl7qFaqW1ALmO4K82DtLZs6LJ5sTykxXhX0yJ7FnDJyOjQnd2+hmql9lTaB1+V7QccXwRhGSAoSdIcZQBWHwwPDz+A/ON+62LXzcBi4CDgoOHh4Y+PjY29rsthVwJXtXl+PfKPSYDT27S7A2iVevXaLuckSZIkSZIkSZI0aEcBhxfrfwEOGRkdumNw01E/FX/LfxbLPVQrtU0YD8iqP5bXFzV02bpYHtFkuFSt1C6ndYnDyxsD+4oAwGPoLPiq7onAUuDkLvpIkqS1iAFYUzQ8PLw+ub741sC5wKFjY2NnDQ8PbwS8Hngv8Nrh4eEzx8bGvtLpuGNjY8+c5LhHAu8H7gS+2abpt8fGxpZ1elxJkiRJkiRJkqTZqlqpvRZ4Z7H5T+DJI6NDNw9wSpphI6NDt5DvyZ3b7PlqpbY57QO0Nio1D3Jlmx2B/ZsMd3e1UruUiYFZ6wMP6GHqh2MAliRJc5YBWFP3cmA34DbgKWNjY5cAjI2N3Qa8f3h4eDvgVcD7hoeHvz42NraqT8etlxc8cWxsbGWfxpQkSZIkSZIkSZqVqpXaocDHis3LgCeMjA5dM8ApaRYaGR26Hrge+Gvjc0X2qq1oXd5wF3IVmroFpeem6unVSm1hUYJRkiTNMQZgTd2hxeO36sFXDf6XHNG+PXAg8IupHnB4eHg/4L7FZsdZtSRJkiRJkiRJktZG1UrtKcBxxeZ15OCrZvdlpJZGRocScHWxnNr4fLVSmwdsR+vsWTsB83s8/EJgMWDQoCRJc5ABWFMwPDy8CbBPsfmzZm3GxsYuGR4e/gewB/A4+hCABSwrHq8Aft6H8SRJkiRJkiRJkmalaqX2aOC75MCXW4GDR0aH/jHYWWkuGhkdWgNcXiy/b3y+WqktAEaBZ/V4iDt7n50kSZrNDMCamt3JtaGhRZ3p0nN7FMuUDA8Pbwg8t9j82tjY2OpJujxueHj4AmBn4A6gBvwE+OTY2NhVU52PJEmSJEmSJEnSdKlWansBJwIbAHcBh4yMDp022FlpXTUyOnR3tVI7nd4CsK4Abu7zlCRJ0iwxb9ATWMttV1pf0aZd/bnt2rTp1DOARcX6cR2035GcFvVWYBPgIcDbgb8PDw8/rg/zkSRJkiRJkiRJ6rtqpXZvciWQRcAa4AUjo0MnDXZWEl8F7u6h3xeLEoiSJGkOMgBrajYprd/Wpl39uU37cMzDisc/j42NtUuvewZwOLALsP7Y2NgScl3pQ8kR9kuAE4aHh+/bhzlJkiRJkiRJkiT1TbVS2w74JbBNsesVI6ND3xvglCQARkaHVgDdfhZXAV+YhulIkqRZwhKEa5Hh4eGdgMcWm8e1azs2NvbxJvtuBr4xPDz8e+CvwObAUcALep3TihUrduy1r/5j2/L6ihXtkqlJmk5LliyZX6zOv+aaazy/STPPa6I0C3g9lAbO66E0S3hNlAbOa+I67PI/3b0o5vPdtJp7Aay/KI5+9Ls2/Kn3JGae18Pmdnviwv+98Jer9k9r2KGT9htuEe941Ns2TH6Gu7P99ttfNug5SJLUKQOwpuaW0vpGwE0t2m1UPE61rvMIOWvZHcBor4OMjY1dPDw8/EngHcBThoeH542Nja3pcbhLe52HmrJuvTRAK1eurK9ui+c3adC8JkoD4vVQmlW8HkoD5DVRmlW8Jq5DVt+VWHHq3aTVeXuXpQsYetrCI4EjBzqxdZTXw+Z2e+JCtnvYfP76hTu57eo2VQUD7vfMhez0qIXHAMfM2ATnjhj0BCRJ6pQBWFNT/ic329M6AGv74vGKKR7vxcXjCWNjYzdMcaw/F4+bAVsA10xxPEmSJEmSJEmSpJ6tWZ04+7g7ufGi/G/Gt3v4fIaetpAIYzA0+2y4xTwe8YYNuPKvq7nsD6u4+bLxQKwFG8B2D1/AjvstYOOt5w1wlpIkaaYYgDU15wGJHH39gGK7mQcUj3/v9UDDw8OPAu5TbH6l13GmwU6DnsAcsC3j/4JrH+DKAc5FWqctWbLkNPJ38sqVK1fuM+j5SOsgr4nSLOD1UBo4r4fSLOE1URo4r4nrmFW3pfj9+27/2Oo7eAbA/PX4+W5PWPjfEbF60HNbl3k9bG/+esEOj1jAtg+Zz8W/vnvrW69as2TBBnHHDvvOX7HZzvPvGvT8JEnSzDEAawrGxsZuGR4ePhV4BPAk4HuNbYaHh3cE9ig2fzWFwx1WPF4GnDSFceoeUTzeDFzX6yDWXp66FSvKidS40vdUGpxVq1bV/2fOar+L0szzmijNDl4PpcHyeijNHl4TpcHymrhuqVZqAXwccvAV8JvVd3HIvffc8Y4BTkt4PezGTofh+yNJ0jrMnJdT943i8fnDw8PNskG9iZwhawVwci8HGB4e3gh4TrFZHRsbWzNJ+7a5eIeHh3cGXlVs/niy8SRJkiRJkiRJkqbRO4FXF+tnAMMjo0MGX0mSJGmtYQasqfs8cASwG/Cj4eHhF42NjZ09PDy8IfA6xv+D4e1jY2Oryh2Hh4cvAnYBvjo2NraszTGeCWxWrB/XwZwOHR4ePgT4GvD7sbGxa4vjbQI8DfhfYAlwC3BUB+NJkiRJkiRJkiT1XbVSezXj9youAA4eGR26aXAzkiRJkrpnANYUjY2N3Tk8PDwM/Bp4EHDW8PDwTcDGwPyi2SfGxsa+MoXDLCse/zA2NnZBB+3nk4O2ngkwPDx8C3AnsDnjWc+uBipjY2P/nMK8JEmSJEmSJEmSelKt1F4AfKLYvBw4aGR06OoBTkmSJEnqiSUI+2BsbOxvwAOBjwI1YH3gRuAk4BljY2Ov7XXsoqzhgcXmcR12Oxl4O/BT4N/AGmARcD3wO+CtwO5jY2M9lUSUJEmSJEmSJEmaimqldjDw1WJzJfCEkdGhiwc4JUmSJKlnZsDqk7GxsauBNxRLp3127aDNpYxn0up03IuB93fTR5IkSZIkSZIkaSZUK7X9gO+R71PdCjx5ZHTo74OdlSRJktQ7M2BJkiRJkiRJkiRpRlQrtQcCPwY2BFYBzxwZHfrzYGclSZIkTY0BWJIkSZIkSZIkSZp21UptN+DnwGIgAYeOjA79YqCTkiRJkvrAACxJkiRJkiRJkiRNq2qlti3wC2C7YtcrR0aHjh/glCRJkqS+MQBLkiRJkiRJkiRJ06ZaqS0Gfgbcu9j1tpHRoc8NbkaSJElSfxmAJUmSJEmSJEmSpGlRrdQ2Ak4E9ip2fRT4wOBmJEmSJPWfAViSJEmSJEmSJEnqu2qlthD4NrB/fRfwxpHRoTS4WUmSJEn9ZwCWJEmSJEmSJEmS+qpaqc0Dvgw8tdh1IvCykdGhNYOblSRJkjQ9DMCSJEmSJEmSJElS31QrtQA+Ahxa7Pod8LyR0aFVg5uVJEmSNH0MwJIkSZIkSZIkSVI/vQ14XbF+FvC0kdGh2wc4H0mSJGlaGYAlSZIkSZIkSZKkvqhWaq8E3lts1oAnjowO3TjAKUmSJEnTzgAsSZIkSZIkSZIkTVm1Unse8Kli8wrgCSOjQ1cNcEqSJM15EZGKZemg5yKtywzAkiRJkiRJkiRJ0pRUK7UnAl8DAriBHHx14UAnJUmaEyJi31KQ0cUR0TbOISKWRsRREbFsknZHFO327ud8+yUiDinmd8ig59JvEbG8+HseN+i59FPx9zoqInYd9Fw08wzAkiRJkiRJkiRJUs+qldq+wPeBhcBtwJNHRofOHeysJElzyGGl9Z2Bx07SfinwLmDZJO2OKNrt3du0pt0h5PkdMkm7fxbLbdM8H03uXcWy64DnoQEwAEuSJEmSJEmSJEk9qVZqDwB+AmwE3A08a2R06I+DnZUkaa6IiA2B5wEJ+Hyxe9nAJjQLpZTuXyynDnou0rrMACxJkiRJkiRJkiR1rVqp7Qr8AticfGP8RSOjQz8b6KQkSXPNM4BFwCnA+8jXm2dExGYDnZUkNTAAS5IkSZIkSZIkSV2pVmrbAL8Eti92vWpkdGh0gFOSJPWotqyyZ21Z5VO1ZZWza8sqFxWPn6otq+w56Lkxnu3q6ymlS4HfkLMuPrexYUTsGhGJXAIO4ICISA3L0og4qmi3S9HuKw1tLmoy9vyIeGlEnBQR10TEXRFxRUR8PyKWNpt4caxUHIuI2D0ivl70uzMiahFxTERs0qwf8OJi14ubvI5dS+3/89pazGOLiDg6Is6JiFuK5ZyIeH9ELOnn3PshIpYXxz4qIuZFxKsj4oyIuDUiboiIX0bEgS367lp+jyJij4j4ZjHvOyLi/Ih4d5FZre2xO5lfad9x9feqcHLD32t5b++G1iYLBj0BSZIkSZIkSZIkrT2qldoi4KfAULHrnSOjQ58Z4JQkST2oLatsCVSBg5s8/UDg8Nqyyk+BFw0dN3rdjE4OiIidgMcBq4Dji91fA5YChwFfbOiyGrgK2ATYuOi3sqHNXcAtRbutyElrbgJuL7W5pmEeWwNjwCOKXQm4GdiWnKHrGRHxnpTSu2ghIh4P/JAcPHYjOVbj3sCbyYFij0kprSrN8Spy5q8NgDuKPo2vdVIRsTfwM2CbYtdtxeOexfKSiHhiSunsPs29nxaQ3/enkP+Wd5Lfk8cDB0bEs1JKP2zT/5HAF8ifhZuAAO4DvBN4WkQ8NqV0Q5/meiP5b1Z/n68n/x3rGj+HmoPMgCVJkiRJkiRJkqSOVCu1Dck3Qx9c7Po4uSSUJGktUgRf/Y7mwVdlBwO/ry2rbDH9s7qHEXJMw09TSvUAlu+SA5L2i4j7lBunlC5NKW0LfLjYdUpKaduG5ZSU0oeLdpcW7V7X0Gaf+pgRsQD4ATn46vfAgcCGKaVFwBbAO8jBQe+MiGe3eS3HAz8C7pVSWgxsBryVHMy1L/DS0us4pZjft4td327yOi5lEhGxiHzN3ga4ADggpbRxSmnj4nX8mxxENjZJSceO595nh5ODqJ4HbJJS2hTYCzgXmA98KiLmt+n/GeDvwF7F32sTclax28i/Y/oWPJ5Sel3xN6t7ZsPf65n9OpZmLwOwJEmSJEmSJEmSNKlqpbYAGAUeU+z6BvD6kdGh1LqXJGmWqgL377Dt/cmZp2basuLx6/UdKaWbyEFF5een06HAfsCpwEEppeUppTuLuaxMKb2PnFGJ0mMzfwEqKaWLir63ppSOAX5cPH+Pkop98CpgJ3LGr4NSSr+tP5FSWg48gZz5axfglW3GGcTcATYHnp5SOj6ldFdx7LMZ/7vvADyqTf87gSfVs3ullFallKrAfxfPVyJij2mZudZJBmBJkiRJkiRJkiSprWqlNo9c6mm42PVj4LCR0aE1g5uVJKkXtWWVBzJ55qtGB9eWVfacjvk0ExH7k0vd3gic2PB0PRjsRREx3TEPLykeP5FSuqNFm28Ujw+MiG1btDkmpdQsYPmE4nE63tt6YNRXU0oXNz6ZUvoXORAPcpapVgYxd4DfpZR+37gzpXQ6cFkHx/5sKXNa2TeA+vvxnKlNURq3YNATkCRJkiRJkiRJ0uxVrdQC+BC5bA/AH4DnjowOrRrcrCRp3VZbVhlE9sFzassqPXUcOm40uuyyrHj8XpPAp58B15CzOz0e+EVPk5pEUd7u4cXmsRHx4XbtCzsDVzbZf1qL9pcXj5t3Ob22ImI9xoOTft2m6UnkjFAPioiFKaVm1/YZnXsHx60fe8dJjr282c6UUoqI3wIvAh7S8+ykBmbAkiRJkiRJkiRJUjtvAd5QrJ8NPHVkdOi2Ac5HkjSHRcRGjGdv+nrj8ymlu8klcWF6yxAuAdYv1rcAtmmz1G3UbKCU0s0tjlEPLut38pwlwPxi/fI27eqZpOYXfe5hAHOva3Xc8rEXtmnT7nXXn9u6qxlJbZgBS5IkSZIkSZIkSU1VK7WXA0cXm/8GnjQyOnTD4GYkSVoHPAvYtFj/dUTb5FmHRMSilNKN0zCP+aX1R6aU/jQNx5A0R5gBS5IkSZIkSZIkSfdQrdSeDXy22LwSOGhkdOiKAU5JkrRuOKyLthsCz5umeVwHrC7Wd56mY0yXlYzPfYc27XYsHlcXfeaS7Tt47uqG/XcXjxu06buo5xlpTjMDliRJkiRJkiRJkiaoVmoHAd8EArgReOLI6NC/BzsrSVLd0HGjbdNCtVNbVvkUcHgPXT81dNzoq3s9biciYldgabH5GOCcNs3/BziSXIbw86X9a+rDTXK4tu1SSqsi4jRgX+CpwPGTjNdvnb6Oe0gp3RUR5wB7A48Dvt+i6eOLx7NTSqu6nuHsdgCwvHFn5JRqjyk2z2h4+vricUeaiIiNgd3bHDOR/149fz+19jIDliRJkiRJkiRJkv6jWqk9HPgBsBC4HXjqyOjQ2YOdlSSpjz4zw/268WJy8MqFKaXfpZRuaLUA3yr6PDIi7lca46bicfEkx+qk3ZeLx0pE7NdusIjYfJLjdavT19FKPWBsJCLukcErIu4NjBSboz0eYzZ7ZUQsbrK/AuxarH+n4bl6wN8TIqJZFqzXA+u3OeZU/2ZaixmAJUmSJEmSJEmSJACqldruwE+BjclleJ49Mjr0+8HOSpLUT0PHjZ5LPtd346dDx43+bTrmU1dkJnpxsfndydqnlM4Fzi82l5WeOrd43CMiHtFmiHq7Z0ZEq7JyXwF+Tw5K/nlEvDYitijNefOIeGpEfJv+BzHV57d/RNynh/6fBi4FNgF+GRH1rE9ExAHAz8klHC9mZoLrZtoGwM8iYk+AiFgYEYcCXyieH00p/b2hz3fIWay2AqoRsXXRd1FEvA04CrihzTHrf7Pntwjg0hxmAJYkSZIkSZIkSZKoVmq7AL8ElhS7XjwyOvSTAU5JkjR9RoDzOmx7HvCiaZxL3QHAvYr1SQOwGtq9KCLq8Q/LgQuABcCfIuK6iLioWPYt9f0iOdhmf+DaiLi8aPOfwOOU0t3A04FfkwOZPgZcExHXR8RNwErgROC59D/+4gfAdeTr8j8j4urS62haIq8spXRjMfergfsCv4mIWyLiVvJ7dG/gSmA4pXRzn+c+G7wS2BM4JyJuAG4GvkYOMv9r8fwEKaV/AB8uNp8DXBUR15P/zu8D3gmc1eaY9eCu5wA3RsSlxd9rLmYYUwMDsCRJkiRJkiRJktZx1UptK+AXwA7FrteOjA59c4BTkiRNo6HjRq8lBx5Nlgnrp8D+Q8eNXjf9s/pPFqtLUkqndtjne8XjDsBB8J+gqccBxwGXAJsCuxTLf7ISpZSWkwOUTiYH52xbtJkQ3JRSWgk8HngWcAJwBbAROSvWv4HvA68gB2H1TUrpOnJQ2nfIgVKbl17Hgg7H+CvwAOAY4O/k8o4AfwM+AOyZUpqrZYb/CDycnJnsdvJrvwB4D/CooozlPaSU3gS8HDgDuKPo91vgaSmlo9sdMKX0VeCw4th3kD+Xu5A/W5rjIqU06DlI67QVK1bsSE79CLDT9ttvf9kg5yOty1atWnUZ+YfQ5QsXLpz0Xw5I6i+vidLs4PVQGiyvh9Ls4TVRGiyviTOrWqltRs7s8dBi17tHRoeOGtyMNFt4PZTWDbVllT3JAUSPBjYDbgJ+B3xmussOSv0UEbsCFxab90opXTS42Whd01FUpCRJkiRJkiRJkuaeaqW2ATmbRz346lPAuwc2IUnSjBs6bvRc4NWDnockrc3MgKUpWbVq1RuANwx6HmuzlNL8NWvWbAswb968KyNi9aDnJK3DtgXmA6vJqVwlzSCvidKs4fVQGiCvh9Ks4jVRGiCviTNjzerEbz96zRZXnHX7BgA77rPR7fu/esuVMS8m66p1h9dDDYxZ1yR1ywxYGiQzYGmqNmO8Hrx6EBHMnz+/vmntV2l2mI/nNmnGeU2UZh2vh9IAeD2UZiWvidIAeE2cfiklTvvydVxx1u0AbPegDdjv8C03jHnhOU/NeD2UJElqwwAsTdVNwOWDnsTazH/JJc0q/msuaYC8JkqzhtdDaYC8HkqzitdEaYC8Jk6/M75+/aJ///bWTQCW3Gu9ux71mq2unb8gLJuiRl4PJUmSOmAJQmnAVqxYsSNwabG50/bbb3/ZIOcjrctWrVp1GflfcV1uamNp5nlNlGYHr4fSYHk9lGYPr4nSYHlNnF7VSu1NwAeLzXOBA0ZGh1YOcEqapbweSpIkdWbeoCcgSZIkSZIkSZKkmVGt1F7GePDVRcATDb6SJEmSpsYALEmSJEmSJEmSpHVAtVJ7JvC5YvNq4KCR0aEVA5ySJEmSNCcYgCVJkiRJkiRJkjTHVSu1xwLfIt8buomc+ao22FlJkiRJc4MBWJIkSZIkSZIkSXNYtVJ7GPBDYD3gDuBpI6NDZw50UpIkSdIcYgCWJEmSJEmSJEnSHFWt1O4P/BTYBFgNPHdkdOi3g52VJEmSNLcYgCVJkiRJkiRJkjQHVSu1nYBfAFsWuw4bGR06cYBTkiRJkuYkA7AkSZIkSZIkSZLmmGqltiU5+GqnYtcRI6NDXxvglCRJkqQ5ywAsSZIkSZIkSZKkOaRaqW0K/AS4f7HrfSOjQx8b4JQkSZKkOc0ALEmSJEmSJEmSpDmiWqmtD/wA2KfY9VngnYObkSRJkjT3GYAlSZIkSZIkSZI0B1QrtfnAN4DHFbuOB149MjqUBjcrSZIkae4zAEuSJEmSJEmSJGktV63Ugpzt6lnFrl8ALxoZHVo9uFlJkiRJ6wYDsCRJkiRJkiRJktZ+RwMvK9b/DDxrZHTorgHOR5IkSVpnGIAlSZIkSZIkSZK0FqtWam8E3lJs/h14ysjo0C0DnJIkSZK0TjEAS5IkSZIkSZIkaS1VrdQOAz5UbF4MPGFkdOi6AU5JkiRJWucYgCVJkiRJkiRJkrQWqlZqhwBfLDavIQdfXT64GUmSJEnrJgOwJEmSJEmSJEmS1jLVSm0pMEq+13Mz8KSR0aHzBzopSZI04yIiFcvSQc9FWpctGPQEJEmSJEmSJEmS1LlqpfYQYAxYH7gTeNrI6NAZg52VJEnTIyL2Bf5YbF4C3CultKZN+6XAUuCilNJxbdodASwGTkgpndmPufZTRBwC7A2cmVI6YaCTmUYRsR3w38ATgXsDi4CVwPnAT4DPp5Sun6ZjLwaOKDaPTSndMB3HmYqI2Bs4BLghpXTsQCejtsyAJUmSJEmSJEmStJaoVmr3BX4GbAqsBp47Mjr0m8HOSpKkaXVYaX1n4LGTtF8KvAtYNkm7I4p2e/c2rWl3CHl+h0zS7p/Fcts0z6fvIuI1QI38OvcFlgC3AlsDjwGOAWoR8dxpmsLi4tjvKtZno73J8ztisNPQZAzAkiRJkiRJkiRJWgtUK7UdgV8CWxW7XjoyOjQ2wClJkjStImJD4HlAAj5f7F42sAnNQiml+xfLqYOeSzci4j3Ax4GNgOXkwLoNUkpLin1PBf5KDsoajYiXDWiqUkcMwJIkSZIkSZIkSZrlqpXaFsDPyZk/AP7fyOjQVwc4JUmSZsIzyCXpTgHeRw7EekZEbDbQWWlKIuJg4O3F5ueAx6aUTk4p3Q2QUrozpfRjclasHwEBfDIi9hrIhKUOGIAlSZIkSZIkSZI0i1UrtU2AHwN7FLs+MDI69JEBTkmSpJmyrHj8ekrpUuA35OxI9yhJFxG7RkQil2sDOCAiUsOyNCKOKtrtUrT7SkObi5qMPT8iXhoRJ0XENRFxV0RcERHfj4ilzSZeHCsVxyIido+Irxf97oyIWkQcExGbNOsHvLjY9eImr2PXUvv/vLYW89giIo6OiHMi4pZiOSci3h8RS/o59y58kBxUdTbw6pRSatYopXQX8CLgSmB94L1N5npRMddlrQ7WrE1ELAcuLDW7sOE9Pq7U9qhi3/Ji+0UR8ceIuKlYlkfE01oce9dmf7dO2hTv/1eKzV2afA5avmbNPAOwJEmSJEmSJEmSZqlqpbY+8H3gEcWuLwBvG9yMJEmaGRGxE/A4YBVwfLH7a8XjYU26rAauAm4ttlcV2+XlLuCWYn1N0e6mhjbXNMxja+APwBeL+WwB3A5sS87QdXJEvHuS1/J44C/AC4ENgQXAvYE3A7+MiIWl5ncV87ij2L6jyetY3e54pePuDfwNeCuwJznoKYr1I4G/RcSD+jj3Tua0H/DAYvOD9axXraSUbgA+WWw+NSJ26OZ4bawEri1tX8vE9/jGZp0i4iNAFXg4+e+wCXAAMBYR72rWZwquIn8+IX9eGz8Ht/f5eJqCtToAKyI2i4hXDHoekiRJkiRJkiRJ/Vat1OaTbzQfVOz6HvDKkdGhplkiJEmaY0bIMQ0/TSmtLPZ9lxyQtF9E3KfcOKV0aUppW+DDxa5TUkrbNiynpJQ+XLS7tGj3uoY2+9THjIgFwA/IgdC/Bw4ENkwpLSIHYr2DHOj1zoh4dpvXcjy5lN69UkqLgc3IQVGJXGbvpaXXcUoxv28Xu77d5HVcyiQiYhEwBmwDXAAckFLaOKW0cfE6/k0OIhubpKRjx3Pv0IHF45pifp04oXgM4DFdHq+plNIzgX1Ku/ZpeI9f16Tb3sDryRm8tkgpbQ5sRw7IAjgqIp7Yj/kVc9wWqM/j0iafg2+366+ZtVYGYBXp7r4GXAF8atDzkSRJkiRJkiRJ6qdqpRbkeyDPKXb9CnjhyOhQRxkvJEmaA5YVj1+v70gp3cR40M4ypt+hwH7AqcBBKaXlKaU7i7msTCm9D3hn0fadLcaAnEGqklK6qOh7a0rpGHKJYWhSUrEPXgXsRM74dVBK6bf1J1JKy4EnkDMo7QK8ss04/Z57vaTyv1JKt3TY5x/kzGDl/oOwCPhiSuktRWYuUkpXkT+LJxVt7lEmUeuGtSYAKyJ2iIi3RUSN/B8ZLyCnt5MkSZIkSZIkSZpr3gv8d7F+GvCMkdGhOwc4H0mSZkxE7A8MkcvAndjwdL0M4YsiYrpjHl5SPH4ipXRHizbfKB4fGBHbtmhzTEqpWQbLE4rHPXucXzv1wKivppQubnwypfQvxjM3Pa/NOP2e+5Li8bpOO6SU1gDXF5tbdHm8fju6cUfx/tT37xMRu87ojDQrzOoArIhYEBHPjoifABcB7wHuxXhd0rvJqe4kSZIkSZIkSZLmhGql9nrgbcXmecCTR0aHbh7glCRJmmnLisfvNQl8+hlwDTm70+OnawIRMR94eLF5bERc2WwhB0rX7dxiuNNa7L+8eNy8D1P+j4hYj/HAqF+3aVrP2vSgiFjYos2Mzn2WuySldGGL504hx7AAPHiG5qNZZMGgJ9BMROxJrhP6QsajF6PU5ExyJOY3UkrXzOzsmhseHt4KeAswDOwI3AqcAXx6bGzshB7G2xVo9cUte87Y2Nh324yzN/A/wFJgS/KFaDnwobGxsbO6nZckSZIkSZIkSZo+1UptBPhIsXkp8ISR0aFrBzglSZJmVERsxHj2pq83Pp9SujsiRoHXkAO1fjFNU1kCrF+sd5p1aaNmO1NKrQKp68Fl/Y7dWALML9Yvb9PusuJxftHnqsYG0zD3lcVjx5msikxn9UCvle3aTrOW72VK6c6IuBbYFth65qak2WLWZMCKiE0j4r8j4s/AWcBryQFD9WxXiXzi3Cul9JCU0rGzKPjqAcC5wBvIaRBXAYuBg4AfDA8Pf2yKh7iWfKJrtrRKc8jw8PALyLVoXwBsT67fugM5sO3U4eHhdmkEJUmSJEmSJEnSDKpWak8DvlxsXksOvrp0gFOSJGkQngVsWqz/OiJS40IOvgI4JCIWTdM85pfWH5lSig6W5dM0l7nkH8XjvSNikw777A6sV6z/vf9TkqZu4AFYEXFARFSBK4BPAw9jPOjqLuB7peanpJTOmflZtjY8PLw+MEaOYDwX2HtsbGwzYDPg7eTAsdcODw8fNoXD7DM2NrZti6VpCcYiKOwrwELgeGD7sbGxxeRArO+QT05fHR4evv8U5iVJkiRJkiRJkvqgWqk9hvz/8+cDtwAHj4wOnTfYWUmSNBDd3FvfEJiuxCPXAauL9ValBWerlYzPfYc27XYsHlczc5ml6iUR55ErjHXikOIxAb9peK5e9m+DNv37FaS3fasnirKPWxabV5eeuru03mqO0xVEqBk0kACsiNg+Io6MiAvIX64XklPx1QOvTgVeBWyXUnrOIObYhZcDuwG3AU+pl/UbGxu7bWxs7P3koDKA9w0PD7eqmTod3kMOsjoTeOHY2NgVxbyuIGfEOpOcLvE9MzgnSZIkSZIkSZLUoFqp7Q2cSL4pdxfw9JHRob8MdFKSJA1AROwKLC02H0MuO9dqObpot6xhmDX14SY5XNt2KaVVwGnF5lMnm/s06PR13ENK6S6gntzmcW2aPr54PLt4vdMupXQKObkNwJsjom0Jw4hYDLy62PxxSqmxDOD1xeOONBERQ+QKZs2sKTdtN4/CLsVntJn9GC/HeEaT+UGLOQL7tDlmz58DzawZDcCKiGdFxI+Bi4H3kgOX6kFXlwPHALunlPZNKX0mpXR969FmjUOLx2+NjY1d0uT5/8/efcfJVZV/HP88u9n0kEILIaFekDL0jkiRjjo0xYvAElARARfEAmJQfoCAioKjgAUlLO0CUhw0dATpIH0AlQuEkkAgpPdN9vz+OGfYYdkyW2eTfN+v17zu3HvPOfe5U3IX7jPP+QU+C3MMsFdvBJTNZofTdAH4VT6fL82oJKz/qql5dhgiIiIiIiIiIiIiItLr6uN0I+Bu/MwajUBcm0QPtN1LRERkhXUsPn/gTefcw865Wa09gBtCn13M7DMlY8wJyxHtHKucdsWpgWMz27WtwcxsZDvH66hyz6M1N4VlrZl9qoKXmW0I1IbVpJPH6Kwzw3JL4Hdm1mJyUagqdQ0wGp+kfnYLzYqJZq1V0zqzle3Q9BpD+a/zj5pvCPEXj/O0c+6t4j7n3HzgzbB6cAt9BwCnlRGjqmT1cb1dAetm4AB8+VwDFgHXA/sD6zjnznLO/beXY+q0bDY7lKZMxLtaahOSsopzmLaVWdqddqNp/tO7W2lT3D4gtBcRERERERERERERkV5UH6djgHuANcKmb9Ym0W0VDElERFZCuTTO5NL4slwav5hL48lheVkujTO9GUdIYjk2rP61vfbOuQLwv7A6vmRXsbrSZma2UxtDFNsdZmatJbdcBTwC1AB3m1mdma1aEvNIM/uimd1I9ycxFePbzcw26kT/y4F3gKHAvWa2e3GHme2BzxkYhC+gc0UXY+0Q59w/gAvD6reA+81sTzOrDvH1N7MvAI/ji884oM4593wLw90YlluY2W9CxSzMbA0zywHH4Gc0aymOWfhiQeAT1arbCX0OcIKZXVD8zJjZmvhEvf1Dm5aSxIoxftPMjgtJV5jZ5sAk2pjakKbPwXAzO7yd+KSCKjIFIf7L8RdgTefc0c65e51zrkKxdMWmNJV5K7TR7uN/4Dt5nJuy2ezMbDa7OJvNvpvNZm/JZrNfaKN98TjT8vn8hy01CNs/aNZeRERERERERERERER6QX2cjsLf+FwvbPphbRL9pfUeIiIi3SuXxqvl0ngSvoLQScAWwLpheRLwUi6NJ+XSeNU2hulOewDrh+ftJmA1a3eMmRXzHx4EXsNPB/eEmX1kZpPDY+eSvlficxd2A6ab2ZTQ5pFiA+fcUnzVogfwiUy/AT40s5lmNgeYgZ9G+Ai6P//iNuAjYBTwXzP7oOQ8WpvK7mPOudkh9g+AjYGHzGyemc3Hv0YbAu8DWefc3G6OvV3OubOA04GF+NnE/gksNrOPwra/A9sCs4CjnHN/aGWcu2mq9lUHzDSzmfhzOwn4BtBi3kTwp7A8DZhnZm+F1/jiFto+B1yCr4L1kZnNAN6jKQHwnBBPcxfhP5MD8Lky88xsNj6XZCuaEg9bOr8U/34B/NXMZpV8Dr7cxnlJL6tUAhbAccDTZnZWS+XulhNrlTyf2ka74r612mjTlh3w79UyYG3gMODv2Wz2pmw227+F9sXjtBVTd8QlIiIiIiIiIiIiIiIdVB+nQ/A3FYuVRX5Rm0S/rGBIIiKyksml8WrAw8CB7TQ9EHikl5Kwxofl2865p8rsc0tYrg3sCx8nTe0NTATeBobhE8vWBQYWOzrnHsQnKP0TmIuf5m5d4BPJTc65GcA+wOHA7fiEm8H4qlhvALcCJ+KTsLqNc+4jfFLazfhkopEl59GvzDGeAzbHJwC9QlOBmZfxFagyzrkXuzPujnDOXQJsBJwLPIVPthoGTMdXHjsL2NA5d0NrYwRHA2fgz3ExPrdiErC7c+6advqeB3wPn1y1DFgH/xqv1krMp+MTpv6Nfx/mAQ/hE9n+r5U+s4HP4quSvYtP/JsL/BnYhqZpFFtzGJADXsd/houfg6Ht9JNeVNaXshudgU+82iSsb4z/MJ9rZg/h/wG8xTnXYvm3Pqj0w9xWzMV9wzow9iL8ly8Bns/n83MBstns5vjX8RjgK/h/gE5oJa72XsfOxCUiIiIiIiIiIiIiIp1UH6f98TeLdwmb/gycWbmIRERkJVVP03379mwCXAMc1HPhgHNuPJ+cSrCcPs/SlFRUuv0dfG5Ce/3vwFewaq+dwyda3dqB2B5sKbaOtHHOvUw7iV3OufaOMR1fselHbbXrSFzltinzWFOAn4ZHZ8doAH4RHi3tX6+Nvo3Ar8Oj3OPV479DHYnxQ+Dk8GhJW5+DmcCp4SF9VK8mYDnnfgn80sx2xZd5+wowBP9B2jM8LjezW4B659wDvRlfX5LP59+nhS9ePp9/GajNZrMf4svxfSObzf4qn8//t7djBJg6dWq7pQ2lXaNLn0+d2l7hMhHpKaNGjSrO61z94Ycf6t83kd6na6JIH6DroUjF6Xoo0kfomihScSvkNXHxHFdVPYDfLlvM/gDV/blz5x8OPG/QqKq1V5RzlBWLrodSSWPGjHm30jGsqHJpvAXtV75q7sBcGmfqoqTQEzGJiCzversCFgDOuceAx8ysDoiB44HiXKtD8NWdjjGzd4DrKhFjmeaVPB8MzGml3eCw7M55U38CfBsYBHwRKE3AKsY1uHmnHojrnS70lU97utIBiKzMZsyYUXw6Gv37JlJpuiaKVIiuhyJ9iq6HIhWka6JIn7JCXBOdc7x5bwPLFvv1kVEVW39zwIHVNTa5ooGJtEHXQ6mwLlfWWZHl0thV4LAv5dK4Ux3rokTvp4is0KoqeXDn3Dzn3JXOuV3x845eAnyIv5gaMI5Plt3NmFlvzC1brtKfo4xpo11x33vddeB8Pj8fPy8rwAatxNVWTD0Sl4iIiIiIiIiIiIiIfNobdzfw7qNLARg2toqtjh9AdY3uRYuIiLTFzP5oZs7MvtwDY5uZnWhm/zaz+eE4zsz27O5jSdeY2YPhvTmnD8SyZ/Gz0o1jrlfy+Vuvu8btKjPbxMyWmdkTlY5leVCRClgtcc69CnzPzM4AsviqWPsD1UDxg3s4cLCZ3YmfT/OOMJdnpfwHH5vhE8j+00q7zcPyld4IquQ4a2az2dXy+fz05g2y2ezqwBrdENe4LvQVbzRNv+DaAXi/grGIrNRGjRr1NP47+f6MGTN2qHQ8IishXRNF+gBdD0UqTtdDkT5C10SRiluhromPnL/wuEUz3LkAVsUbo7etPrTfQJvRXj+RStP1UEQqycw2xecNvAzcUkb7u/A5BgDfdM5d2U6XM4ELwvMGYFp4vsTMtgYOAWY55y7tUOC9zMweBPYArnbOjW+n7UTgWOAh59yePR1bpYVkpjdLNk10zh3XTp8rga+XbFq/B0JbLjjn/mNmNwNfNbPDnXPtfg9XZn0mAavIObcUuBW41czGAMeFR7HKUw3wpfCYaWaJc+6USsSaz+fnZbPZp4CdgANo4R/9bDY7FtgsrN7fXcfOZrNDaErserPZ7keAJUB//AWmpWkc9wvLxaF9p2ju5a6bOrW0kBrv6zUVqZyGhoZl4ekyfRdFep+uiSJ9g66HIpWl66FI36FrokhlrUjXxPo4PQo4N6y+6xrZa5evrft2JWMSKZeuhyJSYRfiC7ac75xrs9qQma0N7FuyaTzQXgLWaWF5CfDDkKtQHG888FPgLeDSDsQsfduXzewU59z8lnaa2WDgK823O+fOAc4JbXYE/tvNcTWUjFnJIkQtOR/4KnCBmf2t9Hsin9TnErBKOeemAj8DfhbK/H0DOAwYGJqMAr4NVCQBK7gOn4B1ZDabPTefzzef//qH+ApZU4F/ljtoNpu1fD7f1kXkHGAQvgLX30t35PP5Odls9u/41+r0bDab5PP54h/IZLPZfsDpTc3zc8uNS0REREREREREREREylMfp18AJobVj4D9apNIyVciItJldVHS6Xlsc2l8GXBSJ7peVhclvXJv3swi4GD89bOcqju1QBVwI754ymfNLHLOpa2MvwZNM0b9RUklK4W3gHXxM6/Vt9LmMGCVkraf4px7CtikOwNzzk3p7jG7i3OuYGaPA7sAXwD+VuGQ+qyqSgdQLufcg865o4G18AlXz1Y4pKI/Am8AQ4C/Z7PZLQGy2eygbDZ7Jk3JYRPy+fwnMhWz2ezkbDbrstnsxBbGfTCbzZ6VzWa3DAlTxT6bZbPZvwDfD5v+nM/nW5r68Cf4KljbAtdls9nRof9o4NqwfXFoJyIiIiIiIiIiIiIi3ag+TncD/or/Mfx84KDaJHq1slGJiIgAcEUv9+uMb4Tlzc65cioCjQ/LK4Gbm21ryaCS5/M6FJksr64Jy2PbaFPc11qC1srq+rD8RputVnLLTQJWkXNutnPucufc9vgkossqGU8+n18MZIEPgC2BF7LZ7GxgLr4kogG/zefzV3Vw6HXx1b9eABZms9np2Wx2Pn5+2+KcpNcDJ7cSV7FdA74c3NRsNjsTeC+sLwHGt5K8JSIiIiIiIiIiIiIinVQfp1viZ68YiP//8YfUJtFTlY1KRETEq4uSAnBnB7vdWRclL/dEPM2ZWRVNiTA3ltF+V2Bj/KxUD+ALkgDUhrFK2+5pZg6YXLL5TTNz4TEx7C/e31+3ZF/xMb6FGDYysyvM7H9mtsDM5prZ82b2UzMb3krcD4bxzjGzAWb2YzN7MfR1ZjaivXPvCWa2lpn90swKIZYFZvaKmV1sZqNb6TPYzI4ys+vCOcwws0VmNtnMrjGzbcs47lfM7OFwzNlm9piZHdWNp3YHMAPYy8zWaeH444DPhzZ/b76/pN2exc9Cs+17m1lj2HdYK30PDvsbzWy/ku3rlXy+1mvWZ3zYPjms72xmt5vZB+E1ftnMfmRmNW3EXGVmJ5vZs2Y238w+MrP7zewLYf/k1j7bwU342dkONLO1WjvOym65S8Aq5Zx73jlXV+k4QrLTFvi5YVNgADAbuA84NJ/PdybGH+Craz0HTAeGhe0pPjPz8/l8/qh8Pr+kjbiuB3YEbsAnXg3GX3SuB3bM5/NJJ+ISEREREREREREREZFW1MfphsDdwHCgEfhabRLdV9moREREPqUWKLdYx3+AY3owlua2BEYDS4Eny2hfLGByg3OuEfgXfgq5ccDezdouAabh78EXTQ/bpuHv808D5oR9jSX7io+FpQOa2dfxhVROBDbCJ6oMALYCzgGeM7MN24h/YIj5fPw0dMvaP+WeERJy/oefkWtzoAZfdGZT4HvAS2a2Ywtdj8Anvn0t9CP0Wxc4GnjSzFr9DJnZr/FJPrvhZx9rBHYCrjWzi7t+ZoB/75MQV20L+4vTWN4Q2naIc+5+4Ndh9U9mtnbp/pC8dmVYzTnn7unoMcJr+DC+SFB//OdsM+CCEHdLfWqA24HfAduEPgbsBfzdzNrNZ3HOfQC8BlTz6e+UBP3abyLlyOfzHwCnh0e5fdZrY9/NNJVG7Epcz+P/kRMRERERERERERERkR5UH6drAffgbxoDnFibRLdUMCQREZEW1UXJ9Fwa74Yv/nFgG03vBI6pi5KPeicyAD4Xli875xa21dDMBuGTfwCuA3DOOTO7DjgLPw3hvcX2zrnHgNGhytCbYfMOzrnJJcOeGioBXQW845xbr43jHwT8CZ+UdS5wpXPufTPrhy+W8htge+BWM9smJIg1dzI+2SwGbnPOLTGzdfFTGPcaM9sauAWfdHUxfjayt/DJOhngl8B+wO1mtolzbk5J95nAL4Bbgeedc4vNzPAJaRPwCXx/NLOHnHNvNztuDHw3rF4B/NQ596GZjQJ+jE/8mt1Np3k1cBI+2er8ZvtqS9p01lnAPvjku6vNbN/weTRgIrAaUADO7MTYq+MTuH4PnO+cmxaqpJ0LfAc43MwOcM7d1azfmcCX8ImBPwZ+55yba2ZrAhfh3+typvl8Cl9pbneaqsxJieW6ApaIiIiIiIiIiIiIiEhfUB+nI/GVrzYIm35Um0R/qmBIIiIibaqLko/qouQg/GxPlwEv4qfmezGsZ+qi5KBeTr4CX/mIEEd7DgdWAV5xzj1Xsr2YIHKoma3SncEVmVk18Ft8gtLRzrnznXPvAzjnloZkr/3xs1VtCRzSylBDga865250zi0J/d9yzpWTFFPqq2b2flsP4Ktt9L8UXx3p+865HzjnJjuv0Tn3Ij6J50VgLeAbpR2dc39zzp3hnHvSObc4bHPOuf/hp5O8F1/p67jSfiEx6dyw+lfn3EnOuQ9D/xnOue/hE5danMaxo5xzTwGvAhuZn7qyGEdxGstXnHNPd2H8JfgCOQvxlaK+H3bV4T8Li4GjnHOLOjH8YKDeOfcd59y0cLxZYda4l0KbI0o7mNlQ4Idh9ULn3IXOubmh7zTgePy0nYPLOP7zYblzJ2JfKfRqBSwz64lSec45p0peIiIiIiIiIiIiIiJSEfVxOhi4A38DG+BXwM8rF5GIiEj56qKkAJxS6ThKrBWWH5bRdnxYfqIij3PuVTN7BtgOn3TUE0nRe+ATr193zt3WUgPn3AwzuxOf6LIfvkJUcy92Zjq6FgwMjw4zsw3w5zMfuLylNqEy11/xyWT70TTdXptCBahJwL7AZ5vt3gpfJQv8NHotOZ+m97k7XI2v/DQeeCxsG1+yr0ucc6+Y2Q/wU/6dHxLfLgq7fxSS2Trrwla2/w3/d2im2fb98Al+Dfi/T5vH6szsQnxyWHuK03au1WarlVhvV8CykmV3PkRERERERERERERERHpdfZzWAH+l6YbiROAHtUnkKhaUiIjI8m21sJzZViMzWwfYCz+12nUtNLkmLI9rYV93KFZQGltm1al1Whnn8W6K52rnnLX1oPUEo+K5DADeauNcihWdPnUuZjbWzH5uZs+Y2SwzW2ZmzswccEloNqZZt+3CcnazCmYfc869DrxT5mtQjmuBRuAIMxtoZgPxlaMa6aap9ZxzlwH/APoD9fjEuPvwVcY6a4Zz7o1W9k0Jy5HNtm8Tlq8452a00vdx/BSY7R4/LEeZmWbba0ElKkcpYUpERERERERERERERJZ79XFahU+4OjBsygPfVPKViIhIlxSrOC1up92x+KIz/3LOvd3C/huAi4FdzGzjMB1edypWAhoArFlG+9ameSun0ldPK55LPzpxLma2B/B3fLWlojnAInyC3CD8VJFDmo2zelhObed4U4BxZcTVLufcFDO7D18d6hB8Dstw4G7nXHtxdMQ3gLeBGmAecKxzrit/I85tY19xSsOaZtvbfX1DZbPpwOh2jl88RhX+M7+wnfYrnV7NSnPOVfXAo7o3z0FERERERERERERERKQ+Tg34DfC1sOkhIK5NonIqCIiIiEjripV2mlfzae7YsNy9WGmp9AFMo6kozfgeiLOYq3B3e5WnwmPPVsZZ1gOxdVTxXP5b5rmsV+xoZjX4ylFDgZfxUw0Occ4Nd86t6ZwbDZxebN6L59SWYiWw8XTj9IPN1NKUEDWUpmpUy6tRYbnAOafkqxaoLJiIiIiIiIiIiIiIiEjHnQ2cEp4/Bxxcm0S6GSUiItJ108Oy1QQsM9sd2LADYx7TA9OmTQvL1qYWXJ4Uz2VtM+toEZxdgLHh+Zecc/c55xY0a9NaVa1i9a+1WtlftHYHY2rPbfgKXfsC+4Tnt3fX4Ga2NXBeWH0pLP9iZmt01zHK1O7ra2b9gVXLGKv4fZzeZquVmBKwREREREREREREREREOqA+Tk8G/i+svgYcUJtEsysYkoiIyIrk1bBcv40248NyEj4xpLXHaGABPkFonw7E0BiWbVVseiwsNzGzjiSD9UXFcxkK7NHBvsXkq4+cc2+20mavVrY/E5YjzGyrlhqY2QZ00/SDRaGC0834nJkq4KbuqupkZoOA64H+wJ3AzvjP9BrAn7vjGB3wXFhubmatJTTuwqenLmxJ8fv4aputVmJKwBIRERERERERERERESlTfZweCfw2rE4F9qtNog8qGJKIiMiK5pGw3KGlnWY2BPhKWL3ROTerjcc0fBIMwHEdiGFOWA5vo80DwFv4JK1L2qqwZWY1Zja0A8fvVc65/9KUhPWLkETUIvNKX5diEvooM1u9hfZ7AXu3ctzn8cnsAGe1csgftxF6V/wG+FV4/KYbx/0lsCm++tRxoRrYUUAD8EUzO7Ebj9Wee4B5+ASr01tpc0aZY+0Ylg93NagVlRKwREREREREREREREREylAfpwcA9fgbrTPwyVeTKxqUiIjIiudRfAWqUWYWtbD/y/hKTUuAfBnj3RKWhzRLHGpLISyHm9nhLTVwzjUAJ4VYvwTcbWY7FxOxzKzKzDYzszOB/wFbl3nsSjkFWAhsBzxsZvuYWb/iTjPb0MxOAV7En2/Ro/gqYwYkZrZ+aD/AzI7GT/c3o43j/iQsjzCz35nZaqH/SDP7JXA8TUle3cY595Jz7vvhUWi/R/vM7EDg5LD69ZAAiHPuOfz01QC/MrPPdMfx2uOcm4dPMAM4y8zOKCYCmtmaZvZnfGW45lNGfkL4TG8XVh/qqXiXd0rAEhERERERERERERERaUd9nO4K3Ar0A+YDB9Um0cuVjUpERGTF45z7CLgvrH6hhSbFSlb3O+dmlTHk34HFwEAgLjOGFHgwrP7VzGaZ2eTw+HJJu0nA0fjEpX2Ax4EFZjYdWAS8DFwIrAe4co5dKSFJ6EvAR/hkm3sJ52Jmi4AUXwU0Q8m5hPfgR2H188AbZjYbmAtcA7xJ09TNLR03AS4JqycD08xsBjAd+D4+gej5bjnJHhSqf10VVn/vnLujWZNf4pOXBgPXmlk50/51h5/hp+qsAi4CZoXX9z38d+m7+Nca/Ge2JbsDw4B3aKqUJs30agKWmS3rgcfS3jwHERERERERERERERFZudTH6RbAP4BB+OljDqtNoicrG5WIiMgK7U9h+bXSjaG60u5h9a/lDOScm4ufig1gfAdiOAzIAa/jk7fWDY9PTCXonLsB2Aif3PI8PtlrBD4B6Qng18BuzrlHO3DsinDO3Y8/lwn42Ofhz2UR8CxwObA/cEOzfjn861WshlUN/Bf4KbAr/rVo67inA0eU9O8HPAkc7Zz7frecXM+7ElgTf97fa77TOdcI1OKreW0PnNMbQYVKbVmgDngBXzkO4H7gQOfcZTRNtTmrlWGK38OrwnlIC8y53kuyNLNGfCakdeOwzjlX3Y3jifSqqVOnjsVnigKMGzNmzLuVjEdkZdbQ0PAusDYwpaamZmyl4xFZ2eiaKNI36HooUlm6Hor0HbomilRWX7om1sfp+vibgWvh73HEtUl0U6XiEelNuh6KSKWE6kBvA6OBjZ1zr1U4JJEVUpjms/j9Wtc593az/QOAqcAqQOSce6uXQ1xuVGIKwu5MvhIREREREREREREREekR9XG6Jn76nbXCppOUfCUiItLzQtWec8Pq8lIBSWR5dGZY/qd58lXwdWAU8GclX7WtVxOwnHNVPfBQ9SsREREREREREREREelW9XE6Argb2DBsmlCbRL+vXEQiIiIrnT/hK/OMNzNV4RPpJDP7q5l9ycxGlmyLzOyP+AQrgF+20K8GOAM/LeQ5vRHr8qxfpQMQERERERERERERERHpS+rjdDBwB7BV2HQpcEHFAhIREVkJOeeWmtlxwL7AOkDFpiQWWc4dAhwOYGZz8TPXDS3Z/0fn3F9a6DcOuAp4yTn3fk8HubxTApaIiIiIiIiIiIiIiEhQH6c1wI3AbmHTNcD3apPIVS4qERGRlZNz7lHg0UrHIbKcOxE4ANgSWBMYCEwFngKudM79o6VOzrk3UOWrsikBS0REREREREREREREBKiP0yrgL8AXw6a/A1+vTaLGykUlIiIiItJ5zrkrgSsrHceKrqrSAYiIiIiIiIiIiIiIiFRafZwa8Gvg6LDpYeCI2iRqqFxUIiIiIiKyPFACloiIiIiIiIiIiIiICJwFnBqevwBka5NoYQXjERERERGR5YQSsEREREREREREREREZKVWH6cnAueH1deBA2qTaFblIhIRERERkeWJErBERERERERERERERGSlVR+nRwCXh9X3gH1rk+j9CoYkIiIiIiLLmX6VDkBERERERERERERERFYMuTT+CnAysBXQH0iB64BL6qKkodxx/rXo99sNrVqNWY1TmNn49tMudSOBZcBbwP3Ar+qiZHKZMW0FPA3UAK/XRUlU3Fcfp/sB1wIGzAL2r02iN8uNU0REREREBFQBS0REREREREREREREukEujS8FbgI+CzwF3AWsA/wceCCXxoPKHWt243uff2PpYyxyc6ii3xvAbcBDwCjgFKCQS+M9y4ipP1BPCz9Ir4/TncO4NcAC4KDaJHqp3BhFRERE+gIzc+GxZ6Vj6cvMbL2S12q9SscjKx4lYImIiIiIiIiIiIiISJfk0vgQ4FRgHrBTXZTsXxclhwMbAS8BuwHnlTvemtUb33bgwAkcNOhsDh3886/WRcmRdVFyED6h6zfAEOCaXBpXtzPUT4AtgctKN9bH6ebAP4DBwFLg8Nokerzc+ERERKT3mNnOJYkzb5lZm3kOZranmZ1jZuPbaXdaaLd1d8bbXczskBDfIZWOpbuY2YPhfXy4A33ikvd/k56Mr68zs43M7Bdm9qyZfWRmi81sipndbWYnm1nZP3joxLHXC5/Hc3rqGF1V7ne/pygBS0REREREREREREREuuqssLyoLkqeLW6si5LpwElh9ZRcGg8vZ7AdBxyVDqka9antdVGyBPgBsAgYC2zW2hi5NN4BOBO4GbiluL0+TtcD7sFX03JAbW0S3VVOXCIiIlIRx5U8Xwf4fDvt9wR+Coxvp91pod3WnQurxx2Cj++Qdtr9NzwW9HA83WFiWO5mZhuW2efYsHzCOfef7g+p7zPvAuBl/N/C2wCr4N/zMcB+wO+A//VgJbT18J/Hn/bQ+N1hT8r77vcIJWCJiIiIiIiIiIiIiEin5dJ4bWCHsHp98/11UfII8A4wADioGw7pgMbwfHErMQ0ErgZm4qcs9BqtGp98NSZsOaU2iW7ohphERESkB4SKPl/FX///GDaPr1hAfZBzbpPweKrSsZThZnzFVIDa9hqb2VrAvmF1Yg/FtDy4GvgRfursW4Cdgf7OuZHAMOBI4A38DxTuNrMDKxXoykwJWCIiIiIiIiIiIiIi0hXbhOWMuih5s5U2/27WtlPClIM/xU8d+AqQttL0PGBToK4uSj4obrQ5q6yFnxYR4Ke1SXR5V+IRERGRHncoMBx4DDgfn4h1qJmtUtGopFOcc/OBv4bVWjOzdrocA1Tjq58mPRlbX2VmJ+JfB4CznHNfds496ZxzAM65ec65BNgWeBroD1xjZmNaHlF6Sr9KByAiIiIiIiIiIiIiIsu19cPy7fo4/QpwMrAV/uZPClzHT/tNoWZpads2Pfv7RdsNXq2KWVOWMPeDRf9j8dwBmHPDRpzmlm70Wr/Fuzw+uXHVGUfURUljab/6OP3M0nFvf3vIwNpTq98du6hq4eCr60n/MHzAhPcW7f4vGjb+74DQ9Lf4JC0RERHp28aH5bXOuXfM7CH8NGNHAFeWNjSz9YDSZPA9zMw1G28vmqYpK7rKzK4qWX/LObdes7GrQyxH4v/OGQ58BDwO5JxzDzYPPEwF908A55yZ2abAj4G98VMhv4NPRjrfOTevpX7BsWZ2LJ+0vnNucmhfPMe9WoljVeB7wJdo+lvsTSAP/Mo5N6O7Yi/TVfjXcj1gd+ChNtoWz/s259zsENsW+Pd/d2BdYDSwEHgVX2HrCufcoo4EZGbn4D8TDznn9uxCm42A0/Gv01hgGfA6cBtwafEcOhDXQODcsHqnc+7C1to652ab2Vfx0xSuip+Ku67ZeG1+VlprY2aT8a918zZF/+ecOyfsm4h/364Gjge+g59GNAKW4L8zFzjnHm3h2HtS8rlrJb5PtSn3u9/aOXcXJWCJiIiIiIiIiIiIiEhXDAMYfMthawA3AUuBB/DTy3we+Pmw337nnbnf+S3ULC2rWsW89xo/P+N/jfQf4Vg6+v1Bbug8bPEAqqeOYcATu9D/yZ3WNFe1evM6CA73QL931hnj+jWAsxfwN2JGs6T/7oPu3Y8BT+0IcDtwWm0SNb8pIyIiIn2ImY3DJ7I04P/GALgGn0B1HM0SsPDJLtOAocCQ0K95ctES/N8o04DV8bOGzcEn8BR92CyONfDJSjuFTQ6Yi0/8ORRfketc51xpUlfzc9kH+Bu+iudsfK7GhsAZ+GSR3Z1zDSUxTsMneQ3EV39qnrizrLVjNTvu1sBdwJph04KwzITH8Wa2v3PuxW6KvRwP46fL2wCfqNNiApaZbQ9sFlYnluy6g6ZkoAXhMRLYJTy+Zmafd87N7UBMXWZmXweuwE8TWIxtAD5hbyt8It2+zrnXOzDsYfjPKfgKcG1yzr1pZtcB3wCOM7PvdfC9ac2HwCr41xn857NUS0l4hv/eHo7/74N5of9BwAFm9g3n3FUt9OuMcr/7PUpTEIqIiIiIiIiIiIiISJfUvLIp/Z/bdgz+xspOtUm0f20SHY6f7u+l6hmrjht43z5ljzdq4+rbPjthILv/ZBgHficzbsFXb6qaX3vN2Lmn/uZri7d/eqa5qkGu/+Lb6uO0urRf4xofNC449FbmnHbpCcdet8nOwFHAW0vXfrdq2RrTqJo9AmBEbRI1tnBYERER6Vtq8TkNd5ZUaforPiFp11Bt6GPOuXecc6OBi8Omx5xzo5s9HnPOXRzavRPandqszQ7FMc2sH7560U7AI/gKWoOcc8PxVYbOxid7/MTMvtzGudwE/B1fuWoEPpnlR/hkrp2Br5ecx2MhvhvDphtbOI93aIeZDccnjq0JvAbs4Zwb4pwbEs7jDXwSWb6dKR3Ljr0cYeq8iWH1y2Y2uJWmxepX7wL3lWx/ED8l39rhfEbhk26OAt4Dtgcu6khMXWVmBwF/wn8WzgbWCq/zYOCz+Om41wduNbOO5OnsFZYfOOceK7PP7WE5FP9adFn4ThxWst7883hxC90OBg7BVwQb7pwbiU+6uwv/vf6DmWW6Kb6yvvvdcay2KAFLRERERERERERERES6Yu6Ah/YoPr+oNomeLa7UJtF04CSAAU/uhM0bsqCF/p+SOWpAOmhU0y2MuihxdVEy5TtbTLxhydbPb+z6NWBLBoxY9Ll/HVJsk0vjPefW/XbtJds9O+k72//hT/VxasAvgWOpWcqCbL7YdM/6OB3bhfMVERGR3jE+LK8tbnDOzcEnFZXu70lHA7sCTwH7OucedM4tDrHMcM6dD/wktP1JK2OAT8CJi9MGOufmO+cuAv4R9h/RA7GfDIzDJ8jv65z7V3FHmIptP3zlr3WBb7cxTk/EfjU+gWsYJYk9RWbWHz/dI8A1zrmPk+edc+Odc9c656aWbFvgnLse+ErYNL6NxK5uFaan/C2+4tPRzrnznXPvh7iWhsSf/fHJYVvik5LKVawA9kIH+pS23azVVj1vOPBT59wlzrkF4Ct04c//FXylsLa+M8sdTUEoXdLQ0HA6PmNROmm11Varbmz014uqqqqnGxoayioXKSI9YnRx2dDQ8G5FIxFZCemaKNJn6HooUkG6Hor0KbomipRpj6pTB78wxc+M8sVfjjmloaHhEzfwjrxmXZLvvOqYNdjWe/eQAxo2b/871dY18Ru7ncdNV7+JA6LdR0xsaGj4DcBmQz8//JV5D9iIfmP2u+mdsxc3ntFYPX/6smqAqmGL3bJVpltxjP4nT3pjyrz9Z64xYIPumJJFZEWm66FUTE1NjZJlV2JmthsQ4ae8u6PZ7mvwST/HmNnZpck5PeD4sPytc25RK22uAy4EtjCz0cXkm2YuCpWfmrsd+CJ+OsDuVkyMuto591bznc65182sHvgW8FXg562M0+2xO+feNrMH8FNMHktJkl3wRXyFMfjk9IPtjfuomc0CRgBbAz1e9QjYA1/Z6XXn3G2txDXDzO7Ef572A24tc+xRYflRB+KZXvJ81VZb9bwFwKXNNzrnFpvZxcBfgIPNrL9zrsenB+wNSsCSrloFWLvSQSzPzIzq6o+rZI9uq62I9Jpq9G+bSK/TNVGkz9H1UKQCdD0U6ZN0TRRpx4Bp6wALaRy0ALfqzNGwxqfaLFt7ClWzNqL/tHEDKeM71do1sbHRUbhtNq6hmmVrTMOtNnMofnoVqq0GgFlLp/ZjKb6ewrBwfLDq95ourdMHvlLT4Pb8dKAi0hpdD0Wkt40Py1taSHy6C/gQX91pH+CengggVDbaMaxeGpJG2rMO0FIC1tOttJ8SliM7GF6bQgWpYmLUA200vQ+fgLWlmdU451pKTu+p2CfiE7A+b2ZjnXOlib7F6Qcfc879r3lHM/sKfsrBbYHVgYEtjD+mk3F11K5hOdbMWnrvi4aG5To9HE9f8W/n3PxW9j0Ulv2BzYHneieknqUELOmqOTT9wyqd4JyrbmxsHA1QVVX1vpnp180ilTMa/z8SltHyH8ci0oN0TRTpM3Q9FKkgXQ9F+hRdE0XKtGTagCGwcETjiFnkP3pvwRuLN+v37pIlNcscrF7Tb+nmg+cvWTps+pCBbETV9FGLKOMX/K/OXzjwiTnzV50ybQnD7mtwDQ7rvxCGT4P+c8CtOpf5X72RVWo+PwuYD7DbqKPZbdTRTH5s/qD7bpw+Kv0sfLQhNAyFwdWNjZs9OKsKYMS4moYjt/7tBz36ooisOHQ9FJFeF6aOK1Zval4ZCefcUjNLgO/gE7V6JAELX31oQHhebiWhFqe9c87NbaV9Mbmsu3M3RuH//Ya27+cXk56qQ59pzRv0YOy3Apfhi74cg68ihpmtDhwY2kws7WBm/YCbgENLNi/B/325NKyvDlQBQzoZV0etFZYDgDXLaN+RqRFnhGVHKlmt1kL/Smjrc1e6b4X5UYQSsKRLampqfg38utJxLM+mTp06FngnrO4wZswYlfAVqZBQQntt4H2VNhbpfbomivQNuh6KVJauhyJ9h66JIuV7Ppl1FvCzRf2G8q/ZYwbDoqVg9wLzpixZsveUJf2HZ6o3ZR3gveeW/KumpmZ/gFwaH4q/0TalLkr2Lh3zyqn3PPpB42d2XXsubPgCVrpv9mgofGkAa682dP7js5JNdljtkI9vFNbH6T4zxjHp6W/Asv5Q1UDaCM+MemrZ3qu+PGq1RnO8sEbDKdmamj/2+AsjsgLQ9VBEKuRwPq5jyQNm1lbbQ8xsuHNudg/EUV3yfBfn3BM9cIyVlnNugZndBHwDX/HqwrDrKKAGWAjc2KzbN2lKvjoXn6A1uXSKRDN7BxgLtPnB6UbFz8ndzrkDunnsV/EVtrbqQJ/Stq90bzjSFiVgiYiIiIiIiIiIiIhIly1xq1DFYjL9/8zQqqmNgGtwg3l5yfEsaXlm3eHAZ2hhypgx/e7adRz/YM11BzHv/OmTlrrFc6tmjly/6t11thzwwBcG7nLlYP77+WPdK7tUT68LferjdMelNdz+3KHULOsPwz7gmj99Pqqtj9O9Hf0OA/jv3sabOzMhLqTXJJloYU+9FiIiItIlx3Wg7SDgq0BPJFd/hK8AWI2fNm55SsCaQVPsbU0hW0yuXUZlqiVNxCdgfcbMdnLOPUnT9IO3OefmNGv/lbCsd879tPlgYdrI1ZpvL0OxelZLUxkWDW9le/HHAD0xteADwNeBNcxsV+fcY2X0OSQs5wP/brav+Jlo8TzNrLVz7Iy2poAs3Vdambb4PmBmA1uYfhRafx8qrqrSAYiIiIiIiIiIiIiIyHJtLkB1AwyreicZWjX1Mfwv9Q+qsQXvjK5+4vfVS3zDZdUsaG+wXBp/ZljVrAdHVjvmueksZfHeGIc3jpq50dItX3h+4bF/+MWSIY5NHqgeuuk9/gZTfZxuCkyasiVDFq8C1YuZMncNxtfH6W7A3wyrWbjXA0zdZUYDMA6o7ZFXQkRERLrEzNYD9gyruwMj23hcENqNbzZMY3G4dg7XZjvnXAPwdFj9Ynux94Byz+NTnHNLgJfC6t5tNN0nLF8M59urnHOPAq+F1WPNbEtg67B+VQtdigljT7ewD2Bn2k6ias3MZuO3ZIdWtheTojYxsw07cey23AZMD88ntNfYzNYHvhZWrwqfg1LtnWdr5whNn0esnbJ0wfZhOtGW7BGWi4GXW4gPuhZjb1U/+wQlYImIiIiIiIiIiIiISKfNHs0cgEFzYHZjdFZdlOxRFyXD66JkcF2UbHHBJt//9pCZLASYs1bTzBx1UTKxLkqsLkrWA8ilcSaXxvcC/1lGw54L3EyWshhgAH5GjxeBkx8f9N0fT9vYlpqDNf/H5+rjdB3gHmDV9z/jx142gD8fdD47A5OAIcDPvvWtE2yxG3VROPxhPf7CiIiISGcci0+eeNM597BzblZrD+CG0GcXM/tMyRjFqkkj2jlWOe3+Epaxme3a1mBmNrKd43VUuefRmpvCstbMPlWdKSQLFZPSk04eoztMDMsY+FZ4/g6++lNzxakmN2m+w8yq8NMSdkYxWW1tM9uuhbE/B3y2lb4PAG/hP7eXhDhaZGY1Zja03KCccwuBc8LqgWb2ozbGHo6fsnEwvprZRS00K57nwS30N+CMNsIprUY2oo12RUOAU1s4Tn/ge2E13yxJ7DWgWPWqpRhXxVdMay/GcuLrdkrAEhERERERERERERGRTvtf+P16/4Vw0Pkttxk+1S8/3KDl/bk0/iy+esA+LbcAYA/n7OEhNmXi0gE+kWvQLIbik6/GAswcx3yADR9hLnAXMAy4oDaJihUDitOwbNP+mYmIiKwc4kKaiQvpZXEhfTEupJPD8rK4kGZ6M46QAFKcfu6v7bV3zhWA/4XV8SW7CmG5mZnt1MYQxXaHtTH12lXAI0ANcLeZ1YUkkGLMI83si2Z2I92fxFSMbzcz26gT/S/HJzINBe41s92LO8xsD+Bu/BSObwFXdDHWrqjHVy4aCZxY3Oaca2yh7b1heYKZ1ZpZDYCZRcCt+CSp+Z2I4VHg3fB8opltEcatMbOvALfzyepMHwuVw04K5/Al/Odk52IilplVmdlmZnYm/vO6dUcCc85dBlwfVi8ws5vNbIdiFSozG2JmXwWewVeHagBqnXNTWhjuxrD8gpmdYWZDwhjr4RMaP5V8VuJ/YWwob5rQ2cB5ZnaqmQ0qOc5tQCaM9YmEuZCMdXtYnWBmWTPrF/ruDNwH9G/jmOV+93uEErBERERERERERERERKTTPtyIVWaN+Xj1a83318fpbgMWMGhZNby9HUub78+l8TrAHfhkqU9Y7Ibz2pLDeG3JYby65CieWfz9wfPd2ket/ob/pbzBfsBnABYN4aLGGoYMnwIbP8RPaUq++nHJkO+E5epxIR3S+bMWERFZ/sWFdLW4kE7CV8U5CdgCWDcsTwJeigvppLiQrtrGMN1pD2D98LzdBKxm7Y4pqTz0IL6STj/gCTP7yMwmh8fOJX2vBBywGzDdzKaENo8UGzjnluIr8TyAT2T6DfChmc00szn4SkN3AEfQ/fkXtwEfAaOA/5rZByXn0dZUecXYZ4fYPwA2Bh4ys3lmNh//Gm0IvA9knXNzuzn2sjnn3sUn1kDTazixleYXA6/jE+KuBhaY2Sz8+/1FfALX9Fb6thXDMvxnfhk+OehFM5sLzMNXEnsCn9DWWv9JwNHAQvwPCh4PsU3HV3R6GbgQWA//meuoY4BfAEuBLwNPAYvNbAZ+OvAE/35OBQ50zv2jlXH+jP/Rg+ErZM0xs5nAm/jksSPaOMcFwHVh9Vfhs1T8PJ7WQpe/hcelzY5zED5Z7VshibK5HwEf4qtY/Q2YZ2bz8K/pcOA7rcVI+d/9HqEELBERERERERERERER6YphrzdNyHJmfZxuW1ypj9NVCTer3toBlgxhcMm+Q+vj9D/DflP3CL7iwccGPLQ7Nn8wS90gPmzclg8bt2Vm46Y0LhzOVnfPY5Vp1h9/A2pc6HL5g6fw2+FTYcfrwRxD+XTyFfibaEWrdMO5i4iILJfiQroa8DBwYDtNDwQe6aUkrPFh+bZz7qky+9wSlmsD+8LHSVN745N43sYnZa8bHgOLHZ1zD+ITlP6JT2IZHdp8IrnJOTcDn1RzOL46z3v4ad5qgDfwlZdOpI3klc5wzn2ET0q7GZ8oNbLkPPq10bV0jOeAzfHJNq/gE2+gKSEo45x7sTvj7qSrSp4/6pxLW2oU3oud8RW73sUnMy3Cvy97OOcmdjYA59wd+M/Nvfip7KqB/wDfxScnfeqHBM363wBshH+tnwcW45OI5uITuH4N7Oace7QTsTU6587AJ4f9CngB/3ftUGBaiLkO2Mg5d38b4ywF9se/92+Ec1qCr4y1o3Puvtb6Bt8GzsN/lqpp+jyOaOlwwFeA0/Cft/7ALOBOYHfn3FUt9ME5NxnYCbgmnJvhE7IuwVfoaqmyV+n5tfvd7ynmXGeS60Sku0ydOnUsTb+6GjdmzJh322ovIj2noaHhXfwf6FNqamra/eWAiHQvXRNF+gZdD0UqS9dDkb5D10SR8sWF9CzgZ1v+jaljX2IMfjqR+/HTv+wNjJg/kncfPoGxjTXck2Si/QHq43Q8cFXj8FmNc35w8Sd+MD5iwvm4qmUsW3MajaNmgDXC3JH0e280VUtqaBjgmL7BMtZ6tR/4X/wfNWkCo/e5mCn9F4Ez5pjjtuaxLhnEsA824rDXd4X5qzEmyUTv9eiLI7Kc0/VQZMUVKl+1l3xV6s4kEx3UU/GIyIrBzCbipxK92jk3vrLR9K6ysiJFRERERERERERERERaMRfgxYP5YOxLfBc4GdgVXxXideCiR77J2MYaTsFXE/gkc5+arWPBF++g31vrUv3eWvR7fUNsSX/cgMU0rvkei6LJ/HurbZg3ZCgD6nls1DscW5tEjZMK6dz+i4pDsgr+xs8n9F8IY1+Ed7eE+au1EIuIiMhKIC6kW9Cx5CuAA+NCmkkyUUtThomIrPSUgCUiIiIiIiIiIiIiIl0xOSzH1SbRTcBNzRtMKqS3NmtLbRJNzKXxYuD65u2X7PwkS3Z+stUDDm3oz7xlO/P0kdx/3bbREoAkE82NJ6QzgFFANslEdzTvFxfSLPA3YHqSieaXeX4iIiJ9TlxIKzHV1UtxocWZ4dqVZCJrv5WIyPLrU78qERERERERERERERER6YDnwnLVuJCu30qb7cPy2WbbF3XmgFUsAWBZf0Y221Ucf3ta1locIiIi0geZ2R/NzJnZlysdS3PmnWhm/zaz+SFOZ2Z7Vjo2aZ+ZnRPerwcrHQtAT3x+zGxyGHN8d43ZVWZWE+KaY2arVzqe7qQELBERERERERERERER6bQkE70LPB1Wv9Z8f1xIdwPGAYuBSc12v9SZY85u3KD49H/Ndt1WPGxcSD9xDySsfzWs3oqIiIj0aWa2KXA88DJwS7N965UkrDR/LDazd8zsdjM7tAdDPBO4AtgOP/XytPBY0oPH7BUlyUmTy2g7vvja90JofUJJYpMzszfMrM0Kb2Z2dLPP6PheCrXPcc41ABcBw4CfVDicbqUELBERERERERERERER6aoLwvLMuJBuW9wYF9JVgcvD6u+STDS7ZN+hjy06/+8vLThxYfPB3l26Ow1u8KcOstQN5I2GLzDfjQWYzaenO5wITAU2Bs5rtu+8sP1doL78UxMREZEKuRCoBs53zrWV3DOTpuSnaWHbWOBg4FYzu769BJlOOi0sLwEGO+dGh8djPXAs6bvWBz7XTpvxZYzz3/BY0NWASrwexpzdXsPu4pwb75wz59z4NppdBUwBvmVmG7TRbrnSr9IBiIiIiIiIiIiIiIjI8i3JRLfHhTQH1AFPxIX0fmA+sDcwAngUOLtZt+HAZxoWrwbNcq3eXrofby/dmyE2jQE2A6ORJW4V5ru1aGQA/ZYuZa0BD/5lXM2DH0BSGseCuJAeAdwDnBUX0ixQADLhMR/4SpKJPpX0JSIisjxJMlGnE4riQnoZcFInul6WZKJTOnvcjjCzCJ9A9RHNql+14DDn3IMlfQ3YEPg/fHXOI/FVOK/txvjWANYIq39xzi3trrFlufIWsC4+wepfLTUws3HAXvjP8kBgSEvtnHObdHdwzrm9u3vM7uCcW2xm9cCPgO8A361wSN1CFbBERERERERERERERKTLkkx0Kn6Kv8eBXYGD8NWmzgQ+X5r0lEvjVcb1u/cEAFs8gP5P7viJsdbvdwerVr3MMmqY3bghMxo3Z6Fbg8E2jXU/fIXdf1vNeu+9/l3g5lwaj2wWx6PAVvgqV6OAw8OyHtgqyURP9MwrICIisty4opf7dcY3wvLmMGVZ2ZyXArU0TVec7c7ggEElz+d189iy/LgBWAp82cw+Xb7Vq8Xn5lwf2op3fVgeY2b9KxpJN1ECloiIiIiIiIiIiIiIdIskE92UZKI9kkw0PMlEg5NMtEWSiX6eZKIlxTa5NN4ZeH5cv4d22eP169nrd8agv3+RAY/uinNVfLRsU2Y3bsgitxrVNLBK1WQ2qrmZ7Qf8nO2ff57N/vQZBs43qj5aFXxy1Qu5NP5cszjSJBMdm2SitZNM1D8sj00y0eu9+4qIiIj0PUkmKgB3drDbnUkmerkn4mnOzKqAY8PqjZ0dxzm3DHgprLZYdSgcb4SZ/cTMnjGz2Wa2yMxeN7Pfh0pcpW33NDMHTC7Z/KaZufCY2Kx9jZmdZGaPmNnMMPabZvZnM2u14lHJeHua2dpmdrmZvWFmi83s+WZtB5nZd83sUTObEdq8bWbXmNk2Zb1YPawzMZpZlZntY2aXmdlTZvaemS0xs2lmNsnMDivjuNuY2a1mNt3MFpjZK2Z2tpkN6KZTmwbcDQwDWoun+Fm+up1YP37PS7ataWYfhO2/baXfOmY2K7T5WbN9k8P28c22r1dyvPXCZ+yK8J4sNrN3wvoatMHMDjCze8Px55nZc2ZWF967iS19J4qccwXgZWBVfLW75Z6mIBQRERERERERERERkR6XS+Nq4AzgXKAawGYPfwrY0VwVc147iJc/sz+LhlZ/ot98N4aZjZvSf8EyNn+tmsGNfnvVzJFPAjsB44AHc2n8M+DcuihRZQEREZH21QIPA+VMe/Yf4JieDecTtgRG46sFPdnZQUIiVyaspq202QG4A1gzbGoIjw2Ab+Gr83zVOff3sH8JPummGlgtbJsOLAvPZ5eMPQr4B7BzydgLgPWA48PY451zxUpALdkYuDkca0EYozT+CD+94kZh0zJgIf7vo6OBI83s2865P7VxjB7VhRjXAe4tWZ8DLMZP/XggcKCZ/cE5d2Irxz0cP1d1MS9mNhDh/xY9AHioi6dWdDXwBfw0hJ+Y5tLMPos/75edc8/42THL55ybZmbH4z+jp5jZJOfcx8mT4TN+DX5q738D53Qi/i2Av+A/Y3PxhZzGAicC+5rZ9s65Wc07mdkZwEUlm2aFsX4D7BHGas+jwObA/vjP+XJNFbBERERERERERERERKRH5dJ4bfwNtJ/hb1guBL416M4DfwgwJQPPHMGnkq9KLRlczXNfhrdDjYSB/9zrPHxFgXn4+x1nAw/l0ni9njsTERGRFUOSiaYDu9F+Jaw7gd2STPRRz0f1sWJly5edcwvbbNkKM9sAmAh8Bp+0c3kLbcbiz29N4CpgM2Cgc24IPlHnemAwcIOZrQfgnHvMOTca2KFkqB2cc6PD49SS7RPxyVfz8Mk5Q51zI/AJOfcANcBEM9uujVP5FfAe8Fnn3BDn3FDgyyH+YSH+jYA8sH2IfxiwNj4Rphq4wsx2avsV6xldjHEpPrnoQGCEc2546LcGcFbY/y0z+0oLx90AP/10P3yi4WbhtR8KfB3YFjipm04zD8wE9jKzcc32lVX9qi0h+a84/edVzapSnQnsjk/OO6qj03WWxPYisIVzbhWaXqPFwIbhGJ9gZrsDF4bVm4BxzrmRwCrAd/EVrcqpavVUWO7eibj7HCVgiYiIiIiIiIiIiIhIj8ml8cH4mzp7hU0vAtvXRckfzVU9Ons0H774Jcq+Y1E4EGaMZba5qvvroqQe2Ab/i3+AXYHnc2l8RLeehIiIyAooyUQfJZnoIHzVmsvw1+jJYXkZkEky0UG9nHwFvsIlIY5y3Gpm75c8FgGvA0fik392d879t4V+5+OnP8s55453zr3qnGsEcM697pw7CrgLn5ByekdOwMx2Bb4UVo91zl3tnFsSxk6BLH56xJoQR2uWAvs65x4rbgj9Ab6HTxS7HTjEOfeMc25paDPVOXca8Ad8gtOPOxJ/iXHNXttPPfBJVK3pdIzOuXedc7XOubucc7NLtn/onLuQpmpPLSVSnYVPnnsLONA592rou8Q59xd8dafhHXwtWuScW4yfKrOKkkpxZjYIOAJf8evalnuX7Xv4SnRr4qtVYWbb0/QafNc5979Ojj0VOChMCYhzbnF4jYoVyVr6u/r/AAMeAY50zr0b+i5wzl2K/2HEiDKO/XxYbhQqxi3XNAWhiIiIiIiIiIiIiIh0u1waDwIu5pM3xXLAGXVRsiisf/bNnRjuWi989WlV8Oq+vP+H/aNFAHVRkubS+LPAecAP8TfTbsyl8f5AXV2UzO/yyYiIiKzAkkxUAE6pdBwl1grLD8tsP7KV7f3wSSCfSuwIyTFxWL24jbGvx09Xt1+ZsRQVk1Zeds7d2nync26xmV0A3ADsb2YjWprmDah3zk1r5RjHh+WvnHOulTbX4adS/LyZVTvnlrXSrjVVNE3P2Bk9GeM/8MlrO5f2Mz/P3+GhzW+ccy39LViPn4pwnTKP1Z6J+KSuY4ELwrZD8X+X3umce68rgzvnFprZ14AngC+Y2Q/xVapqgLxz7o9dGP7XIYmsudvx/y6sb2ZDiq+jma2Kn2IQ4BfFpMVmcviEuiHtHHt6yfO1gBkdCbyvUQUsERERERERERERERHpVrk0zuCnFCkmX00HvlQXJafWRcmi+jgdVB+nlywZxIPvb0r/jo4/ewxRXEjHFNfromRJXZScgb85+n7YfDzwbC6Nt+3a2YiIiEgvWy0sZ5bZfi/nnBUfQH9gY/wUaTsBfzezo5r12Q4YADjg6TKqO3U0Uac4reADbbS5LywNX9GzJY+3tDFMn1ic7u6WNuIvJn8NwVf76qi3Sl/blh7AcT0Vo5kNMrPvmtmDZvaBmTWYmTMzBzwXmg3kk0l4G9BUfenBlmILyWAPdeiVaINz7kngv8DGZrZL2Nzl6QebHeM5YEJY/Tn+M/4+8I0uDv10K9unlDwfUfJ8a/xnFvz0jp8SkrWeKePYpQlXq5fRvk9TBSwREREREREREREREekWuTQ24NvAr/A3w8DfXKyti5L3AOrjdCf8jajPzFobGjtzp8KoBnYDbirdXBcl9+bSeEvgKuAL+BtTT+TS+Ezg0rooaekX+iIiItK3FP+GaKkqT7uccw3Aa8BZZlYDfB+41Mzyzrm5oVmxypZRXoWnQR0Mo5hMMqW1Bs656Wa2GJ8ItkYrzVqrArZWyfPW+jY3uMx23aVLMZrZWvgEqo1L9i/AJ+Y14qctLCbrDaGpmlJpIk+rr387+zrjanz1q2PN7G1gH2A28LduPMavgKOBLcP6t5xz5VaKa83cVrYvKnleU/K8+PoubKVqW9HUMo5deoyOfsf6HFXAEhERERERERERERGRLsul8arAbcBl+BunS/FTAu5fFyXv1cdp//o4/RnwGPAZgAUjeLkLhxze0sa6KPkQ+BJQh79xW4O/WfWPXBp3ZQodERER6R3FqjitTS3YEX8Oy9XwCTFFxQmQF7dX4amk0lMltDYdX+kEzmuVeQ6TeyHe7ozxUnzy1RygFljdOTfEObeGc240sHNJ20q9P6WuwSeGfRU4AZ+Pc6NzblGbvTpmJ2DzkvXPdePYlVA6Pej0VlstJ5SAJSIiIiIiIiIiIiIiXZJL472AF4GDw6YU2KUuSn5ZFyWN9XG6FX56k7Pw9yYWAqe8ui9ndeGwrd5oq4sSVxclvwV2BF4Nmw8AXsyl8QFdOKaIiIj0vGIiRnckYL1d8nzDkufTwnKAmZVbnakjilWJ1m6tgZmthq9+BfBBB8efVvK8o9Mj9pZOx2hm/Wn6u/I059w1zrnmCTqtJdaXVoQa00obaOO96Qzn3Lv4KSdHAD8Km7tl+kEAMxsGXItPbHspbP6eme3VXccoU/H1HWRmLf4gIlirjX1Fpd9xJWCJiIiIiIiIiIiIiMjKKZfGNbk0vgC4n6YbXFcD29ZFyb/r47RffZyehU++Kk6V8jiwVW0SXeaqeQpfKaszLosL6R1xIT0iLqQtTllSFyUvAtsDfwyb1gDuzKXxr3NpPKClPiIiIlJxxeTp9bthrLElzxtKnj9dsv7FbjhOc/8Oy8+30aZYkcsBz3VkcOfcm8D7YbUn4u+yLsZYmpz2dCttWks8egOYFZ7v0VIDMzNg9w7GVI5iwlUN8Jpz7rFuHDsHbICfOnHPcCwDrjazEd14nPY8X/K8xQpcZjYY/zd4e4rf8fnAO10Lq/KUgCUiIiIiIiIiIiIiIh2WS+MNgIfxv/A3YC5wVF2UjK+Lkrn1cboJ8CjwM/xNqCXAGcDnapPotTDMKsDMTobQD38z70bg/biQ/jkupHvFhfQT9z7qomRBXZR8Czi85FjfBR7PpfFnOnlsERER6TmPhOUO3TBWXPL8meIT59w84Oaw+tNQjapVZtbRalw3heXmZnZYC+MNgI8rgd7lnJvVwfEB/hKWdWYWtdWwE/F3l87GOBefmAawSQtt18BPN/0pzjkH/DWsnhqSgZo7Gli3rXg66VbgYvz01z/orkHN7HBgPP41OdY5NwP4DvAmMA74fXcdqz3OuY+Ah8LqD0IyW3OnAEPKGG7HsHzcOdfZH2X0GUrAEhERERERERERERGRDsml8VH4X7/vFDY9CWxdFyXX18dpVX2cnoav5FC8qfIcsF1tEv2iNomWxYW0Ji6kZ+GnLVy9EyGcBPyGpul6VgGOx0/78lZcSC+KC+nmpR3qouRWYCvgX2HTNsCzuTT+ei6NW53OUERERHrdo0AjMKq9pJ3WmNlwM/sO8OOw6Zkwbqkz8dOprQM8bmaHmtnAkjHGmdlxZvYYcHJHju+cexzIh9Wrzaw2TKtHOKc8sAW+CtfZHTu7j/0c+B8wHHjEzI4N09QV41/DzI4wszuBX3TyGF3VqRidc3OBJ8Lqr8zss9Zkd+Cf+Kn4WnMhfsrr9YFJZrZJOF5/MxsP/AGY3W1n2RT3AufcD5xz33fO/a07xjSztWmq5vpr59z94VhzgWOAZcBXzeyY7jhemf4vLHcHrgsxYmaDzKwO/wOMWWWMU0yyfKjNVssJJWCJiIiIiIiIiIiIiEhZcmk8LJfGVwPXAsPwv8K/APhcXZS8UR+n6+OToC4BBuJvCP0fsFNtEhUA4kK6E/4m6M/wU8ssw09hWK4JSSa6IslEpwFrAwcB1+NvsoGfaugMoBAX0ufiQnp6XEjXAqiLknfwUwH9JBx3MHAlkOTSeETHXxERERHpbqHCzn1h9QtldLnVzN4veXyEr3qZA/rjE4AOD5WRSo/zDrAf8BYQ4asXzTOz6Wa2AHgbX8FpF5qqMXXEcfgk9aH46eLmmtlM4LVw3KXAcc65Z1ofonXOuTlhnBeANYGJwCwz+8jM5gHT8JVCD+jM+N2hizF+F//33Tr4qmjzgXn4ZJ3RwNfbOO4b+OSkpfhpCF8Nr/1c4Cr8jwMu7/oZ9qxQXWoiMAr/Gp5Vut859yg+2Qzgd2bWE1W9PsU590+akhuPBN4xsxnAHPyPJG4F7gj7F7U0hpkNoWkayRt7Ltre06/SAawostns6vgM2Sz+P+7mA88Cl+fz+ds7Md4qYaz98HNjrovP4HwfeAy4Ip/PP9xG/4nAse0c5uV8Pp/paGwiIiIiIiIiIiIisvLJpfEO+ESnYiWKqcDRdVHyz/o4tXrSbwK/xt9kBHgVqK1Non8DxIV0KHA+frqYYsWpZ4BvJpnoubiQHo+/YVPs39wC4IdJJrqsuCHJREuBO4E740I6DDgUf7Nt73CMrcPjl3EhvQ+4Fs6/LclE5+XS+P5wPusCRwA75dL4qLooaV4dQ0RERHrfn/D3yr+G//ugLc2n11sGfAQU8IkgVzrnFn6qF+Cce97MNgO+CRyCr0o1HJ/48xLwNPAP4O8dPQHn3Awz+xxwQjiPzfHJ32/jk89/6Zx7taPjNjvGW2a2A35KvSOAbfHJOouB/+L/1roTuK0rx6lEjM65J81sF+AcfBLVEOA94C58In9bFbBwzt1iZjvhK4ztjn/tXwduwFfb+lH3nGGP+i6wDz6J6Sjn3JIW2vwf/ruyI3CNme3pnGvs6cCccxeY2XPA9/E5Lf3x35k/45Pbbg9NZ7UyxMH49+RfzrnXWmmzXLFmSZ7SCdlsdnP8L3rWCJvm4j8oxS98Lp/Pn9rBMV+j6T9iwX+hGsO4RRfn8/kW5w0tScBaROul8/6Tz+f37Ehc0v2mTp06FngnrI4bM2bMu5WMR2Rl1tDQ8C7+V5NTampqxlY6HpGVja6JIn2DrocilaXroUjfoWuiSJNcGlfhb6z8jKYfdv8N+HpdlHxUH6dr46tIFSsXOOBXwNm1SbQIIC6kBwFX4CsYgE+mOhvIhSQqQrthwNH94JuDjG0AFjheWOarT9QnmWhWOTHHhXRt/K/xj8ZPO1hqAf5m7LVb9r/830Orpl4GfDXsa8TfxPpZXZQsK+dYIisyXQ9FpFLMrAafqDQa2HhFSdAQWRmEyl1v44sXHeucq2+hzT/wlWyPds5d18sh9gglYHVRNpsdALwCbIDPoD06n8+/kM1mB+OzEc/D/8rm+Hw+f1UHxp2MT5y6EpiUz+dfz2azBmyELyF3WGj67Xw+//sW+k/EJ2Bdnc/nx3fu7KQ36H+ui/Qd+p8JIpWla6JI36DroUhl6Xoo0nfomiji5dJ4LaAe/8t78D/6PR34/YgJ54Ov5vA7YETY/wZwbG0SPQIQF9I18FUr4pJh7wFOTDLRm60dtzuviXEh3QJfFesoYEyz3e+Du2HDfrfNXqP62R+YMSRsfxhf3evtzh5XZEWg66GIVJKZfRtfSeePzrlvVToeESmPmR0NXIOvRreuc25Ks/0Z4EV8rs1WzrkV4ocPVZUOYAVwAj75agHwhXw+/wJAPp9fkM/nf0bTvKHnZ7PZmg6MW5vP57fK5/O/zefzr4cxXT6f/x/wFeDB0K7FClgiIiIiIiIiIiIiIl2RS+Mv4m+MFJOvCsAOdVFyxYgJ568G3AxcS1Py1eXAVrVJ9EhcSC0upOPx0xAWk68+widCHdBW8lV3SzLRS0km+iG++tY+wERgXtg9Guy7ry897JwnFp/z3lsN+05Z7IYDfA54IZfGh/dWnCIiIvIpfwJeA8abmZJARfoQM/u5mX3TzMaEileY2apm9j38dxfg+ubJV8GP8YWMzlpRkq+gqVSwdN7RYXlDPp9v6ZcwvwBOwv+qZi/8L3valc/n/9XGvsZsNns1sCewQTabHZnP52d2KGoRERERERERERERkRbk0ngg/v9tf6dk82XAD+qiZGF9nB4C/BFYPex7F/h6bRLdAxAX0g2BPwB7l/S/Fjg9yUQf9nD4rUoy0TLgfuD+uJCeDGTxCWH7A9WOftGUZXswZdkerFL1BqtXvTBi1erCX3Np/Cfgu3VRMr9SsYuIiKyMnHNLzew4YF98IrWqRIv0HdsBPwzPF5vZAvwPMyxsewo4tXmnML3oK8D3nHP5Xoiz1ygBqwuy2exQYIeweldLbfL5/NvZbPZVYDP8f2yWlYBVhuklz/U+ioiIiIiIiIiIiEiX5dJ4M+AGYMuw6SPg+LooydfH6Yh60j/gk5aKrgZOq02iWXEh7YefnvAcYFDYPxk/3eDdvRF/uZJMtABIgCRMkxjjf3C9A8Ccxg2Y07gBbyz9IqOq/vPNVatfPvCXr33rkB9s9IdnKhi2iIjISsc59yjwaKXjEJFP+TnwJrAzsBYwHJiBr5p7I/AX59zi5p2ccw3Aeb0YZ69R4k7XbEpT9l6hjXYFfALWZt147D3CchqfTMZqbu9sNvsaPiN4EZACk4Df5fP5ad0Yj4iIiIiIiIiIiIgsp3JpbMAJwCU0JU/9EzimLkqm1MfpfsBfgLXDvg+AE2qT6G8AcSHdDrgS2DrsbwQuBX6SZKI+XTkqyUQfADkgFxfSTYCj8MlY6zlq+KhxCz5q3GJsP+b/+5sv3/fwXLfOD6DqqSQTuYoGLiIiIiJSIc65e4F7Kx1HX1JV6QCWc2uVPJ/aRrvivrXaaFO2bDY7FjgxrE7M5/Nt/UfeWGA9YD4wFNgWmAC8ks1m926jn4iIiIiIiIiIiIisBHJpPAr4K/B7fPLVMuAsYN8RE86fXR+nVwB305R89Vdg89ok+ltcSIfEhfRi/BQjW4f9zwM7JZnoe309+aq5JBP9J8lEZwMbAp8D90ejYSHAUoYw1633Oah6AhrTuJD+JEy3KCIiIiIiKzklYHXN0JLnC9poV9w3rKsHzGazNfjyz0OBt4ALW2n6LHASsC4wIJ/Pj8LPt3k08B4wCrg9m81u3NWYRERERERERERERGT5lEvjPYAXgMPCpjeBz9ZFyYUjJpz/2bCv+IPgmcDXgCNqk2h6XEj3A14Cvoe/37AIOBPYMclE/+7F0+h2SSZqTDLRI0lmo285akaOqPrv90dU/W+esTS0qNoA+D8gjQvpo3Eh/XZcSFetYMgiIiIiIlJBmoJwOZLNZg34E7Ab/j9k43w+P7ultvl8PtfCtrnAddls9hHgOWAkcA7+P5g7ZerUqWM721c+Nrr0+dSpbRVTE5GeNGrUqOrwtPrDDz/Uv28ivU/XRJE+QNdDkYrT9VCkj9A1UVZ08xtnVP9z0W9OA+oIP9bux8Dbtu3/5bOGTd5q6Q0Xpn8AvgkYQFV/Hlh3j34/3PDA/tP+taghM/7l9KfA4cXxauDR7Qb0O/OIIf0nA2t2wzWsz1wTfz1qMLDNje8teyX/8KLLLvxg2XqHf7hsa+a6dYtNdg2P39QW0gdWq7ZbDh/c/4H1a6oXVyxokW6i66FU0pgxY96tdAwiIiLlMuc0RXlnZbPZLPC3sDo8n8/PaaXdJcBpwDP5fH77Lhzvt8ApwFLgsHw+f0cXxjoXOBuYA4zM5/ONnRln6tSp+gCJiIiIiIiIiIiILEfmN87gqSXX8lHjZAD6MYBt+h/Ouv22Z/bby3jlhiXMn+b/12/1ANj44P6M2cnnYDy7ZBm3L1jC/PB/hgcZHDy4Pzv0r8bMKnE6ve6dpc/z7JKbmNs4iA8bt2LGsu2Z70Z8os0gg636V7Nd/36s36+KqpXktRER6U5jxozRP54iIrLcUAWsrin9yc0YfDJTS8aE5XudPVA2m70Yn3y1DDi6K8lXwZNhuQqwKvBhF8cTERERERERERERkT7unaXP8eySm2lgEQAjq8axU/+jGdy4Gq/fuYTJ9y/FhZ/rjtywis2O7M+gUVXMWNbIzQuW8N+Gpt/ybtO/mkMG92dY1cp1f3xcv60ZVbUOTy25joGNDzK2+kEWuvVodIfy+tJVme9goYMnFi/jicXLGFllbNe/mu0G9GPN6qpKhy8iIiIiIj1ACVhd8x/A4Uswbx7WW7J5WL7SmYNks9kLgO+FY30jn8/f2Jlxesi4SgewAhgNPB2e7wC8X8FYRFZqo0aNehr/nXx/xowZO1Q6HpGVkK6JIn2ArociFafroUgfoWuirGimLH1p8L+XJOc2sPCrYZMbwNArtuv/1YvfvHaVDT94aeGlbtnH/y970cBRdsGWxw2YOHeAq7p41sLjZza6HwCDAKpg6trVVT86ZuiAB3ow5D59TRxSNYod+x9V/c9FuVMX2ZxTB9vkKriEbaoG/+3tpXV3TVs29MDFsB8wcGaj475FS7lv0VKq4cVVquyWXQb0y+8zqGZ6pc9DpD26HoqIiIiURwlYXZDP5+dls9mngJ2AA4BbmrfJZrNjgc3C6v0dPUY2mz0H+FFYPSmfz0/sVLCftlNYzgU+6uwgmnu566ZOLS2kxvt6TUUqp6GhYVl4ukzfRZHep2uiSN+g66FIZel6KNJ36JooK5JcGm8LJMBGYdN7wDGDJpz54L/hB7DsXKAm7HsCGH/E5Rv+Ny6kW7GIK4Htwz4H/LYRJvxy0w3m9mTMy8c1cQwb8cfTc2l8G3AdMK7RFhw8tuaircbWcORji86vBQ4DjgH2BGwZbDmz0W05aWHDTyYtbLgHuAb4W5KJFlTsNETaoOuhiIiISHlU67brrgvLI7PZbEvVoH6Ir5A1FfhnRwbOZrNnAj8Nq9/N5/O/L7Nfm/Wes9nsOsDJYfUf+Xy+sa32IiIiIiIiIiIiIrL8yaVxVS6Nv4dPqiomX/0d2GrEhPPfAR4BLsQnXzXgfwz8uUkTeDsupBcBz9CUfFUAdkky0alJJurR5KvlTV2UPAxsBfw1bFoPeGTXgRNO2XXghPokE30eWBc4E3g5tKkGDgSuB6bFhXRiXEj3iQtpde9GLyIiIiIi3UEJWF33R+ANYAjw92w2uyVANpsdFBKoTgntJuTz+YbSjtlsdnI2m3XZbHZi80Gz2eyp+P/wBTgzn89f2oGYjs5ms7dks9lDstnsaiVjDs1ms0cCjwKjgHnAOR0YV0RERERERERERESWA7k0Hg3cCVyMT7BaDHxn8A3xwSMmnB8DzwM7h+YvANvXJtFFkyawO/AScAY+SWgxMAHYLslET/buWSw/6qJkJnAE8E1gIf61Ox+4L5fGY5NM9E6SiX4ObAFsA/wKX4kMYChwLHAv8HZcSH8ZF9Ite/scRERERESk88w5V+kYlnvZbHZz4AFgjbBpDj4hq/hLld/m8/m6FvpNxv/q5ep8Pj++2b5GfOUsB3zQTgiH5fP5x0r6jgeuKtk/D/8fySNpSrr7AIjz+XyHqnJJ95s6depY4J2wOk4lfEUqp6Gh4V1gbWBKTU3N2ErHI7Ky0TVRpG/Q9VCksnQ9FOk7dE2U5VkujQ8EJtL0/6xfBeIRE86fjf9/x3uF7cvwPwQ+b9IEhuKTtY4rGeoh4IQkE/2vN+IutTxfE3NpvClwA74qFsAM4Ot1UXJ7abtQ7Wpv4Gj8VIVDmg31En6KwuuTTDSlJ2MWaY2uhyIiIiLlUQWsbpDP51/G/2rlEiAFBgCzgfuAQ1tKviqDlSzXbOfRv1nff+J/kXQnvjpXIzAcmAk8jC8jvamSr0RERERERERERERWHLk0HpBL40uASTQlX/3B5g7dfsSE83fAJ/QUk6/+A+w6aQI/mTSBw/BJWsXkq9nACcDnK5F8tbyri5JX8dXFfhM2jQJuy6XxFbk0HlRsl2SiZUkmuifJRLX4/9d/NHA3/v/pg7/v8AvgnbiQ3hcX0vFxIR3WayciIiIiIiJlUwUskQpbnn/JJbKi0a+5RCpL10SRvkHXQ5HK0vVQpO/QNVGWN7k03gRfdWnrsGkm8I0RE85/HPgT8IWw3eF/TDxh0gRWBy4v2QfwV6AuyUTvUUEryjUxl8YH4auRrR42vQwcWRclL7XWJy6ko4Ej8QlZ2zbbvRC4HbgWuCfJREu7OWSRT9D1UERERKQ8/SodgIiIiIiIiIiIiIiIdE4ujQ04HsgBg8Pmh4CjR0w4fzd8ws/IsP1N4LhJE3gEOAm4ABga9k0BTk4y0d96K/aVQV2UTMql8ZZAPbAvsDnwdC6NvwdcXhcln/qVfJKJ3scnyV0SF9LN8IlYRwPjgEH45KwjgQ/iQprgpyl8JslE+sW9iIiIiEiFaApCEREREREREREREZHlUC6NRwA3Alfik6+WAWevcvH3jhgx4fxf4ytiFZOv/gBsNWkCM4BH8QlbxeSry4HNlHzVM+qi5H3gAOAHQAMwAPgdcHsujVdrq2+SiV5JMtFZwHrAnsCfgTlh9xpAHfA08GpcSH8cF9L1euAURERERESkHUrAEhERERERERERERFZzuTSeDfgBeArYdNk4HMjJpz/YtWskS+VbJ8CHDhpAqdNmsAZwLPATmHfq8BuSSY6OclEc5AeUxcljXVRcjGwC/Ba2JwFXsil8efb659kosYkEz2UZKJvAKOBI4A8UJyC8DPA+cCbcSH9V1xIT4gL6chWhhMREVnhmZkLjz37QCwPhljOqXQsItJzlIAlIiIiIiIiIiIiIrKcyKVxv1wa/xQ/zeA6YXMy8L69dx8x4fwTgL/hKyOBn5pui0kTWIBP1vox0A9fhekcYJskEz3am/Gv7Oqi5BlgW2Bi2DQGuC+Xxhfm0rimnDGSTLQwyUQ3J5noYGAt4GTgiZImn8NXPHs/LqS3xIX0kLiQ9u+2kxAREelhZjaxJIGq9NFoZrPN7Fkzu9DM1qp0rH2FmY0wsx+EZK/3zGyxmX1gZk+a2f+Z2ZgePv454bFeTx6ns8xsvWKMlY5FVlz9Kh2AiIiIiIiIiIiIiIi0L5fG6wDXAbuFTfOBU4affe675qoeBcaF7R8CJ06awAPAL4BvlgzzGPDNJBO90kthSzN1UTIPOC6XxnfjE6VWAc4EPp9L46/VRcnr5Y6VZKLp+CkkL48L6UbAUcDRwIZAf+Cw8JgRF9Kb8El5jyeZyHXnOYmIiPSQBmBGyXoNMArYJjy+bWYHOOeeaKnzysLMvgL8Hv/aADhgVlhfHdgR+L6Z/cg5l+uhMH4alg/iK7P2NevRFOM5lQtDVmSqgCUiIiIiIiIiIiIi0sfl0vjL+CpWxeSrZ6vfWuezIyacv4O5qntpSr66bVk/MpMmYPgpBovJV3OBk4DPKfmqb6iLkgTYmqbqVTsCz+XS+OjOjJdkoteSTHQOsBGwK3AFTTetRwEnAo8CaVxIzw0JWyIiIn3ZY8650SWPVYFBQAx8BAwHrjEzq2iUFWRm3wBuxF/rn8dPcTzIOTcKGADsBfwTGAz8xszOrVCoIis8VcASEREREREREREREemjcmk8BLgU+EbJ5l+t8rOz7qhaOPgWfKUj8FUOTrnrDB5srOGPwMEl7f8GnJxkoim9ELJ0QF2UvJlL493xFRnOAoYB1+TSeH/g5LoomdPRMUN1q8eBx+NCehpwIL4q1pfwN2I3AM4Gzo4L6ZPAtcCNSSb6sBtOSUREpEc55xYBN5pZDb6yYwRsCqx0CeZmthXwO8CAvwOHO+eWFPc755YBD5rZQ/jE7G8BE8zsMefcXZWIWWRFpgQsEREREREREREREZE+KJfGWwM3AJuETdNszrCvD//FGXvgKxkUqz3c1TCAb977A76Ev/m4Stj+PnAKcKumnOu76qKkAZiQS+N78clQY/EJU7vm0vjIuih5qrNjJ5loCT4B729xIR0BfBk4Btg9NNkpPC6JC+ld+BvZdySZaGFnj9lT4kL6FeBkYCv89IopfkrOS5JM1NCBcVbFJ6NtFx5b46up3J9kon3a6LcZcAKwLX4ao9Xw38EpwEPApUkmeqmj5yUi0hfUx2kG+DbwOfzfEXOAh4ErapOoUMnY2vB8yfMhHe1sZqsC38NfE9YPm98E8sCvnHMz2uhbjb9Wx/jrwgh8Ra7XQ/9rnHPvdyCWc/HJ0YuBI51zt5XZ9Xx8cvV7wNGlyVelnHPOzL6Dr5C5BfBz4BMJWGb2ILAH8H/OuXNaifNTbcxsInBsSbN/NitI9pBzbs/QdjxwFfCWc249M/sCcDp+OskBQAG43Dl3dSvHL/49u5dz7sFy25jZZGDdFtoUtXrOIh2hBCwRERERERERERERkT4kl8YGnIq/OdY/bJ405M/HX1Lz5ga/ATYL2+YDp9/5Ix5x1STAZ0uG+SNwRpKJZvVS2NJFdVHyUC6NtwKuBA7FV6p6NJfGZwO/qIuSxq6MHz4LVwJXxoV0XeAofDLWJvj7RV8MjzlxIf0rPhnrX0km6tJxu0NcSC/FfyeWAg8A84DP478jX4oL6X4dSBr7HP7mb0ftGmKYBvwXX2VsMP5G9vFAbVxIa5NMdEMnxhYRqYj6OF0NqMdXS2xuC+Ck+ji9EzimNok+6tXg2rdVWDYCb3Sko5ltjU9AWjNsWhCWmfA43sz2d8692ELfNfHJzTuFTQ5fiXQkfqro3YAGfAXT9uKowlew+jb+2nawc+6BMs9hbeALYfV3zrnZbbV3zjWY2c/xyd5bmtkuzrnHyzlWO2bjr43F13ImUJoI1mIim5nVAb/Bv36zgYH46Zh3NLPdgW8457rrBwQf4hMLR4b1ac32z+um48hKrqrSAYiIiIiIiIiIiIiIiJdL4zXwU8hcgk++WsKSmtOHTzjv6Zo3N7iTpuSrf80bxfaTJjDGVfM8TclX/wP2SDLRt5R8tfypi5IZwOHAicAifGLUhcA9uTQe013HSTLRW0kmugD/edoefwP0g7B7FXxC0T+ByXEhvTAupJt317E7Ki6kh+ATn+YBOyWZaP8kEx0ObAS8hL/RfV4HhpwG/AE/DdMO+Ne6HPcBmyaZaHSSifZIMtFXk0z0JXyi3A/w79WVcSEd2eYoIiJ9REi+epiWk69KHQg8Uh+nq/Z8VO0zswFm9mX830rgq02VnRxmZsPxVarWBF4D9nDODXHODQH2widzjQbyZrZKs779Q9+d8ElDJwIjnXOj8Em5mwI/wSf8tBdHDb6S47fx1bM+X27yVbAnTdVQby+zTx6fsFbs32XOuVOdc6NLNh3mnBtd8jishW6rAxfjk//Wcs6NBFYFfhH2H4+vOtktnHM7AIeVrI9u9ri4u44lKzdVwBIRERERERERERER6QNyabwf/kZUsYLAfwfeu89ZAx/a88f46W3AJ+X86K4z+HdjDbfib/SBrwx0EfCzJBMt6s24pXvVRYkD/pBL40fwU1BuAewNvJhL4+PqouSO7jpWmJryGeCZuJB+H9gXP6XSofhp+cYBZwJnxoX0eXxVrBuSTPRed8VQhrPC8qIkEz1bEvv0uJCehE8eOCUupOclmajN6h+h3+P46lUAxIU0U04QSSaa3Mr2RuDiuJB+G5+MtRvQbe+RiEgPqqdpmuP2bIK/BhzUc+G0aFczK53KrwYYFZ6/jU/A7UgSLvjpbMfhE3v3dc69VdzhnHvQzPbDJ/iui0+O+nlJ3+PwVZoagP2dc0+W9HXAf8qJx8wGA7cABwDvAvs5517t4HkUk/IXh+O2yzk318zeBDYs6V8Jg4F7gfHFKlfOuVnAGWFqyK8DPzWzPzvnllYuTJGOUQUsEREREREREREREZEKyqVx/1waXwzcTTH5alnVn4efe3b9wIf2vIGm5KunZo5lt0kT2LixhodpSr56Etg2yURnK/lqxVEXJS/jb/L+LmxaFcjn0vh3uTQe1N3HSzLR0iQT3ZlkoqPwn8Nj8VWfitP/bA38Cng3LqR3x4X06LiQDu3uOErFhXRtfJUqgOtbiPkR4B1gAL2fFNBc8Qbx4opGISJShvo43YL2K181d2B9XF7SajeqwV+Tio9RJftWAUbgrwEdcURYXl2afFXknHsdn5wG8NVmu8eH5Q2lyVcdYWYj8dfXA/AVuHbrRPIVNL0WM51zHZkueHpYVrqi2YWtTDF4QViuxSen1xbp81QBS0RERERERERERESkQnJpvDG+ylExyWpWTWHzs4ckR8b4X/+Dr7Lwf3f/gFeWDeBvwNph+zx8daDLk0y0rDfjlt5RFyWLgO/k0vge4Cr8zdKTgT1yaRyHJK1ul2Siufibz/UhCepIfGWsrfA/7t8vPObHhfQ24Frg/iQTdXeVim3CckaSid5spc2/8ZVMtsF/l3pdXEhPADbGT+P4RCViEJGVT32ctpS80tNeqo/TTnWsTSJrv9WnPOSc27O4YmYGDMcn5lwAfAf4rJnt7pyb395gYQrBYhJZW9P93YefqnZLM6txzjWEKQO3D/v/0eEz8dYC/hVieA44wDn3QdtdVkgNwKMt7XDOvWFm7+Cv7dsCD/VmYCJdoQQsEREREREREREREZFelktjw1cY+h0wBIBGe2TYpafdXT1j1Z/jp2YBeGlaxOnPxJwInF8yxD+Ak5JM9HYvhi0VUhcld+TSeEt8UtTe+Bu3/86l8XeBP4RpC3tEkommABfjp9nbAp+IdRQ+EXBIWD8aeD8upDfgp6h6Pkxv2FXrh2Vbn/N3mrXtUXEhHQxcHlaH49+LCJgGfCXJRHN6Iw4RkZVRqJg0C/iHmT0PvIxP0qkDLixjiFFAdXg+pY1274ZldegzLSyL+RWfqpxVphPCci5+2sHpbTVux4ywHGlmVR2ogrVas/6VMN05t6SN/VPwCVhr9FI8It1CUxCKiIiIiIiIiIiIiPSiXBoPx0+ndhU+gaWx+t21Lxn+k3MXV89Y9Tx88lWjgwvu+R5XPBPzV+Dw0P0DIAa+pOSrlUtdlEzFV506Ez/d3UDgCuDWXBr3yjRCSSZ6KclEZwDr4hPBJuIrsQGMBr4LPAsU4kJ6ZlxI1+niIYeFZVtVTYrHX6WLxypXf3zy5LHAIfjkqzeAOMlED/dSDCIiKz3n3BTgrrAaVzKWDvgHMAd/fftTqKrVWcVpCwcAm5TTwcyG0ZSw/EoXji0iLVACloiIiIiIiIiIiIhIL8ml8S7A8xRvFDreHnJ17UXDfv/trxu2d2j2vykZ4jsnsNvSQVyOr7ID8Bdg0yQT3dhN1YVkOVMXJY11UfJz/NRLr4fNhwAv5NJ4z96KI8lEy5JM9ECSiY4D1sRPUTgJKE6FuRm+EsnkuJD+My6kx8eFdHgrwy1Xkkw0K8lElmQiwyedHQR8CPwzLqS/rGx0IiIrnWIy+oZltp9B07Vq7TbajQ3LZTRVivoIP3Ue+ETkzvg3cCC+AtYhwA1m1tlZyx4Ein8PHlJmnyxNOSIPNttXnEZ4YBv9u+tavlqYDrI1Y8Ky+fSMxfeuxRjNbIX4W0OWX5qCUERERERERERERESkh+XSuBr4EXAOYeqbqhkj7xj2m1P72bJ+ZxXbNVbx2/tPY0bDYK7BVzQAn2hzQpKJHujlsKWPqouSp3JpvA1wGXAM/ibyA7k0vgD4v7ooaWhzgG6UZKIFQAIkcSFdA59ceDSwA2DAnuFxWVxI88C1wF1JJionxrlhOaSNNkPDsten/ksy0TTgzriQ3g88CXw/LqQPJZno770di4isfGqTyDrbtz5OLwNO6kTXy2qT6JTOHrcHFBOlyrruOeeWmNlLwNb4So63ttJ0n7B80TnXEPouNbN/A7sAXwBu6kzAzrnHzOwg4E58hdPrzOxrzrll7XRtPs67ZjYpxHKKmV3mnJvdWvtQbeuMsPqSc+6xZk1mhuVYWmBmQ4BN2woJf90v53NZg38dH2rhOOsDxQqaz7YQ42qtxYj/26M1H0/RaGYWprMU6VaqgCUiIiIiIiIiIiIi0oNyaTwWuB84D598tWDgPw76/bBfn76rLet3YGj21jtbcfJdZ7FXw2B+ik++Wgb8HNhCyVfSXF2UzK2Lklp8stNc/A3PHwP/yqXx+m127iFJJvogyUS5JBPtiL9Jez4wOeweCBwB5IGpcSH9XVxId4oLaVs3aot9x7XRprhvchttelSSiZYA14XVQysVh4hIB1zRy/26nZmtSlOi1DMd6FpMnKo1s09NlWtmGwK1YTVptntiWB5pZjt14Jif4Jx7BJ84NR9/bbzGzKo7MdTZwBJgLeDa1qpKmZkBvwW2CJvObKHZS2G5n5m1VGHquzT9OKAlxUToEe3EXPSjEFdzxdimAo+2EuPBzTuFsc5ovr2F+DoSo0iHKAFLRERERERERERERKSH5NL4UOBFYA8AmzfkpVUu+NE/Bz6+64mGrQqwrB8T7zuNu176Er8DMqHrv4Htk0x0ZpKJFlYkeFku1EXJdcA2wFNh087A87k0PrJyUUGSif6TZKKz8dNCfQ74IzAr7F4NOBl4AvhvXEh/EhfSlqaPei4sV40LaWtJZduHZfMqGb1tfliuUdEoRETKUJtEBXwFpo64szaJXu6JeDrCvG2B24DVw+bfdGCIy4F38BUU7zWz3UvG3gO4GxgEvMWnE84mAk/jKzjdbWYnmNkqJXFtamY/N7Nj2gvCOfcv4IvAAvxUvlebWYfyN5xzzwF1+OpTXwSeMLMvmtmAEFN1OKd7gW+Fbhc45ya1MNzNYZzVgXozWyOMMdzMfoyv4jqrjXAKYXlkKwlcpRYAnwf+3Ow4FwAnhDbnOueWNut3Y1h+wczOCFW5MLP1gBuA7do45v9oqpR2XDvxiXSKErBERERERERERERERLpZLo0H59L49/ipbUYC9H9859tXuejM1asWDPlCaPbeW9txzt1nsveSoXwLX8FoAXA6sEuSiZ6vROyy/KmLkteB3YAL8TdPVwGuz6XxxFwaD6tkbEkmakwy0SNJJvoWMBo/3dJtNN0E3Qj4PyCNC+mjcSH9dlxIVw1938Xf6Ab4WvOx40K6G74C1mKgpZvJvWnvsPxfRaMQESlfLfCfMtv+Bz/lbW/b1czeL33gE16fwSf3NgITnHN3lDtgmKbvYOADYGPgITObZ2bzgQfxicPvA1nn3NxmfZeEvs8Cw4E/ADPN7KMQ1yvAD4FVy4zlQeBLwELgKOCqTiRh/QF/jZyJT8i+A1gQYloczmnvcIzvOed+3Mo4rwIXh9WvANPMbCYwA1/R8ifAC22E8qeSvrPN7B0zm2xmzauIAXwIfB+fCPW+mc0APsJP1w1wFT5xu7k/A4/h/2a+CJgTYnwT/zoe0VpwzrkFNFWr/FV4zyeHx2ltnJdI2ZSAJSIiIiIiIiIiIiLSjXJpvCU+acRXGlg0YPqwS0+9Z/A/vniIYaMBltZw6/2n8vjLB3IOTVOo3Q1kkkx0SZKJmv/iX6RNdVHSUBclZ+GnY3o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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all_streams_io_name_df = pd.DataFrame(\n",
+    "#     block_output_data+\n",
+    "#     attn_out_data+mlp_out_data+\n",
+    "#     attn_value_out_data+mlp_act_data+\n",
+    "#     mlp_input_data+block_input_data+attn_input_data\n",
+    "# )\n",
+    "# all_streams_io_name_df.to_csv(\"./tutorial_data/all_streams_io_name_df.csv\")\n",
+    "all_streams_io_name_df = pd.read_csv(\"./tutorial_data/all_streams_io_name_df.csv\")\n",
+    "all_streams_io_name_df[\"IIA\"] = all_streams_io_name_df[\"acc\"]\n",
+    "df = all_streams_io_name_df[\n",
+    "    all_streams_io_name_df[\"stream\"].isin(\n",
+    "        {\n",
+    "            \"block_output\",\n",
+    "            \"attention_input\",\n",
+    "            \"attention_output\",\n",
+    "            \"attention_value_output\",\n",
+    "        }\n",
+    "    )\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"block_output\": \"Block Output\",\n",
+    "    \"attention_input\": \"Attention Input\",\n",
+    "    \"attention_output\": \"Attention Output\\n(After Head Mixing)\",\n",
+    "    \"attention_value_output\": \"Attention Value Output\\n(Before Head Mixing)\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "other_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 1)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Correct IO Name (20) w/ DAS (Learned Basis)\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    other_locations_plot,\n",
+    "    filename=\"./tutorial_data/DAS_IO_name_other_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "other_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "69636ded",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all_streams_df = pd.DataFrame(\n",
+    "#     block_output_data+attn_out_data+\n",
+    "#     mlp_out_data+attn_value_out_data+\n",
+    "#     mlp_act_data+mlp_input_data+\n",
+    "#     block_input_data+attn_input_data\n",
+    "# )\n",
+    "# all_streams_df.to_csv(\"./tutorial_data/all_streams_df.csv\")\n",
+    "all_streams_io_name_df = pd.read_csv(\"./tutorial_data/all_streams_io_name_df.csv\")\n",
+    "all_streams_io_name_df[\"IIA\"] = all_streams_io_name_df[\"acc\"]\n",
+    "df = all_streams_io_name_df[\n",
+    "    all_streams_io_name_df[\"stream\"].isin({\"mlp_output\", \"mlp_input\", \"mlp_activation\"})\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"mlp_output\": \"MLP Output\",\n",
+    "    \"mlp_input\": \"MLP Input\",\n",
+    "    \"mlp_activation\": \"MLP Activations\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "all_mlp_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Name Position w/ DAS (Learned Basis)\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    all_mlp_locations_plot,\n",
+    "    filename=\"./tutorial_data/DAS_IO_name_all_mlp_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "all_mlp_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3213917d",
+   "metadata": {},
+   "source": [
+    "### Head's Query Representations to Trace Name Mover Head\n",
+    "To use the name position information to copy the IO name, responsible heads need to take in name position information, and process it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0104da84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = 9\n",
+    "head_query_output_mo_data = []\n",
+    "for i in range(12):\n",
+    "    _head_query_output_mo_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_query_output\",\n",
+    "        heads=sorted(list(set([i for i in range(12)]) - {i})),\n",
+    "        debug=True,\n",
+    "    )[0]\n",
+    "    _head_query_output_mo_data[\"mo_head\"] = i\n",
+    "    head_query_output_mo_data.append(_head_query_output_mo_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2cf16bb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 1000
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 200)>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "layer = 9\n",
+    "# df = pd.DataFrame(head_query_output_mo_data)\n",
+    "# df.to_csv(f\"./tutorial_data/layer_{layer}_head_query_output_mo_data.csv\")\n",
+    "df = pd.read_csv(f\"./tutorial_data/layer_{layer}_head_query_output_mo_data.csv\")\n",
+    "df[\"mo_head_cat\"] = pd.Categorical(\n",
+    "    df[\"mo_head\"], categories=df[\"mo_head\"].unique(), ordered=True\n",
+    ")\n",
+    "head_query_output_mo_plot = (\n",
+    "    ggplot(df, aes(x=\"mo_head_cat\", y=\"acc\", fill=\"mo_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Leave-One-Out (LOO) Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"head {i}\" for i in df[\"mo_head\"]])\n",
+    "    + ggtitle(\"Name Position for Each Head (Query) Before Self-Attention\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_query_output_mo_plot,\n",
+    "    filename=f\"./tutorial_data/layer_{layer}_head_query_output_mo_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_query_output_mo_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a720164",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_attention_value_output_mo_data = []\n",
+    "for i in range(12):\n",
+    "    _head_attention_value_output_mo_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_attention_value_output\",\n",
+    "        heads=sorted(list(set([i for i in range(12)]) - {i})),\n",
+    "        low_rank_dimension=20,\n",
+    "        aligning_variable=\"name\",\n",
+    "        debug=True,\n",
+    "    )[0]\n",
+    "    _head_attention_value_output_mo_data[\"mo_head\"] = i\n",
+    "    head_attention_value_output_mo_data.append(_head_attention_value_output_mo_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a51eb417",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 1000
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 200)>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df = pd.DataFrame(head_attention_value_output_mo_data)\n",
+    "# df.to_csv(f\"./tutorial_data/layer_{layer}_head_attention_value_output_mo_data.csv\")\n",
+    "df = pd.read_csv(\n",
+    "    f\"./tutorial_data/layer_{layer}_head_attention_value_output_mo_data.csv\"\n",
+    ")\n",
+    "df[\"mo_head_cat\"] = pd.Categorical(\n",
+    "    df[\"mo_head\"], categories=df[\"mo_head\"].unique(), ordered=True\n",
+    ")\n",
+    "head_attention_value_output_mo_plot = (\n",
+    "    ggplot(df, aes(x=\"mo_head_cat\", y=\"acc\", fill=\"mo_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Leave-One-Out (LOO) Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"head {i}\" for i in df[\"mo_head\"]])\n",
+    "    + ggtitle(\"IO Name for Each Head (Value) after Self-Attention\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_attention_value_output_mo_plot,\n",
+    "    filename=f\"./tutorial_data/layer_{layer}_head_attention_value_output_mo_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_attention_value_output_mo_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe82670f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_attn_value_out_cumulative_data = []\n",
+    "current_heads = []\n",
+    "for i in [9, 6, 8, 3, 2, 7, 0, 1, 4, 11, 5, 10]:\n",
+    "    current_heads += [i]\n",
+    "    print(\"evaluating grouped IIA adding head\", i)\n",
+    "    _head_attn_value_out_cumulative_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_attention_value_output\",\n",
+    "        heads=sorted(current_heads),\n",
+    "        low_rank_dimension=20,\n",
+    "        aligning_variable=\"name\",\n",
+    "        debug=True,\n",
+    "    )[0]\n",
+    "    _head_attn_value_out_cumulative_data[\"adding_head\"] = i\n",
+    "    head_attn_value_out_cumulative_data += [_head_attn_value_out_cumulative_data]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f2412fc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 1000
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 200)>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df = pd.DataFrame(head_attn_value_out_cumulative_data)\n",
+    "# df.to_csv(f\"./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_data.csv\")\n",
+    "df = pd.read_csv(\n",
+    "    f\"./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_data.csv\"\n",
+    ")\n",
+    "\n",
+    "df[\"adding_head_cat\"] = pd.Categorical(\n",
+    "    df[\"adding_head\"], categories=df[\"adding_head\"].unique(), ordered=True\n",
+    ")\n",
+    "\n",
+    "head_acc_plot = (\n",
+    "    ggplot(df, aes(x=\"adding_head_cat\", y=\"acc\", fill=\"adding_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Cumulative Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"+ head {i}\" for i in df[\"adding_head\"]])\n",
+    "    + ggtitle(\"IO Name for Cumulative Head (Value) after Self-Attention\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_acc_plot,\n",
+    "    filename=f\"./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_acc_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edf2fca5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = 10\n",
+    "head_query_output_mo_data = []\n",
+    "for i in range(12):\n",
+    "    _head_query_output_mo_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_query_output\",\n",
+    "        heads=sorted(list(set([i for i in range(12)]) - {i})),\n",
+    "        debug=True,\n",
+    "    )[0]\n",
+    "    _head_query_output_mo_data[\"mo_head\"] = i\n",
+    "    head_query_output_mo_data.append(_head_query_output_mo_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4e36df68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 1000
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 200)>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "layer = 10\n",
+    "# df = pd.DataFrame(head_query_output_mo_data)\n",
+    "# df.to_csv(f\"./tutorial_data/layer_{layer}_head_query_output_mo_data.csv\")\n",
+    "df = pd.read_csv(f\"./tutorial_data/layer_{layer}_head_query_output_mo_data.csv\")\n",
+    "df[\"mo_head_cat\"] = pd.Categorical(\n",
+    "    df[\"mo_head\"], categories=df[\"mo_head\"].unique(), ordered=True\n",
+    ")\n",
+    "head_query_output_mo_plot = (\n",
+    "    ggplot(df, aes(x=\"mo_head_cat\", y=\"acc\", fill=\"mo_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Leave-One-Out (LOO) Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"head {i}\" for i in df[\"mo_head\"]])\n",
+    "    + ggtitle(\"Name Position for Each Head (Query) Before Self-Attention\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_query_output_mo_plot,\n",
+    "    filename=f\"./tutorial_data/layer_{layer}_head_query_output_mo_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_query_output_mo_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d05b7eb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_attention_value_output_mo_data = []\n",
+    "for i in range(12):\n",
+    "    _head_attention_value_output_mo_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_attention_value_output\",\n",
+    "        heads=sorted(list(set([i for i in range(12)]) - {i})),\n",
+    "        low_rank_dimension=20,\n",
+    "        aligning_variable=\"name\",\n",
+    "        debug=True,\n",
+    "    )[0]\n",
+    "    _head_attention_value_output_mo_data[\"mo_head\"] = i\n",
+    "    head_attention_value_output_mo_data.append(_head_attention_value_output_mo_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8ca0ab50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 1000
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 200)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df = pd.DataFrame(head_attention_value_output_mo_data)\n",
+    "# df.to_csv(f\"./tutorial_data/layer_{layer}_head_attention_value_output_mo_data.csv\")\n",
+    "df = pd.read_csv(\n",
+    "    f\"./tutorial_data/layer_{layer}_head_attention_value_output_mo_data.csv\"\n",
+    ")\n",
+    "df[\"mo_head_cat\"] = pd.Categorical(\n",
+    "    df[\"mo_head\"], categories=df[\"mo_head\"].unique(), ordered=True\n",
+    ")\n",
+    "head_attention_value_output_mo_plot = (\n",
+    "    ggplot(df, aes(x=\"mo_head_cat\", y=\"acc\", fill=\"mo_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Leave-One-Out (LOO) Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"head {i}\" for i in df[\"mo_head\"]])\n",
+    "    + ggtitle(\"IO Name for Each Head (Value) after Self-Attention\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_attention_value_output_mo_plot,\n",
+    "    filename=f\"./tutorial_data/layer_{layer}_head_attention_value_output_mo_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_attention_value_output_mo_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afa2d05e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head_attn_value_out_cumulative_data = []\n",
+    "current_heads = []\n",
+    "for i in [2, 6, 10, 3, 0, 11, 4, 1, 8, 9, 5, 7]:\n",
+    "    current_heads += [i]\n",
+    "    print(\"evaluating grouped IIA adding head\", i)\n",
+    "    _head_attn_value_out_cumulative_data = find_variable_at(\n",
+    "        gpt2,\n",
+    "        tokenizer,\n",
+    "        [17],\n",
+    "        [layer],\n",
+    "        \"head_attention_value_output\",\n",
+    "        heads=sorted(current_heads),\n",
+    "        low_rank_dimension=20,\n",
+    "        aligning_variable=\"name\",\n",
+    "        debug=True,\n",
+    "    )[0]\n",
+    "    _head_attn_value_out_cumulative_data[\"adding_head\"] = i\n",
+    "    head_attn_value_out_cumulative_data += [_head_attn_value_out_cumulative_data]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2b22bed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 1000
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 200)>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df = pd.DataFrame(head_attn_value_out_cumulative_data)\n",
+    "# df.to_csv(f\"./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_data.csv\")\n",
+    "df = pd.read_csv(\n",
+    "    f\"./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_data.csv\"\n",
+    ")\n",
+    "df[\"adding_head_cat\"] = pd.Categorical(\n",
+    "    df[\"adding_head\"], categories=df[\"adding_head\"].unique(), ordered=True\n",
+    ")\n",
+    "head_acc_plot = (\n",
+    "    ggplot(df, aes(x=\"adding_head_cat\", y=\"acc\", fill=\"adding_head\"))\n",
+    "    + geom_bar(stat=\"identity\", position=\"dodge\", width=0.9)\n",
+    "    + labs(x=f\"Cumulative Head Index ({layer}th Layer)\", y=\"IIA\")\n",
+    "    + theme_minimal()  # Add axis labels\n",
+    "    + theme(figure_size=(10, 2))  # Use a minimal theme\n",
+    "    + theme(legend_position=\"none\")\n",
+    "    + scale_x_discrete(labels=[f\"+ head {i}\" for i in df[\"adding_head\"]])\n",
+    "    + ggtitle(\"IO Name for Cumulative Head (Value) after Self-Attention\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    head_acc_plot,\n",
+    "    filename=f\"./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "head_acc_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b848660",
+   "metadata": {},
+   "source": [
+    "### Use Boundless DAS to find information\n",
+    "Instead of starting with a fixed number of DAS dimension, we can also use Boundless DAS (Wu et. al., 2023) to dynamically find a good dimension by learning the boundary. Here, we use Boundless DAS to find alignments for the name position information variable as well as the correct IO name variable. Very similar results, yet alignments with the IO name seems to be consistently better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4310e73e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attn_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_input\",\n",
+    "    aligning_variable=\"position\",  # now we are localizing the IO name\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "block_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_input\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "mlp_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_input\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "mlp_act_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_activation\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "attn_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "mlp_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "attn_value_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_value_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "block_output_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7b94d268",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jjgttSRq0cETuvPn37NuZ/urKpt6dFjO0eWKNvv3tLdvPX3TRjJrMUR/sHst0vW+L5lf8s3m7eF7l83lr0hXLcpU56oPdL7poxtcv6H/wg2OnbJ5Y46bVG6ormxrZtt8YPSl546va/cEPjp3SVM74GpgYX0i27h5jG1aM+kPTHHn66bt8vWUNY2xg4jEMycT4QjIxPyKZUv34VWI5QyX9rGr6EjWNr4+OMpYrLS1LUkzSGZddesyk5uOr2LECxY71K0kPS8qRt3rRRUWmfVGR2fXt7JBUV/m3Nzbf4s+M2OslXeSfznXC1qBO9vcDef/mz5sR+85m/TVKmiNps7wVrY5u0e44SXvIS2y5yK/f1PZOSS/I+7D80k7GgR5SZNr3SvqWvNXNJC9J6YlixypYPHz6KZI0uext50fH/vAGSd+XtLgqe5gKyzc0StLS/N1H3fbEDTt15r5Kh42PxJWhYbHFOmnClh1Pi0I/qZta+smuQdWoPDg2EAzU/rl5u0CgrqQiONpIU42mln66a1HoJ3VN135x2IX7D4l9FWtUplYNHb/VSjhO2DpT0vuS9vOLXpO0uxmx/96ZmJFcgz4efYokrdtjvXP1ucfcMMM2Y5JmZJXl1seGlEmShn04eNR1f4t2aoxlLh8ZSYsZKptYG7vyooP3byq/+qxQXfWOS3f15sj0QCzT+MYYi2UESvLXpBuxLFfVOyzb9eqzQl+PsSsvOnj/som1sbR6Q5krRrS72hKSy4zYjWbEvlbeY9YKv3iGpHedsLVHy/qpfg7WhOf4AAD0HyRgYcC5Y/59Z+evypAkZeSv+EXL65dcdPpZtQVxBRsN1dePvKszfWYvHZ4rSTnbrd3q2pzZ539cMXlzgyTFN449rPm1+KaxB0lSxeSy2JzZ53/csm3jmBWOJGUvG57fmTiQeskcX41jVmy1Zz3ja2BhfCHZkjHGspYOz5GYI8FjGJKL8YVkYn5EMvWSx6+fSRociw+WJFVMKXOrCgZN9q/9yJ5uvuH313x8PSzp536dDfJWvZrXmfjQc5ywNVZeMpQk/bXldTNivyJpuaRMScd3sttT2umvUlLUP225SkdTu6hfr6Wm/ljdoxcqMu1X5Y2lpiS+b7ubB31UHhwbkKRhm9dcLUn2dHOTpDMk1a8YvHNaplsmV+nanF94c2fuZ03u1NGStGtd2VbX5p501eIRNU6lJK0rGHNy82vrBo05UZJG1DgVc0+6aqutwkZtXvKGJK3Jmzq2qcwJWzlO2LpL3spsBZJcSTdIOtSM2Es7Ey+S67oHnzo2f1V6QJIaRm68uql8hm0ulHRN4efD1DRHBssKOjXG8hcXjJakqkkbX2957ZozTlq8eWplpSRlrhpycvNrmasKT5SkzVMqK64546Stxlj1pI1vSFLe4kFjW15DzzMj9n8k7a4tc9I0Sa87YevSphUae8lzMEk8xwcAoD8hAQsDTqxh0IWSVJ/t6uLZ597eWp2aEVVxSYpXD57aUX/z5t+zW86moCRp3Pj0Vuu4BZvWSFL6xvyM5uXpG/IyJMnNL1vTWru0jLInJCl7Y1Dz5t+zQ2t10Lskc3ylZZQ93lodxtfAwfhCsjFHIpl4DEMyMb6QTMyPSKZUP36VWM52ki72zr3deeqGVTRti/OIpFub2jUfX25FTlOSzEeS9iky7Zc7ig0p0bTax0YzYm+VMOB7u0XdNjlhK1+S2aJdZ/vbo8X1ttpNc8JWbkexoOcVmfZySQdL+psk1dePmiBJaW61fvDtKx5qqmdPN9+WdJkkFdR701VF9qBdO+r/tiduMGsCQyVJZtaQVusU1GxcJEnlWcMLmpeXZ3pJCfnV3vWW8moqHpOk6sAQ3fbEDROdsLWLvDF3gV9ltaSjzIh9tRlhFb/eIlCdM0Py5sirv/vth1pcvlnSK9XDayVJaWW5e3fU33UPRc2mOTKeX/Voa3VihdWLJClrffY3xljW+ux8SaovrGp1jDXmVT8mSTkbA7ruoScmdhQLks+M2BsknSzpEnnbqGZIukXSE07YGpbq52DNy3mODwBA/0ECFgaceH3uVEmqL2hou05ObZ0kBapysjrqr9HN/m7T8dgxQ1utE0ivXi5JmZu/+eZ603kgvarVb1UFjdrHJcmQoUY3+5yOYkHqJXN8BY2aVpc+Z3wNHIwvJBtzJJKJxzAkE+MLycT8iGTqBY9fv5WUJqmxaXzF8hokyZF0gT3ddJvauRM+Wyp548tYPUGSHpN0YJHZZmIPUq9pJbNl7dRZ3qJueyY1O26rz7b66yiWpnZGi/tBL1Jk2tWSzpJ0dY1b6EpSdrxMxY5VVOxYRrOq8yTZGQ1eckx1RsGwjvquT884oel4TN6oVutkxWoXSVJNYPA3PtdoOs+K1Wy16owkpcfqn/eOAhpeWvFbSW9K2tG//A9Ju5kR+4WOYkTPClRn7ChJdQUNbstrM2yzUVK4MTvmSlJaZeaQEstpN3nTqE//eoy56bF/tVanMat+kSRlbk77xhhrOo9n17c6xtz02POSP0fWbxnLSC0zYrtmxL5N3hajn/vFJ0r6wKjL2kniNSQAAOheJGBh4ImnZXs38bbrBBsaJCkQS+vw/4gbTxvTdJybk91qHcNo3CRJaXXfLG86NwKNZa137n6y5TBtfEexoBdI4viSG/y0tTqMrwGE8YVkY45EMvEYhmRifCGZmB+RTKl//DpZkhrSFWkaX41piks63Z5ubm5qU+xYsxonLfrj151sHPmhpNOKzFa3kkPv0bRVUVU7dZr+DQvaqdOyv/b6bKu/jmJpPpY6EwtSpMi03SLTviHmFqySpKAbk6Q/Sbqr2LEyJclP3pwZjDc2SFJDID3dWujs316/jYHg11lXWek5rdYJNDZulKQGI/ObbQ1vMZlAPL6ptXaGq68TRbNqGs6UlCUpJumHkk4wI/bWewIj5YyGYK4kxdPcVifJGba5KJ7uPScK1gcMeUnFbWvcMsbkakmrdYLeGGvrOZgCrY8xGfEtyciNwXHtxoEeZ0bs9yXtLSniF40JNmiYxGtIAADQvUjAAgAAAAAAAAYoVyr/z2x9/WFedk3dx/Z0831JKnas9GLHul3SfHkrZUmSjM1D3y8y7XY+sQTQ37lusGUy3fmSXih2rJGSZE83KwJuw/pm1x+yFjqtL/2YZPu/9MV+LYoWSTrQjNg3mxEey/oyN/iNpM6LSyznyJQFg17NjNiVZsT+vqSzJVVIrr9qnysnbJHUBAAAugUJWBh4Ag013k07w78xLU2S4ukNHb4ANwINpU3HVdU1rdZx3WChJDV888tZX5+78eDg1js3dtpy2LC81TroXZI4vmQ07thaHcbXAML4QrIxRyKZeAxDMjG+kEzMj0imXvD4tWpnvVI7SEc3nWdWN5ZLUrFjDZP0L0kXSVKg0lvtRmJ89SEV/m1723Ll+bflXeivvT7b6q+jWPKaHXcmFqRYsLGhSpIajAxX0kt+8f9JeqvYsfaQpHggGJSktHhMksZLKrEWOq0+4AXjjaubjmtj1a3eZzwYHCJJae43l48JuvXe9UCgsHm5E7aCTti6OuC6/2wqM9z4W5L2NCP22537TZEqblpjlSQFGow2J8lAQyBTkuLpX0+R95ZYzuBWKwe3jDEZbWx12uiNsbaegyn+zTG2JdjAlm1Xg40r2ooXqWdG7L9K2tMwYvWSFIgFJekDJ2ydtFVlXkMCAIAuIgELA04go+orScooT2u7TnWW98Itp6a2o/6CRs3fmo5Xlm5otU48ljNekuoKYt8obzqPx3Inttau0c06WZJcuQoaNQ92FAtSL5njq9HNPrW1OoyvgYPxhWRjjkQy8RiGZGJ8IZmYH5FMKXr8miB546kxTas/PFHHSFLdoC3jq9ixdpH0pqTD/Gb/C3y032MS46uPWeLftreyR9O1Je3UabK02fGELvbXdN5RO7fF/aCXyorVfCZJtYECQ9Ixkm7zL42X9L9ixzqjNj0336v7dcbx8ZKuaK2/jFj9P5qOSytXt1ZFtelZUyUpK172jWSIbP+8Nj17WlOZE7bGSHpO0nVrRw4KeqVxLTOHnmlGbJL8+oB4dv1nkpRZnma0VSetKiNfkhry6pq2zR0nb0vMrbgZsa/HmBFLP7q1OsHajKmSVFfwzYSbukHeeaAmY1pr7YxY+pGSN0e6GfXPtBUvegczYjsN2Y1vSlJGRZokFUp63AlbtzphK6upHq8hAQBAV5GAhQEnPW3z3ZKUUWPo9vl/ubi1OtlrcwOSFMjdtKij/ubMPv/d6sJGSdKK5bFW6xjlhSMlKTa0or55eWxYZb0kGRWDR7bWrqF+8EmSVDOkUXNmn/9xR7Eg9ZI5vhrqB5/cWh3G18DB+EKyMUcimXgMQzIxvpBMzI9IphQ9fo2SpNiQCi08XpnxNAUlVceGVsYkyagYPFrSa5KaVvO4R9LhDVUjjpIYX33Me/7tUCdsTW6jzt7+7bsddeYnrTgt2nW2v3dbXG+r3ZdmxK7sKBakXk5t1V8kqcHIVfzzPU8pMu1LJM2UFJOULelvmzNGZUlSfvXGVyQt9Jteby10Dm3Z39yTrvo8O+4lJju1G1u9z/LsIVMlaVDtum8kUBXUrauQpIoc77oTto6X9IGkb0mSs/34KknKiW/U3JOuWpz4b42eFM+t/ovkzZHX//XpM1qrk7MmK0uSYoVVr0t61C+eUWI5J7ese80Zoc+b5shARe53WusvfVPOVEmqHV7zjTFWO6ymQpIyNuVOba1dsDLnFEmqHhLXNWecxBjrA9LSy++UvPG1eNTkplUa50p63QlbO0i8hgQAAF1HAhYGnItmf+8vFaO957f1FeN+1fL6rXc8/GBWeUCNQVcZGWsu7EyfNRPXVUlS9Rcjtro2b/49O+cvGZQmSYEhK19ufi1QuPIVScpfPDh93vx7dmjZNlg6zpSkmgnrKlpeQ++UzPEVLB231TesGF8DC+MLyZaMMVY7cV21xBwJHsOQXIwvJBPzI5Kppx+/Flz/2CH5SwZ5HyZmlcVW7qKmrZRmBQpX/leS8hcPDroVObmSGiUVSbqwyLTrGV99jxmxV0h6yz/9bsvrTtg6SN5qRXWSOrtiy2Pt9Jcn6UT/9NEWl5vahZyw1do2hE39tWyHXurSE37yTEHjyrgkrR808npJKjLtuyQdIWl9w/qJqjMGy1BMgxtWXiXpNEmV8j6TsK2FzlaJBiOrFq2SpA8zB291f7c9ccPkddlmniQNLy99vPm14ZtLn5Sktdlm/ts/+P4CSU9LGuZfvvWTKbtmStLIykUrt/X3Rs+55qwTnqkYHYtLUtqaIde3vH595F9/aJojGweXXy5ptqS1/uU7SyxnqydaFZPLV0lS7pIh+7e8dt1DT0we9FVeniTVjd74ePNrdaM3PSlJg77Ky7/uoSe2WqUoZ8mQ/SSpcvJmxlgf0fw52MdDj2qQ9IJ/aTdJ79x70z2v8xoSAAB0FQlYGJAapnz5miQN+mjc0NvueLBpeWzNm3/vEcFPp1uStHn62qo5s89/s+nan2594rX5F33m3nrFR1vt9505eNEFDemuBi3N1tPPfPZ1+R13lGTXrd7t/fRaQxWj6xWoyD+2ebtAZd6RlSNjSq81VLdm1w/vuKMku+naLbf886tBS7ONhgxXmYMXhbvz90dyJW985Ri33PLPr79tw/gamBhfSLbuHmNDx63+UdMc+fDDH73SVM4YG5h4DEMyMb6QTMyPSKaefPyqjY1/qWl8fbzz5HR5mzrdeWDWNY+6R/1tfeXIeqXXGnKfnCW3MufkItO+tci0XcZXn3aDf3ulE7b2bCp0wtZQSXf4p7eZEXtzs2unOGHrMydsvaCt3SKpWtKRTti6sFmboN/fYHlJX/9q0e4f8lbkGizpDr9+U9uZ8pJ2KtXG1mHonSavW/iYJC0evLd50z9uukqSikz7v1o+6fuf5Hi5eJOr3pA75ct7D8y6pl7SBRPL3lNOfN2owtjiVdZCJ9i8vzHrl4cDqtf69MmKLv/f1+XF0d9mLhqz44cNylZB48p4YzzrvObt4vHMGfmNpW6DsvXw6cfPbAwYkrRR0skPhI/Yd2P6lLSg6jR6w/Jzkvn3QPfbvPOqxyRp+HvDzOv/8uxVTeXX2U/uXfjOhMslaf2uZauvOTP0yQzbXCdp5vpd1qtqSOPwsgl1q0ss5xvbF9aNXxNuSHc1eGlW+m/u+O9rTeXXPxjNzPlswofeHBmLp9e53xhj6fXxGRUjY256raGczyZ8dP2D0cymazfe8d/XBy/NSm/IcFU3bi1jrA9peg5W8NH4wn/ue9Jnkq6S1Li+YEhOzZID95Oksulrq3kNCQAAOstwXTfVMaADoVBouKQrJYXk7WFeJW/Z7jui0ejjKQytT/vjDa9VD/1weHY84Kp8YrXrpjXG85bnB9NrDZWPrVNw2utT5sw+/+vlgm/50zOfDXltu+1rBjVq1oLtt9p3/tbbn/og/5Xtdw24hirG1CmWV9uYvTYvmF0WVF1uXPV7v/mji+ecc1PLdrfNv//qrDf3vS6jOqCawY2qGVHZmF6RHcxflaF4wNXmAz55/dJLTjog2X8PdK/uHl9/uvXJtwe9usNeAddQ+Zg6NTC+OhSLxVZIGitpZXp6+rhUx9OdGF9Itu4cY6WlpeOef+GT5fFnJooxBonHsFTrz/OjxPhCcjE/Ipl6+vGrfPtVeuv4sZL0/i4ZC07PDyx/SNIe+mqacuxzNRDHV3+eI52w9Sd5q5nF5K3wUSUv4WmwpP9JOsqM2DXN6ocl3StpqRmxJ7XS3+mSHpQUlPSGpCWS9pE0RdIaSQeZEdtppd32kv4rabikr+Qlak2WtK+kBklnmBH7sZbt0Ltd8frf1i7L22u4oQYNqV8SS3NjDRsyJ2U3KFuDG5a6O2TcawQyGiRpg6TT1q769gNO4QFjstxNqjUKf21PN3/evL/fvrTgufeGH3akFNSghhXxrIby2s2Zo3JqjSFKdyu017Lnz/3Bt398f/M2Ttg6e+mUYXdFTrkgO2bkKTu+UYPqV9dUpxdklgfHBQw1atc1zz/60yMuPrUH/zToJn+48e21I94fPDwecLV5Ym0snhZvKFiek51ea2jz2PrGzfs54685I7Tq6/p/eHX1iLdHjKwZFFf25sC5M2zzG+PlN3e+/OTol8ae4M2R9fFYfqw2e01WTo4/R6453Dn3mrOPv79lHNc98MwFo16YdldGtaHqwY2qGVlbnV6ekVWwKj0QD7gq/daKR6+68Fv9boz15/lRauU5WLDRzVuRH/Ceg9Xq4Kqbl+bWVp9uRuy3JF5DAgCA9rECVi8XCoV2lrRQ0uWSTHlvlAyWdJSkx0KhEN8KS9BlVx2Qs+nAT9+tGFun3NIco2BxfrAuv0Eb9l62LjjtjRHN39jsjFNP2fPbw2esVNm0CmWWpWvwooKgXGnDLutqY/u8enJrT6olae7sc66v3/fVUzfssq5WrjR4UUEwc3OaNk2riFce9PbtPKnum7p7fF16yYl7Vxz8zh2bplXEsxhfAx7jC8nW3WPsyCN20vAZK8UYg8RjGJKL8YVkYn5EMvXU49fGndfLyN6ohQeMlaTyyWnR3+QHlv9P0h6SpClfPlV30EvnML76FzNiXyrpTEmvSTpQ0vGSVsj7wufhzZOvOtnfw5L2k7dd4BRJp8hLxrpd0m6tJV/57T6XtKtfL+i3m+z3sx/JV33T7/Y/c8Qua/719KCGlY1lGePS12VOzc6Ml7vTNr3y+Y6rXhsayGj4hV91qKTnsrSpulnza6yFzjHN+zt3+xO/f15sqcbUfa7q4ODA2qztciRD46ve3bDPkmcPbZ585YStPCdsRSTdP/Gr9dkX33dbfHLZWzWuYWhN1nbZNYHBgRF1n9Xuvfypq0m+6rt+dOXeI1YevuLpirH1jXmlWemDF+dk1+U3uqv2X/v55n2/HNY8+UqSYjkNzR+DbiuxnG8kDf105mEnrjzuq2s2TKuuzSpLDxQ6OTmGa2jtbps3rDnyi0NbS76SpGvOPv7u1Ud9/q21u23eYLiGCp2cnKzNaYEN06prVx731dX9MflqINjqOdiS/EBdfoM27+nUHVb2O+XWVk+U9KoTtn7khK0OP1PlNSQAAAMbK2D1YqFQKFPSJ/LeyFgo6ZxoNPpBKBTKkXSZpF9LMiSdF41G701dpJC8bzBLWu6fjh8zZsyKVMYDDHT9/dtZQF/B/Aj0LsyPQO/A/IieVmI5x8rbAk5f7S99dqQ0Ovi/Oyan/+NCSel+td9I+lmRaTemKs5UYo4EkqfYsb4j6S+SciSpMj7mgQ/r55wgGYMkrZe0hz3dXCF1fo50wtbukv4maTu/aKWks82I/e+k/SLoM0os50hJz/mn/5J07Azb5MOwBAzU+dEJW4akmfK23s3yi/8p6XtmxF6bqrgAAEDvRgJWLxYKhS6RVCypWtKO0Wh0WYvrt0m6WFKppEnRaDTW81HinttvSw8EYk8UVFUcmhFvzGkMpqkyO+ezqmDO0bNmzVnecQ9A2x76/Y1GZX723QVVm09Pj8Uy44FAY3V2zuqqjILTZs6a826q4+vNBuqbA111z/xbryuoLpubUV+f5RoBtyYra1Nl1qDzL5x10T9SHRv6vrsW3DazoKb8xuxYfaEk1aSnbyzPHnTFhbPm3pPq2ND33bXgjhPyasvuzK6tHWK4ruoyMqvLcwb96YLZl/wq1bH1ZsyPnXPngnl75taXP5JTUz0qEI8HY+npdZtzB/0tv6Jm5hk/vpIX0dgmCxbMG5/bWPVcXk3N9sHGBtUHAlXluQUv5VbWhRhf2FZ3zZ+XnxGv/kd+VcUewYbGtMa0YENFTv4H7mvHFQQaMnauz5ZevljKz/n0/R0zHtjdb1Yr6bwi034whaGnHHNkx/wPow+XdLSkQnnvWb4t6REzYtemMjb0fsWOtZukJyRNlKS1jXssdGKnTvcv/0/St4764K9XD17fMDMrHhjtpgVVkeW+t7nQOGf2aT/7pKkffxxeIun3kjL84iclfd+M2Bt67BdCr1diOU2fn0jSRYvOXhivLMz4SSwtONiQ3Iy6WGnepoY5vzj55FdSGWdvN9DnRydsTZeX7LmTX7Ra0jlmxH6heb27Ftx2fkHN5hsz6+ry5bqqy8qqKM8e9PMLZ82d19MxAwCA1CEBqxcLhUJvSNpX0j3RaPSCVq5PkLRE3ipYx0Sj0X/1bIS4/7bffTrNWbzD0LJNW12rzsrW59OmbN6UO3zazNmz16UgPPRxkTtufslcsviwUWu3Hj516Rn6wpxUu27w6P8jEat1A/3NgY78ed6f7p20Yml4wsrSra41BINypkxqKB025rszZ138cArCQx939/xbfzZ+9fJfTl6yPBDQN59rxg1DiydOiC8fNf7nF8yee32KQkQfdueC262x61f+ZeqiJWlp8fhW15eNG+MuGTfx3vNmX3p+CsLr9Zgf23fngnn7jihb/e9pzldZmbGtv9+yasQILZo06YXwRZcfmYLw0MfdOX/+8MKqtc72Xy4uyKnderev9YWF+nLq5E/OnXvFzikID33cPbfflp7TUL50uy8Xjx5UWbHV9fK8PH02cSe9P+R4rdt/Y/luGbcXBIxGyVsx5uQi0367p2PubZgj2+eErXMkXSNp+1Yub5A0X9K1ZsSu79HA0KcUO9ZwSY9IOkSSvoqdUL66cf+CXZa8qkP++7Y7es1ao2WbuvR0OduN37hqcvbBRz718RpJ90o60b9cL+nHkm41IzYfdOAbSiwnV9L7G3ZbY352dKM2Z47Zqo6hBg2tWV45rLTy1F+edAqfr7SC+VFywlaOpD/KWxFLklxJN0r6xcv7H/TD8auXXz956bJAoMXnrXEZWjJpfHzZqPG/vGD2Jb/u2agBAEAqkIDVS4VCoTxJ5fKSq06PRqOPtFHvY3mZ97+LRqM/6cEQB7SHfn+jETTqyndb+GleR3WXjB/nLhpnTp01a/binogN/cMDxTcu3efd9yd0tKn8muHD9LG58zEzZ83hDYIWeHOgbffdcdMr+73z7v+lNba/s0hZfoHe33nXyy+YPfePPRQa+oE/z/vTvfu8/144u679L8DXZGXpzd33uPv82Zde2EOhoR+4e8GtV+yx8MPfDqrY+oPl5mLBoN7Ya8//hC/64aE9FFqfwfzYtrsW3HHczl9+/MyI9e0vnBCXobf23G3J2UVXTu6h0NAPLFgw3zSXf/nFxBUrt/pguaX3d9mx4rQf/qKgJ+JC/3DXvDuyR29cUb7Dl1+ldVT30+2m6pOTNyg7o0ySXpf0nSLTXpXsGPsC5si2OWHreklXdaLqi5JONCN2dZJDQh9W7FgZ8nZ9mBV3A8p8cy/3iOfeNlp+eaeltcOGusF4zfqhG6uH+0VfSLLMiP1ekkNGH/aLx5751xfTphzlqv0pMsMt1+QlS8O/OvGU+3ootD6D+XELJ2ydLukuSYMk6WNz2topK5aPyK7t4D2wzEy9tceeJefNvvR7PRAmAABIoY4+20fq7Cgv+UqSFrZTr+naTu3UQTdryHSXdib5SpImLV9hjFu37Itkx4T+I3LHTf/erxPJV5I0ct16TVv6xbN3zbsjO+mBoV+4Z37xbft2IvlKkgZXlGuXzz6++c4F81iFAZ1y14Lbzt/7g/fD2XW17b517krKrq3V3h+8f8Fd828/t6fiQ99254J5u+36yccdJl9JUnpjo/Z7971D7plfXNwDoaEfuGveHdnbLfmiw+QrSQrI1T7vfjApcsfNL3RYGfCNX7v0s84kX0nS7h99mv9A8Y18gQedVli1fk1nkq8kaccvFmnKv0ZK0n2SvkXyFTrihK1Z6lzyleRtT3hvEsNBP1Bk2vVFpj1b0kW5X46Kdyb5SpJGrN9g1GbmD48ZhuQ9hu1F8hXa8/OnH7/iy2mTO0y+kqR6o0BLJo6P/Oqxx1tb5Q+QJJkR+2FJe0h6o3TYME1auWJEdm0n3gOrq9Pe778/464Ft7FSNwAA/RwJWL3X6GbHW+8PtfW10e3UQTdaMH/e6J0//WJ8V9ps73yVds/8W3+TrJjQfzz0+xuNaV8tPqQrbcaXrlK6W/tMsmJC/zJ2zco56Z1IvmoytGyT8urK/pnEkNCPDN287tamLZXa+4S56VpuTY2GVKybl/TA0C/k1ZY9PWRzWafrpzc0aMzalRcnLyL0JxluzXNjV63udP2AXJmLFx/+0O9v7FRCDQa2e+YX37TdosXBrrTZ+bMvJt05f/7wjmtioLtrwR0nTP/08/yutNnlI0fZLx78SJFpt79cAwY8J2xlSPpVF5ud4YSt3ZMQDvqZES9Nmr/jy/WdSr5qMnbVGn20z5Q3zYgdNiN2ZRLDQz+wfmT+L+NK73T9usBgVQ5JeziJIaEfMCP2YkkHrx8xNJZb0/n3wHJqazR087pbkx0fAABILRKweq/mqyu1t2x307UuvdmGxOU0VD7X9MS6K4ZuXndZEsJBP1OZn3X/yPXru9xu7JpVXUrawsB014Lbzp+yZFmX5/7JS5aP4wNmdOTOBfN3mLZoSZdX49tu0ZLcBQvmm8mICf3HPbfflj51ybKxXW03ZfHyAKusoTPGrlp1YFfbjFq3TpV52X9ORjzoX4aVrZvb1TZ51dXKaqx4LhnxoH8pqNl0b2dWt20urbFRBdVlrFKEzviOpJEJtJvT3YGg/6nKybl21Nr1XX6vYejqmj2SEQ/6l18+8fjxG7PGdfk9inWFI3f5+RNPdmpVSQxcLxxw2LhpXy3tfHafb9qiJdl3Lpi/QzJiAgAAvQNPJKHS0tIBvW93Vw3buGHHRNpNXFaaecvNL3Y9swYDyrh4xZBE2k1cuiJwy03PrJeR1d0h9VmZGWmFhiG5rkbX1Tfwf0/S0GBdYcDt/DdLmwzfuFHvTj644pabX+Qb8mhTXoZRkF1X1+V22bW1CtQbn9xy84vlSQgL/UR6+pDsoZs2dbld0I0rs74ucsvNL/4xCWH1ScyPranVCctXJJRonF9V/r1bbn7xxO6OCP3LYctWZiTSbtjGjbvxGhId2XPt2qGJtBu9as2wRZfOvr674+nL8g89vMAIpsltbCio+PeL/G0kKRg8WV1M8POds+jS2Tx+oV1DJw69JJF2E5eVpl/57oOv1mVnd/0bshgw0oYE9pa6tACpJKk2UKiMss2lvy9+bXMSwuqT8vMzRgYChuJxd2RFRf2XqY6nNxgUTBuTXdv1t0mz6+qU7dbeW1paenoSwuq3xowZsyLVMQAA0FkkYPVezZdQzpHU1oeSOf5txTbc1/JtaDvgZCXw4bIk5dRUa8ibExJ6YxQDR+YuLyXULiBXg5bnDg2u7vLiIANBIFfi/56kzL3eTLhtzsZAbvDTCbndGA76GWOvzxJum1UTT898lzkSbWvceU3CbTPrGgJD3mF8tYL50dc4ZlnCbTPrYsaQjxhfaFtcceVkJfb5cGZdvYZ8zPhC+7JHJvYdieyaWrnl5Vd1czh9Wnn00abDfEn8bbZNjru5jL8h2pVeOyjhtlU1Iw9YlzGhG6NBfzMy8FXCbTM3Zgwf+cZwtoLeWlqOxArmkow9GxJum5OZub/4TK6r2JkBANBnsAVh71Xa7HhMO/Warq1KYixoJh7o+jdnJKkhmFg7DCxxYxselusT+mI9BpB4At/8a+I2dnlVbQwwrrsN48vlOwHoQGPiY2RbHvswQGzDc6h4gJfUaF9AATUGExsnjC90RmOC7zUk2g4Auou7DdNcLMh7YGif4SaerxGM8RwM7YvHE3+PwgjyHhgAAP0ZM33v9ZkkV15m987+eWt29m8/2Yb7Gr8NbQec8oK8LyV1eZ+3DUMKdeSvcvhbo13/fiT7bUkju9quMidHe83JmzxsaE7iX7/pZ4YMGfKWpFGSVm/cuHGfVMfTG7wQTX9UUpf/Fg2BgAqmx04/cHbO60kIC/3E8//U1ZJmJ9LWGBG/7Yibc37bzSGhH3nlvw2HNC4LPBCMx7vctiE/+PqRN+ewvL+P+XFrpauys6ruzv4yt6brqxTV5GWvOvLmnH2TEBb6ka9uGbJ81Np1XW5XkZ9Xc+TNOdslIST0Ix/cXbB02MaNXf6kuGxwQXzCj382MRkx9VXMkVurvv5n16iqalaXGwaDC3N+/fvjkhAS+pHKp+5+RtIuXW1XnZWtOYOypg4flFOfhLDQT9y1uq54tXRKV9sZatSgfepPPPKcnPeTEFafxPy4tReedX+oz/SDRNpWNjTeLelX3RsRAADoLUjA6qWi0WhlKBR6U9J+ko6V9PeWdUKh0DhJO/mnLyR6X+yf3DUb84feJOnqrrZbNm7sokP4W6MDFZmDzo4Fg8+nNzZ2qZ0zdVLFabvssiQ5UfVNsVis6Y/YyOOcp0aZx5Tn5W0sqKzsuHIzi6ZMajjtjDMfSVJY6CdmnDdrzn8+/2T2mDVd2ypu5ehROvf7sy5JUljoJ84486y/PvPFhyXbLVrcpeU6Nuflq1aZR48ZM2ZbtuvuV5gftzZmzBg98tKkyt0XfprXlXb1aWmqyCg4nb8jOvLSuLFLRq1dN6mr7TbmD/kN4wsd+dfwkc9P+2rJ0V1tt3r4yBePZXx9A3Pk1pyqqlskdT0Bq7Hxdv6G6MjmQuOc+rS0DzIauvZdwi+3H1t66o57J76/HAaGN946J2NkeVW9UdClZkNqVlSf/60jnkpSVH0S8+PWzv3+hZe9/MXHPxi3anWX2pWOHKEZ582+MElhAQCAXoC1VHu3B/zbs0KhUGsrJ10hb4WsUkkv9VhUA9z5sy+5Ztm4MW5X2tRkZak6Y9AJyYoJ/cfMWXNecKZOjnWlTUMgoE15Qy5LVkzoP2bOnrPJmTppU1fbrRk28q/JiAf9z9LxY7u8IueysWM/TEYs6H9WDxv1cFfbOFMnbrhw9hySr9ChsrxhVzZ2cbu3L83J9TNnzflfkkJCP1KVOeiEmsyuLaK8dPxY94LZl/w6SSGhH2mMp5+wobCwS202FBaq0c04PkkhoR8xI/Znkp7rYrPN2vKeJtCmWaf/7ENnu/FlXWnTGAho45hM3gNDh35xyknVI8pWfdnVdoXrKxckIx70P8sTeD9r6fhx27KTDQAA6ANIwOrd7pT0laRcSU+FQqFdJSkUCmWHQqErJc31610TjUa7lLCBbfPVuCk/qMzJ6VTdRiOgd3bd9cWZs2a3tY0k8A2rho49bn0X3kB/Z4/dFl84a+49SQwJ/cimnKG7LR8zutP1399lx4rzZl/6vSSGhH6k3sjZ/XNzcqeX8PvcnNJQb2TvncyY0H+cN+fSsz6YvmOnl/BbNnaMyrKHdnlLEwxMF8y++Pa3d991SWfrrx8yRGsKxxyexJDQj8ycNefjd3bb9eW4YXSqfmVOrhaPncLqkOiU8y+eG/t4+x3uqEtP71T9+rR0fbz9DgvOv3gu7yOhs2ZK6uwSH3FJ55oRmwR4dMqqyTmHrh8ypNNfdH1n32nvXHjmzx5KZkzoP/LXxQ4oiJV2eom10eVfrrruuJMuT2ZM6D/qjey9P582pdPj64upkxvrjZzdkxgSAADoBUjA6sWi0WidpJCktZJ2lfRBKBTaLKlC0m/krX51azQavTd1UQ5MF8yeW/z2rnv8ctOgQe3Wq0tP12v77PWf8EWXH9FDoaEfmDlrzgsf7jD9rNUjhrdbrzEQ0Bt77b7k7Et+MqWHQkM/MGvWnOVfTtxur8UTxnX4Bud7u+y8OR7PbP+BDmjm/IvnxlYOmzj60+2mdviB3qfbmbGVw8aP4MM/dEWjm1nw/i47dfiB3uKJ4+POhGm7z5o9Z1VPxIX+4eyiKye/sdfuSxuN9l8mrxo5Qh9uv/OprH6FrghfdPm3Xttn7//Vp7WfJLNx0CC9vevuP79g9sW391Bo6AfOm33pxW/stXdJVXb7XxSrys7W63vvdf95sy+d3UOhoR8wI/YSSYdJWtRB1WpJp5oR+8lkx4T+Y9bpP/vwg/1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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all_streams_io_name_df = pd.DataFrame(\n",
+    "#     block_output_data+\n",
+    "#     attn_out_data+mlp_out_data+\n",
+    "#     attn_value_out_data+mlp_act_data+\n",
+    "#     mlp_input_data+block_input_data+attn_input_data\n",
+    "# )\n",
+    "# all_streams_io_name_df.to_csv(\"./tutorial_data/all_streams_boundless_das_df.csv\")\n",
+    "all_streams_df = pd.read_csv(\"./tutorial_data/all_streams_boundless_das_df.csv\")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin(\n",
+    "        {\n",
+    "            \"block_output\",\n",
+    "            \"attention_input\",\n",
+    "            \"attention_output\",\n",
+    "            \"attention_value_output\",\n",
+    "        }\n",
+    "    )\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"block_output\": \"Block Output\",\n",
+    "    \"attention_input\": \"Attention Input\",\n",
+    "    \"attention_output\": \"Attention Output\\n(After Head Mixing)\",\n",
+    "    \"attention_value_output\": \"Attention Value Output\\n(Before Head Mixing)\",\n",
+    "}\n",
+    "df.loc[:, \"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df.loc[:, \"IIA_formatted\"] = df[\"IIA\"].map(custom_format)\n",
+    "other_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Name Position w/ Boundless DAS\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    other_locations_plot,\n",
+    "    filename=\"./tutorial_data/Boundless_DAS_other_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "other_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5d54e10f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_streams_df = pd.read_csv(\"./tutorial_data/all_streams_boundless_das_df.csv\")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin({\"mlp_output\", \"mlp_input\", \"mlp_activation\"})\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"mlp_output\": \"MLP Output\",\n",
+    "    \"mlp_input\": \"MLP Input\",\n",
+    "    \"mlp_activation\": \"MLP Activations\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "all_mlp_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Name Position w/ Boundless DAS\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    all_mlp_locations_plot,\n",
+    "    filename=\"./tutorial_data/Boundless_DAS_all_mlp_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "all_mlp_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "091b0a6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attn_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_input\",\n",
+    "    aligning_variable=\"name\",  # now we are localizing the IO name\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "block_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_input\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "mlp_input_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_input\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "mlp_act_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_activation\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "attn_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "mlp_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"mlp_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "attn_value_out_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"attention_value_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")\n",
+    "block_output_data = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [i for i in range(12)],\n",
+    "    \"block_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "92640e82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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F9s8Yfb81hrTaaXxNrtm0lyRJkiRJkiRJWqVYAUu9sh/5y6m6/52qExVL9pzB2C9RryBXzFpI/hJtf/KX5zOK2ycjYmlK6fMdnmZHcthpfXI456fkKiT3k8MeB7U59jOMBjP+Rv7i/gZgPWAP8jKE5efzacYuE7akOOYc8vJsmwAHMlqF4VHkL4l3Tynd3moSEfFYcsigXC3lTnI47KJi7A2Ax5G/pH9ck2FuIS9VNYuxFR2uJS9H1OiGVvPppWJJrT1LTad2u4RiSumBiDgJ+K+iaS45tDNu1bQuvZvRoMk2wBvJYb1euoP8uz8fuJX8mtoC2Juxy6odTf5dfnECY7+O0dfr38iVgW4ENiKHv+pV5NYGToyIncmVXo4o2s8rjrmB/F4dLB2zAXB8ROzRbmnGImj0B/Kyh3VXkquLXU0O5O1CrgZW7/OeiLgvpVReJnWyyp8DZ/dw3EY7lLZvadPviQ37LZeCbNBY8eeJ5J9jSymlWkTczOjP9yDg0g7P12rMmyPiCvKSlpA/l1q9D+e1aGtV1arc/8IOqtg8k/yahrxEZlfVHHtkr4b9ttV0IuJtjA2iLAN+S75u3g1sD7ycXIUPcuh2QUQ8rZNqQSvAzuTPpPXJn18/Af5ODsbsDBwKbF703YtcVW+8oM13Gb2GQl5+90fkz8hl5NBohfw7/xbwlck/jbEiYhPy58RjS803kKtIXU5+fgPk30093HoY+W+G17cYczfgG4yG7BaT3wPnkpeaDPLfSruQl658TJNhej5WH32H/Nqvh5Db/a1WNr+0/auU0l3w7yVAf0Z+zdX7faOD8W5r2H82o6EuSZIkSZIkSZK0ijKApV55VsP+6VNxkmLpnu8wNnx1LPDhlNLyUttnIuKZ5OoFmxRtx0XEKSmlv3dwqsOK+1OAQ1NKtzY8fmybY/8f+YvLw1NKP2jTj4gYZGz46uzifNc2dP1URBxI/iJ6A3LliS/TohpDUe1jmLHhq68B70op3dPimD3I4aB//xxTSu8E3tlkGbp5KaWF7Z7bFHtGw36nlX5aOYXRABbAPkxRACuldGZE/I68rCXAuyPiW50uozWOc8hLjv0mpbS0WYciwPgjcuAA8vvixJTSvzo8x3vJ4bv/arKc2GeKyjv18Mf2wKfIlWqWFsd8t+GYz0bEexldBnJXcpDr5BbznwWcyGjwZwm5Gtq3Gj4D6pXlvkUOYgF8MCJ+mVKa9HJQReWwp5SaLp7smC3Os3XDedqFqnZu2K91eJrG0M3OtPj5N7iY0d/D0zo813gWMDaA9ekW/eYV98sZDYzMa9rzkY8t6GAeLyht9235wSJs+t5S0w3AWW3678rY8NUtwIsbl9WLiGPIVQPfXTRtAnwnIvZNKaVezH0S3kb+nR4P/L+U0n3lB4tlY3/P6PviTRHxkVafYRFxMKOfAZCXqTs4pXRdQ79jyZ+Nz6TzykkT8U1GX9uJ/HfEcY3L90XEB8if40cVTUdExM9SSr9tMub/MBqovRJ4Trtrc1Hp7M3k0NlUjtUXRWDqHEavr4+JiC3bVSeMiG3JFSTrTmjocgKjAay9IuJxKaXLx5nKXxr2Px8RV6aULhrnOEmSJEmSJEmStBJzCUL1Srka0RLGqdAxCQeTq/jUfT6l9MHG4AVASukPRf/6Y2sCE1mG7O/AYJPwVSf+q4Pw1SzGVtn4G3Bgk/AVACmlU8mVMeoOiYjHtxj+jeQqTnWfSCm9sVX4qhj//JTSyxu/lJ6mntqwf94kx2s8fs+mvXrnPeQv4AE2ZWwIr1unppSekVIaaRW+AkgpXUmuxlGv0LE2Y6t/jCeANzcJX9UdRw441B1J/lL/yCbhq7qPkSvM1R3Soh/AfzL29/+qlNI3WnwG3Au8mtHl5mYDH2gz9kQ8mrHLn17Wo3EbvZWx1+oftulbrpS1nFyZrBM3Ugpe0nppyUblMOseHR4zngWl7X2aLb8WEVsyGmS5kLwcLcDuEbFhk/5rMbaK1ILGPk28sLS9QgNYEbFuRDw+Io4mP7/ti4ceBv47pdSs+mDdRxhdxvZh4AWN4SuAlNKylNJ7yKGgumcCL5rk9HthBnBySunwxvAVQErpTvL7uh78mQ28rM145ap3twHPb3adK5awGyRX0uzp38fFksYvKTW9LaX0ocbwVTGPJSmlt5KXea1rtcTuvNL228cLRqeULk0pHdEi8NvLsfqpMaQ63rK2r2H0930nj1xu9FSgHOCa38EcfgcsKu3PBc6LiJ9FxKsjoqPlqSVJkiRJkiRJ0srFAJZ6ZYvS9o3NvlTskf8ubd8KvL9d55TS2eQqGnUvmMAXX+9IKT00sekBcG5K6Xsd9HsZeQk6yGGcw1NKi9sdkFL6PWPDAEc09ikCC28pNV0CvK+D+axMNm/Y77TSTyvXMDaAskWrjr2QUjqfvGRm3dERsekkx+z4tVoEDb5canpOq75NXJJS+lqbsRO5QlXZxSmlbzbr3+KYxoAd8O+qU+XX9o9SSj9pN9liKcOjSk0vKqoKTdajG/a7WgKznYh4MmOf78Xk5VBbKQfC7h0nqPNvRWDv/lLT+q36Nig/5+2LCoWTtaC0vSHNwxPzGvrXj5lJDhE12oscwIUc2vlDkz7/VizfulOxe0NK6YJ2/SfhjIhIjTfy8niXkpez3aroeyHw7JRSYzikPO9tGV3+FuCbKaXxKvm9k7xkad0bJvokpsDD5OBmS0WQ9MxSU6vPjL0Zu7zuh9tVQyqWnntPxzPt3FGl7XNTSp0sO3s0uXIgwFMiotl7obwM61Vdzm0qxuqnhQ37mzTrVDK/tP2jxr9fi2tIOQx3WLNgaMMxi8lB+HI1uZnkfxRwAnB9RNwYESdHxLsi4ulFKF+SJEmSJEmSJK3EDGCpV+aUtu+eihNExNrAfqWm7zerjtFEOSwyk87CJreRKxh0o5PwFYxdPvC0lNKlXYx/QJPHnwJsV9r/fKdBjJXInIb9Sb3migBQuTpY4/hT4f2MVnBZn9GlwFaU00rbT57AcY3LMzXTGFjp5JjzS9s7RMTsJn12Y2yY4gsdjFsPvNUrVM3mkUumdmPbhv2bejDmv0XERsBJjC4VvAx40zjLw61X2p5oePTBFuO0U67wMpvRsFDXinDMFaWm/Zp0m1faPoOxoa3x+l+YUhrv86Jc/eqX4/RdEU4lV507Y5x+z2V0CTkYW92qqSJwVK7WuF9RMayfTk0pdVK9rVzp6LEt+pSXklwCVDsY98fkSkg9EREbk6sO1nX6uXULY5fXbXa9f6C0vVeTxyeil2P1010N+y2v58VS1eUwbatrVbl9LmN/n02llH5OrubYqkLYXHLFuY+Tlw++MSI+HRFTGgCXJEmSJEmSJElTxwCWeqVcMaWTUFQ39mA0jADw204OKiqA3FZqalopo8Ffi6oH3XjEck8tlJdS7Oi5FP5a2t45IhrDEvs07J88gbFXFo0Veu5v2mtiuqkA1LWU0hWMrc72xhW8LNEtpe05Ewhd/HX8LmPG7uaYIFc/avSM0vbddP5ea5xDL5aY3Khhv2efe0X47EeMDQZ8vKjo1075dzjRKoTl6ntrd3hM43Nu9jvrxoLS9rwmj9fb6tWsOu3fOHYrKyqAtQi4usntBsYG4g4EzoqIX0REu2o+5WvbzSmlizqcx69L27MZf8m2qfaXDvuVA4AbtehTfq+f20H4rl4RbkGHc+jE3uTPtLpur/fNPrcuLG1/LiJeXFQK7EYvx+qnxs+ldtfzw0vbC4Gmn7FFFbzykqvzO5lISulHwAC5mt3t43TfnLwc8VURMdTJ+JIkSZIkSZIkaXoxgKVeKf8L/3Wn6Bw7NexfMoFjL24zTjNXT2DsCR8bEVsBm5Wa/jGB8ctBlRk8surMzqXthSmlO1j1NFaU6MVrrjxGq4oVvfZBRisVrQUcM9kBI2JGROwfEV+KiLMjYlFE3N9kmbPGJaY6Dc+0XL6rpDEQ1xjI6uSYZr/TXUvbV6aUljfp00p5Dr0IupVDSksnOJeWisDDd8ihm7oR4NgODi9XvVpjgqdes7T9YMteYzX2W2eC52xlQWl7n/JyXxGxJaPVji5MKd2dUrqV0c/Q3SNiw1L/tRhbzac89iMU1Yrq4dgHGFsprtdenVIaaHLbllyFbFfgS4wuj/oC4JQmodu68rWt2+tj4zj90MlnDIz9zGh1DXhMafvvLfo002lFyk6UP7duSyn9awLHjve59Y3S9obkJUoXFp//L4mIzZoc00ovx+qnxsDVPc06RcS6wMtLTd8fp8JguQrWi4rPinGllG5NKb2NXPHqQOCj5M+VVq+D9YHvRsR0WA5UkiRJkiRJkiRNgAEs9Uo55LPRFJ2j8cuu25r2aq7ct5MvzSYTwOnk2MYqJr9qDMi0uvHI0MNGbcbu9IvslU1jqGyjyQxWhF42aDP+lEgpXQ98tdQ0PyIe06r/eCLiqeSl/E4DjiRXjNqKzoIxnVbAmujSdt0e06zySvm1vWen75niffPO0rEbdTGfRktL2zNb9pq4LwKvLu0vAA7psCJfufLLRJeRKwfKOq3m1bhM5NKmvSZuQWl7A3L1w7p5pe3yknz1Y2YwdonJvRgNl9UrZrXzPEYrLZ6aUurmtTtpKaXlKaVLUkpvJi9jVvck4EMtDitf27q9PjaO0w+9+ryAse/1yfxMJqP8ubXZBD+3yksob9Q4cErpB8C3GpofRf78/wlwS0RcHBEfj4jHNR4/VWP12UYN+62u5y9n7HKr4y2VeyJQD2itCbxyIpNKKS1NKZ2WUnpfSunAlNKm5CqHRwBnNTnkCxHRamlNSZIkSZIkSZI0DRnAUq+UqzTMLZbQ6rVyhYuHi2WCOlWulNGqekjZwxMYe4yUUifH9mqpLnhkuGZFLAfZb7c27D+6aa/O7cDYz8NOKjb1yscZDe3NAj7czSARMY8cQtmtycP3kpfr+iejy5xd2zhEN+ddwXr1vulFpabyZ8qMiJhoxalHiIiPksMOdecCgxMIAZUrvawXEbNa9hx73ll0VwGucanCXiwFSkrpZuCKUtO8FtsLWmy36n9hB0vQlZcf/MU4fVeIlNKPgd+Uml4fERs06Vr+HT4wgfEXk8NpdZ1cI1cW5Z9Jp5XdYAI/vw5M6edWSukI4DCg1uThAJ4IvAu4LCJObFe5qZdj9dH2Dfutlv6bX9o+P6XUthJpEZheUGo6vEXXjqWU/plS+lZKaV9y+LNcFWs28LbJnkOSJEmSJEmSJK04HX05K3XgXODpxfaa5C/pzu/xOcpholkRMXsCIazyl7DTIZTU+OXujXRX9YMmx5XDE6vSF+ll5zbsP5mx1XAm6snjjD9lUkq3R8RnGF1i7uURcVxK6YJOx4iItYHvMhqIWUJeTuqn5C+WH7EEU0TsQA5krUzK75sHyaGybnR7XFljdbnNyO/jrkTEu4D3lJouBZ6bUppINb5rStszga15ZNCumW0YG0Ds9HXRuCRZLyvuLWB0qcH9gE8V2/OK+8ZqVgtK2/uVtue16PMIRRDtucVuAn7V2VRXiF+QAxqQgzjP5JHzK1/bOg4ZRsSajK3iNh2ukb1yP6PVDRsDg+30ajlNGPu5tRS4rstxbmj1QErphIg4kfx32HPIVeD2YmwlvABeBewREU9PKd011WP1ydMb9h/xt2hE7MjYSnl7FBXHJuIpEfH4lNJElrZsKaX024ioAKeUmp/Xqr8kSZIkSZIkSZp+DGCpV84C3lza34/eB7DubNjfjM6DFOWgQOM4/fCvhv3XpJROm4Kxt+zRmNPN2Q37zwY+PYnxDmrY/+MkxurGZ8mVjzYlf7H9MSb2xevB5KWiAJYDz+/g9bTRxKY4LZRf2+ellJ7Zt5k8Mti0NV0GsCLi/5ErodVdBRyUUproUpiXN+w/ms4CWI0V5BrHaWVuafuOlFIvgzsLgNcX2/tExEzy53g9lDUmWJhSujUiLgN2AXaLiI3I4dS9GsZsZx9G3xfnpZRumsT8e21hw/5jeGQAq3xtawzHtdPYt9k1cqLhlLqJhJ6mwl2MBrAm8zOZjPLn1i0ppYEejv1vKaUEnFPc6sG6ZwGvIC9rWv9dPA44BnjrihhrRYqI9RgbwPpHSqnZcpLz6U3Vx8PpYZWqlNKpEXEJ+R8xAGwdEev1+LNVkiRJkiRJkiRNEZcgVK+cwdjlfV43BedoXBJn1wkcW+57VQ/mMlmLGFtlZOsejn1ZaXv7iJjTw7GnhZTSrcB5paYDImLbbsaKiHWAQ0pNNwEdV5/qhaLK0cdKTc+NiGe16t/E/qXt33cY5tthAuNPF+Vl6Xr5nunGZeSwW91O3QwSEYcDXyg1XQccWCzDN1EXN+w3VoJppbHfJR0eV37Ol3Z4TKcWlLbXJ1epm9fi8ca2GYxW7VmzaGusmNXMtFt+sKQxLLJWkz7la+QTmzzeSuO1tNk1slzFqaPqUMVSxP1eou7K0vbjJ3DcE3o4h/Ln1mZTtETzI6SUFqeUTkkp/Rf5uZeX1n1lv8aaYocztvLnKY0dIiKA15Sa7mR0ad5ObuVA16s7Xep1Ai5r2G+23KgkSZIkSZIkSZqGDGCpJ4pKLd8tNe0cES/r8WnOBx4u7T+nk4Mi4smMrWbxl15OqhsppYcZW2Vp3x4O3xgyOLgHYzYu9TgdPju+WNqeAby/y3HeytgvOL9S/H5WtK8C15f2P9aqYxPlSkQXdXjMfuN3mXbOLG3v0G3orheKiiTlcMeEAxsR8Qrg24yGa24mh6+6WqIspVRjbAinsbJbK+V+VxXjdKL8nM9r2asLRQCtHFyZR+cBrGb9L0wp3T3OaadzAGv7hv3bm/QpX9u2jIjdOhy7XG1vKc0DqHeVtucUFZHG82RghYSN2igvJ7tnRGw43gFFQGpeD+dQ/txak7FV2VaIlNI1jK2yt0W34exejtVLEbE58O5S08PA55t03Z/RipEAh6eUBjq9AYOlY7dkdNnSXml8b020EqIkSZIkSZIkSeqT6RCi0KrjM4wN6nw1IrboZqCI2LQxwJVSehA4vdT0qmK5mfH8d2l7GfC7buY0BX5U2j4kIjbt0bjnAf8s7R/VgwoNjcvfTIeKDMOMDSz9Z0TMm8gAEfE44L2lpnuBb0x6Zl1IKS0GPlhqegbwHx0eXq6O06wyztjOOYRwWOezmzbOZexSbEf2aR51Z5W2nzKRAyPiP4ATGL0O/4scvppshb6TS9vPioi2lbmKx8vV1n7eyUmKynG7lJrObNV3EhaUtucxGop5mObVrFr1b3zsESLisYxW9LohpbRCq+B14EUN+43VzgB+S77G1b2+SZ8xis+CcgWj01JKDzXpWg4bzqSz1/trxu8y5X5Z2l4DOLSDY15CDyt3FWHC8rK5/frcurJhfzJ/F/RyrEmLiPWBHwJblZpPKMJijQ4vbd9Nft90LKX0Z8Yu7Xp4q74TFREzGBvQW9Ti/ShJkiRJkiRJkqYhA1jqmaJqSrn6wGbAmRHxqBaHNBURTyeHiPZp8nA5HLM58OEOxnptqekXKaUbJzKfKfQ9oD6XdYHvRsTMiQwQEY8I26SUljN2SbMnMs7PaTxF5Zi7Sk29XJ6pKymlJcB8IBVNAZwcEXt0cnxEbAf8Hli71HxkSqlZZZkV5XjGVv15X4fHlYNozy2+xG3nK8C4lWCmm5TSMuDTpaajImJC1eOavWcmofzF/d6dLi1WBAV/zGh1oHuA56SU/t6DOX2d0SBsMPbn1cxnSttLga91eJ69GZ3/UsaGY3tlQWl7HvDYYvv8YtnOMVJKtzG6fNdujA0yLGjs36Bc/epXE5nkVIuICvDsUtONjK3sBEBK6Qbg16Wm/4qI8YJSHwc2Ke1/vVmnlNK15Aptda9t1q805z2B/xzn3FMupXQOcHmp6QPtguFFIG0i1Qc7dVxp+xURMaFl+yJiZmOQOiLWiYjNWh3TRHlZygcoVVHr5VgrWkTsRX4/lK8Fl5OrWzb23YAcsKs7uQg/T9RJpe0XRET5PURE/GdEDE30bzrynLcs7f+6VUdJkiRJkiRJkjT9GMBSr32WsRVYHgtcEBFvHW/JoojYLSJ+BJzD2OVhyk4uHq87KiLe3yxwEhHPIFdzqT+2mM4DLVOuCBC9gdEA0fOB0yLiMe2Oi4i1IuJFEfE74J0tun2dvGRj3bsi4ivFl4+txt0tIk5qE5j7a2n77RGxfbt5rggppdMZuxTShsAfIuJtrcIwkR1GDvmVl7A7MaVUnbrZjq8IGJWXUtyyVd8Gp5S2Hwt8ttkXvxGxQUR8B3g1sLzrifbXN4E/F9trAL+JiDeNF36KiJ0i4ligq+X9Wvg9cH+xvQ7NQ6ON89gTGGG0Utn9wPNTSj1Zwi+ldDXwf6WmwYj4RESUq6TV3wefZGzw6H9TSuXqee2Uly08rVkgqgcWlLbXbtHe6pgZjC7ltYzmFbPKXlDa7vvygxExIyKeGBFfBk5sePg9RdC2mfcxGsCbBfyiCKg0jj8zIj5EvgbVnUV+bbZSDp3Mj4hDWsx9Hjk4MovR61s/lYPhmwO/arZ8abGE3c+BHenx52NK6VfAT0pN34uIYyJi3XbHRcQ2EfE/wNXANg0Pbw4sjIgvRMTu44zzDOA9paZfNryGejnWlIqI2RGxR0S8MSLOBv7EaDgT4BbgBSmlu5ocXmHsZ8lJTfp0onzcGuRratn25GW5/xER74iIrWij+LvuGOBTpeZWSyhKkiRJkiRJkqRpKlKaDt+NaVVSBCG+Sa5OVHY/uUrKecBt5EDUFuSw1bOBHRr6fyGldFST8R9DDmCUlwj6B7mizLXkEM5+wPPISyXVvTWl9Pk2817AaAWFD6aUjm3Vt+G4ecAZ9f2UUrTs3Pz4DzB26blEXs7rbHKlk4fIz2kbYHfg6eSKWW3nWSwtdjb5i9W6O8hLMl0I3EleSvAx5Oddr2q1Q0ppYZPxXgl8v2Ge15MrY9U/SBallJ7f7vmOJyLKH0rfTSnN7+CYjzL2C2HIz2+E/Nq4jfx6eTR5Ka+5DX2rwOtSSg93Oe1W81oIbFfsfiOl9N9tutePCeBvQLNKXq9MKQ03OWYW8Hfy77LuMvJ7YiE5HPRE4KVAfanLDwAfKvVv9XvfHrhmvH4r8piI2Ab4I2ODmjeRK1JdQv7drwnMIS+TtyelL+gn+h4dZ94nAq8qdr+YUnrLOP1PA/YvNd1HDgxMxKtTSn9pc44NyKGE8hKBl5FDA4uArclBhMeVHv87sHdK6Z5OJhARVwEDxe6hKaXGkFBPRMTljA1XADwvpdR02bCIeDl5KbKy81JKLStBRcTGwK3kwNADwCZTsexXRMwHvlNqWgQ82KTrGuT36dpNHuvkNfY2xgY5lpEDUWeQq61tB7yCsT/XO4CnFgG+VuM+ivw6KoeGfkte1vducmD0IPL1F/Jz3Z/Rz8DDU0rHtxh7YSf9Go6Zz+jP89qU0vZt+g4D5cDYfeT3w/nksNUTyUsxbky+5n4F+J/xxp7I9b94X55Frs5Wdyf5Z3g++Xcws5jDY4EnN/Qd85nY5DPzanJA/e/kilSJfK17FnAgo0vVPgTsllK6cirG6lbDa+AhRiuE1q0BbASsVzp/o1PJr58bWpzjHPLfUJCXfd0qpbS0Wd8O5nslo8uWXpBS2qP02EcYu7zxcvLP8k/kJaL/VbRtSv4dP5/83MrenVI6DkmSJEmSJEmStNKYNX4XaWKKL7MOj4i/AccyGvhYl1xt5YUtDq1bTv5S9RMtxr8yIvYnf2lZX0poZ8ZWDhpzCPDOduGrfkopfSgibgK+RA6NBHm5rXkdHL6szbhXRcTe5C/e68GcOcBQcZvoPH8QEc8HDi2aghyAKYdgNprouL2QUnpvEQj5JHnpS8hfYr9mnEPvAz4CfDJNkzRqSilFxHsYu7zdeMc8XARPzmT0d7ALOWT1iO7k5/w9xgawVhoppRsi4qnAzxj9Mn0r4PAODu91pZb/ZTSA9bKIOLqoZNZKY2Wy9YrbRDQL5vxbSumeiPgP4DeMhqx2YWzQs+xycsWYTsNXezAavrqT/HuYKgsYGxR6mBwsbde/k7ay5zH699CpUxG+aqExCNrOneTr2LfG65hS+nQRhP4o+XN6Ju2vvTeRl8BsGb4qxr0uIo4gf3bUK0s+t7g1+jW5utYVTR7rh9eQl1o8sNhfD3hdcStbQl46saPlRCeieF/uQ65oNlg0b0wOfnWyJOF4n12PLm7t3Au8tIPAVC/H6sZaHZy/bjk5BPe5otJYUxHxWEavFwA/7TZ8VTiJ0aqqT4qIXVNKFxf7DzT0nUEO+T2R8T1ErnL3uUnMTZIkSZIkSZIk9YFLEGrKpJS+Ql7K573ABYy/FNH15NDVziml/0wp3dRm7AvJoasvkr8EbKb+pdzTUkqfatFnWii+VH8MuXLY3eN0vx34AfkL3I+361h8ob4r8Hbyz7edS8gVPxa1Ge8w8pf4w8BV5ADTdAkuHU8OhXyI8b/0vw74ArBTSukT0yV8VZdS+h05TDWRYy4mV3o6pU23i8hBm2bBrJVKSukW8pJ/ryJ/vrSzHDiXHNJsrLQ32XmcTn7vQA7UPK+X43erqJSzB/AZcninmTuLx/cYr0JZg3Jo5VsppcawQS8taNg/L6V0X6vOKaXbyJVm2o3RqBxM+mXHM5s6D5OrIf0dOIEcmN26k/BVXUrp4+SwyQJaf0bfQ17ibJeU0iUt+jSO+31ytZ5WoZtbgXeQP2cWdzrfqVbM5bnk5QjvatHtXGDeVFVzK+ZxX0rpReSf4R8YP1R1KXAc+e+ixuVTbwKOIL9mx/u74X5ysH2XlFKza0Qvx5oqS8i/u6uA04AvA4eR3xsHtgtfFRoDut0uP9jq+H+Pn1L6GDlsdQz599zJe+E2chD/8YavJEmSJEmSJElaObkEoVaYiNiMHBDZnFwVaxb5y7SbyF+qN10ypoNx1wCeSQ57bUr+cvAm4MyU0q2Tn/mKVSwnV18ybVPysjv3AjeQl3+6stvAUEQ8kbyM4ebkChP3kJcdOj+l1DJ4tTKKiAHyF6BbkCt/3U0OB1xRqlKxSoqIHcnhpK2ApeT3w0Uppcv6OrEpFBFbAnuTl0HbmPyF9x3kL+svSSndNYXnPoy8jCXAr1JKL5iqc3Wj+IzcF9ie/JlyO3lpyjNTSksmONZ65DDnRuSf8UC3n93TQfF5exv5+SRymKNl+HdlFBFbkJeO24pcifJ28hJzZ0/0918aM4CnkEN+c8hhvquBBZOsKjTlImJN4AByhaW1yaHjC1JKjcG9FTGXjcmf1XPJFboeJv9dVCN/bt3W4TgzyKH0x5KXK16/GOtO8jK853UalOzlWMqK19zjyEsWbkX+mS4n/213C3AxUEsp9bpCoyRJkiRJkiRJWoEMYEmSNAkRMZMcjnwMOcTz+JTSP/o7q6kREUeTq2YBfDml9P/6OZ/Jioh55EqJAH9LKe3Zv9lIkiRJkiRJkiRJWlm5BKEkSZOQUlpGXt4QIIB39XE6U6ao4vLWYvc+4GN9nE6vlJcf/EXfZiFJkiRJkiRJkiRppWYAS5KkSUop/RA4q9g9NCJ26ed8psgbyUuSAXx0FVmqrxzA+mXfZiFJkiRJkiRJkiRppeYShJIk9UBEPA6oFLt/Sin9rp/z6bWIeBOwGbAM+GRKaXGfpyRJkiRJkiRJkiRJ04IBLEmSJEmSJEmSJEmSJEnqkksQSpIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXDGBJkiRJkiRJkiRJkiRJUpcMYEmSJEmSJEmSJEmSJElSlwxgSZIkSZIkSZIkSZIkSVKXZvV7AtLqbtGiRdsA1xe7286dO/eGfs5HWp0tXbr0aGAD4J7Zs2d/tt/zkVY3XhOl6cHrodRfXg+l6cNrotRfXhOl6cHroSRJUmcMYEmSNOpoYGvgRsD/mSBJWl15PZQkKfOaKEmS10NJkqSOuAShJEmSJEmSJEmSJEmSJHXJAJYkSZIkSZIkSZIkSZIkdckAliRJkiRJkiRJkiRJkiR1aVa/J6CpMTg4uDGwL/Dk0m3z4uH9RkZGFvRpapIkSZIkSZIkSZIkSdIqwwDWqutFwHf6PQlJkiRJkiRJkiRpOqpWai8H3gTsBqwB1IATgc8NDQ8sneBY6wBvAQ4BdgKWA5cDVeCrQ8MDy8Y5/lnAG4F9gM2Ae4FrgbOAd0x0PpIkacVyCcJV283Ar4APAYf2eS6SJEmSJEmSJEnStFCt1D4P/BB4BvBX4LfAo4BPAKdXK7W1JzDWHOAc4GPAjsAfgbOBAeCLwG+qldoaLY6NYi5nAi8BrgF+ApxPXt3mKGDNiT4/SZK0YlkBa9X1vZGRkePrO4ODgxv1byqSJEmSJEmSJEnS9FCt1A4mV6u6D9h3aHjg/KJ9U+B0chWqDwNv63DIr5OraF0KPH9oeOD6YrwtgBHgIOAY4L1Njj22mMs5wKuGhgeubZjrnsBDnT87SZLUDwawVlEjIyNty5hKkiRJkiRJkiStLPq9VFy1UpsHnDHO0G8YGh74+kTmor55T3F/XD18BTA0PHB7tVJ7I/AH4MhqpfbhoeGBu9sNVK3U5gIvK3b/Xz18VYx3S7VS+y/gIuCt1UrtuKHhgXtLxz62mMstwH8MDQ/c1Tj+0PDAuV09Q0mStEK5BKEkSZIkSZIkSZKmremyVFzhFuC7LW5XTOR5qT+qldrWwJ7F7vcbHx8aHjgbuJ687N/zOxjyKUAAS4Czmox3MXAbsHaT8d5ALpjxrWbhK0mStPKwApYkSZIkSZIkSZKmpWm2VBzA5UPDA/O7eS6aNp5U3N8xNDxwTYs+fwO2Lfr+YJzx1ivu7xoaHljeos/twGbAk4GTSu3PKe7PqlZqG5Grsu1Krsp2KfCToeGB28c5vyRJmgasgCVJkiRJkiRJkqTpquVSccAbi90jq5XahuMNNN5SccB/FbtvrVZq60965pqudijur2vTp/7a2KFNn7pbi/vNq5Xaeo0PViu1GcB2jeMVldYeW2q/ghwQfCNwZLF9TbVSq3QwB0mS1GcGsCRJkiRJkiRJkjTtTLOl4rTqqIfr7m/T577ifoMOxvsL8ECx/Z9NHh8C1mky3hzy6xHgy8DNwLyiz+OA48nVtU6oVmrP7GAekiSpj1yCUJOyaNGibfo9h1XAluXtRYsW9W0i0upuzpw5M4vNmbfddpufb9KK5zVRmga8Hkp95/VQmia8Jkp95zVRrL/tjAPuvX45BHcd+Jl1ljb7TmLmGly6bAnbzl6PZy5atOgP7cZbd4t41P23JAjuOfAz68xt9rqKGdyVlrPZ7PXYd9GiRX+st2/06Bmb3XX1cmIGWw8fcfWnly9NW0aweNbaUdvsiTNPe8zgGqvki3RVvB6uuWFssPjuxIxZrNHqe6411mf9JffCjNmsOd53YQd+dh3OOuaBby65l6OAj//wjVevP3fPWb+aMZtlN/754WeTl7RcCsyeMZvZ9fF2fM7szf/5u6X1YR7aaXD2YdvNm30HsCE5HPb+09/1wHbLl7DfjNl8fNGiRatdJay5c+fe0O85SJLUqUgp9XsOWgEGBwc3Au4sdvcbGRlZ0ItxFy1a5AtIkiRJkiRJkiT13HV/WMqVP1vKelsHe/3P2k37XPGzJVz/h4fZfLeZ7PqaNduO968rl3HB1xcDMO/jazNrzRjzeFqeOOM9D7J8CY8Y747aMs7/6uKm48YM2PaZsxh4wWxmzIymfTR99Pp1Bfm1c/lPl3LjOQ8/4rGNdpzBulvM4MY/PcyWe8zkCYfm8R5enFjw7gcB2GrPmTz+lY88z21/X8ZF/7uYmAn7fXxtZsxavV5fc+fOXb2esCRppWYFLEmSJEmSJEmSJE07y4q808w1WmcwZhaZlYcfGv/fi2/4qBnMWAOWL4FFf36YR+07e8zjN/1tGcuXNB9v1lrBts+axeZPnMk6m81g1lrw4O2JRec+zPVnP8x1Zz7MssWJnV8xflhH/bX2nPx6WnxX69fMQ8Vja2/cWf4nZgQ7v2wNtt17Frf9fRkP3ZmYuSZsPDCDTXeeyaUn5hfWelvN+Pcxs9YMZq8HS++DtTeZ0XTctTfJ50/LYOn9iTU3NI8kSdJ0ZQBLk7VtvyewCtgSOLfY3pO8xrekPpgzZ8655PfkzXfcccee/Z6PtBrymihNA14Ppb7zeihNE14Tpb7zmihu+OPDRwLvvPf65ecCL2nWZ9FfHn4H8P/u+ufyM4FD2403a61g1pr8z5IlHHXlz5c+dN0fHv5Yeam4B29PxwBrALPv+ufyM4Ch+rEbbDODDbZZY8x4680NHvOiNbjjqmXPu29R+uaNf17GkvsXP2e3w9e8bHLPfPpYFa+Hd1+zfEvg3KX3w+U/XbL3416yxvWNfW77+7K/AHNvv3zZkTsN8vNOx15v7gzWmzs2TLVsaeLWi5b9Gdj6roXLnw9c8u/HFlMF9rv+rKX/t+OzZx/TON4VP13yNODHAHdevXznLfeYcV+nc5EkSSuWASxNimsvT17DGvM3+zOV+mfp0qXLis1lvhelFc9rojQ9eD2U+svroTR9eE2U+strogAW3127EWD5w8xu9RpYcm8tASxfym2dvE6W3Ft7G7AW8N8P3ZE+9M/fLf1Q6eGzgH8Ar1++lJs6fd295LN8q1qpvRHY/bZLlj1t7ty5v+/kuJXBqng9nHsENyw8vXYusOcNZz+8//5Hbv/R8uPVSm0fYC6w+P6b0wlz5869ezLnq1ZqhwBbA396/vt3+E35seVLaycA+y19gKeeevQDi4aGB5aXHz+1Vnt9sXnFHi941OWTmYckSZpazetZSpIkSZIkSZIkSf21sLhvtxpH/bGFbfr829DwwLKh4YE3ALsC7wO+CXwWGAT2AzYoul7SfISW/lHcbzPB49QfHyvu31Wt1PaoN1YrtU2Arxa7Xx4aHri79NiLq5Xa5dVK7bTGwaqV2txqpfaI12m1UnsB+TW2GPjvJvM4AbgaeALwoWqlNqN07H7A0cXuFyfy5CRJ0opnBaxV2ODg4Kal3Q1K2xs2PHb3yMjI0hU0LUmSJEmSJEmSpE5cUNxvUq3UdhgaHrimSZ+nFPfnT2TgoeGBS2gIWVUrtQCeUeyeMpHxgE2K+3sneJz6YGh44ORqpfZF4M3An4tQ1f3AAcBGwB+B9zcctiHwWHIFtUZPBX5ardQuAq4BlpJDfo8D7gMOHhoeuLjJPJZUK7WXAGcA7wUq1UrtQnLFrKeSi2l8F/jaZJ6vJEmaelbAWrXdVrpdXWo/ueGxZzziSEmSJEmSJEmSpD4aGh64ATi32H1V4+PFUnHbkqsL/boHp3wF8CjgT0PDA+d1elC1UtsaeGax+9cezEMrwNDwwFuAQ4A/AXsDzwduAN4F7D80PPDgBIa7h/xa3Rb4D+BFQACfAx43NDzw2zbzuJhcAesrwEzgheTg1pnAK4eGB+YPDQ+kiT07SZK0olkBS5IkSZIkSZIkSdPVx4CfkZeK+83Q8MD5MP5SccDHgRuHhgcOKA9WrdTmAjOHhgeub2hvu1RctVJ7C3Di0PDA7Q3tuwLHA2uT/zH8z7t/qlrRhoYHfgj8sMO+x5N/1/9WrdT2JlfKem6TQ9YGbgFu7WDsm4Aji5skSVoJGcBahY2MjES/5yBJkiRJkiRJktSt6bJUHPBB4DPF8nDXAMuBRwNPIq84cx3wwqHhgcVdP1mtVKqV2qvJgaxW37c+CjgOOKBaqR08NDzwwIqamyRJWvFcglCSJEmSJEmSJEnTVq+WiqtWajPIwanTgE2BZ5OXe+tkqbiPAr8kh7sOAl4C7ACcA7wdeMLQ8MA/unl+WvlUK7UDgO/SWbGLg4BqtVKzcIIkSaswK2BJkiRJkiRJkiRpWpvMUnHVSm094L+ANwA7NXRfAHwF+OnQ8MDyNmN+CvjUROasVVMRpPoUMHMCh72UHB7845RMSpIk9Z0VsCRJkiRJkiRJkrRKqlZq2wF/BT7LI8NXAPOAHwHD1Uqt2ZKFUqOnkpeenKg39noikiRp+rACliRJkiRJkiRJklY51UptE+BUYKCD7i8HUrVSqwwND6SpnZmmo6Ky1ezitkaT7fr9W7o8xUurldprhoYHHu7BdCVJ0jRjAEuSJEmSJEmSJEmrovfRWfiq7hXA94BfTs10Vn0tQkyN9+0e61Wfbo6f6u9N1wQ2Am6f4vNIkqQ+MIAlSZIkSZIkSZKkVUq1UlsXOLyLQ99InwNYpRDTVAWNOu6z6U5rbgqwbMnyze+8dukFHYzjd4/tLe33BCRJ0tTwjyBJkiRJkiRJkiStal4EbNjFcc+tVmqvBR6gf8GnafP93e1XLa5vzgZ2799MAFhCDjA13jdr63Wf8vYhwGu7mP9twD1dHCdJklYC0+YPOEmSJEmSJEmSJKlHduzyuAD+t5cTmWaWMIFQ0ha7rPWMWWvGminx4KILH/x5i75TGXyq3y8bGh5IU/VDmYhqpXYFMB+YMcFD/2+6PAdJktR7BrAkSZIkSZIkSZK0qploOKZTiRVfcalXfSYcYlq6dOkNwNbAHbNnz37lRI5dVQ0ND1xbrdR+Qa6y1qnlwDemaEqSJGkaMIAlSZIkSZIkSZKkVc2Nkzh2HrCQJqGmoeGBZZOemVYFbwH2ArbosP87h4YHrpnC+UiSpD4zgCVJkiRJkiRJkqRVzcnAl4G1JnjcOUPDA2f2fjpalRRVsPYHfg1sN0739wKfmfpZSZKkfpqq8quSJEmSJEmSJElSXwwND/wLGO7i0K/1ei5aNQ0ND1wG7AYcDdQaHn4IOB54ytDwwMcmuvSjJEla+VgBS5IkSZIkSZIkSauiayfY/2zgpKmYiFZNQ8MDdwOfq1ZqXwAeC2wKPAhcVTwmSZJWEwawJEmSJEmSJEmStEqpVmr/AxxT7CYgxjnkz8CLhoYHlk7pxLRKGhoeWA78o9/zkCRJ/eMShJIkSZIkSZIkSVplVCu1dwOfLnZvBg4kLy14X5Pu1wPvAfYfGh64Y8XMUJIkSasaK2BJkiRJkiRJkiRplVCt1D4AfLDYvZEcrLoSOL1aqb0LeB6wFbAUqAGnDg0PLOvLZCVJkrTKMIAlSZIkSZIkSZKklVq1UgvgQ8D7iqbryOGrq+t9hoYH7gFO6sP0JEmStIozgCVJkiRJkiRJkqSVVhG+Og54R9F0DTl8tbBvk5IkSdJqxQCWJEmSJEmSJEmSVkpF+OqzwFFF09XAfkPDA9f3bVKSJEla7RjAkiRJkiRJkiRJ0kqnWqnNAL4IvKlougI4YGh44Mb+zUqSJEmrIwNYkiRJkiRJkiRJWqkU4auvAUcUTZeRw1c3929WkiRJWl0ZwJIkSZIkSZIkSdJKo1qpzQS+Dcwvmi4hh69u69ukJEmStFozgCVJkiRJkiRJkqSVQrVSmwV8Bzi0aLoAOGhoeOBf/ZuVJEmSVncGsCRJkiRJkiRJkjTtVSu12UAVqBRN5wLPGRoeuLN/s5IkSZIMYEmSJEmSJEmSJGmaq1ZqawDfB15aNP0JeN7Q8MDd/ZuVJEmSlBnAkiRJkiRJkiRJ0rRVrdTWBH4IDBZNZwPPHxoeuLd/s5IkSZJGzej3BCRJkiRJkiRJkqRmqpXaWsBPGQ1fLSBXvjJ8JUmSpGnDCliSJEmSJEmSJEmadqqV2jrAycBBRdOpwIuGhgce6NukJEmSpCasgCVJkiRJkiRJkqRppVqprQv8ktHw1W+AFxq+kiRJ0nRkAEuSJEmSJEmSJEnTRrVSW58cuNqvaPoF8OKh4YGH+jcrSZIkqTUDWJIkSZIkSZIkSZoWqpXahsDvgGcWTT8DXjY0PLC4f7OSJEmS2ouUUr/noJXY0qVLjwaO7vc8VmYppZnLly/fEmDGjBk3R8Syfs9JWo1tCcwElgE393ku0mrHa6I0bXg9lPrI66E0rXhNlPrIa+LqafG9y+KMT9y66Z3XLlkDYOsnr/3gPkdudseMWdHvqa3OvB6qb2bPnr1Nv+cgSVKnDGBpUpYuXXoscEy/5yFJkiRJkiRJklZei+9dxhmfvJU7Fy4BYLu912WvIzZhxkzDV9Lqavbs2X4ASJJWGrP6PQGt9O4Bbuz3JFZm/ksuaVrxX3NJfeQ1UZo2vB5KfeT1UJpWvCZKfeQ1cfXy4N3LZpz+8Vs2vefGpbMBtnv6Og/sdcQmdxq+mha8HkqSJHXAClhSny1atGgb4Ppid9u5c+fe0M/5SKuzpUuX3gBsDdxoaWNpxfOaKE0PXg+l/vJ6KE0fXhOl/vKauPqoVmpbAKcBjy+avg28fmh4YHn/ZqU6r4eSJEmdmdHvCUiSJEmSJEmSJGn1U63U5gILGA1ffRXDV5IkSVoJGcCSJEmSJEmSJEnSClWt1LYFzgQeVzR9ATjS8JUkSZJWRrP6PQFJkiRJkiRJkiStPqqV2nbAGcAORdOngHcODQ+k/s1KklZPEbE9ML/YXZBSWtC3yUjSSswAliRJkiRJkiRJklaIaqW2I3A6sF3R9DHgfYavJKlvtgeOKe0v6M80JGnl5hKEkiRJkiRJkiRJmnLVSm0n8rKD9fDVsRi+kiRJ0irACliSJEmSJEmSJEmaUtVK7XHkyldbFU3vHRoe+FgfpyRJkiT1jAEsSZIkSZIkSZIkTZlqpfZ4cvhq86Lp7UPDA5/u45QkSZKknnIJQkmSJEmSJEmSJE2JaqW2G7CA0fDVUYavJKn3ImJGRLwqIk6OiGsj4sGIeCgiboyIiyLiRxHxxojYpOg/LyIScEZpmGMiIjW5bV86z/xS+/yibY+I+HpEXBkR95Yfa5jjhhHxPxFxakQsiojFEXFHRJwXER+PiK07eJ7bFM9jOCIuK863NCJuj4i/FONs28E4C4t5Liz214iIIyPinIi4LSLuK35u74iIdRuO3SIiji0ev7uYw58j4j8jIsY7t6RVkxWwJEmSJEmSJEmS1HPVSm0P4BRgTtH0pqHhga/2cUqStEoqQlW/BPZq8vDc4rYr8DJgHaBnQdiIeAfwMWDmOP1eDnyd0WtC3RrAxsAewFER8d8ppe+2GGMeuaJis5DTJsXtqcDREfHGlNL/dvgctiT//J7c8NCuxe1lEfHslNJdEbEXcDKwRUPfpxW3/SPi1Sml1Mm5Ja06DGBJkiRJkiRJkiSpp6qV2lOB3wEbAQl4/dDwwLf6OilJWnV9i9Hw1fXAMHAVcCewLrAT8HTgmaVjLgVeDDwB+HDRdlJxbKNbW5z3FcDzgPuAKvBXYCmwC3BzvVNE/BfwDXJwagnwc+As4BZgPWAf4FXAWsDxEbEkpfSDJudbqxjjCnLlrsuA24GHgS2BZwEHk0Nd34qIW1JKv2wx97rZwE/I4atTyOGq24EdgTcB2wB7Ap+PiGPJ17a1geOL5/Bg8fgbivZXAqcC/zfOeSWtYgxgSZIkSZIkSZIkqWeqldrTgd8CG5DDV68dGh44vq+TkqRVVERsDryo2D0HOCCl9FCLvpsBmwKklG4HTo6Iu0pdLk8pnTyB0z8PuBI4KKV0XYtz7gp8mRycugoYTCld3tDtOxHxaXJwaS7wjYj4XUrpjoZ+/wB2Tyld1GI+X4qI3ckhqc2Bz0bEr8apRjUX2Ap4XUppTGgqIr4DXEgOdx0K7EYOmM1LKV1Q6jocEb8CTiv2j8YAlrTamdHvCUiSJEmSJEmSJGnVUK3Ungn8nhy+Wg4cZvhKkqbUjox+739iq/AVQErptpTSP3p47gRUWoWvCseSK1I9BDy/SfiqPrd/APOL3fWB/2rS59o24at6nwuB9xS7OwF7t+tf+HZj+KoY6xZyeAzyEou7A29qCF/V+57OaADr8RGxbQfnlbQKMYAlSZIkSZIkSZKkSatWavuRK1+tBywDXjk0PHBif2clSau8+0vbT17B5z67WRipLiI2YrQ6189SSrV2g6WUTgFuKnafM5l5lbb3atlr1Jc6HOsW4Edt+v6htL1LB+eVtApxCUJJkiRJkiRJkiRNSrVSOwgYAdYCHgYOGRoe+Gl/ZyVJq4XLgBuBrYHXRsRM4FvAn1NKy6b43H8Y5/FnMFoUZnFEHNzBmPeSlwRsGWAqlhk8FHg6ucrVBsCaLbpvM8757gcubfP4zaXt81JKyzvsu/E455W0ijGAJUmSJEmSJEmSpK5VK7XnAT8jf/m9BHjZ0PDAL/o7K0laPaSUlkXEEcBPyZ/Drylu90TEX4A/AqcC56SUUo9Pf8M4j29f2p7P6BKDnZjT2BARs4CvkJcnjA7H2WCcx+8Y5+eyuLT9r3HGKvdda5y+klYxBrAkSZIkSZIkSZLUlWqlNkhejmkN8hfPLx4aHvhNf2clSauXlNKvI+IpwDHAIPkzeQPgoOJ2LHBNRHwgpXRCD0/94DiPbzSJsWc3afsCcESxvZS87O1fyUGw+4s2gM2BbxTbM8c5T7uKVpPpK2k1YwBLkiRJkiRJkiRJE1at1F4KDJO/b3oQGBwaHji1v7OSpNVTSulS4OURsS556b+9gGcWtzWBHYDvRcSjU0ofXEHTuq+0/eaU0pe6HSgitgX+u9i9EdgvpXRVi76P7/Y8ktStGeN3kSRJkiRJkiRJkkZVK7VDgJPI4asHgOcbvpKk/ksp3Z9S+n1K6UMppYOAzYD3l7q8NyK2XEHTKS9RuO0kxzqQ0XzDca3CV4UdJnkuSZowA1iSJEmSJEmSJEnqWLVSOxT4PnlZp/uA5w4NDyzo66QkSU2llO5NKX0E+HnRNJtcHauuvKxe9Pj0fwBSsf3cSY5VDo3Vxun7vEmeS5ImzACWJEmSJEmSJEmSOlKt1OYDVfJ3TPcAzx4aHvhDXyclSerENaXtWaXt8jKB6/byhCmlW4HfFLtPjIhXTmK4+0vbA606RcSOwGsmcR5J6ooBLEmSJEmSJEmSJI2rWqkdAXyHXCHlLuDAoeGBP/V1UpK0mouI50TEWyNi4zZ9NgdeWmq6qLRdDmbt0ev5Ae8FlhTb3x4vhBURcyLi6Ig4sOGhc0vbb4uITZoc+yjgF/Q4SCZJnZg1fhdJkiRJkiRJkiStzqqV2puALxe7d5DDVxf0cUqSpGwr4LPAJyJiAfBn4J/kylabALsCrwTqAa0fppSuqh+cUrozIs4nh6/2i4hvAKcC95bOcWZK6cFuJpdSujAiXg/8L7AO8P2IeAc5KHUV8CCwIbmq1VOBZ5FzDIc1jPOniPgL8DRgO+DyiPgm8A/ykrh7FcesCxwPzO9mvpLULQNYkiRJkiRJkiRJaqlaqR0FfK7YvR04YGh44OL+zUiSVJKK+9nAQcWtlR8Dhzdpfw/wS3J+4IjiVrYDsLDrCaZ0fETcBPwfMBfYvbi1sph8vWlUAU4v5rNpMe9GXyJfs+Z3O19J6oYBLEmSJEmSJEmSJDVVrdTeDnyy2L2FHL76ex+nJEkaqwpcBhxIrg61MznktDbwAHAduSrW91JKZzYbIKX0u4h4OvBmYG9yVa11ejnJ4hw7Aq8Cng88GdgMWItcbWsheWnE04FfpJTuajLGwoh4EnAU8BJy1SyAm4FzgP9NKS2IiO17OXdJ6oQBLEmSJEmSJEmSJD1CtVJ7L/CRYncRsP/Q8MAVfZySJKlBSikB5xa3yYzzN2Cog37Hk5f46+Yci4HvFLeupJTuBj5Y3Fr1WQjEOONs3+H5xh2r1Pd4uvzZSFr5GcCSJEmSJEmSJEnSv1UrtQCOKW4ANwD7DQ0P1Po3K0mSJGn6MoDVI4ODg5sB7wIGgW2A+4Hzga+OjIyc3MV4C4B9O+x+/MjIyJi1egcHB48HXjPOcX8fGRl5wkTnJkmSJEmSJEmSVk1F+OojwHuKpmvJ4atr+jcrSZIkaXozgNUDg4ODjyevRbt50XQvsBFwEHDQ4ODgF0dGRt4ywWHvIK+l3soawMbF9nlt+j0E3N3isdsnOCdJkiRJkiRJkrSKKsJXnwTeVjT9k7zs4LX9m5UkSZI0/RnAmqTBwcE1gRFy+OpS4NCRkZGLBgcH1wHeCnwYePPg4OCFIyMjHa9lOzIy8pJxzvse4KPAYuD7bbqeNDIyMr/T80qSJEmSJEmSpNVPEb76PPDmoukqcvjqhr5NSpIkSVpJzOj3BFYBRwA7Ag8A/zEyMnIRwMjIyAMjIyMfBb5a9PvI4ODg7B6et7684C9GRkbu6OG4kiRJkiRJkiRpNVKt1GYAX2E0fHU5sK/hK0mSJKkzBrAm79Di/gcjIyPXNXn8k0AC5gL79eKEg4ODewOPKXY7rqolSZIkSZIkSZJUVoSvvgG8oWj6OzBvaHjgpv7NSpIkSVq5GMCahMHBwfWAPYvd3zbrU4Sy/lHsHtCjU88v7m8CftejMSVJkiRJkiRJ0mqkWqnNBP4P+M+i6SJgv6HhgVv6NytJkiRp5TOr3xNYye0MRLF9aZt+lwK7FLdJGRwcXBt4RbH7vZGRkWXjHHLA4ODgVcCjgIeAGvBr4MsjIyP+B5QkSZIkSZIkSauhaqU2C/gu8Kqi6Tzg2UPDA3f0b1aSJEnSyskKWJOzVWl7UZt+9ce2atOnUy8GNiy2j++g/zbA9sD9wHrAHsD7gMsGBwd7VZFLkiRJkiRJkiStJKqV2mzg+4yGr/4KHGj4SpIkSeqOAazJWa+0/UCbfvXH1u/BOQ8v7v8yMjLyjzb9zgfeCGwHrDkyMjIH2Ag4lLx04Rzg5MHBwcf0YE6SJEmSJEmSJGklUK3U1gBOAl5eNJ1Drnx1V98mJUmSJK3kXIJwJTI4OLgtsH+xe3y7viMjI19s0nYvcOLg4ODZwAXAxsCxjP4LlwlbtGjRNt0eq3/bsry9aFG7YmqSptKcOXNmFpszb7vtNj/fpBXPa6I0DXg9lPrO66E0TXhNlPrOa+IUuOe6ZWvMXINvLFvCgQAzZvHnx796jddusdus9RctWtSLf0SuVYzXQ/XT3Llzb+j3HCRJ6pQBrMm5r7S9DnBPi37rFPf3TvJ8Q+SqZQ8Bw90OMjIycu3g4OCXgfcD/zE4ODhjZGRkeZfDXd/tPNTUuf2egLQ6u+OOf1dY3xI/36R+85oo9YnXQ2la8Xoo9ZHXRGla8ZrYA8uWJK7+7VKWLcn7G+80g91fu+ZeM9eMy/s7M01nXg/VZ9HvCUiS1CmXIJyc8j+5mdumX/2xmyZ5vtcU9yePjIzcNcmx/lLcbwBsMsmxJEmSJEmSJEnSNLVsSeKi/1vMvy7P/xZ7zmNnsPvr1mTmmmYbJEmSpF6wAtbkXA4kcvr68cV+M48v7i/r9kSDg4PPAHYqdr/T7ThTYNt+T2AVsCWj/4JrT+DmPs5FWq3NmTPnXPJ78uY77rhjz37PR1oNeU2UpgGvh1LfeT2UpgmviVLfeU3skVsveXidS09YcvzypTwdYMYanL7js2cfMXONWNzvuWn683ooSZLUGQNYkzAyMnLf4ODgX4GnAc8FftLYZ3BwcBtgl2L3tEmc7vDi/gbg1EmMU/e04v5e4F/dDuLay5O3aFG5kBo3+zOV+mfp0qXLis1lvhelFc9rojQ9eD2U+svroTR9eE2U+strYm9UK7UNgGHI4Svg58uXcMguz9jW8JU64vVQkiSpMy5BOHknFvevHBwcbFYN6h3kClmLgDO6OcHg4OA6wMuL3erIyMjycfq3rRk8ODj4KOBNxe6vxhtPkiRJkiRJkiStXKqV2obA74BnFE0/AV4+NDxg+EqSJEnqMStgTd43gaOAHYFfDg4OHjYyMnLx4ODg2sBbgCOLfu8bGRlZWj5wcHBwIbAd8N2RkZH5bc7xEmCDYvv4DuZ06ODg4MHA94CzR0ZGbi/Otx7wQuCTwBzgPuDYDsaTJEmSJEmSJEkriWqltjHwe+ApRdMwcNjQ8MDD/ZuVJEmStOoygDVJIyMjiwcHBweB04FdgYsGBwfvAdYFZhbdvjQyMvKdSZxmfnH/x5GRkas66D+THNp6CcDg4OB9wGJgY0arnt0KVEZGRq6YxLwkSZIkSZIkSdI0Uq3UNgVOAXYvmr4HvNbwlSRJkjR1XIKwB0ZGRv4OPBH4HFAD1gTuBk4FXjwyMvLmbsculjXcr9g9vsPDzgDeB/wG+CewHNgQuBP4A/BuYOeRkZGulkSUJEmSJEmSJEnTT7VS25z8D8Z3L5r+Dzjc8JUkSZI0tayA1SMjIyO3AkcXt06P2b6DPtczWkmr03GvBT46kWMkSZIkSZIkSdLKq1qpbQmcBuxSNH0DeOPQ8MDy/s1KkiRJWj1YAUuSJEmSJEmSJGklVq3UtgbOZDR89WXgDYavJEmSpBXDAJYkSZIkSZIkSdJKqlqpPYocvnpM0fQ54M1DwwOpf7OSJEmSVi8GsCRJkiRJkiRJklZC1Upte3L46tFF0yeA/zF8JUmSJK1YBrAkSZIkSZIkSZJWMtVK7dHAWcD2RdOHgXcbvpIkSZJWvFn9noAkSZIkSZIkSZI6V63UHgOcDmxdNH1gaHjgw32ckiRJkrRaM4AlSZIkSZIkSZK0kqhWajuTw1dbFk3vHhoeOK6PU5IkSZJWewawJEmSJEmSJEmSVgLVSu2JwGnAZkXT/wwND3y2j1OSJEmShAEsSZIkSZIkSZKkaa9aqe0OnApsUjS9eWh44Ev9m5EkSZKkOgNYkiRJkiRJkiRJ01i1UnsycAqwcdH0hqHhga/3cUqSJEmSSgxgSZIkSZIkSZIkTVPVSu1pwO+ADYEE/OfQ8MD/9XdWkiRJkspm9HsCkiRJkiRJkiRJeqRqpfYMcuWrDYHlwGsMX0mSpLKISMVtXr/nIq3ODGBJkiRJkiRJkiRNM9VKbV9y5av1gWXAoUPDA9/r76wkSVrxImKvUsjo2ohom3OIiHkRcWxEzB+n31FFv917Od9eiYiDi/kd3O+59FpELCh+n8f3ey69VPy+jo2I7fs9F614BrAkSZIkSZIkSZKmkWqldgDwG2Bd4GHglUPDAz/o76wkSeqbw0vbjwL2H6f/POAYYP44/Y4q+u3e3bSm3MHk+R08Tr8ritsDUzwfje+Y4rZ9n+ehPjCAJUmSJEmSJEmSNE1UK7XnAL8E1gaWAi8fGh74UX9nJUlSf0TE2sAhQAK+WTTP79uEpqGU0uOK21/7PRdpdWYAS5IkSZIkSZIkaRqoVmovAEaAtYAlwIuHhgdO7uukJEnqrxcDGwLnAB8hB7FeHBEb9HVWktTAAJYkSZIkSZIkSVKfVSu1g4GfAmsADwGDQ8MDv+rrpCRJq4Xa/MoTavMrX6nNr1xcm19ZWNx/pTa/8oR+z43RalcnpJSuB84E1gFe0dgxIraPiEReAg5g34hIDbd5EXFs0W+7ot93GvosbDL2zIh4XUScGhG3RcSSiLgpIn4aEfOaTbw4VyrORUTsHBEnFMctjohaRBwXEes1Ow54TdH0mibPY/tS/38/txbz2CQiPhYRl0TEfcXtkoj4aETM6eXceyEiFhTnPjYiZkTEkRFxfkTcHxF3RcQpEbFfi2O3L/+MImKXiPh+Me+HIuLKiPhgUVmt7bk7mV+p7fj6z6pwRsPva0F3Pw2tTGb1ewKSJEmSJEmSJEmrs2ql9jLgB+TvbR4EXjg0PHBaf2clSVrV1eZXNgWqwPOaPPxE4I21+ZXfAIcNHD/8rxU6OSAitgUOIC/J+8Oi+XvAPOBw4NsNhywDbgHWA9Ytjrujoc8S4L6i32bkojX3kK+/dbc1zGNzcoXKpxVNCbgX2JJcoevFEfGhlNIxtBARBwI/J4fH7iZf8x8NvJMcFHtWSmlpaY63kCt/rUUOZt/d5LmOKyJ2B34LbFE0PVDcP6G4vTYinpNSurhHc++lWeSf+3+Qf5eLyT+TA4H9IuKlKaWftzn+6cC3yK+Fe4AAdgI+ALwwIvZPKd3Vo7neTf6d1X/Od5J/j3WNr0OtgqyAJUmSJEmSJEmS1CfVSu2VwDD5S8b7gecZvpIkTbUifPUHmoevyp4HnF2bX9lk6mf1CEPkTMNvUkr1AMuPyYGkvSNip3LnlNL1KaUtgU8XTeeklLZsuJ2TUvp00e/6ot9bGvrsWR8zImYBPyOHr84G9gPWTiltCGwCvJ8cDvpARLyszXP5IfBLYIeU0kbABsC7yWGuvYDXlZ7HOcX8TiqaTmryPK5nHBGxITnAtAVwFbBvSmndlNK6xfP4JzlENjLOko4dz73H3kgOUR0CrJdSWh/YDbgUmAl8JSJmtjn+a8BlwG7F72s9clWxB4AnFY/3RErpLcXvrO4lDb+vl/TqXJq+DGBJkiRJkiRJkiT1QbVSGwJOIH+JeC/wnKHhgTP7OytJ0mqiCjyuw76PI1eeWtHmF/cn1BtSSveQQ0Xlx6fSocDewF+Bg1JKC1JKi4u53JFS+gi5ohKl+2b+BlRSSguLY+9PKR0H1JcbfsSSij3wJmBbcsWvg1JKZ9UfSCktAJ5Nrvy1HfCGNuP0Y+4AGwMvSin9MKW0pDj3xYz+3rcGntHm+MXAc+vVvVJKS1NKVeD1xeOViNhlSmau1ZIBLEmSJEmSJEmSpBWsWqm9Fjie/F3N3cBBQ8MDf+zrpCRJq4Xa/MoTGb/yVaPn1eZXnjAV82kmIvYBBsjXyF80PFwPgx0WEVOdeXhtcf+llNJDLfqcWNw/MSK2bNHnuJRSatJ+cnE/FT/bejDquymlaxsfTCldTQ7iQa4y1Uo/5g7wh5TS2Y2NKaXzgBs6OPfXS5XTyk4E6j+Pl09uitKoWf2egCRJkiRJkiRJ0uqkWqm9Hvh6sXsn8Oyh4YG/9XFKkqSVTG1+pVkgZqpdUptf6erAgeOHY4KHzC/uf9Ik+PRb4DZydacDgd93NalxFMvbPbXY/XxEfLpd/8KjgJubtJ/bov+Nxf3GE5xeWxGxBqPhpNPbdD2VXBFq14iYnVJa2qTPCp17B+etn3ubcc69oFljSilFxFnAYcAeXc9OamAFLEmSJEmSJEmSpBWkWqn9P0bDV/8C9jd8JUnSqIhYh9HqTSc0Pp5SehgYLnbnT+FU5gBrFtubAFu0udWt02yglNK9Lc5RD5f1unjOHPISxzAalGqmXklqZnHMI/Rh7nWtzls+9+w2fdo97/pjm09oRlIbVsCSJEmSJEmSJElaAaqV2tHAZ4rd24ADhoYHLunjlCRJmo5eCqxfbJ8e0bZ41sERsWFK6e4pmMfM0vbTU0p/noJzSFpFWAFLkiRJkiRJkiRpilUrtXcyGr66GZhn+EqSpKYOn0DftYFDpmge/wKWFduPmqJzTJU7GJ371m36bVPcLyuOWZXM7eCxWxvaHy7u12pz7IZdz0irNCtgSZIkSZIkSZIkTaFqpfZ+4EPF7o3kZQev7OOUJEkruYHjh9uWhWqnNr/yFeCNXRz6lYHjh4/s9rydiIjtgXnF7rOAdmHltwPvIS9D+M1S+/L6cOOcrm2/lNLSiDgX2At4AfDDccbrtU6fxyOklJZExCXA7sABwE9bdD2wuL84pbR0wjOc3vYFFjQ2Ri6p9qxi9/yGh+8s7rehiYhYF9i5zTkT+ffV9ftTKy8rYEmSJEmSJEmSJE2BaqUW1Urtw4yGr64D9jV8JUnqs6+t4OMm4jXk8Mo1KaU/pJTuanUDflAc8/SIeGxpjHuK+43GOVcn/f6vuK9ExN7tBouIjcc530R1+jxaqQfGhiLiERW8IuLRwFCxO9zlOaazN0TERk3aK8D2xfaPGh6rB/6eHRHNqmC9FVizzTkn+zvTSswAliRJkiRJkiRJUo9VK7UAPg68r2haSA5fXd23SUmSBAwcP3wp8JsJHvabgeOH/z4V86krKhO9ptj98Xj9U0qXAvVQ8/zSQ5cW97tExNPaDFHv95KIaLWs3HeAs4HZwO8i4s0RsUlpzhtHxAsi4iR6H2Kqz2+fiNipi+O/ClwPrAecEhH1qk9ExL7A78hLOF7LignXrWhrAb+NiCcARMTsiDgU+Fbx+HBK6bKGY35ErmK1GVCNiM2LYzeMiPcCxwJ3tTln/Xf2yhYBLq3CDGBJkiRJkiRJkiT1UBG++jTwzqLpanL4amHfJiVJ0lhDwOUd9r0cOGwK51K3L7BDsT1uAKuh32ERUc8/LACuAmYBf46If0XEwuK2V+nYb5PDNvsAt0fEjUWfs+sdUkoPAy8CTicHmb4A3BYRd0bEPcAdwC+AV9D7/MXPgH8Bc4ArIuLW0vNoukReWUrp7mLutwKPAc6MiPsi4n7yz+jRwM3AYErp3h7PfTp4A/AE4JKIuAu4F/gesC5wQfH4GCmlf5D/hgN4OXBLRNxJ/j1/BPgAcFGbc9bDXS8H7o6I64vf16pYYUwNDGBJkiRJkiRJkiT1SBG++iJwdNF0JTl8dV3/ZiVJ0lgDxw/fTg4ejVcJ6zfAPgPHD/9r6mf17ypW16WU/trhMT8p7rcGDoJ/h6YOAI4nL/+7PrBdcft3VaKU0gJyQOkMcjhny6LPmHBTSukO4EDgpcDJwE3AOuSqWP8Efgr8NzmE1TMppX+RQ2k/IgelNi49j1kdjnEB8HjgOOAy8vKOAH8nV+p8Qkrp4l7Oexr5E/BUcmWyB8nP/Sry0tDPKJaxfISU0juAI4DzgYeK484CXphS+li7E6aUvgscXpz7IfLrcjvya0uruEgp9XsO0mpt0aJF25BLPwJsO3fu3Bv6OR9pdbZ06dIbyH8I3Th79uxx/+WApN7ymihND14Ppf7yeihNH14Tpf5aWa+J1UptBnm5n9cXTZcBBwwND9zcv1lJ3fN6KK0eavMrTyAHiJ4JbADcA/wB+NpULzso9VJEbA9cU+zukFJa2L/ZaHXTUSpSkiRJkiRJkiRJrVUrtZnAN4HXFk2XAAcODQ/c2r9ZSZI0voHjhy8Fjuz3PCRpZWYFLE3K0qVLj2a0jLK6kFKauXz58i0BZsyYcXNELOv3nKTV2JbATGAZuZSrpBXIa6I0bXg9lPrI66E0rXhNlPpoZbsmLl+W+NPXbt/4ur88sA7AhtvMXrr/u7a4fa0NZy7v99ykSfJ6qL6x6pqkibIClvrJCliarA3IpWfVpYhg5syZ9V3XfpWmh5n42SatcF4TpWnH66HUB14PpWnJa6LUByvTNXH5ssSfvn471/3lAQDm7LAG896x+ew115u5VZ+nJvWS10NJkqQ2DGBpsu4Bbuz3JFZmK9u/5JJWcf5rLqmPvCZK04bXQ6mPvB5K04rXRKmPVpZr4vKHE3/44m1zFl3w4NoAc3ZYY8l+79z89jXWnenyI1pVeD2UJEnqgEsQSn22aNGibYDri91t586de0M/5yOtzpYuXXoD+V9x3WhpY2nF85ooTQ9eD6X+8nooTR9eE6X+WhmuidVKbU3gJOBFRdMfgecPDQ/c079ZSb3l9VCSJKkzVsCSJEmSJEmSJEmagGqlthbwY+A/iqYzgRcMDQ/c179ZSZIkSeoXA1iSJEmSJEmSJEkdqlZqawMnA88umk4FXjQ0PPBA3yYlSZIkqa9m9HsCkiRJkiRJkiRJK4NqpbYu8EtGw1e/BQYNX0mSJEmrNwNYkiRJkiRJkiRJ46hWausDvwb2L5p+CRw8NDzwYP9mJUmSJGk6MIAlSZIkSZIkSZLURrVS24Bc7epZRdPPgJcODQ8s7t+sJEmSJE0XBrAkSZIkSZIkSZJaqFZqGwGnAHsXTT8CDhkaHljSt0lJkiRJmlYMYEmSJEmSJEmSJDVRrdTmAKcBTy2avg+8amh4YGn/ZiVJkiRpujGAJUmSJEmSJEmS1KBaqW0GnA7sUTR9FxgaGh54uH+zkiRJkjQdGcCSJEmSJEmSJEkqqVZqW5DDV7sVTd8GXjs0PLCsf7OSJEmSNF0ZwJIkSZIkSZIkSSpUK7WtgAXAE4qmrwGvHxoeWN63SUmSJEma1gxgSZIkSZIkSZIkAdVKbRvgTOBxRdMXgDcZvpIkSZLUjgEsSZIkSZIkSZK02qtWatuRw1c7FU2fBt46NDyQ+jcrSZIkSSsDA1iSJEmSJEmSJGm1Vq3UdiCHr3Ysmj4OvMPwlSRJkqROGMCSJEmSJEmSJEmrrWqlNgCcBWxXNH0QeK/hK0mSJEmdmtXvCUiSJEmSJEmSJPVDtVJ7LHAGsFXR9L6h4YGP9nFKkiRJklZCVsCSJEmSJEmSJEmrnWqltgt52cF6+Oqdhq8kSdLKJiJScZvX77lIqzMrYEmSJEmSJEmSpNVKtVLbFTgV2KxoeuvQ8MDn+zcjSZLUSkTsBfyp2L0O2CGltLxN/3nAPGBhSun4Nv2OAjYCTk4pXdiLufZSRBwM7A5cmFI6ua+TmUIRsRXweuA5wKOBDYE7gCuBXwPfTCndOUXn3gg4qtj9fErprqk4z2RExO7AwcBdKaXP93UyassAliRJkiRJkiRJ6olqpfZy4E3AbsAaQA04Efjc0PDA0kmO/XzgV8XuaUPDAwe26DcLOAIYAnYhfxdyNfBD4NPAzsApwJzikCOHhge+Mpm5SZKkKXV4aftRwP7kIHUr84BjyJUuj2/T7yhgO2AhcGH305syBwOvAb4LnNym3xXF/QNTPJ+ei4j/BxwHrFM0LQPuATYHtgSeBbwjIt6QUvrhFExhI/JrBfJr5a4pOMdk7U6e47XA5/s6E7XlEoSSJEmSJEmSJGnSqpXa58khp2cAfwV+S/6S9BPA6dVKbe1JjL0x8C0gjdNvTeA3wFfIX1adT/6CdnPgQ+QvV09nNHz1esNXkiRNXxGxNnAI+W+AbxbN8/s2oWkopfS44vbXfs9lIiLiQ8AXyeGrBeRg3VoppTlF2wuAC8h/tw1HxH/2aapSRwxgSZIkSZIkSZKkSalWagcDbwHuA542NDzwnKHhgZcCOwGXAPsAH57EKb4EbAF8fZx+HwYOBG4EnjI0PDBvaHhgEBgA/gg8hrysTQJeOzQ88M2WI0mSpOngxeRr9znAR8jX8BdHxAZ9nZUmJSKeB7yv2P0GsH9K6YyU0sMAKaXFKaVfAXsBvwQC+HJE7NaXCUsdMIAlSZIkSZIkSZIm6z3F/XFDwwPn1xuHhgduB95Y7B5ZrdQ2nOjAf/7Mg88FXg18llxZq6lqpTYbeEOx+76h4YFLSw/vRq6IVff2oeGB70x0LpIkaYWbX9yfkFK6nrys4DrAKxo7RsT2EZEYXVJu34hIDbd5EXFs0W+7ot93GvosbDL2zIh4XUScGhG3RcSSiLgpIn4aEfOaTbw4VyrORUTsHBEnFMctjohaRBwXEes1O468/CDAa5o8j+1L/f/93FrMY5OI+FhEXBIR9xW3SyLioxExp8UxXc19Aj5BDlVdDByZUmpa5TSltAQ4DLgZWJMmgf6IWFjMdX6rkzXrExELgGtK3a5p+BkfX+p7bNG2oNg/LCL+FBH3FLcFEfHCFufevtnvrZM+xc+//jfrdk1eBy2fs1Y8A1iSJEmSJEmSJKlr1Upta2DPYvf7jY8PDQ+cDVxP/tLs+RMZe8l9ifsWpY8DVwAfGKf7zkD9S8BTS/ObB/wOWLfUd9ZE5iFJkla8iNgWOABYSl7mGOB7xf3hTQ5ZBtwC3F/sLy32y7cl5IqdtwDLi373NPS5rWEem5MraX67mM8mwIPAluQKXWdExAfHeS4HAn8jh8rXJv8t8mjgncApETG71H1JMY+Hiv2HmjyPZe3OVzrv7sDfgXcDTyCHnqLYfg/w94jYtYdz72ROewNPLHY/Ua961UpK6S7gy8XuCyJi64mcr407gNtL+7cz9md8d7ODIuKzQBV4Kvn3sB6wLzASEcc0O2YSbiG/PiG/XhtfBw/2+HyahJU6gBURG0TEf/d7HpIkSZIkSZIkrcaeVNzfMTQ8cE2LPn9r6NuRy3+yBBJzgNcNDQ88NE73cgWGfwFUK7UDgV+TK2U8DNSrcz15IvOQJEl9MUTONPwmpXRH0fZjciBp74jYqdw5pXR9SmlL4NNF0zkppS0bbueklD5d9Lu+6PeWhj71YDkRMQv4GfA04GxgP2DtlNKG5CDW+8lBrw9ExMvaPJcfkpfS2yGltBGwATkUlcjL7L2u9DzOKeZ3UtF0UpPncT3jiIgNgRHyMs5XAfumlNZNKa1bPI9/kkNkI+Ms6djx3Du0X3G/vJhfJ04u7gN41gTP11RK6SWM/iMCgD0bfsZvaXLY7sBbyRW8NkkpbQxsRQ5kARwbEc/pxfyKOW5JXuYb4Pomr4OT2h2vFWulDGAV5e6+B9wEfKXf85EkSZIkSZIkaTW2Q3F/XZs+9S8Jd2jTZ4ybL3iYWy9axux1+c7Q8MAfOzjk1tL2jtVK7bnkLwvXJn8x+lJGq2B1PA9JktQ384v7E+oNKaV7GA3tzGfqHQrsTV4G+aCU0oKU0uJiLneklD7CaJXOdtU6/wZUUkoLi2PvTykdB/yqePwRSyr2wJuAbckVvw5KKZ1VfyCltAB4NrmC0naMLuPcTK/nvktxf3VK6b4Oj/kHuTJY+fh+2BD4dkrpXUVlLlJKt5Bfi/UKrI9YJlGrh5UmgBURW0fEeyOiBpwGvIr8H02SJEmSJEmSJKl/1i/u72/Tp/7lWrvqCv/2z98v3eyKnyxh7U2CJxy65nGdHDM0PFBjNAT2MeDn5GUPFwMHk5dveexE5iFJkvojIvYBBsjLwP2i4eH6MoSHRcRUZx5eW9x/KaXUqhrnicX9EyNiyxZ9jksppSbtJxf3T+hyfu3Ug1HfTSld2/hgSulqRis3HdJmnF7PfU5x/69OD0gpLQfuLHY3meD5eu1jjQ3Fz6fevmdEbL9CZ6RpYVoHsCJiVkS8LCJ+DSwEPkT+Vyn1dUkfJv/rFUmSJEmSJEmStIq49vSln1j6IOx8yBps8tiZ4y09WPbB4n4QWIMcvnoleQnCk8iVsCAveSNJkqav+cX9T5oEn34L3Eau7nTgVE0gImYCTy12Px8RNze7AeeWDntUi+HObdF+Y3G/cQ+m/G8RsQajwajT23StV23aNSJmt+izQuc+zV2XUmq15PY55AwLTHDZba0aZvV7As1ExBPI64S+mtH0YpS6XEhOYp6YUrptxc6uucHBwc2Ad5H/o24b8r/0OR/46sjIyMldjLc90OqNW/bykZGRH7cZZ3fg7cA8YFPyhWgB8KmRkZGLJjovSZIkSZIkSZIa3Fvcr9umz3rF/T3jDVat1F4DHLTN3rOYMzBzonO5jxyuqv8D9DWBnxbblwLfBt4D3DHRgSVJ0ooREeswWr3phMbHU0oPR8Qw8P/IQa3fT9FU5pD/loDOqy6t06wxpXRvs3agHi7rdXZjDlD/Q+rGNv1uKO5nFsfc0thhCuZe/zus40pWRaWzetCrn3/HtfxZppQWR8TtwJbA5ituSpoupk0AKyLWJy8r+FrgKfXmUpcEnAK8PaV0yQqeXluDg4OPJ6dG62+ie4GNgIOAgwYHB784MjLylkmc4nZgWYvHWv7Ln8HBwVcBxwP1pOrdwNbkYNvLBwcHh0ZGRk6axLwkSZIkSZIkSVpY3G/bpk/9sYVt+tS9GOCe65fzt688xD3XLv/R8odri4vH6sv6PLlaqS0otitDwwM3Vyu1V5P/8fYM8j+S/m6xvQT4M/AT4NjimGn1PYMkSRrjpYwucXx6RLTre3BEbJhSunsK5lFOgj89pfTnKTjH6ugfxf2jI2K9lNJ9bXtnO5OrmwJcNjXTkian7wGsiNiXXO3qJcDa9ebifgl5PdeXFvvnTMPw1ZrACDl8dSlw6MjIyEWDg4PrAG8FPgy8eXBw8MKRkZHvdHmaPUdGRhZOcF6PB75DDl/9EDhqZGTkpsHBwa2ALwAvB747ODh40cjIyOVdzkuSJEmSJEmSpAuK+02qldoOQ8MDzVZ3qP/D6/M7HfSe6/+9SuBeTR7eCNi32F6rqJr1HfL3C/cAzxsaHjin8aBqpfbMYvOUTuchSZJWuMMn0Hdt4BDgm1Mwj3+RC6XMJC8tuDIFsO5gdO5bt+m3TXG/jBVXWep0co5iBnmFse93cMzBxX0Czmx4rL7s31ptjt9wAvNrZ26rB4plHzctdm8tPfRwabvVHHs1P/XRjPG79F5EzI2I90TEVeQ316vJpfiiuP0VeBOwVUrp5f2Y4wQcAewIPAD8R31Zv5GRkQdGRkY+Cny16PeRwcHBVmumToUPkROgFwKvHhkZuamY103kSmMXksslfmgFzkmSJEmSJEmStIoZGh64ATi32H1V4+PVSm0fcgWsxcCvOxjv4AM/u862B352HYrbtkPDAzE0PBCMfiF7WqntIEbDV3cBB7UIX+0F7ANcD/x8os9TkiRNvYjYHphX7D6LvOxcq9vHin7zG4app7jbls4ar19KaSmjf+O8YLy5T4FOn8cjpJSWMFrx84A2XQ8s7i8unu+USymdQy5uA/DOiGhbOCgiNgKOLHZ/lVJqXAbwzuJ+G5qIiAFyeL+Z5eWu7eZR2K54jTazN6NFkMr/6ODO0nbTOQJ7tjln168DrVgrNIAVES+NiF8B15ITjTsyGrq6ETgO2DmltFdK6WsppTtbjzZtHFrc/2Bk5P+zd+fxcVXlH8c/T9J0h0LZyl7ksg+oCLLvO+iArBeBUECRRSMq/kRFRUVQNnEUFUWBIHgBBRyQRfat7Isw7FcoUMrWhdItzXZ+f5wzZAiTZJJMmrT9vl+ved25955z7nMnk95p7jPPyb9RZv85+CzM1YBdFkVA2Wx2HB0XgPPz+XxpRiVh/fyO5tllEBERERERERERERHpu+IN0NMa43Tz4sbGOF2Bji8q/64+iWaX7PtSY5y+2Bind/b1oI1xehK+4oXhqzYcAHxiCqKQfPVP/N/rj69PotbObURERGRIOBp/XX/NOXe/c+6Drh7A30Ofbcxsg5IxPgzL5Xo4ViXt/hqWsZlt291gZrZ8D8frrUrPoyvXhGW9ma3VeaeZrQvUh9Wkj8foq9PCcjPgd9bFPJOhqtQV+Gmom4EflWlWTDTL9nCscj4seb5cN+1Kfb/zhhB/8TiPOedeL+5zzs0DihVi9y/TdwRwSgUxqkrWELeopyC8Fv+fm+IvzwLgevw87Hc459wijqdfstnsWDoyEW8t1yafz7+RzWZfADbGZ5b+ZxGEtj0d85/e1kWb4vYRof0tAx2UiIiIiIiIiIiIiCyZ6pPohsY4zQENwMMhqWoe/u/iywEP8skbZuOADeh+upguNcbpN4ELw+p0fAUHA15sjNMXgDTEsAHwWaAFOK4+icr+PV9ERGRplUvjDHAisAOwLD7h437gDw1RUuiubzWFJJajw+o/emrvnCuY2cvA+vgqWMXEmGLMG5vZVs65R7oYogB8GjjQzP7qnPtEEje+ymY9/p76bWb2Q+BK59yMEPPywHbAUfjPPHv1FHcvFM9jezNbzzn3Si/7/x7/c10TuN3Mvuqcuw/AzHYC/oKfwvF14A9Virkizrl/m9nZ+J/Z14D1zexnwP3OubaQeLUHfkavzfF5Jg3OuafLDHc1vkrqpmb2G+AnzrkPzGxl4HT8z2Y+fla2znF8YGZv4adprDezZ5xzbd2E/iFwvJnNAH7lnJttZqvgiw0Vf/blksSuxidofdXMngSucs4tNLNNgBzdTG1Ix/tgnJkd5Jz7ZzdtZRANyhSE+F+OvwKrOOeOdM7dvrglXwUb0ZFM1t2F56N/4Pt4nGuy2eysbDa7MJvNTs1ms//MZrP7ddO+eJx38/n8++UahO3vdWovIiIiIiIiIiIiItIn9Un0TeAw4CH8FCz7AlPxN5t2rU+iBVU83Fp0JF+9C+xcn0T/xc+2cTF+qpadgC/hb4b+Gdi0PokurWIMIiIii7VcGq+YS+Ob8RWETgI2BdYOy5OAZ3NpfHMujVdYRCHtBKwTnveYgNWp3VFmVsx/uAd4BV+Q5mEzm2FmU8Jj65K+l+BzF7YHppvZW6HNA8UGzrlWfNWiu4CxwG+A981slpl9iK/AeSNwKNXPv7gemAGMB14ys/dKzqOrqew+EhLK9sfnBawP3Gtmc81sHv41Whd4B8g65+ZUOfYeOed+AHwbX7hnF+BuYGFIbloA3IRPvvoAOMI5d3EX49xGR7WvBmCWmc3Cn9tJwFeAsnkTwZ/D8hRgrpm9Hl7j88q0fQr4NT5xbIaZzQTepmMazDNCPJ39Ev+eHIHPlZlrZrPpSAI8ukyf4vml+J8XwD/M7IOS98HB3ZyXLGKLugJWqWOA7czsCuBvzrly0/cNdauWPJ/WTbvivlW7adOdLfGZlG34zMsDgQOz2ey1wJH5fL65i7i6i6m4f+V+xCUiIiIiIiIiIiIi8pH6JLqGjhtgPbW9DLisdFtjnBqwy7CRnLDM6jU4YM5b7b9pa0r/BNxen0SXNcbpasAvQpe38cldL4Yx3wdOqMrJiIiILMFyabwivsrVhj003Qd4IJfG2zdEyYwBDmtSWL7hnHu0wj7/BH6Av4++B3Cbc67VzHbDV0/aFX8/fHxo/1HlTefcPWa2P/At4DP4ae4+kUTlnJtpZrvjE7uPAj4PrIhP+H4VeBo/E1ZFn4Eq5ZybESpV/QSfJLZSeECFuR7OuadClaXv4Kfomxh2PQfkgfOL1bwGg3Pu12Z2DXA8sDc+KWxZfHXTl4GbgYudczN7GOpI4Al8ItO6+NyKm4GznHOTzezn3fT9OTAnjLE+PtEf/M+4XMzfNrOn8cldGwNzgSfxr+WNXfSZbWbbAWfgfw6rhGNeC/wUqO3h/A4MffcD1sAnSoJPCpQhYlEnYH0Pn3hV/Ed8ffyb+Wdmdi/+P1r/dM7NX8Rx9VXpm7m7mIv7lunF2E34koAJ8HQ+n58DkM1mN8G/jkcBh+CzPY/vIq6eXse+xCUiIiIiIiIiIiIiUnWNcboz/u/iG7U2waz/tRd3HRgeaWOcPoT/+zj46lq71idRb6fjEREREWik5+Srog2BK/DVLQeMc24SHUlYlfZ5ko5Zq0q3v4nPTeip/434ClY9tXPAdeFRaWz3lIutN22cc8/hq2t1N0ZPx5iOr9j0/e7a9SauSttUeKy38ElmP+nHGC3AOeFRbv/Ebvq2AxeER6XHa8T/DvUmxveBk8OjnO7eB7OAb4aHDFGLNAHLOXcucK6ZbYsv83YIMAb/Rto5PH5vZv8EGp1zdy3K+IaSfD7/DmV+8fL5/HNAfTabfR9fju8r2Wz2/Hw+/9KijhFg2rRpPZY2lB5NKH0+bVpPhctEZKCMHz++mF1e+/777+vfN5FFT9dEkSFA10ORQafrocgQoWuiyKL1yPkL9gT+CNR10ywKD6yGt1bfZthhGx40fIH+Ti0ycHQ9lMG02mqrTR3sGJZUuTTeFF/Zqjf2yaVxpiFKCgMRk4jI4m5QpiB0zk0GJptZAxADxwLFuVbH4L+9cpSZvQlcORgxVmhuyfPR+GkCyxkdltWcN/XHwInAKOALQGkCVjGu0Z07DUBcb/ajr3zSY4MdgMjSbObMj6qXTkD/vokMNl0TRQaJrociQ4quhyKDSNdEkUVn3rvtzHvX9arPZscOX32ljYc9OEAhiUig66EMsn5X1lmS5dK4dxfP6ng2l8Z96tgQJfp5isgSbVASsIqcc3OBS4BLzGwjfFWsI+mYt3RN4LSSLhkzW2Ew5yDtpPRrqKvRdQLWamH5drUOnM/n52Wz2eeALYBPdRHXanSv6nGJiIiIiIiIiIiIyNJrauvTpK0PMrt9Gu20MdZWYK1hn2O9YTtRY7Vl+7xxbwvtrT2P3bL+S8yrvwKAx95bl33LzN7yYfs7vNr6EB+0v8U8N5NmNw+HY5SNY6WadVmvbkfG1fT0p3MREREZKszsT8BXgUOcc/+o8tgGfA2fp7ARHQVMdgnT68kQYWb3ADsBP3XOnTHIsewM3A09T//YizEnAq+F1XWcc1OqMW5/mdmGwHPAY865rXtqv7Qb1ASsUs65F4DvmNn3gCy+KtZeQC1QzN49CNjfzG7Bz6d5Y5jLc7C8iI/NgE3CejmbhOXziyKokuOsks1mV8zn89M7N8hmsysBK1chrjX70Ve8CXR8q3lL4J1BjEVkqTZ+/PjH8L+T78ycOXPLwY5HZCmka6LIEKDrocig0/VQZIjQNVGk9/Lzf3RGM/OOA1prqXvQqJk/272z7bMtN417ruXWR3cYcfwRK9VGTaV93n68dZm3Hm57Aj/bQ5faRy5g/gE3QLtBjaN5jnNT7mr57MRd6z72he3JC/96+Fw3/Ryw92uo/V8Nte+DGzXfzdpwStuja0xpe7R1WZtwyp6j/u9fVX8BRJZAuh6KyGAKRVyOxSeA/LOC9rficwwAvuqcu6SHLqcBZ4XnLcC74XmzmX0GOAD4wDl3Ya8CX8RKkpMud85N6qHtZcDRwL3OuZ0HOrbB1imxCeAy59wxPfS5BDiuZNM6AxDaYsE596KZXQscZmYHOed6/D1cmg2ZBKwi51wrcB1wnZmtBhwTHsUqT3XAF8NjlpklzrmvD0as+Xx+bjabfRTYCtibMv/oZ7PZNYCNw+qd1Tp2NpsdQ0di12uddj8ANAPD8ReYctM47hmWC0P7PtHcy/03bVppITXe0WsqMnhaWlrawtM2/S6KLHq6JooMDboeigwuXQ9Fhg5dE0V6J5fGB+BvVM0Fdjo5uuLJsH1F4K52Wj9/78Lfn9AQJaeW9rvjqnQfeki+AljwhZtwY+Yx/LEtad7qUQBLb2pZb9sj1/5vabu56fRrgRsbor9/7AvTuTSuAb4NnPuhe+ecf8z/9lUNUTKrzycsspTQ9VBEBtnZ+IItZzrnup1y0cxWB/Yo2TQJPxtXd04Jy18D/xdyFYrjTQJ+ArwOXNiLmGVoO9jMvu6cm1dup5mNBg7pvD1U3TojtPk88FKV42opGXMwixCVcyZwGHCWmf2r9PdEPm7IJWCVcs5NA34B/CKUcfsKcCAwMjQZD5wIDEoCVnAlPgHr8Gw2+7N8Pt95/uv/w1fImkYoQ1eJbDZr+Xy+u4vIGfj/lDrgptId+Xz+w2w2exP+tfp2NptN8vl88QMy2Wx2GP4/mqF5fk6lcYmIiIiIiIiIiIiIlPGDsPxlQ5Q8WdzYECXTc2l8EnA/8PVcGv+8IUpml/RbrqeBmzd6npbP/JcR929PzXsrg0/AAli+c9uGKJlSboyGKGkHzsul8Yn4L3xvD9zY82mJiIgMTQ1R0uepz3JpfBFwUh+6XtQQJYvk3ryZRcD+wAwqqH4F1AM1wNX44inbmVnknEu7GH9lOmaM+quSSpYKrwNr42dea+yizYHAsiVtP8E59yiwYTUDc869Ve0xq8U5VzCzh4BtgP0AVZLtQs1gB1Ap59w9zrkjgVXxCVdP9tBlUfkT8CowBrgpm81uBpDNZkdls9nT6EgOOz2fz38sUzGbzU7JZrMum81eVmbce7LZ7A+y2exmIWGq2GfjbDb7V6D4LaG/5PP5clMf/hhfBWtz4MpsNjsh9J8A/C1sXxjaiYiIiIiIiIiIiIj0SS6NV8dPnQtwVef9DVHyAPAmMALYt9Pu+d2N3T56Hgv2/xc176/IyDt367y7bOWCHhRvri7sQ18REZElxR8Wcb+++EpYXuucq6Qi0KSwvAS4ttO2ckorcM7tVWSyuLoiLI/upk1xX1cJWkur4mf8r3Tbaim32CRgFTnnZjvnfu+c2wKfRHTRYMaTz+cXAlngPWAz4L/ZbHY2MAdfEtGA3+bz+Ut7OfTa+Opf/wUWZLPZ6dlsdh5+ftvinKRXASd3EVexXQu+HNy0bDY7C3g7rDcDk7pI3hIRERERERERERERqdRnw3JmQ5S81kWbxzu1LXoCaO9q4AXZPG70fEZf/yWsta6rMSuSS+PjgfXxf89/uDd9RUREliQNUVIAbullt1saouS5gYinMzOroSMR5uoK2m+Lv8ZPA+7CFyQBqA9jlbbd2cwcMKVk82tm5sLjsrC/eH9/7ZJ9xcekMjGsZ2Z/MLOXzWy+mc0xs6fN7CdmNq6LuO8J451hZiPM7Idm9kzo68xsuZ7OfSCY2apmdq6ZFUIs883seTM7z8wmdNFntJkdYWZXhnOYaWZNZjbFzK4ws80rOO4hZnZ/OOZsM5tsZkdU8dRuBGYCu5jZWmWOvyawa2hzU+f9Je12Lr4XOm3fzczaw74Du+i7f9jfbmZ7lmyfWPL+mtipz6SwfUpY39rMbjCz98Jr/JyZfd/MPvFhuWSMGjM72cyeNLN5ZjbDzO40s/3C/ildvbeDa/Czs+1jZqt2dZyl3ZCegrAnzrmngYbBjiOfzz+XzWY3BU4DvgisCczGV+m6KJ/P39CHYb8L7I7/1tCq+OkWW4EUeAi4NJ/PdzulYT6fvyqbzT6PnwZxJ2BF/EXnHuCcfD7/3z7EJSIiIiIiIiIiIiJSap2wfCMupIfgvzj8aWA4/m/aV249YthbNdZa2haA+iSa2hinN+G/6PwxzZs+Q0vmOYZP3oZhb6zNe+vC4/ttDk2bM26lt1ouTqKXO/eJC+kYYH+jZatRNvOwJjd+fDt1dSOY1fK5kdQB7wKHNETJh9V8AURERBZD9fgpgiuZ9uxF4KiBDedjNgMm4O+PP1JB+2IBk78759rN7D46ppDbDbi9pG0z/vNALf7+OcB0oC08nx32j8JPRdcOvN/peAtKV8zsOHx1sGICzHx85c9Ph8fRZraHc+5/XcQ/ErgP+Dy+wEq3FUIHUkjISYCxYdNCfOLNRuFxtJntF6bhK3UoHUlr7fjX0fA/g7WB2MyOdc5dQRlmdgHwrbDqQv+tgG3MrHMCf18148/tJPz7/8xO+4vTWP49tO0V59yd4Ty+A/zZzB4JUwsCEJLXLgmrOefcf3p7DDM7Cvgr/v37If59tjFwFvA54OAyferw03h+MWxqw/9cdwF2NbNvVnBu75nZK/hEx93oSHKUEot1AtZQks/n3wO+HR6V9pnYzb5r6SiN2J+4nga+3N9xRERERERERERERES6sAzAK80Hroz/dnwrvvrEXHwVgV893fyNNz89/LfUWuuyZfpfSKcErPaxc1jwxRupmTGeUbfvQctIeHY/wDkwo32Y62oKwfWAKx11zHerfLTRrL0OeBU4riFK7u/X2YqIiCwBGqJkei6Nt8dPy7ZPN01vAY5qiJIZiyYyAHYIy+eccwu6a2hmo/DJPwBXAjjnnJldCfwAPw3hRwlYzrnJwIRQZahYuXNL59yUkmG/GSoBXQq86Zyb2M3x9wX+jE/K+hlwiXPuHTMbhk+o+g2wBXCdmX3WOVeu8ufJ+M9PMXC9c67ZzNamb9Mt95mZfQafqFMHnIefjex1fCJVBjgX2BO4wcw2dM6VJrTPAs4BrgOeds4tNDPDfzY7HZ/A9yczu9c590an48Z0JF/9AfiJc+59MxsP/BCf0DS7Sqd5Od0nYBXb9NUP8IV2Pg1cHhLvXHgtLsMn/RXwxX16ayV8AtcfgTOdc++GKmk/A74BHGRmezvnbu3Ur1hIyOFfz9855+aY2SrAL/E/60qm+XwUn4C1I0rAKmuxm4JQRERERERERERERESGlhltG/F+++ar4ZOutkoy0V5JJjoIf9Pt2Sa3wppvtu7eVfdPdd4wf/9/4UY2MeqGA7CW4Ty3FzSPhdXfmA6Aq+2yKsEc/M3SbwDbA18AaHbLvI2vXnF3Lo3P7fuZioiILDkaomRGQ5TsC2yKT7Z5Bj813zNhPdMQJfsu4uQr8JWPCHH05CB8parnnXNPlWwvJoh8yczKJYD3m5nVAr/FJygd6Zw70zn3DoBzrjUke+0FvI2v6nVAF0ONBQ5zzl3tnGsO/V93zlWSFFPqMDN7p7sHcFg3/S/EV1Q61Tn3XefcFOe1O+eewSfxPIOfwesrpR2dc/9yzn3POfeIcz5RPvR9GT+d5O34Sl/HlPYLiUk/C6v/cM6d5Jx7P/Sf6Zz7Dj5xqew0jr0VKne9AKxnfurKYhzFaSyfd8491o/xm/EFchbgK0WdGnY14N8LC4EjnHNNfRh+NNDonPuGc+7dcLwPnHMNwLOhzaGlHcxsLH7GNICznXNnO+fmhL7vAsfivzgxuoLjPx2WW/ch9qXCIq2AZWZtPbfqNeecUyUvEREREREREREREZHBMeet1p2Kz3+ZZKIniytJJpoeF9KTgPvfbtuK1Yfd/7EpdRrj9Mv4qhHgk7dGNH/2ybrWjV5k+COfp+61T/HOBjBtU1jnIdptrRlPwkpbdBVIkon+h7+RBEBcSHcGaGf4fPy39R8BTs2l8b0NUXJTv89cRERkCdAQJQXg64MdR4lVw7Lz1H/lTArLj1Xkcc69YGZP4KdlO4yOzxvVtBM+kfx/zrnryzVwzs00s1vwn0/2xFeI6uyZvkxHV8bI8Og1M/sU/nzmAb8v1yZU5voHPplsT+CCSsYOFaBuBvYAtuu0+9P4hH3w0+iVcyYdP+dquBxf+WkSMDlsm1Syr1+cc8+b2XeB3wFnhsS3X4bd3w/JbH11dhfb/4VPpMx02r4nPsGvBTi/TKzOzM7GJ4f1ZHpYrtptq6XYoq6AZSXLaj5ERERERERERERERGQQzGpb78O5bo3i6lWd9yeZ6IFhzFvgqOOdts9/9IXqxjg9EGjE/53/A3zFqjWbtnvweYC2Nd5i1lev4NmDFjCy/YOWlbf7xaNu7ZfXAKix5mVzaXxPeEyoJM6GKGkmTE0EfKlvZysiIiKLwIphOau7Rma2FrALfmq1K8s0uSIsjymzrxqKFZTWqLDq1FpdjPNQleK53Dln3T3oOsGoeC4jgNe7OZdiRadPnIuZrWFmvzKzJ8zsAzNrMzNnZg74dWi2WqdunwvL2Z0qmH3EOfc/4M0KX4NK/A1oBw41s5FmNhJfOaqdKk2t55y7CPg3MBz/eXckcAe+ylhfzXTOvdrFvrfCcvlO2z8bls8752Z20fch/BSYPR4/LMebmWbbK2MwKkcpYUpEREREREREREREZAnxRutuAAxjPp8feRaQfKLNMjVTmdW+AR+0RQA0xum+oWEtftrAveqT6L8AufTdV4CN21Z/i5ebY1raR7DB8CvqamsWbF3b6mfiMdwwfJUG6F2lh3lhuXLvzlJEREQWoeK1fWEP7Y7GF525zzn3Rpn9fwfOA7Yxs/XDdHjVVKwENAJYpYL2XU3zVkmlr4FWPJdh9OFczGwn4CZ8taWiD4EmfILcKPxUkWM6jbNSWE7r4XhvAWtWEFePnHNvmdkd+OpQB+BzWMYBtznneoqjN74CvAHU4Su9Hu2cc/0Yb043+4pTGtZ12t7j6xsqm00HevpSQ/EYNfj3/IIe2i91FmkClnNOWXAiIiIiIiIiIiIiIkuQeW6NZQFG2AcAXwZ+Ubo/l8bbj7R9RwHMdau3NsbprsA/8TeIFgD71SfRo8X2DVFywLRp09Z4amHrm5Pbmxlj/OX09c/5CkDDC1dfAhzX5kbMbIiSFfoQ7m5hWe0bsCIiIlI9xUo7nav5dHZ0WO4Yqix1ZxLwg/4EVUZtWN7mnNu7H+O0VSOYfiqey0vOuQ1709HM6vCVo8YCzwGnAJOdc/NL2hwHXMLQKdhzOT4BaxIdMfV7+sFO6ulIiBqLr0ZVzQSvRW18WM53zin5qgwlRImIiIiIiIiIiIiISH8sA1BDC8BpuTTevLgjl8YrAL+voRmAunnDVgPywMjmjZ9r+eBHP5v+wZmnn9F5wP8saFnpn/ObWaHGOHLsiF9WGkgujU/JpXGZ6gjOcml8OnAQfoqVSys/PREREVnEpodllwlYZrYjsG4vxjxqAKZNezcsu5pacHFSPJfVzay225aftA1QnI/6i865O0qTr4KuqmoVq3+t2sX+otV7GVNPrsdX6NoD2D08v6Fag5vZZ4Cfh9Vnw/KvZraoq7D2+Pqa2XCgki82FH8fp3fbaimmBCwREREREREREREREem3FsZMw3+7/+FcGt+SS+N/ACmwaRujpgIs8x6b46eeaW3Z7JmLGNG8JmVunt61oOVXCxwcNmY4G9TVNnXe341TgNdzafxcLo2vW78uOR1ghM2aiL8JthA4tiFKCn0/UxERERlgL4TlOt20mRSWN+MTQ7p6TADm4xOEdu9FDO1h2V3FpslhuaGZ9SYZbCgqnstYOqZ5rlQx+WqGc+61Ltrs0sX2J8JyOTP7dLkGZvYpqjT9YFGo4HQtPmemBrimWlWdzGwUcBUwHLgF2Br/nl4Z+Es1jtELT4XlJmbWVULjNnxy6sJyir+PL3TbaimmBCwREREREREREREREemPOQBNbsX3gMOAh4BtgX2BqcBps2dtcT9A3QKG4W9ofrkl89x/yw12VOHpE5phjw2GTWOhu51bF5x1eC6Nx5drW8YPgEbAATvWMW8XAOdqWoDzgI0bouSKvp6oiIiILBIPhOWW5Xaa2RjgkLB6tXPug24e7+KTYACO6UUMH4bluG7a3AW8jk/S+nV3FbbMrM7Mxvbi+IuUc+4lOpKwzglJRGWZV/q6zA7L8Wa2Upn2u9AxDXTn4z4NvBJWu5oi8ofdhN4fvwHOD4/fVHHcc4GN8NWnjgnVwI4AWoAvmNkJVTxWT/4DzMUnWH27izbfq3Csz4fl/f0Nakk1bLADEBERERERERERERGRxdqUsFyzIUquAa4p3dkYpxsscyg/XbAsjPK35ybVJ9G1kABcVmyXS+NVgTPG1hxx3Kz2jXizrZ1r560JrHkO8MtjnnvwvSb3mXkAc90aNXEhvSd0jZNM9A5AQ5Rcha844HcU0p2Bu5tZbmpDlHy3yuctIiIiA+NBfML2eDOLnHNpp/0H4ys1NeOnNu7JP/HTEB9gZuOcc7N76gAUq2WOM7ODnHP/7NzAOddiZicBNwJfBG4zsx8Bjzrn2kNC1oZAFvgacBQdyWVD0dfxr/3ngPvN7DTgHudcK0Co8rUP/lx+Bfwt9HsQX2VsNJCY2Vecc6+Z2Qh8otzvgJl0Pc3dj4G/A4ea2fvAGc656aFi0w+AY/FJXt0lw/Wac+5Z4NRqjmlm+wAnh9XjQgIgzrmnwnvjl8D5ZnZ3SHobUM65uWZ2PvAT4AdmNhe4KGxfBTgLXxmu+PMrK7yXPxdW7x3gsBdbqoAlIiIiIiIiIiIiIiL9UZzaZIW4kH5sqqDGOP0UcOeHqzACoL2Wv9Qn0ScqUOXSeEPgUeB4w9UCzHNr8KFbp/ioWeBWmeCoLU7vsxx+epydgJEDclYiIiIyKJxzM4A7wup+ZZoUK1nd6Zz7oIIhb8JPQzwSiCuMIQXuCav/MLMPzGxKeBxc0u5m4EhgAT6R5SFgvplNB5qA54CzgYn4Cp1DlnPuKXwi2Qx8ss3thHMxsyb81NK/BTKUnEv4GXw/rO4KvGpms/FVUq8AXgN+2s1xE+DXYfVk4F0zmwlMxydInQ88XZWTHECh+telYfWPzrkbOzU5F5+8NBr4m5lVMu1fNfwCP1VnDT4B7IPw+r6N/136Fv61Bv+eLWdHYBngTToqpUkni7QClpm1DcCwzjmnSl4iIiIiIiIiIiIiIoMgyURT40L6GH6aoC/jb/LQGKdrAnfOXJPVm8aBtdP6+uf5Tuf+uTReAbgVWANgw+FXdW7ykfdaP0vaehBGy/1/z2y04wCcjoiIiAwNfwb2xH+2+Gh6ODNbB58MAvCPSgZyzs0xs//gk4smARdXGMOBwBn4JLA1gLXD9o9NJeic+7uZ3YevILU38Cl8svhs4GV8wsp1zrkHKzzuoHHO3Wlm6wEnAV8ANsCfy1x8MtnDwL/oSJAr9suZ2ZvAd4DPArXAS8C1+MSjw3o47rfN7CHgm8Cn8bksj+CrNV1pZvdU6RQH0iXAKvjz/sRn3lAVrR54BtgC/94aqOkVS4/bYmZZ/M/0OGD9sOtO4Dzn3G1m9ouw7YMuhvlyWF7qnGsfsGAXc+bcokuyNLN2fCakVXFY55z/NozI4mjatGlr4DNFAdZcbbXVpg5mPCJLs5aWlqnA6sBbdXV1awx2PCJLG10TRYYGXQ9FBpeuhyJDh66JIr0TF9IDgOvxN+d22vdMpgH3No9i/UeOhDmrAHB+kolOLenzJeDskTZ9+OYjLlynzLCfUEzAGmXvvX7pJttOrCCunYG7gf8lmSjq5WmJLPV0PRSRwRKqA70BTADWd869MsghiSyRzCwCir9fazvn3ui0fwQwDVgWiJxzry/iEBcbg1E5qprJVyIiIiIiIiIiIiIiMsiSTHRDXEhzQAOOhx8+ioXD5zN2+kRoHQXAg8CPOnUbB2zQ7ob1evaMOpu7Wi6NRzVEyYLO++JCej2walhdNizXiAvpwyXNLkky0SW9Pa6IiIgsGqFqz8+A3+OnofvaIIcksqQ6LSxf7Jx8FRwHjAcuVvJV92oW5cGcczUD8FD1KxERERERERERERGRQZZkom+O/JBjxr3Nwg9XYex7EdS2MAPHacCuSSb6RLIUgPHJv/M7V0OzG0OzG0O7++StDMPVAbt2Ecpnga3CY6OwbUTJtq0I0x2KiIjIkPZnfGWeSWama7dIH5nZP8zsi2a2fMm2yMz+hE+wAj9VZOd+dcD3gPn4KROlG4NRAUtERERERERERERERJYwjXE6dlc4HhgbNl0MnFifRK5c+yQTXZZL4wVAUtw2r30C77Rtxfttm9HOCABqaGbF2meZUPsIKw97ipWHPVVsPqGLcSdW54xERERkMDnnWs3sGGAPYC1g6iCHJLK4OgA4CMDM5uBnrhtbsv9Pzrm/lum3JnAp8Kxz7p2BDnJxpwQsERERERERERERERHpl8Y4HQXcCGwTNl0BnNRV8lWJVgDnjCmt+/B227afaNDOcN5r+xzvtX2OlWsf51PD8tRYO0BL9c5AREREhiLn3IP4qYxFpO9OAPYGNgNWAUYC04BHgUucc/8u18k59yqqfFUxJWCJiIiIiIiIiIiIiEifNcbpCOA6YOew6Vrg2Pokaq+g+yvOwautWd5t27LHxu+1bUG7q2O9un9g5l7pc9AiIiIiIksJ59wlwCWDHceS7pMTp4uIiIiIiIiIiIiIiFSgMU7r8FMI7h023QQcWZ9ErZX0b4iSZ95v/0xaSfJV0fT2T/Nu2xbTgId7G6+IiIiIiMhAUAKWiIiIiIiIiIiIiIj0WmOc1gKXAweETXcAh9QnUXNvxnmzZbe23h57auvObQ1R0tP0hiIiIiIiIouEErBERERERERERERERKRXGuO0BvgzcHjYdD9wQH0SNfVmnK8/f93WC1l+g94ev5lxa570fH6P3vYTEREREREZCErAEhERERERERERERGRijXGqQE54Jiw6VHgC/VJNK834+TSeJ3la1+8rq9xLF/z0t9zadzr5C0REREREZFqUwKWiIiIiIiIiIiIiIhUJCRf/Qo4OWz6L7BPfRJ92Jtxcmm8GTAZWLUf4awAPJBL4y36MYaIiIiIiEi/KQFLREREREREREREREQq9WPgu+H5C8Ce9Uk0szcD5NJ4B+A+YEIdvSqa9THDbD7AisDduTTevc8DiYiIiCzGzMyFx86DHctQZmYTS16riYMdjyx5lIAlIiIiIiIiIiIiIiI9aozT/wPOCKv/A3avT6L3ejNGLo2/CPwHGAe40TXvfgOY1Zd4Xm354ksz2zZod87GAjfn0vjQvowjIiIiQ5uZbV2SOPO6mXWb52BmO5vZGWY2qYd2p4R2n6lmvNViZgeE+A4Y7FiqxczuCT/H+3vRJy75+W84kPENdWa2npmdY2ZPmtkMM1toZm+Z2W1mdrKZjRrAY08M78czBuoY/VXp7/5AUQKWiIiIiIiIiIiIiIh0qzFOv46fehDgTWC3+iSa1psxcmk8CbgeGAk0A4eeut4lvwMu7UtMLSy7wYstR9U81fxN907rlnVtri7JpfGJfRlLREREhrRjSp6vBezaQ/udgZ8Ak3pod0po95m+hTXgDsDHd0AP7V4Kj/kDHE81XBaW25vZuhX2OTosH3bOvVj9kIY+884CnsNXo/0ssCz+Z74asCfwO+DlAayENhH/fvzJAI1fDTtT2e/+gFACloiIiIiIiIiIiIiIdKkxTo8FfhtW3wZ2rU+i13szRi6Nv4tPtKoF5gL7NETJP+JCOgrYqJchzQEuAmYANLkV7dXW/Xli4an2Rstuvz/75YZzc2lsvRxTREREhqBQ0ecwwAF/CpsnDVpAQ5BzbsPweHSwY6nAtfjPggD1PTU2s1WBPcLqZQMU0+LgcuD7QB3wT2BrYLhzbnlgGeBw4FVgDeA2M9tnsAJdmikBS0REREREREREREREymqM08OBS8LqdPy0g2ml/XNpbLk0Phc4J2x6H9i5IUruigvpSsBdQPEGUXsFQ84D9k8y0dfxFTBOAF4GaGUMU9t24Znmk059rnnS80cUnvt0pXGKiIjIkPUl/NTFk4Ez8YlYXzKzZQc1KukT59w84B9htd7MekqaPwqfwN8EJAMZ21BlZifgXweAHzjnDnbOPeKccwDOubnOuQTYHHgMGA5cYWarDU7ESy8lYImIiIiIiIiIiIiIyCc0xumXgCsAAz4A9qhPoucr7Z9L42H4qlenhk1TgO0aouSJuJCuBzyE//Y+wL+BrYDbuhnyLmD7JBPdDZBkovlJJroYX0HrizU0PwTgGMbs9mjDNkY8HRde/k9cSPeKC6kqYomIiCyeJoXl35xzbwL3AqOBQzs3NLOJZubomCJtJzNznR47m9kZod3aod2lndpMKTN2rZkdZ2Z3mNn7ZtZsZm+b2XVdTfkWjuXCsTCzjczsb6HfQjNLzeyXZja2XD86pt47usx5TCxp/9G5dRHHCmZ2lpk9a2Zzw+NZM/uFmY2vZuwVKk4/PRHYsYe2xdfgeufc7BDLpmb2czO718ymmFmTmc0ys8lm9i0zG9nbgIrvCTO7p59t1jOzP5jZy2Y238zmmNnTZvYTMxvXh7hGAj8Lq7c4587uqm14fQ4DFgArAKeVGa/b90pXbcLvxN1l2hQfZ5Tsuyxsu8zMaszsm+E1mGtmM83s32a2XRfH/tj7rtI2lf7udzVmtSgBS0REREREREREREREPqYxTvcGrqZjysC965Po6Ur759J4NHA9HTfNCvjkq1fiQrodPvlq3bDvD8ABSSZ6PMlEewPrLWP8esvhtWw5vJZljBywUZKJdksyn4whyUTtSSa66arMxtuOq0l3G1/z/EyjLeyt2QO4FXg2LqTHxoW01zfkREREZHCY2ZrAbkALcE3YfEVYHlOmSxvwLr5iJqHfu50ezfjPNu/SUX3zw05t3u8Ux8rAg/iqoLvhk1sWABPwFbruNrOf9nAuuwOPA0cAo4Bh+M9C3wNuN7O6kubNIY6msN5U5jzaqICZfQZ4Dj99XQafWG/h+Q+A58xssyrGXon78dPlQcdnxXLH3QLYOKxeVrLrRuB0fPLWSsB8YDlgG+AC4H4zW6aXMfWbmR2Hf61PANbDV2sbAXwaOAN4yszW7XKA8g7EnyP4CnDdcs69BlwZVo/pw8+mK+8Ds0rWO78f55bpY/jf2wuBTfC/j8sD+wL3mVm53+G+qvR3f0ApAUtERERERERERERERD7SGKe74JOn6vA3F/erT6JHKu2fS+Pl8ZWsvhA2PQjs2BAl0+JCeghwJ/7GJfgbdycnmai12D/JROlPlx99weFjR3D42BH8dPnR5yaZ6MVKjn3xxnvfteHwqyZ+ZvhvHli99l5qWVDctQnwF+D1uJD+OEx/KCIiIkNbPT6n4Rbn3Myw7R/4hKRtzWy90sbOuTedcxOA88Kmyc65CZ0ek51z54V2b4Z23+zUZsvimGY2DP+5aCvgAWAXYJRzbhz+88yP8MkePzazg7s5l2uAm4B1nHPLAcvik6IcviLocSXnMTnEd3XYdHWZ83iTHoSKS3lgFeAVYCfn3Bjn3JhwHq/ik8jy1v2UjhXHXokwdd5lYfVgMxvdRdNictZU4I6S7ffgp+RbPZzPeGAMPkHsbWAL4Je9iam/zGxf4M/498KPgFXD6zwa2A6fwLYOcJ2Z9SZPZ5ewfM85N7nCPjeE5Vj8a9Fv4XfiwJL1zu/H88p02x84APg2MM45tzzwKfyXI2qAi80sU6X4Kvrdr8axuqMELBERERERERERERERAaAxTrfFVxUYif+W+P71SXRfpf1zabwafmqg7cOmm4A9Jzed+UFcSE/F38AbASwEDksy0TlJJupyipG+aIiSOaNqZu6+dt3t/9hixLmsM+xGRjCrJexeGfgp8EZcSC+OC+lG1Ty2iIiIVNWksPxbcYNz7kN8UlHp/oF0JLAt8Ciwh3PuHufcwhDLTOfcmcCPQ9sfdzEG+ASc2Dk3JfSd55z7JX4aZigzpWIVnAysia9OtIdz7qPPdM65e4A98cn2awMndjPOQMR+OT6BaxlKEnuKzGw4cHhYvcI5V6xWhnNuknPub865aSXb5jvnrgIOCZsmdZPYVVVmVgv8Fl/x6Ujn3JnOuXdCXK0h8WcvfHLYZvikpEoVK4D9txd9Sttu3GWrgTcO+Ilz7tfOufnwUYWuA4Dn8V/26O53ZrEzbLADkMVbS0vLt/EZi9JHK664Ym17u79e1NTUPNbS0lJRuUgRGRATisuWlpapgxqJyFJI10SRIUPXQ5FBpOuhyJCia6IsdaanC+uGjbSVWpucWQ1sd/KKc9b8/JhLW1paeu4MzGqZNmxs7fgV57bNrAWIRm89f9cVv/ZZqH257t2Zy907e+4YgNE1Ne3fXH2lDzcePfKClpaWC8qN1d9r4olrX0G7a+PeGX+dVzvvvjETah+tm8cW7bPZv+31hdThE8yOB47/+atTm/Ydv+zcTUePXGhmvTmMyNJA10MZNHV1dWsMdgwyeMxseyACZuOTw0tdgU/6OcrMflSanDMAjg3L3zrnmrpocyVwNrCpmU0oJt908stQ+amzG/BVQ6tSCaiTYmLU5c651zvvdM79z8waga8BhwG/6mKcqsfunHvDzO7CT+l4NCVJdsEX6KiYelkvxn3QzD7AT0n4GWDAqx4BO+ErO/3POXd9F3HNNLNb8O+nPYHrKhx7fFjO6EU800uer9Blq4E3Hz/94Mc45xaa2XnAX4H9zWy4c27ApwdcFJSAJf21LLD6YAexODMzamtri6sTumsrIotMLfq3TWSR0zVRZMjR9VBkEOh6KDIk6ZooS4UP3mzm3vPfo7XJYQbbnrQia35+TMU3bN5fOIWb3juHpvY5AHx62X3ZZrl49ELnRl807X2enuenAly5bhinrrFyzarD67qdArAa18Qaq2XnFb7C2GEr8Pjs6xnLYzUr1Dxfk131VB6bO45H58ynHXhuftPI5+Y3jVxrRB37LL8sWy87hmFKxBLpTNdDEVnUJoXlP8skPt0KvI+v7rQ78J+BCCBUNvp8WL0wJI30ZC2gXALWY120fyssl+9leN0KFaSKiVF3ddP0DnwC1mZmVuecK5d5P1CxX4ZPwNrVzNZwzpUm+hanH5zsnHu5c0czOwQ/5eDmwEr45PrOVutjXL21bViuYWblfvZFY8NyrQGOZ6h43Dk3r4t994blcPxU4U8tmpAGlhKwpL8+pOMfVukD51xte3v7BICampp3zEzfbhYZPBPwf0hoo/yHYxEZQLomigwZuh6KDCJdD0WGFF0TZakxe2rzsLvOfnel5rntNQBbHDN+1lpbjZlfaf83Fjwz4j/v51ZocQsNYMtxB83eYrkD5s5qaa057633VnxjYUsdwMQRw5u/s8bKM5YbVttjlYpqXRPNjC2XO5C6mpFjHpqVLLewfR5Pf3CW23fFk2ccsuJmrbfOmjP2/g/njmlqd/bGwhYufmcGf39/Vvtuyy0zd4/llpm7zLDaqk6PKLIY0vVQRBa5MHVcsXpT58pIOOdazSwBvoFP1BqQBCx89aER4Xmliellp71zzs3pon0xuazauRvj8f9+Q/f384tJT7Whz7udGwxg7NcBF+GLvhyFryKGma0E7BPaXFbawcyG4ae0/lLJ5mZ8hajWsL4SUAOM6WNcvbVqWI4AVqmgfW+mRpwZlr2pZLVimf6Dobv3Xem+lQc6kEVFCVjSL3V1dRcAZUtES2WmTZu2BvBmWN1ytdVWUwlfkUESSmivDryj0sYii56uiSJDg66HIoNL10ORoUPXRFlaNMbpOsD9+JtUACdutOf4P1baP5fGBwFXAYZP0PjqNisdcmlcSDcCbgHqQtPrpyxsPnKlUSMrSuyq9jVxyxX256FZf4+BxjbXUnfr+xcuBxzTECV/u70wZ1ngOOCbwNoftrXXXD9j9rLXz5g9DH/T78IkE73Sn+OLLK50PRSRQXIQsEx4flcPUwQfYGbjnHOzByCO2pLn2zjnHh6AYyy1nHPzzewa4Cv4ildnh11H4D9DLgCu7tTtq3QkX/0M/1ltSukUiWb2JrAG/vPpolB8n9zmnNu7ymO/gK+w9ele9Clt+3x1w5Hu1PTcREREREREREREREREljSNcboGcCcd04p9uz6JepN89TXgWvzUIU3AgQ1RcmlcSHcCJgNrh6a/AQ5JMlHFVbUGQkOUJMAXgHn4L6hfkUvjU5JM9GGSiX4NRPhqG4+ELqOBk4CX4kJ6Q1xId4gLqeYmFBERGXjH9KLtKOCwAYpjBj7BHBa/aeNm0hF7d1PIFpNr2xicakmXheUGZrZVeF6cfvB659yHndofEpaNzrmfOOde65R8VcvHK0BVqlg9q9xUhkXjutherBo2EO+R4vSRK5vZtt227HBAWM4DHu+0r/ieKHueZtbVOfZFd1NAlu57r+R58eeAmXX1s6hmjFWlBCwRERERERERERERkaVMY5yugk++WidsOr0+iX5dSd9cGlsujU8H/oivLDAb2LMhSvJxIT0CuB1YDnDAKUkmOiXJRENiWt2GKPkPsCv+hirAr3NpfFYujS3JRK1JJro2yURbA9sB/wTa8ee4P3Af8GhcSA+PC2ldufFFRESkf8xsIrBzWN0RWL6bx1mh3aROwxSnO+4pcbrbds65FuCxsPqFnmIfAJWexyc455qBZ8Pqbt003T0snwnnu0g55x4EipVGjzazzYDPhPVLy3QpJow9VmYfwNZ0n0TVlVmdxi9nyy62Tw7LDc1s3T4cuzvXA9PD89N7amxm6wBfDquXhvdBqZ7Os6tzhI73I9ZDWbpgizCdaDk7heVC4Lky8UH/YhyUL00oAUtEREREREREREREZCnSGKcrAHcA64dNZ9cn0S8q6ZtL4xp8Raufh03vADtObjrzgbiQ/hD4G37KmCbgoCQT/aaqwVdBQ5Q8CmxPxxSH3wf+nEvjYcU2SSaanGSig4H1gBy+ggDAFvgpF/8XF9JT40K63CILXEREZOlwND554jXn3P3OuQ+6egB/D322MbMNSsYoVk1arodjVdLur2EZ91SByMyW7+F4vVXpeXTlmrCsN7NPVGcKyUL1YTXp4zGq4bKwjIGvhedv0lH9qVRxqskNO+8wsxr8tIR9UUxWW93MPldm7B3wCfrl3AW8jn/f/jrEUZaZ1ZnZ2EqDcs4tAM4Iq/uY2fe7GXscfsrG0fhqZr8s06x4nvuX6W/A97oJp7Qa2XLdtCsag5/iu/NxhgPfCav5Tklir+D/H9FVjCvgp6zsKcZK4qs6JWCJiIiIiIiIiIiIiCwlGuN0HHAbkAmbfgP8sJK+uTQejk+w+kbYlALbTm468wXgT8CZYfv7wM5JJrq+WnFXW0OUvAhsC7wQNh0HXJtL449VTEgy0atJJvom/hv4/wdMDbvWBM4F3owL6YVxIV0HERGRxVRcSDNxIb0oLqTPxIV0SlheFBfSTM+9qyckgBSnn/tHT+2dcwXg5bA6qWRXISw3LpnWrpxiuwO7mXrtUuABfIL5bWbWEJJAijEvb2ZfMLOrqX4SUzG+7c1svT70/z0+kWkscLuZ7VjcYWY74T8TjsInD/2hn7H2RyO+ctHywAnFbc659jJtbw/L482s3szqAMwsAq7DJ0nNK9OvJw/S8TnvMjPbNIxbZ2aHADfw8epMHwmVw04K5/BF/Ptk62IilpnVmNnGZnYa/v36md4E5py7CP8FAICzzOxaM9uyWIXKzMaY2WHAE/jqUC1AvXPurTLDXR2W+5nZ98xsTBhjIj6h8RPJZyVeDmNDZdOEzgZ+bmbfNLNRJce5Hv9/kRY6JcyFZKwbwurpZpY1s2Gh79b4L5EM7+aYlf7uDwglYImIiIiIiIiIiIiILAUa43QscDMdN1b+BHyrPolcT31zaTwWuBE4PGx6Cth+ctOZ08P24jfRXwa2STLRI9WMfSA0RMlUYAfg4bDpAODWXBp/4gZskok+SDLRucCngCOAJ8Ousfhv9qdxIb02LqTbDHjgIiIiVRIX0hXjQnozvirOScCmwNpheRLwbFxIb44L6QrdDFNNO9ExPXKPCVid2h1VUnnoHnwlnWHAw2Y2w8ymhMfWJX0vwU+ZvD0w3czeCm0eKDZwzrXiK/Hchb/u/wZ438xmmdmH+EpDNwKHUv38i+vx0yaPB14ys/dKzqO7qfKKsc8Osb+Hr3x6r5nNNbN5+NdoXXw106xzbk6VY6+Yc24qPrEGOl7Dy7pofh7wP3xC3OXAfDP7AP/z/gI+gWt6F327i6EN/55vwycHPWNmc4C5+EpiD+MT2rrqfzNwJLAAP63jQyG26fiKTs8BZwMT8e+53joKOAdoBQ4GHgUWmtlMYA4++W9dYBqwj3Pu312M8xf8lImGr5D1oZnNAl7DJ48d2s05zgeuDKvnh/dS8f14Spku/wqPCzsdZ198strXQhJlZ9/Hf6FjudB/rpnNxb+m4+j4Mkg591DZ7/6AUAKWiIiIiIiIiIiIiMgSrjFORwF5fNUngCuAEytMvloRuBPYM2y6G9h5ctOZw4D7gb3C9geAbZNM9L9qxj6QGqJkBv4m2a1h007Avbk0nlCufZKJWpJMdBV+KsKd8K+pw99vORiYHBfSh+JCenBcSIeVG0NERGQoiAvpivjr+D49NN0HeGARJWFNCss3nHOPVtjnn2G5OrAHfJQ0tRs+iecNYBl8YtnawEfVLp1z9+ATlO7GJ7FMCG0+ltzknJuJ/7xwEL46z9v4ad7qgFfxlZdOoJvklb5wzs3Af964Fp8otXzJeVT0OcM59xSwCT7Z5nl84g10JARlnHPPVDPuPrq05PmDzrm0XKPws9gaX7FrKv5zWBP+57KTc+6yvgbgnLsR/765HT+VXS3wIvAtfHJSaw/9/46fvvqXwNPAQnwS0Rx8AtcFwPbOuQf7EFu7c+57+OSw84H/4pPDxgLvhpgbgPWcc3d2M04r/rP72fj3bivQjK+M9Xnn3B1d9Q1OxE9F/jz+9Sm+H5crdzjgEOAU/PttOPABcAuwo3Pu0jJ9cM5NAbbC/3/lXfx79n3g1/gvkpSr7FV6fj3+7g8Uc64vyXUiUi3Tpk1bA1/6EWDN1VZbbWp37UVk4LS0tEzFf0B/q66ursdvDohIdemaKDI06HooMrh0PRQZOnRNlCVJY5yOwFdQKN5g/QdweH0SdXsTCSCXxmvhp6fZMGy6DjhictOZ6+GraRV/P64Bjk4yUVM1Yl7U18RcGtfhb/wdETa9CuzZECU9JpPFhXR9fBWsY/DT+BRNwX/j/69JJhq0qhIi/aHrociSK1S+6in5qtQtSSbad6DiEZElg5ldhp9K9HLn3KTBjWbRUgUsEREREREREREREZElVGOcDgP+TscN1n8DR1SYfLUR8CAdyVd/Ag6d3HTmDmF7MRnjHODwaiVfDYaGKGkB6vHTCoGfavDBXBp/pqe+SSZ6OclEJwNrAj/EV6gAP8XMhcDUuJCeGxfStaoctoiISJ/EhXRTepd8BbBPXEgzAxGPiMiSQOVvRURERERERERERESWQI1xWgtcDnwpbLoDOLg+iZp76ptL463wFa7Gh01nAj+e3HTmJHwi1jCgHTg5yUR/rHLog6IhStpzafwt/FQnZwGr4KcjzDZEyb099U8y0QzgrLiQng/EwLeBzYBlgVOBb8WF9BrggiQTPT5Q5yEiIkuHuJAOxlRXz8aFsjPD9SjJRNZzKxGRxZcqYImIiIiIiIiIiIiILGEa47QGuBj4ctj0AHBAfdJzlapcGu8F3EVH8tU3Jzed+ePJTWeeAfwVn3w1D8guKclXRQ1R4hqi5Gzgq/gEs2WB23JpvH+lYySZaGGSiS4HPgPsDtwSdtUChwOPxYX0vriQ7h8X0tqqnoCIiMgSxsz+ZGbOzA4e7Fg6M+8EM3vczOaFOJ2Z7TzYsUnPzOyM8PO6Z7BjARiI94+ZTQljTqrWmP1lZnUhrg/NbKXBjqealIAlIiIiIiIiIiIiIrIEaYxTw099d1zY9BiwX30Szeupby6NDwduAkYDrcARk5vO/CNwGfDj0OwdYKckE/27upEPHQ1RcglwMLAQGAFcl0vjY3szRpKJXJKJ7kwy0b7AJsCfw3gAOwA3AC/GhfTkuJCOqVrwIiIiSwgz2wg4FngO+GenfRNLElY6Pxaa2ZtmdoOZfans4NVxGvAH4HNAHb6K5rtAj9VGh7qS5KQpFbSdVHztF0FoQ0JJYpMzs1fNrNsKb2Z2ZKf36KRFFOqQ45xrAX4JLEPH/y+WCErAEhERERERERERERFZQoTkq18C3wibngH2rk+iD3vqm0vjBuAqfIWr+cAXJzedeTO+glN9aPY8sHWSiZ6oduxDTUOUXA/sDczB30/5Sy6Nv5dL415PoZRkoueTTHQ8sBZwBvB+2BUBvwPejAvpWXEhXa0qwYuIiCwZzsZXkDzTOdddcs8sOpKf3g3b1gD2B64zs6t6SpDpo1PC8tfAaOfchPCYPADHkqFrHXxyfXcmVTDOS+Exv78BlfhfGHN2FcfslnNuknPOnHOTuml2KfAW8DUz+9SiiWzgDRvsAEREREREREREREREpGp+BPxfeP4isEd9Es3srkNIKPoZcHrYNBPYd3LTmW/jpy7cJGy/GzgwyUQfVDvooaohSu7JpfFOwK3AyvjktpVzafzdhihp7+14SSZ6D/hpXEh/BRwBfBvYGFge+D5walxIE+CCJBM9XaXTEBGRJVCSifqcUBQX0ouAk/rQ9aIkE329r8ftDTOL8AlUM+hU/aqMA51z95T0NWBd4Kf46ZgPB24G/lbF+FbGfzYA+KtzrrVaY8ti5XVgbXyC1X3lGpjZmsAu+PfySKBs5VPn3IbVDs45t1u1x6wG59xCM2vEf/79BvCtQQ6pKlQBS0RERERERERERERkCdAYp6fibzQCvArsXp9E73XXJ5fGtcAf6Ui+mgpsP7npzBbgETqSr64A9l6akq+KGqLkKWA74LWw6dvAZbk0ruvrmEkmakoy0V+ADLAPcHvYVQccBTwVF9I740K6X1xIdS9HRESq7Q+LuF9ffCUsrw1TllXMeSm+gufLYXO2msEBo0qez63y2LL4+Dt+2u6DzWx0F23q8bk5V4W24l0VlkeZ2fBBjaRK9KFdRERERERERERERGQx1xinJwHnhtU3gd3qk+it7vrk0ngkcA1wfNj0IrDd5KYz18F/g39C2P5z4OgkEzVXPfDFREOUpPgkrGfCpqOAG3Jp3NWNtookmcglmejWJBPtCWwGXAYUbzLvCtwEPBcX0uPjQjqqi2FERER6JclEBfwUw71xS5KJnhuIeDozsxrg6LB6dV/Hcc61Ac+G1bJVh8LxljOzH5vZE2Y228yazOx/ZvbHUImrtO3OZuaAKSWbXzMzFx6XdWpfZ2YnmdkDZjYrjP2amf3FzLqseFQy3s5mtrqZ/d7MXjWzhWb2dKe2o8zsW2b2oJnNDG3eMLMrzOyzFb1YA6wvMZpZjZntbmYXmdmjZva2mTWb2btmdrOZHVjBcT9rZteZ2XQzm29mz5vZj8xsRJVO7V3gNmAZoKt4iu/ly3uI9aOfecm2VczsvbD9t130W8vMPghtftFp35SwfVKn7RNLjjcxvMf+EH4mC83szbC+Mt0ws73N7PZw/Llm9pSZNYSf3WXlfieKnHMF4DlgBXy1u8WeErBERERERERERERERBZjjXF6DHBRWH0Hn3w1pbs+uTReFj8VT/FG0aPADpObztwHuBF/k7IVOC7JRD9OMpEbiNgXJw1R8jawE3B/2LQvcHsujcdXY/wkEz2bZKJj8NPYnImfChJgQ+Bi4I24kP40LqSrVON4IiKy1KvHJ19X4kV88vGishk+EbwVX5GzT0IiVyaspl202RJ/fj8FNsdXtmoDPgV8DfivmX2hpEszPulmesm26WHbu8DskrHH45PaL8Inco8BmoCJwLHAM2b25R5OY33gaeBEYBU6ErWLx4iA/wIXANsCy4YY1wSOBB4zs6/2cIwB1Y8Y18JXCT0J2BIYDSzET/24D/BPM/tjN8c9CP8Z90v4JJ9mIMJPvX0XUK2qS8XEqkllYtgOWA94zjn3RG8Hds69i3+vAHzdzPbpNH4NvlLtOOBx4IzeHgPYFP8eOwFYDp9HtEZYn2xmy5XrZGbfwydy7h6O3xLG+g1wbYXHfjAs9+pD3EOOErBERERERERERERERBZTjXEaA38Jq9PxyVevdNcnl8arAPcAu4RN/1nQPn73yU1nnoqfjrAGmAPsl2Sivw5I4Iuphij5AH+DKB82bQvcl0vj1at1jCQTvZ1koh/hb0qeSMfUSSsCP8YnYv0lLqSZrsYQERHpSZKJpgPb03MlrFuA7ZNMNGPgo/rIDmH5nHNuQV8GMLNP4StLboBP2vl9mTZr4M9vFeBSYGNgpHNuDD5R5yp80s/fzWwigHNusnNuAj4hqGhL59yE8PhmyfbLgK3xUxROAsY655bDJ+T8Bz/18GVm9rluTuV84G1gO+fcGOfcWODgEP8yIf718J9NtgjxLwOsjk+EqQX+YGZbdf+KDYx+xtiKTy7aB1jOOTcu9FsZ+EHY/zUzO6TMcT8FNALD8MnzG4fXfixwHD7Z7qQqnWYemAXsYmZrdtpXUfWr7jjnbqJj+s9LO1WlOg3YEZgPHNHb6TpLYnsG2NQ5tywdr9FCYN1wjI8xsx2Bs8PqNcCazrnl8cl138JXtKqkqtWjYbljH+IecpSAJSIiIiIiIiIiIiKyGGqM0wOAvwEGfADsWZ9Ez3fXJ5fG6wAPAMWpXpIpLXsd9FTzt/8EfC9sewt/o/U/AxH34q4hShYAB+Fv1AJsAkzOpfEG1TxOkonmJ5noj8BGQBafNAe+WsOxwLNxIb01LqR7xoXUqnlsERFZOiSZaEaSifbFV625CJ+EMSUsLwIySSbadxEnXwEUE3Ge6bZVh+vM7J2SRxPwP+BwfPLPjs65l8r0OxNfGSnnnDvWOfeCc64dwDn3P+fcEcCt+ISUb/fmBMxsW+CLYfVo59zlzrnmMHaKv7Y/i0/COrOboVqBPZxzk4sbQn+A7+ATxW4ADnDOPeGcaw1tpjnnTsFX0awFftib+Eus2em1/cQDn0TVlT7H6Jyb6pyrd87d6pybXbL9fefc2XRUeyqXSPUDfPLc68A+zrkXQt9m59xf8dWdxvXytSjLObcQP1VmDSWV4sxsFHAovqLa3/p5mO/gK7WtAvw1jL8FHa/Bt5xzL5fv2qNpwL5hSkCccwvDa/TnsP/QMn1+iv8/yAPA4c65qaHvfOfchcCP8NW0evJ0WK4XKsYt1pSAJSIiIiIiIiIiIiKymGmM073xN3pq8VUV9qlPoqe665NL482AyfibYAC/e6zpe1+f1rbDzUActj0DbJ1kokpveC6VGqKkFV8Z4Fdh01rAA7k03qLax0oyUXuSiW5MMtEuwOeAK/E3Y8FX47oNeCYupMfEhXREtY8vIiJLviQTFZJM9PUkE306yUTrhOXXk0z03CCFtGpYvl9h++XxiSnFR/F6OAyfBPKJxI6QHFP8/HNeN2NfFZZ7VhhLUTFp5Tnn3HWdd4aknbPC6l5dTfMGNIZp6MopTk13vnOuq+mirwzLXc2stoeYy6nh469tucey3fQfyBj/HZZbl/YzM8MnywP8xjk3r0zfRuCNXhyrJ5eF5dEl276ET/L6j3Pu7f4MHirBfRk/jeJ+ZvZ/+NetDsg75/7Uj+EvCO/Hzm4Iy3XMbExxo5mtgJ+WG+CcYtJiJzmg3OveWelUnqt22WoxoQQsEREREREREREREZHFSGOc7gxcj6+EtAD4Qn0SPdxdn1wa7wDcB0wIm370UNMZv25hmQfpmObnP8AOSSaaOiCBL2EaosQ1RMlpwKlh04rA3bk03n2gjplkoieTTHQksA4++euDsCuDr4bwelxIfxQX0hUHKgYREZFFoHgdm1Vh+12cc1Z84D8jrY+fIm0r4CYzO6JTn8/hE7Uc8FgF1Z3W6uU5FKcVvKubNneEpdFRnbSzh8ptDNMnFqe7+2c38ReTv8bgq3311uulr225B3DMQMVoZqPM7Ftmdo+ZvWdmLWbmzMwBxS8fjMQn4RV9io7qS/eUiy0kg93bq1eiG865R4CXgPXNbJuwud/TD3Y6xlPA6WH1V/j3+DvAV/o59GNdbH+r5PlyJc8/g3/Pgp/e8RNC0tsTFRx7ZsnzlSpoP6QpAUtEREREREREREREZDHRGKfbADfhbzQ1AwfUJ1G3N49yafxFfHLVOPxNxhMmN535H8ewh4HitHl/Ab6QZKIPByz4JVRDlJyPv8HWhp+i6OZcGpebqqVqkkw0NclEp+FvajYAr4ZdqwA/A96MC+kf40K64UDGISIiMkBGhmW5qjw9cs61OOdecc79ALgAXzH0QjNbpqRZsdqO0X11p2Jiz6hehlFMJnmrqwbOuel0nOPKXTTrqgpYabWglek6/tKk7NHdh1x1/YrRzFbFT1F3Ab7i0kr4z7/vA+/y8epJY0qelybydPn697CvL4qJVkeb2erA7sBs4F9VPMb5fHxqzq855yqtFNeVOV1sbyp5XlfyvPj6LnDOfdDNuNMqOHbpMXr7OzbkKAFLRERERERERERERGQx0BinmwO34G8wtQKH1CfRf7rrk0vjSfhqWcWErUMnN535Dr4aQPHmyY+AryaZqGVgIl/yNURJI7A/viJZHZDk0vjEgT5ukonmJpnot/gKCAcCD4RdI4GvAS/EhfTGuJDuEhdS62ocERGRIaZYFWf5bltV5i9huSI+IaaoOGXdwp4qPJVUehoMbV1sL52qb9UKz2HKIoi3mjFeiP+M8yFQD6zknBvjnFvZOTcB2Lqk7VD4nHMF0A4cBhyPz8e52jnX1G2v3tkK2KRkfYeuGi4mSqcHnd5lq8WEErBERERERERERERERIa4xjjN0FHFqh04sj6J8t31yaXxd4FL8Te/5gL7TG46czV8QtYooAU4KslEZyaZyA1k/EuDhij5N7AHflpAA36fS+Of5NJ4wG8IJpmoLclE1yeZaAf8jbmr6bhh+wX89EdPxoX0qLiQDh/oeERERPqpmIhRjQSsN0qer1vy/N2wHGFmXVWf6o9iVaLVu2pgZivip0EEeK+X479b8ry30yMuKn2O0cyG45PbAU5xzl0RKoaVWqWL7qUVoVbr5jBd/mz6wjk3Ff+Zazng+2FzVaYfBAgV3P6G/2z/bNj8HTPbpVrHqFDx9R1lZuO6abdqN/uKSn/HlYAlIiIiIiIiIiIiIiIDpzFO1wfuAFYIm46tT6Kru2qfS2PLpfG5wDlh0/ttrm6XyU1nfgH4DT45aDawV5KJ/jaAoS91GqLkQXwlguKUK2cAv8ulcW2XnaosyUSPJpkoxt9kvoCOaWU+AzQCr8WF9LS4kI7vYggREZHB9kJYrlOFsdYoeV5a7fOxkvUvVOE4nT0elrt206ZYkcsBT/VmcOfca8A7YXUg4u+3fsZYmpz2WBdtuko8ehWfEA9+6sJPMDMDduxlTJUoJlzVAa845yZXcewc8Cn81Ik7h2MZcLmZLVfF4/Tk6ZLnZStwmdloYIsKxir+js8D3uxfWINPCVgiIiIiIiIiIiIiIkNUY5xOBO6k4xv+J9UnUZffpM+l8TB81atTw6Ypc9tX2/WRhT/5PvCtsO11YNskE909MFEv3RqipABsB7wSNp0EXJVL4xFd96q+JBO9nmSi7+BvPH8b/3MHXwnibODNuJD+Ni6k0aKMS0REpALFKXW3rMJYccnzJ4pPnHNzgWvD6k9CNaoumVlvq3FdE5abmNmBZcYbAfwgrN7qnPugl+MD/DUsG8ys2+t5H+Kvlr7GOAefmAawYZm2KwMN5cZxzjngH2H1myEZqLMjgbW7i6ePrgPOA84HvlutQc3sIGAS/jU52jk3E/gG8BqwJvDHah2rJ865GcC9YfW7IZmts6/jp03vyefD8iHnXGs14htMSsASERERERERERERERmCGuN0dfw0JsXKDd+pT6I/dNU+l8aj8dMLHh02Faa27vDFZ5pP+jNQvPH3BLB1komeH6CwBWiIkinA9nTc6D0UuCmXxsss6liSTPRhkol+DUTAYcCjYddo/M2xl+NCen1cSHeIC+mAT5coIiJSgQfxUy6P7ylppytmNs7MvgH8MGx6Ioxb6jT8dGprAQ+Z2ZfMbGTJGGua2TFmNhk4uTfHd849BBSni77czOrDtHqEc8oDm+KrcP2od2f3kV8BL+OnqH7AzI4O09QV41/ZzA41s1voqIy6qPUpRufcHODhsHq+mW1nHXYE7sZPxdeVs4EF+ApLN5vZhuF4w81sEnAxviJsVTnn5jvnvuucO9U5969qjGlmqwN/CqsXOOfuDMeaAxyFn3b6MDM7qhrHq9BPw3JH4MoQI2Y2yswagF/QUYWsO8Uky3u7bbWYUAKWiIiIiIiIiIiIiMgQ0xinq+ArXxWn5fhRfRJd0FX7XBovD9xGx/QuDz7ffPQxb7TudQOwddh2E7BzkoneKTOEVFlDlLyHnxrnzrBpd+DOXBqvNBjxJJmoNclE1+DfD9vjKzQ4/NQ1BwD3AY/EhTSOC+mwwYhRREQEPqqwc0dY3a+CLteZ2TsljxnALPyUbcPxCUAHhcpIpcd5E9gTXyUywl8b55rZdDObD7yBr+C0DR3VmHrjGOARYCx+urg5ZjYLXyVzT6AVOMY590TXQ3TNOfdhGOe/+GqplwEfmNkMM5sLvAtcDezdl/GroZ8xfgufRLUWviraPGAuPllnAnBcN8d9FZ+c1IqfhvCF8NrPwVeLfQr4ff/PcGCF6lKXAePxr+EPSvc75x7EJ5sB/M7MBqKq1yc45+6mI7nxcOBNM5sJfIif8vw64Mawv6ncGGY2ho5pJLucXn1xog/QVZLNZlfCZ8hm8d9Gmgc8Cfw+n8/f0Ifxlg1j7YmfG3NtfAbnO8Bk4A/5fP7+bvpfRse3nLryXD6fz/Q2NhEREREREREREREZOI1xugJwO7BB2PRL/LfIy8ql8WrArfgqCgA3Pd506q+bWe5WYIWw7Q9AQ5KJFvupPRYnDVEyJ5fG+wF/Aw7Gf8v//lwa79UQJa9333tgJJnI4SuAPBgX0nXx0/cch58mZkvg78Cv4kKaAy5JMlHVq0OIiIhU4M/4e+Vfxid0dKfz9HptwAyggE8EucQ5t6BcR+fc02a2MfBVfELypvhqTQuAZ4HHgH/jE9l7xTk308x2AI4P57EJvgLlG/gE7XOdcy/0dtxOx3jdzLbET6l3KLA5PllnIfASvvLXLfgqqYOirzE65x4xs22AM/BJVGOAt/Gfe39B9xWwcM7908y2wlcY2xH/2v8P/1nnHOD71TnDAfUtfBJ/E3CEc665TJuf4n9XPg9cYWY7O+faBzow59xZZvYUfurzLfDJjs8Cf8Ent90Qmn7QxRD7438m9znnXumizWLFOiV5Sh9ks9lN8GWgVw6b5uDfKMVf+Fw+n/9mL8d8BZ9lW9SEL7NYOj/pefl8vuy8oSUJWE10XTrvxXw+v3Nv4pLqmzZt2hrAm2F1zdVWW23qYMYjsjRraWmZCqwOvFVXV7dGT+1FpLp0TRQZGnQ9FBlcuh6KDB26JspgaYzTcfiKD1uETTnglPokKvvH/Fwarwf8B5gYNl3+cNNPbm2n7jJgRNj2f8B5IfFmsbCkXRNzaVwL/A44IWx6C9irIUqeG7yoOsSFdHn8jecG/L99RXOBS4DfJJloyiCEJoNM10MRGSxmVodPVJoArL+kJGiILA1C5a438MWLjnbONZZp829gX+BI59yVizjEAaEpCPspm82OwM/PujI+g/Yz+Xx+WWBZ4HR8KcKGbDZ7TC+HrgOewf9nJ8rn86PwpQk3wGfpApyazWZP6KJ/0dX5fH5CF4+dexmTiIiIiIiIiIiIiAyQxjgdC9xMR/LVn+k++WpzfCWjiQDOcd7kpp8X2qn7Oz75aiFwWJKJzl2ckq+WRA1R0gachK9QAD6h5f5cGm8zeFF1SDLRrCQTnYOf8vJI/Awf4O9LnAL8Ly6k18SFdOsuhhAREakq51wL8LOweupgxiIivXYEPvmqjY7puD9iZhlgH+A5IFm0oQ0cTUHYf8cDnwLmA/vl8/k3APL5/HzgF9lsdlXgZODMbDb7t3w+31LhuPX5fP6+0g35fN4BL2ez2UPwb9Kdge8Cf6zKmYiIiIiIiIiIiIjIoGiM01HAv4Btw6a/ASd2k3y1K35aj2UA2t2w0x5eeMZawLmhyUxg/yQTPTCQcUvlGqLEAWfk0ng6vrLZ8sCduTQ+qCFKbhnc6LwkE7UAV8aF9Cr8VD3fAb6I/0L/IcAhcSF9CDgfuCHJRG2LKra4kB6Cv9/yafwUNylwJfDrEHel43wW2Bs/nU8GPwXSXPyX7BPgT92NFxfSCP8F/N2BlYD38VXrfpZkold7f2YiItKNP+OnYJtkZj93zi3WFTFFliRm9iv857F/A28755yZrQBMAs4Mza5yzr1VpvsPAQN+4JxbZJ8nB5oqYPXfkWH592LyVSfn4KtgrQbsUumgnZOvOu1rBy4Pq5/KZrOd57QVERERERERERERkcVEY5wOB/4B7Bo2/RM4pj4pn9ySS+ODgFvwyVdtC90yJzy88Izt8RWWAF4FtlHy1dDUECW/A74MtACjgHwujY/svteilWQil2Sie5NMlMXPzPEHYEHYvQ3+/fpKXEi/GRfSZQY6nriQXghcA2wHPArcCqwF/Aq4Ky6koyocZxi+utdZwOfxVRf+gU++2gY/TeQDcSFdrov+2wH/BY4GPgCuD8ujgWdUIUxEpLqcc63AMcDZ+H/3RWTo+BzwJ/zU2gvMbCY+Mf08YCT+M9s3O3cK04s+D3zHOZdfdOEOPCVg9UM2mx0LbBlWby3XJiRlvRBWd6vi4aeXPFclMxEREREREREREZHFUGOcDgP+DuwbNt0MfLk+iVrLtc+l8deAa/EVgJpmt61z7BMLv/dV4AuhySPA1kkmenlgI5f+aIiSBP8zm4f/G/8VuTQ+ZVCD6kKSiV5OMtFJ+BvfpwPvhF3rABcCb8aF9Jy4kK45EMePC+kB+Jt3c4Gtkky0V5KJDgLWA54Ftgd+3oshnwAOBVZMMtGuSSY6PMlEOwCfBd7GJ2ZdUCaO0fgksNHA2UkmyiSZKE4yUQafGDAGuKbSZDAREamMc+5B59wZzrnJgx2LiHzMr4BL8Insc/FfDpkJ3Iv/YsiOzrlZnTs551qccz93zn3i89biTok7/bMRviwa+DdVVwrAxuFRLTuF5bt8PBmrs92y2ewr+P8YNeFLwN0M/C6fz79bxXhEREREREREREREpBca47QWP9vBgWHTncBB9UnU3LltLo0NP1VHMdFk9rTWbb8+pXXfM4G1w7brgSOTTDR/YCOXamiIkv+EqSRvBlYAfp1L45WBH4bpCoeUJBNNB34RF9LzgBg/PeGmwDjgu8C340J6DXB+komeqOKhfxCWv0wy0ZOl8cSF9CTgfuDrcSH9eZKJZvdwDq3AFl3sezYupP8HXAHEcSH9WqepCCfhZzt5GZ+IVup04CBgfaAeuLjSkxMRERFZHDnnbgduH+w4hhJVwOqfVUueT+umXXHfqt20qVg2m10DOCGsXpbP57v7j9gawET8t2jGApvj/yPwfDabrWZFLhERERERERERERGpUGOcGvBH/FR0AA8C+9cnUVPntrk0rgF+Q0fy1TsvNx/y7Smt+/6WjuSrC4FDlHy1eGmIkkfxFZzeDJu+D/w5l8ZD9gv0SSZamGSiy4FPA3vgp8MEqAUOBx6PC+m9cSHNxoW0X/eh4kK6Oh0zkVxVJpYH8K/dCDqqyPXHU2E5Clix074vFQ+bZKL2TnG0A1eH1QMRERERkaWOErD6Z2zJ8+7+U1vc1+950LPZbB2+HPVY4HV8WdtynsSXdVsbGJHP58cDywFH4kvojgduyGaz6/c3JhERERERERERERGpXEi+uhD4Stj0OLBffRLN69w2l8bDgb8B3wib0qcXnvSr6e2f/iP+b74O+GaSib6VZKK2gY5dqq8hSl4EtgVeCJuOA67NpfHIwYuqZ0kmckkmuiPJRPsCGfwUNAvD7h2BfwEvxoX0pLiQjunjYT4bljOTTPRaF20e79S2P9YLy2b8FDrlYnmc8qoZh4iIiIgsZobsNyjkk7LZrAF/xn8bpgmI8/l82XK6+Xw+V2bbHODKbDb7AP5bHMsDZ9DxDatemzZt2hp97SsfmVD6fNq07oqpichAGj9+fG14Wvv+++/r3zeRRU/XRJEhQNdDkUGn66HIEKFrogyUthZH3Vi+3zKXkwCslhc2OKDumDW2q1tm2rRpH/sS71utz46upe5PbbTsBGCutvB8y/fume9G/zo0aVq91r7+nXGjblsC/1a7VF0TDx59AWnL/Yc+03Lj5e20bg4cUMOwux96I3/s2sO2mDPY8fXkgvGjZwM/vWtBy+/ua2qpn+M42vlpFdcDLjL4xVefS6/YesSwy/cZPfzdSscdX2OfndnuqIW3u3qPjzZmzXcwAjbuz+9Bi3MMg9NbgRFw59njR69UfN/9t7l1DP582KyutqnccTJ1tQsLLW0AK90x5c31Nh5eu6CvsQwluh7KYFpttdWmDnYMIiIilVICVv/MLXk+Gviwi3ajw7K//0nKAUcDrcCh+Xz+4b4Mks/nX89ms78DfgTsl81ma/L5fHtP/brwZs9NpBceG+wARJZmM2d+9KW2CejfN5HBpmuiyCDR9VBkSNH1UGQQ6ZooA+X1u1tpCX9ZHr2yscXJIzcavow927ndQjeXF1vvpI0WAFaw9Xin/ejMrHYyAGMNjltmxMi1h9VesgjDHyxLxTUxqtuBicM+z0MLL+fd9hdpp3Xrl1vueX6V2vUZacsOdngV2XVUHbuOqqPZOZ5c2Ma9TS282+5wsNwcxzfuamr9xqx2x04j61h9WM+TtGw9Yhg3L2hhrWE1m9DFv8XbjhjGHU2tTBxWs19XbSpx54IWQvIV3x43cp/SsSaWxLrf6Lr/lOv/xdF1FGb7InRrDKt5ua9xDDW6Hsogs8EOQEREpFKagrB/Sr9ys1o37Yr73u7rgbLZ7HnA14E24Mh8Pn9jX8cKHgnLZQnf2hARERERERERERGRgfP6PS28eqtPqBq1grH5CSMYvswn7y3Pb5/FPU2/Y1b7GwCsXPNZXmqZxGPNfv9KNcY3lx3J2sNqP9FXFm/DbATbjTiOtWo3B2C2m8bdTb9lbvv0QY6sd4absfXIYXx33Ei+OnYE64cEpjbg8eY2zv+wiT982MTzzW20Oze4wQKPLWzl9qZWDDhs7HBWqtXtMxERERHpHVXA6p8XAYfPvt4krJezSVg+35eDZLPZs4DvhGN9JZ/PX92XcQbImoMdwBJgAh3f4NoSeGcQYxFZqo0fP/4x/O/kOzNnztxysOMRWQrpmigyBOh6KDLodD0UGSJ0TZRqe/AXC+oXzHC/AMB4e8WNaw8cuVzNJ6ZWenThldGbbU9e5XCrArS0r/HPG5sO3KgNtzHAMHh0+5HDjluhtuaDRRn/IFhqr4k1Vstnhh9o7yx46cfNzPvKPDeDW5vOfn/12syR24w4pk/3GQZLjRkbDa9lo+G1XD5n4YYvtbYd3+Q4AKh7pbWdV+YupAZeGV9jfz50zPDro7raptL+Dy9sPQb42Rut7c8Be5c7xuSFrT8Fjp3S2v5v4ITexnju7AX7vd3mLgJqVqqx735m+LBP3IOZ0to+hnAP6N/zW/actMyIFzq3uXF+yybArQBTW9vXX4KmINT1UERERKQCSsDqh3w+PzebzT4KbIX/4P/Pzm2y2ewawMZh9c7eHiObzZ4BfD+snpTP5y/rU7CftFVYzgFm9HUQzb3cf8U55IN39JqKDJ6Wlpa28LRNv4sii56uiSJDg66HIoNL10ORoUPXRKmmxjidBPwirL6DY6ddTp74Sud2uTTeCv935vEAM9s2uPjFliP3A1sjNLm6FSYd9qm1mzr3XdLomgjN6bzjgf8BZ4Nb6a22Z6/9x/xvZxui5N7Bjq0vvg9TgTviQroqcDJwIjC+Hdab3u7O+f2chd8Ffg/8PslE7wHMnJk+DdAGE7p6D8yfmS4PsBBe6O37JC6kBwK/w88Y87Xfbrzun8u1Ww24vJDOBMY/09I2stxxCoV08/B0+u4T1/zE7/fiStdDERERkcqohmr/XRmWh2ez2XLVoP4PXyFrGnB3bwbOZrOnAT8Jq9/K5/N/rLBft/MhZ7PZtfD/uQH4dz6fb+9NXCIiIiIiIiIiIiJSmcY4PQz4S1idAexen0Tlkq/2Au4iJF+91brDRS+2HPXlkuSrXwFfTjLREp98JV5DlLiGKPkl8FWgHVgWuC2XxvsPbmT9k2Sit5NMdDp+ho2TgOLvw0r4eyJvxIX0kriQbgI8FfatEBfSdboYcouwfLI3ccSF9AAgAWqBE5NMVDb5qkRx/C262N+nOERERERkyaAErP77E/AqMAa4KZvNbgaQzWZHhQSqr4d2p+fz+ZbSjtlsdko2m3XZbPayzoNms9lvAmeH1dPy+fyFvYjpyGw2+89sNntANptdsWTMsdls9nDgQfx/4ucCZ/RiXBERERERERERERGpUGOc7g/8Df+3+NnAnvVJ9Fzndrk0Phy4CRgNtL7SfPDFr7fu9TVgGXzizYlJJjotyUT6Mu1SqCFKLgEOAhYCI4Drcml87OBG1X9JJpqfZKI/ABsCWaBY2WsEcBxQAP4MvBS2f7nzGHEh3R6fyLUQuLnSY8eF9IvANfiZYk5MMtHFFXS7vtg9LqQfu78W1g8Lq9dVGoeIiIiILDk0BWE/5fP5hdlsNov/ZtJmwH+z2eyH+ISs2tDst/l8/tJeDv3rsHTAt7LZ7Le6aXtgPp+fXLJeCxwYHmSz2bn4/3wsT0fS3XtAnM/nX0JEREREREREREREqqoxTveiI8FjLrB3fRJ9ojJOLo0bgN8AOMf8Z5tPuH6uW+NrYfc84NAkE1WcWCJLpoYouSFUScvjK2H9JZfGKwHnNESJG9zo+ickFt4I3BgX0s8B38InMw0D9i5penpcSO9IMtEjAHEhXQE/ZSHA75JMNLvYMC6kX8J/yf2tJBPtVnq8uJDuC/wjjH9Ckon+VGGolwE/BNYHfh6eF/08bJ8KNFY4noiIiIgsQZSAVQX5fP65bDa7KXAa8EX8ty1m48vMXpTP52/ow7BWslylh7bDO63fDZwObAdsAKwIjANmAc/jvwXyp3w+P7MPcYmIiIiIiIiIiIhINxrjdCfgBvzfbpuAL9Qn0cOlbXJpbMDP8H/Lpd3Vznxy4bcfaWbcEaHJO8B+SeaTSVuydGqIkntzabwTcCv+vsEvgZVzafzdhihZIqqjJZnoCeDIuJCeBnwD+Br+/gbASODhuJC+ArwI7AAsh5/140edhhqHvz8ysnRjXEhXxleoGo5Plto2LqTbdhHOqUkmml4S2/y4kB4K/Af4QVxIs/gqXZnwmAcckmSiBX04dRERERFZzJlzi/UXI0QWe9OmTVsDeDOsrrnaaqtNHcx4RJZmLS0tU4HVgbfq6urWGOx4RJY2uiaKDA26HooMLl0PRYYOXROlrxrjdGvgdmAs0Axk65PottI2uTSuxVfuOR6gxY1668mF33mzjZFbhybPA/smmej1RRf50KJrYtdyabwuPgnoU2HTFcBxDVHSMnhRDYy4kI4FjgFOoeN8i2YCfwV+mGSi5k79JgGXAq8nmWhiyfaJwGsVHn6dJBNNKRNThE/42h1YCXgfuAP4WZKJ/lfh2IsNXQ9FREREKqMELJFBpj8kiAwd+mOCyODSNVFkaND1UGRw6XooMnTomih90RinmwN34avvtAEH1SfRv0rb5NJ4JHAlcCDAgvbx6dPNDW2OYRuEJncDByaZ6INFFvgQpGti93JpvCpwC/DpsOlm4JCGKJk/eFENnLiQ1gL7A9/Gz/5R6ibgfODeJBN1edMrLqTDgP3w0xtOAFqBFPhLqLwlZeh6KCIiIlKZmsEOQERERERERERERERkcdcYp5vgqxKNA9qBI8okXy2LT5Q5EODD9rWffar5lGVKkq+uAPZe2pOvpGcNUfI2sDNwX9i0L3B7Lo3HD1pQAyjJRG1JJrouyUTbA1sD1+B/zwC+gE9cfCIupEfGhXR45/5h6sBX8VODHg7sAuwBnAg8HhfSh+NCuunAn4mIiIiILKmUgCUiIiIiIiIiIiIi0g+NcboecCewQth0XH0SXV3aJpfGqwD34BM/eL/t008Umr/yKahZJTT5OXB056nURLrSECUfAHsDxUS/bYH7cmm8+qAFtQgkmeiRJBMdBqwLXADMCbs+i09ifDUupN+LC+nyAHEhbQCuBtbsZtitgAfjQrp1N21ERGQxYmYuPHYeArHcE2I5Y7BjEZGBowQsEREREREREREREZE+aozTifjkq2Ii1cn1SXRZaZtcGq8DPIBPEOGNlt0ee6Xl4M+CjcFPg3Zckol+3N30aSLlNETJAuBg4K9h0ybA5Fwab9B1ryVDkommJJnoO/jEqu8Ab4RdqwO/BN6MC+kNwG8qHHIZ4Ma4kK5c7VhFRKR3zOyykgSq0ke7mc02syfN7GwzW3WwYx0qzGw5M/tuSPZ628wWmtl7ZvaImf3UzFYb4OOfER4TB/I4fWVmE4sxDnYssuRSApaIiIiIiIiIiIiISB80xunq+OSrYmWdU+uT6PelbXJpvBkwGYicM15qPuyJqW27bAlWg6/cs2+Sif6KSB81REkr8BV80hHAWsADuTTeYvCiWnSSTDQ7yUQX4CtixcBjYdcYYP9eDrcicHwVwxMRkf5pAd4tecwClsUntZ8GvGBmS331QjM7BPgfcA6wE/6LAfOA8cDngR8Dr5hZwwCG8ZPwmDiAx+iPiXTEKDIglIAlIiIiIiIiIiIiItJLjXG6MnAH8Kmw6cf1SXR+aZtcGu8A3AdMaHfDeKb5hGdmtG/6ubD7LWD7JBPdvsiCliVWQ5S4hij5Pr4SFPhEortzabz7IIa1SCWZqDXJRFfjpxPcHv/72RfHx4V0WPUiExGRfpjsnJtQ8lgBGIVPuJ0BjAOuMDMb1CgHkZl9BT/V7njgaSALjHLOjQdG4Ke/vhsYDfzGzH42SKGKLPGUgCUiIiIiIiIiIiIi0guNcToeuB3YMGz6FXBmaZtcGn8R+A8wrsWNan9y4bfSeW71zcLu/wJbJZnomUUVsywdGqLkAqAeaAPGAjfn0vjQwY1q0UoykUsy0YPAv/s4xJrARlUMSUREqsg51+Scuxo4JWyKWEr/3TazTwO/Awy4CdjKOXejc24hgHOuzTl3D7AbcHHodrqZ7T0Y8Yos6ZSAJSIiIiIiIiIiIiJSocY4HQfcBhSTqX4LfL8+iVyxTS6NJwHXAyOb2pdvfnLhd95pZlwUdt8G7JhkorcWYdiyFGmIkivwU+8tAOqAJJfGJw5uVINi+X70Xa5aQYiILA4a4zTTGKcXNcbpM41xOiUsL2qM08xgx9aNp0uej+ltZzNbwczOMrNnzWxueDxrZr8ws/E99K01s6PN7BYze9fMFprZNDO738y+a2YTehnLz8zMmVmTmX2pF13PxFe5ehs40jnXXK6Rc84B3wCexSdr/apMDPeEGM7oJs5PtDGzy8zMlTS7O7QpPu4paTspbJsS1vczszvNbKaZzTOzR8zs6G6OXxxz5960Cce7u0wb19M5i/SGSqiKiIiIiIiIiIiIiFSgMU7H4KvqbBE2XQKc0in56rvAOQBz2tecV2g+rsUxbLWw+y/AiUkmalmEYctSqCFK/h2mH/w3Ppno97k0Xhn4WUOUuG47LznmD1JfEZHFRmOcrgg0AvuU2b0pcFJjnN4CHFWfRDMWaXA9+3RYtgOv9qajmX0GuBVYJWwq/rufCY9jzWwv59wnqpWa2SrAv/BT3gI44AN84u/24dECXFhBHDX4ClYnAnOB/Z1zd1V4DqsD+4XV3znnZnfX3jnXYma/Av4GbGZm2zjnHqrkWD2YDbxLx2s5CyhNBJtZrpOZNQC/wb9+s4GRwOeBz5vZjsBXQuJYNbwPLEtHcva7nfbPrdJxZCmnClgiIiIiIiIiIiIiIj1ojNOR+Jtt24VNVwEn1CdRO0AujS2XxucSkq+mt20y+9nmr9Y6hi0X2p8OfFXJV7KoNETJZGAHYFrYdAbwu1wa1w5aUIvWo33s54D94kK6YjWDEREZakLy1f2UT74qtQ/wQGOcrjDwUfXMzEaY2cHAr8OmK5xzFSeHmdk4II9PGHoF2Mk5N8Y5NwbYBZ/MNQHIm9mynfoOD323wicNnQAs75wbD4zGT4X4Y3zCT09x1AFX4pOvZgC7Vpp8FeyMr2YFcEOFffL4hLVi/35zzn3TOVda8etA59yEkseBZbqtBJyHT/5b1Tm3PLAC4XM0cCxwfDXiCzFuCRxYsj6h0+O8ah1Llm5KwBIRERERERERERER6UZjnA4H/gHsFjZdBxxdn0RtALk0HgZcCpwK8FbrDjNfbomXhZqR+AoIRyaZ6BdJJlpaKg/JENEQJQVgW+DlsOkk4KpcGo8YvKgWmXuAl/rQz4CfAlPjQnpZXEg/V9WoRESGjkZgwwrbbghcMYCxdGVbM3un5DEDaAKuxU+1+3Pgq70c82RgTXzVoz2cc/cVdzjn7gH2DGOvjU+OKnUMvkpTC7CXc+7iYuUp573onPu5c+7K7gIws9H4ZKgYmArs4Jx7rJfnsXFYLgRerKSDc24O8Fqn/oNhNP46Pck59y6Ac+4D59z38BVjAX5iZprRTRYrSsASEREREREREREREelCY5wOw1e7Kk7xcjNweH0StQLk0ng0cD1wtHNG2nLA9Ndb9xoPZvjpaPZMMlG3N+FEBlJDlLyOnw7p8bDpUOCmXBovM3hRDbyQ8PibXnZrB/4bno8AjgYejwvp5LiQHh4X0uHVjFFEZLA0xumm9Fz5qrN9GuM0MxDxdKMOX6mq+Bhfsm9Z/DS7vU0qPjQsL3fOvd55p3Puf/jkNIDDOu2eFJZ/d8490svjAmBmywN3AHvjK3Bt75x7oQ9DFV+LWc659m5bftz0sBzsimZndzHF4FlhuSodlWdFFgvKGBQRERERERERERERKaMxTmvwla0OCpvuAg6uT6JmgFwaL4+vXrB9m6vjxZYjZ8xuX7c4bdnrwL5JJnp+Ucct0llDlLyfS+Nd8cmCuwG7A3fm0ni/hijpcZqkxdjFwI74CiOVODHJRH+KC+kWwDdCv+HANuHxTlxI/whcnGSidwYiYBGRSjXG6WBU1ny2MU771LE+iaznVp9wr3Nu5+KKmRkwDp+Ycxb+3+rtzGxH59y8ngYLUwgWk8i6m+7vDuBrwGZmVuecawlTBm4R9v+712firQrcF2J4CtjbOfdeH8danLUAD5bb4Zx71czexFcp2xy4d1EGJtIfqoAlIiIiIiIiIiIiItJJY5wa8EfgyLBpMrB/fRItAMil8Wr4G0Lbt7jRPNN84qzZ7esWKwk8AWyt5CsZShqiZA6+ktu1YdOWwP25NF578KIaWEkmageOAi7qoel84OgkE/0p9Hs8yURH42/+ng68FdpNAM4A3ogL6VVxId0mLqR9SSgQEZE+CNP8feCc+zewLzAbn6TTUOEQ44Ha8PytbtpNDctaOipNjaejwM0nKmdV6Hh88tUcYM9+Jl/NDMvlzaw3eR/FLwvM7LbVwJrunGvuZn/xZ7PyoghGpFqUgCUiIiIiIiIiIiIiUiIkX10IfDVsegLYtz6J5gLk0ng9/Lf2N13QvgJPL/zGnAVu5eVD25u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w/K9JAUMVY+t02ml7HdZ0/IILZtalDl++TyDVUfamFIWzq55r3i6cVf1i1pZkBdIcpQ5fvs8FF8z85smxX/3q+AkVY+ucpEZDDeUTl37Hbxk9pLv7VkP5xLsjfavW+dWvjp/QdJy+NfDQtxAvjImIB65ZiBf6FuKF8RDxwnUL8dJb+1bW64eOlPSipCF5H49TxdhaJTUaGv9BSFK4XDJ+aHvMh7/7TwBxdG309cbmS/yZS+3tki6I7l7kn+XN7czJgu++da4CjXKZk5V++bXfLO1mLrVDisyYU6HIjFbHtmh6gqR9FVly8IJo/aa2SyS9JClLkWQx9FJFpv2epMNDKXIkKbdUWvM9/bDY702SpJNm2SuGrtRlkrRtog54+l7v6M6ct3qwFoVSpNyNKjv1VPuspuMnnmMHBm3UIUn1UnWBMgPpuqZ5u8YMXVc9VOlJ9XIGbdShJ57z7XKap55qn5W7UWWhFKl6sBZ3w7c/oNgeM2h7zEdsj3mkIkuX3iWp6feN/STdK2m9t8R/g7fE36l/585YPdTzY0kaX/6+v/kSfxdb17yzx6b3b5Gktdn7DJ//xA17duZ8pUNGLw0rRfmBVYHf/uD8g5uOF1lXNUws/Xxvt+pU6R7pcrvq/9a8ncvVsKzKXWgkqU4TS7/Yu8i6qqGp7Lc/OP/g/MCqQEip2jR49G4zZwEAgN6FBCygCxYsuu8X2ZsiS5KnZG/4bcvyiy847Yz6nLDcIUONjcPu6sw509YOzZCkjMlbdyubN/fcz6rGVwQlKVw28gfNy8I7Rx4uSVXjywPz5p77Wcu2oREb/JKUvm5odmfiQGLFo2+lrx2aKUmhERu+bllG3xo46FuIF8ZExAPXLMQLfQvxwniIeOG6hXjprX0r89UjqxVJiimUpNXHb1bNxC2SpKxVQyS5DrI95n87+32i5/lneUcqkgwlSQ+2LDeX2m9IWi8pVdKJnTppXe0JkpQ0fb/disyldrUkX3S35Qx8P46++qL1WmqKj5n7erkJ/1Vm9VAZklQzSJJ0rqSHi/3eNEmyfm4vTK1UKJwkNWbpys6cs3K49pWk7G16vGXZiWfbW3JLtUqSagfpzOZltXn6uSTllWrViWfbW1q2zd6mJySpcpgO6PQ3iN3YHvMT22POVmRJwiskrYkWDZF0jaTV3hL/I94S/5HeEr/Rxmk6dPuTfz6+0j3SJUlDKrb8pmX5Fcf96vJUp1yOklWRPeiWzpxzS+bEQkkqLF/9dsuyi2Zcu7qgzl8tSdtyRpzSvGxb7oiTJamgzl910Yxrd1v+d3jFmnckaUvWxJGdiQMAACQOCVhAFwSCuedLUmO6owvnnnVna3XqCmrCkhSuzZvY0fkWLrpnesZOtyRp1OjkVus4OTu3SFJyWXZK8+PJO7JSJMnJLt/tjz1JSkopf0KS0svcWrjonikdxYLEimffSkopf7y1OvStgYG+hXhhTEQ8cM1CvNC3EC+Mh4gXrluIl97Yt5Rdvl2R5KsxkrTyqAp9ccBwlQ+P3LpOL3PryDdec3f83SHB9o2+lplL7d0SCKLeb1G3Tf5Z3myFw+MkyTVyVFvV2jrfvi3K22o3yT/Lm9lRLEicYKosSUquk9OQq7eih0+R9FyxPzKTWsZObZGkxnR1OGvfM/d6C+vylBw959Ot1Umt1keSVJcTuSY1qc/VaElKiZa3lFwfOV9dnpKfudc7tMNvDu2yPWaZ7TH/KsmUNEORGRIlyS3pVEmvSlruLfGf7y3xZ3T1/LXpmTMlKcmp1a9OuvKfrdXJbdxcL0lV6bl7d3S++U/cYNa5BkuSMuuqHm2tTk5d2UpJqkwbmtP8eGVqJIk9uzZS3lJWXdVjklTrytf8J24Y21EsAAAgcUjAArog3Jg5UZIac4Jt18mob5AkV01Gh2uSh5z0nzdtjxwxuNU6ruTa9ZKUWrHrzfemfVdyzdrW2rmN+sclyZChkJN+Zmt10HvEs2+5jbp/tVaHvjUw0LcQL4yJiAeuWYgX+hbihfEQ8cJ1C/HSG/tWUkM4V9JESVr1/Tp9dUhkdbrq7NTXJfpWHzI++rqunTrrW9Rtz7imDSNvUFfP11EsTe2M5u+D3ieYoj0lKaVG9ZKOkfRstOhISa8V+73Dkxu0VZIC6RrR0flCSTqkadsV1Hut1UkKyC9JDVnaJamnIUvp0fIVrbVzBfWBJMnY9X3w3dgeM2R7TJ/tMY+RtKekBZJqosV7SVoiaaO3xP9Xb4m/M9cWSVJ9cvpUSUoLVzht1UkL1FRJUl1ydl5H52tMTvlR03ZKoPGF1s9Xv1KS6lx5u3w227SfFqjbbSZJSUoONEaTz1y7vA8AAOh9SMACuiKclB55Cbddxx0MSpIrkNTh/y8nnPTNH4WZGemt1jGM0E5JSmrY9XjTvuEKlbd+cufzbzeTum1ddMRJHPuWHPcXrdWhbw0Q9C3EC2Mi4oFrFuKFvoV4YTxEvHDdQrz0wr7lChoZkrT6kIC+PCJy7TMUvN1cvbZpGTn6Vt/QtAxpTTt1mpYDzGmnTsvzSckpbdVp63wdxdJ8WcLOxIIEcdyRfx93UIEi065RZCakB6LF0yX9V45CkhROUuu/PDU/n0tDmrYNR7uv9yzJCGunJAVTtMvydk37RljlbbT7ZqZIx6VhHcWCrrM95he2x7xQ0khJl0hqSljKk/RrSSu9JX6ft8R/TEfLE4bcSZmSlOQE2hwQ3eFggyQF3SlJHcUWcrmHN207cq1prY4rFCqTpKCRumtbI3KNc4XDO1trZzj6ZlbBsMvd5pSAAAAg8UjAAgAAAAAAAAD0mGVe/y5JL2sPCOuLHyZLhiMpdMlDnim/Sm1oaHNWEgADU5FpByT9UtJt0UMTgqnqcHk49C+2x6ywPWaxpCmSTpD0TLTIkHSypBckfe4t8V/oLfFnt3EaAACAbkcCFtAVrmBd5KWd/zqhpCRJCicH23mUMMJwBUubtmtq61qt4zjuQZIU3PWhiG/2nbA7r/WTG3t+uxlc32od9B5x7FsyQlNbq0PfGiDoW4gXxkTEA9csxAt9C/HCeIh44bqFeOkFfWuZ158h6amm/bps6bPjXDKMUFByTrE9exRHzkff6mOqoq+Z7dTJir5WduF8UqCxq+frKJasZtudiQUJYoQi/z6hJH2zhmmRaYclXSbpGklyjEiZEVTH16ywtjdtO4YKWqvjuDRIkpIatUsSaNO+41JeG+2+mfWq+WxYiB/bY4Ztj/mc7TFPkjRJkcS8pv/TUyTNV2R5wmJviX9y87buULBGkoJGcpsDYsiVlCpJSaHGttftbTpfOLS5adtQeFxrdcJud74kJTm7TgnpdiLXuLDL1ep6q47x7TKrrnBoQ0exAACAxCEBC+gCV0rNKklKqWx7xllXbVqqJIUz6uo7Op/bqPtH0/bG0h2t1gkHMkZLUkNOYJfjTfvhQObY1tqFnLRTJMmRI7dR91BHsSCx4tm3Qk76qa3VoW8NDPQtxAtjIuKBaxbihb6FeGE8RLxw3UK8JLpvLfP60yQ9Lun7TX0rmJIkl9FY5ch9mO2Z/MS356Nv9TFroq/tLRfZVLamnTpN1jZtOOWtrsrV3vma9sd00M5p/j7ofZIa9YUkNWYqrfnxItN2ikz7Rknnh92RY4ajIcV+74z2zucK6p2m7XCSDmytTjBZpiSlVmuXbPim/WCyJrfWLpyk/SVJjuRu9j7oGbbH9Nse81JFlie8QFLTEsnZki6W9JW3xP+ct8R/krfE70oL1H0pSfWunDaXKqxPzsyWpPRAdUVH758SaHy2absxOeXY1s+XNlGS0sLluyQLpkf365PTJ7XWLpCccnRkK6yUQOMzrdUBAAC9AwlYQBckJ1XcLUkpdYbuXHT/ha3VSd+a6ZIkV+bOlR2db97ccz+sHRSSJG1YH2i1jlE5aJgkBQZX7fKoV2BIdaMkGVV5ra4nH2zMmyFJdfkhzZt77mcdxYLEimffCjbmndJaHfrWwEDfQrwwJiIeuGYhXuhbiBfGQ8QL1y3ESy/oWw9LOiayXy1JStmeo7BS9rE95rvN29G3+pyPoq+D/bO849uoc0D09cOOTmYutSvlcq2RpPDGNid8aet8H7Yob6vd1+ZSu7qjWJA4SQ3ySVIgXcZTy7zfb1leZNp31+dEZr4Ku2VIerTY7z2nrfOddLa9Mb1cgeg5T2qtTkOW9pWk9Mpdk/PSKrRekhqj5S0F0iLnSy9X4MSzbWbAShDbY1bbHnOhJI+koxRJ+m1KeDpO0lOSVmxPHVslSUEjU7c9/ZfTWztXRcrwNEnKritf3tH7XjTj2q/Sw5EHKGrSs3/SWp3K9PyJkpRbv22XmfdyGrZVSVJVRqS8per07B9LUka4TBfNuHZ1R7EAAIDEIQGrn7Msa6hlWTdblvW1ZVl1lmVttyzrBcuyTkl0bH3RBXN/eX9VYeQ+ZGPVqN+3LL9jwcMPpVW6FHI7SknZcn5nzlk/dlutJNWu2H3G44WL7pmWvSY3SZJc+RtfbV7mGrTxDUnKXp2XvHDRPVNatnWXjjIlqW7MtqqWZeh94tG36sZuq5Ekd+mo3Z6coW8NHPQtxAtjIuKBaxbihb6FeGE8RLxw3UK8JLJvJSWXlUn6kSRtnShVjS+XJOWsytWRb7yW0rItfatvMZfaGyS9F939ecty/yzv4YrMPNUgqXMzuKRnPCtJweW752v5Z3mzJJ0c3X20RfFj0VfLP8vb2jKETfG1bIde5ke/tN/P2qoaSarL1XUty30Peuc15MjlCkpVQ1WpyGde9xT7vVcV+72tzmyUszmSLFg1VKe0LHvmXu+wihGR5d4yduqB5mUZ5XpQkspHaMIz93qHtmxbNVQzJClni97v4reJOLA9pmN7zJdtj/ljSRMl/UVSWbR44sZBU8/PCW2UJG3LLbypZfubnr/trw1GngwFlFu187LOvOewmpWbJGlT3viDW5bNf+KG8dvSzSxJGlpZ+njzsqEVpU9K0tZ0M3v+EzfsNuPo5txx35OkYdUrN3YmDgAAkDgkYPVjlmVNk1SiyHropqSApDxFnjJ7zLKs2xMXXd8VnPD1W5KU++mowfMXPDS/6fjCRfce5f7C45WkCs/Wmnlzz/3mqb3b73jirUUXfOncceWnu61DP3jU5suDyY5y16br4Yc/faPp+IIFy9IbNk//OLneUFVho1xV2cc3b+eqzjq6elhAyfWGGrbs/cmCBcvSm8puu+25Vblr041giqPUvJWzuvUHgLjp7r6VmrfyvEjfyjBuu+25b55apW8NPPQtxAtjIuKBaxbihb6FeGE8RLxw3UK8JKpvZX08cbgk7RgrffhT6avp+f+mb/U7N0Rfr/bP8u7XdNA/yztY0oLo7nxzqV3RrOzH/lneL/2zvC+1PFnSQYfco+QUhf0rVHfzDWc0a+OOni9PkaSvF1o0fVaRGbnyJC2I1m9qO1uRWXGqJXF/vA/IX687JGnrJB3je8D7TXLf00u95raJukWShq7U+6E0HSlpiyTlbtCN6TsVvO91b1nL82Xt0Fx3o1QxUvn/+pd3WdPxZ/7mTd45Um8G02RkbVVNcp3+1LxdSq2uz9qmumCajPKReuuZv3mTm8r+9S/v/RUjle9ulLJ2aE63/xDwndgec43tMa+SNErSeZKWS9Kgqq2SpDV5+4254j+P+b0l/h97S/xJd/j+dMBXhQdcJkljqz7efNGMa5uWM9SfXrrzX+d88lb4go/+vdt0tSO2r5/lUqPKkick//7VJW81HS/2/Tl15YipnwSVrpzQxnAonLbLLG3hcOrM7NAmJ6h0rRox9dNi359Tm8p+/+pdb5clT0h2q0GFO9af2b0/GQAA0N0Mx3ESHQPiwLKsVEXWuJ6gSBLWmT6fb7llWRmSLpV0vSRD0jk+n+/exEXaN916w1u1gz8Zmh52OaocW+s4SaFw1vpsd3K9ocqRDXJPenvCvLnnfjMV7G23P/Nl/luT96jLDWnO4j12efKmtLR01Isvfb4+/MxYuRxDlSMaFMyqD6VvzXKnl7vVkBlW4wHvXn7hvDNvbhnH/EUP/Cbt3YP+mFLrUl1eSHUF1aHkqnR39qYUhV2OKg75/O1LLp5xSE/8TNA9urNvSdLtdzz5fu6bU/anb3VOIBDYIGmkpI3JycmjEh1Pd6JvIV4YExEPXLMSi/GQvoWuYzxEvHDdSizGxO7rWxq0SakbRmrnKOndM6SktJ13NTiD5h7+xtvXDMS+1Z/5Z3lvl1SkyMPAL0mqUSThKU/SfyUdYy6165rVnyXpXklrzaX2uObnKi0tHRX89OP1jf94QAqHJekdSWskHajIfe8tkg43l9r+VuLYQ9J/JA2VtEqRRK3xkg6SFJR0urnUfqxlO/RO9jPej7ZO1j5GSMrdpO2ukBoqh2lEME1GzmZVFnytcSeebe8s9nsnSnohb50mlI+R0irl1Ocotci0d0mWeeyf3ps37KPLHJeUvUXVyXUqqx2kEfW5SkqpUXjUch3zo5n2yy3jePJ+7/EbpuuZQIaMtAoFM3aqNJChwVUFyjRC0uiP9edTfmZf3WM/mB7S38ZDb4nfkHSYpItH1Sw/fUPmdBkKKr9xrdxO0ClLHWMEla684LqQZ+3boy+ace2mprbXv7L4tc+GHnVEmrNTS/c6cLfx8P9eWvDkp8OO+pHkVk5wQzg9WFlfkTo8o97IV7JTpf3XvXjWr0664oGW7W595qbzPhx9zF0BI0tpTplyGzbX1iXnpFW6R7kMhbT3lhcfveaoC0+N708GAAB8VyRg9VOWZV0sqVhSraSpPp9vXYvy+ZIulFQqaZzP59stWx/tu734yQ9c68fvl7E1Va6QVDcoqIaxm7alZa2ZNm/uOdua123rJtXiRQsLM526F7PqavYsdw1WxfaDlVmapaQGQw1ZYdWOKatPHfyV94K5s55oK44Fi5b+pGHHHn/PWJefllrtUjDVUc3ImrC78MuFF11wxkXx/BkgPrqjby1ZvHC/zMbKRzLqaofvTB6WXLXj4CT6Vsf6282Elrqnb905I7u+YlF6ff2gspThbvoWpO7pW3cvmn9pTl3F/0sLBvLKkoepYsf3lLmRvjWQdUe/kqR7FhXPz6kpP7PKNSSTa1bnMB5+q62+9c+bbjRqslIfz6mp/GGFe2gafQtS9/StuxYtzE5T/avZtTX7lbvy+RsRkrrp/sPiRWZmoPLJzNqasfyN2HmMid9q5/7DkVkN5Q9k1NUPKUsZltSyb9WN3hnK2SR30o4hqhguvXNmWEnpZVcvnXbwn5vOMRD7Vn/nn+U9XZF7z/tISpa0UtIDkm41l9qNLerOUisJWP5Z3tFGTu5c99Rp1zrV1Qp98dkKhUODJWVL2iTpKUnXm0vtLe3EMVzS/yqy9GWhpApFkrL+z1xq776uIXq1x/7pvbWiUGfX5is37JLSK9WQs0kvZ5bptBPPsWua6hX7vYX5a1RSNk75qZVSQ46ekXRakWnXStILi7wnp1fo14EUTdw8VcMrC5UUSJVSaxXK3qLPc7bojJNm2Z+1FcfT93n3qizQ36uGac+GDLmTGuVkb9OmnE26wjrTfrAHfhQ9rj+Ph94S/8hJO958ZVvuyElVScPkyKX08E4NqV7vlKbv+fdAcsYttsf8qKl+WwlY9l1XG7XpGX/K3d54ZkVeav4ne05P35E+RkEjTalOlYbWrt4xeuuqnxSdfPXrbcVS/OSNP1hfMOGRbRnjBzcY2Upy6jWocUP92K1fXf/rE359Q1vtAABA70ECVj9lWdY7ijzNc4/P5zuvlfIxijwtZEg6zufztZymGXG0ZNHCQXm1O1bu8fWqQZl1tbuVl+XmaYU53p9S70w+/Yqr+U+KTluyeOF+Q8s3/Xeyf01aaqBxt/JNBQVaOW7cy7MuuOyoBITX6/Xnmwnf1V2L7jxrxPaNf5u4ak1SUni3FSe0dtRIZ+2oMUvOmXvJ3ASEhz7s7kXFN40tXf/r8evW7/bUYMhwadX4MaGNBaMvOW/uhXcmIj70XfcuvPXJCWvX/Gjk5t0/j2lMStLX5vjGLYNGHDt7zrzXEhBer8Z42L7759/06aRVqz1DynZbzUS1aelaMWlc1Y7Mgj3mzJ23qZXmQKvuuXN+ckawcv0eX68allNdvVt5RXa2vjInbExqNEbzNyK6YvHiReOHVG35ZPLXq7PSG+p3K982OF9fT5jw4cwLL98/AeH1eoyJbbtr8YIThpWVPj5p5ZqU5GBwt/INhYVane/50v3BkVMkqWqo9O5ZgbArs/yMe6cd8s8eDxh9hn+Wd5Kkv0iyJLlaFDuSnpF0pbnU/rxlW6BJsd+bJ+kJSUdED7056TX9o8Cv3+duUl7L+mG3VDZGq8oL9ZPjZ9vLezDUPmEgjIfeEn+qpNMlXazILHvN/VfSHZIetT3mbpMZ3Lf09/80P9v202Hbtu92b6s+JUVf7zF625axad+b+9PrVrcsBwAA/QsJWP2QZVlZkioVSa46zefzPdJGvc8k7SnpLz6f76oeDHFAW7xoYeGETas2jF+7vuUNhN18Mm1KbVBpWdxgR2csWbzw2Gn+z54ftm17u/XCMvTe/tPX/uLiq8f1TGR9x0C4mRCLuxfdcd1+Jcv/0NqHgc0FkpL0zn77vTTrgsuO7qHQ0Mfdu/C2xw/68MMZrSWMNleTnqH3p+9zy7lzi37dQ6Ghj3tg/l++OPCDj6e4nd0TRpvbnp+vT/aYdsbsORfaPRRan8B42LaHb76+fN9PP8vtqN66kSMc/5jJe86eM/fLnogLfdtdCxekj9ixvmoP/2p3R3W/mDwxsDW3MPPcCy9iFmt0aMnihdMnr/nq41GbNndY94Ppnh0/u/R/h/RAWH0KY2Lr7lo8/9zpX5TcnV9e3m69kMuld6Ycqeo1R+n9mbUNTnb1UfdOO/S/PRMl+iL/LO8Bkp6XlN9B1UpJJ5pLbfoT2lTs96ZLekjSjIKvpH0fldyh9tvUZyu00aNjj5uz+/KDA9lAGw+9Jf6DFEnE+pkis/g1KZW0SNIS22NukaSHFv72kwPf+Wqvjs65qWBo+PMDC/adc9p1n8QjZgAA0Dt0mACCPmmqIslXklTSTr2msj3jGw6aG1G2cW1nkq8kae/PvswIpTgb4x0T+r67Fi5In7xmRYfJV5LkkqMDP1g+dumCm1+Nf2To65YsXnjo9M9LOky+kqTkYFAHffjRUfcsuuPPHVbGgHf34juuPPCjjzpMvpKkzLpa7ffpJ5fdtXjBCT0QGvq4exfe9nhnkq8kaUhZmfb0f/nQkkWLhvZAaOjj/n7Hn1d2JvlKksZsLDVGb1nT3t9iwDfyq7du60zylSRNXbEyOae+rONf+jHg/fOmG41xm1Z/2JnkK0naf3nJ4Pvn/6XN5ZaAJosXLxw9bcUXHSZfSZI7HNb3vnxNNYe/3RjOrp1G8hXa45/lHSHpaXWcfCVJOZKe9M/yjo9vVOjLiky7TtJPB6/Wq51JvpKktCq5Cz/Xc8/e7R0W9wDRa9ke813bY54labSk/6dI4pUkjZD0B0nrvCX++++6/8+vdib5SpIKt25zTVpe9sE9D/02KT5RAwCA3oAErP6psNl2aZu1vi0rbKcOutHdi+64buoKf3LHNb/l+eKrwsWLF3EzAe1KduqeG7Wp86vcuOTIXL3myH/edONu0yIDzeXUlT02qLKi0/VTggEVbitlliJ0qKBsy/VpjQ3q7BSP2TXVyqvZ8VBcg0K/MG792hmdSb5qMnzbNqUHq16MY0joB5YsWjhozy9XTOhKm0mr1rjvWVQ8P14xoX9YsvjOn3i+WJHZlTaez7/KWbJ4IUuKo13V2WnLJqxZ16X7flNX+Pe8a+GC9HjFhP4hM1D5YsGOHZ2u7w6Hte8XHyUvnXbwyjiGhf7hEkkFXag/SBL3H9CuItMOTnpdUzqTfNUko0LJ6RW6I35Roa+wPeYW22NeL2mcJK8iSxFKUoqc8Jl7fbT2yK6cb8yGTUmugHthN4cJAAB6ERKw+qesZtu17dRrKsuOYyxoJr9y+zVdbZNeX6/MQMVz8YgH/cfIzZu/39U2w7dtU3V2+tI4hIN+4p475ydPXL2uKzc/JUkTV691L1l850/iERP6hyWLFx5mrlyTIn07ZWdnmCvX5i5ZtHBQnMJCP3D3ouKbRpd2PiG5yajS0r3jEA76kbRQzb+za2q63G7ozq3nxyEc9CN5tTvvdoc7nzQqScmhkLLrd/49TiGhnxi2Y6u3q23yKiuV7NRx/wHtGrt+4+Suthm3fqNx96L5v4lHPOgf/LO8aZLOjaHpTP8sL/e20abnl3gPHrRBw7vabtAGzXj2Hi8PrkKSZHvMgO0x/2F7zMMl7Sfpb3uufz84pKysy+casabqzG4PEAAA9BpMdYnvpLS0tN+v992dxmzYFNOTpEO375h8y22vr+/ueNBPOPVJP1q3IaYbAjlVlWfdctvrP+zukPqqjIyU4YYhOY6G19Y2Dvj/c2lJg3IHVXR+9qsm7nBYqQ2Bf9xy2+udW+8EA05eUqggJRjscrvsmmqFg2kbbrnt9a7f4cKAMCJcMyKWdqNLN+n24ie3hMK5Ha+JOQAwHu5ucvXOmP7uGbd2Ywq/x6M9B27aElNiceHmrcPoW2iLEw7r+DXrY7rnl19RfgR961uMibtKcu1IP3HL1pja5tRXXLnq+utY5hKtyxu0t8p3Do6hZbZycq9Zdf1173Z7TOgXMtOTLjecrt9/yNyplNqhSS88+MbZ7a0wMmCMzj9wkMtIUtgJDlpf9t59iY4nkazoa9pXhSHF8BnruNUb0975+LUpowsmVXdvZP3XiBEjNiQ6BgAAOosErP6p+S9uGZIq26iXEX2t+g7vNeBvPnVFRm17E5K1La2+UUPeHkGyG1oVHL5erk4v4rWr1MZGY8gH9K1WuDOkAf9zCe+1Lua2qY2BpCHv07fQOvf+H8XcNr3aycj4eERGxzUxEKVOjz1/KrvUXZC0Jqb8rf6M8TAqbVJDTO0y6uuU/27BKFeYP73Ruowh9TG1S69r4G9EtCmcVqNUBWJqm1rfqCHL6VutYEyUFDR3xtw2uaExJ7zy68e6MRwgorLimnBl1x8ew8CQPCb2trVZwaN3Du968lZ/tF2vN21maLhmJjKW3mJiQ2y/a7mdsDLScr7o5nD6O2ajAwD0GSxB2D81fyqjvU+Rmsq6vk4LYhJyx/bBS8jNf1W0ozE15qYhl7sbA0F/44SSY24bJscb7XC+w6+gTpjrFtoW/i7jWiCl+wJBvxN2xXbdCrlcUpjf5dG2kDu26xZ/I6I9TuA7/B5P30I7jFDsf+c5Lj47BdC38AwF2hNyxz6upSRx/wEAgP6KXyH7py8lOYpkhU+L7rdmWvT18+/wXqO/Q9sBZ0d+3vpRm7q+IldVdlbg8Gn3nR6HkNAP1Biu1OrSDDsrhhnW6jNSdxw+7b7z4hBWnzR4xql3y+UerHBox44n/jXgfy5fZOccFDJc17idcNcbp4c+OnzafX/o/qjQH7yTnvcrSUfG0jYna/uSfabd92z3RoT+4p2MrAWSCrvarj4lVZ4hb549JO+18u6Pqu9hPNzdR+mZD0lK62q77fn5OmL8/T+OQ0joJ74O5TyaX1He5U9vyvNywofn3XdqPGJC/7C1bNBjQ3Z2fbaimqz02sOn3feLOITUJzEm7mpjWtqIxm3Jd6YEuz7rR316xlrXxEm/6v6o0B84VZUjnK1b7oylrTF4yOVG3qCV3R0T+odA5vqfa139aV1tF3ZJ+7w9bG52Q86WeMTV1zAe7u7DQcYdimF2zIrsbOW5U/hcDQCAfooErH7I5/NVW5b1rqTvSTpe0r9a1rEsa5SkPaO7L8X6Xqy93DUvjxzx5ahNm6d0td32vCF3Tphb9HgcQkI/8cjNv79rn0+/yO5Km0BSkirT8k6dcMk1r8Urrr4mEAjMj27W5+69z+OJjKU3mCA9/tzvr7rKXL22S4/B78zJVY0r47AJ111eF6/Y0Le9f9ONT2wbnB8euqOsS+3WjB7l/KTo13PiFBb6gRcXL/y6PiW1JK2xa8vFfW2Oq/vxlf9vaXyi6nsYD3f38qL5v5f0p662Wztm5PrDiq55vPsjQn/x+oKbX5+4Zl2Xk5K3DCl45sR5lz4eh5DQT7xz+w1bhuzcOawrbcKGoZ2Z+ZdOuOyix+MUVp/DmLirCZKeuPG6W6d9+XWXpu6oTUtXVXL29ydcd+X6OIWGfsA/y+uV9P0uNvvQ2bH9lok3z3fiERP6vq/u8T6fl6lT02q6NhV32RitOfjK2xfHK66+hvFwd//+5/VlQZfrtaRw1x5cXTV5+KrTxk/iczUAAPop5hXvv/4efT3DsqzWsumvVGSGrFJJr/RYVANcTXLOiTXpGV1qs35Eoc6dW3RpnEJCP7Eza/DloS4ujfP1xHGNs+fMI/kK7dpUMPyprrZZOWHM1vPnXUDyFdp0+hVXO6vGjVnb1XYbCgv/E4940H/MnjPvsxXmuC5df8IytCNnyJ/jFRP6h/PmXnTj6jGjuvTBXn1KqqpT806JU0joJwJG+glluXldarMtP1+Z1Q1WfCJCf1GVNuhnjUldW4pw1bgx4fPnXLQkTiGhn9ieN3R+x7V2tcIcVz1nzjySr9CRLvctSfPNpTbJV2jTCefadWVjuv75R1WBbohHPOg/5px+3esrzdHVXWkTdLtVNixlXrxiAgAAiUcCVv+1RNIqSZmSnrIsa29Jsiwr3bKsqyVdFK33vz6fr+vzhiMmc+bMXf3h3ns929lEmdq0dK0cO+GqOIeFfuD8ORcteX/fvdd0tv72QYO0OX/ksXEMCf3E2fMunfHJtCmdXt9y7aiRTmXaYE88Y0L/UJucu9fKcWM6/ZjgZ1MmNWbUBH4Qx5DQT2zPHX7IlqFDOl3/g3332nLe3It/H8eQ0E+sGTlublVmVqfqhg1DH+yz939nz5n3YZzDQh93/rwL6j6dOvXuxqTOTVDekJyizydPueP0K67mw2a0a/acea+9v+/0Dzr7y1ZFdrbWDx87M65BoV84d27Rrz+Y7tnR2fqbhhWoLHvYgfGMCf3Gw5Lu70L9f0laFqdY0I/UDNGpFcNV0dn6m/fQW8dcYN8Vz5jQP2yYlHVsWW5up+u/f/Ck1+ecft0LcQwJAAAkGAlY/ZTP52uQZEnaKmlvScsty6qQVKXI0hmGpDt8Pt+9iYtyYDp73qUnvnXAAS/Vp7Q/W3t5do7e22e/G8+bc/Ffeig09HG/uPjq8e/sv8+ajhL8NhUU6JMpntOZ/QqdtTNjSN5yz9QOn+haNW5MeOVoc6/Zc+du64m40LedP3de1ZrC8eNWTBwf6qjup1Mn12/NLczjw2Z0xuw585Z/Zk47bkPh8HbrhQ1D7+279+YzLrm2/YpA1PlzLlrywd7TryrLy2u3XmNSkt46YP93fnnBrw/vmcjQ150795Lz397/QLs2Lb3detUZGXpn//3/du7coqIeCg193MwLLz/g7QP3Xx5wu9uttz0/Xx9Nm37J+XMu+Hu7FYEoI5g09IN99toeltFuvXUjR+iLCVN/MHvO3C97KDT0YdGZrM6V1JnElwck/cJcanf49yRwwrl2xTZT03aO1PaO6m6aoterCnRYT8SFvm/Oade99fFho07aMmRwu/ergi6X3j5syn/OPP93XV56HAAA9C2G4/A5Vn9mWVaBpKslnSxptKQaSR9KutPn8z2ewNAGvLsWL/hRTt3OeyeuXDskr6rym+Nbhg7RmjGjV9ek5J7IDSrE4u5Fd144qHr7jZP8a7Iy6r9dhWndyBHOhhEjPqx3Zx4ze+68nQkMsdcKBAIbJI2UtDE5OXlUouPpbe5ZVHzrkPJtF0zyr0lJCUYmTwwbhlaNGxPeVDD8+czqhpNIkEFX/fOmG42arNRHh2/fevKE1Wvd7nBknoZAUpL8E8YFtuUP/ds5cy+Zm+Aw0QfdtWhhdkq49vmRmzYdPG79hm8+HaxLTdPX5rjasuwh158396IbExljb8V42L7FixeNzwxUPjt2/YY9Crds/eZ4ZVaW/BPH7izPyJ8ze86FDycwRPRRSxYvPCy7fufDE1avLxxc/u2v69vz87Vq3OgN1al5p86eM+/dBIaIPuquxQt+kVuz485JK9fkZtfUfHN84/BhWjdq5Oe1ydnHszxc6xgT23f3ojuuG1y5/ZpJ/jXpaY0N3xxfPWa0s3H48P8EjPTjWZ4esfDP8h4s6QJJP5PU9BRrQJFZrxZIeoOlB9FVz97jdafU6ursLbp40AYNa/ojMZgslY3VZ9VD9P+OnWs/mtAgeynGw/YtfuQPeekVzkNjVu48ZlTp5m8y32vSM+TfY8SGHYWpRbN/dt1jiYwRAAD0DBKwgAT75NNPxzXU162ur63Rps2bf3H6z854MNExoX+4a9HCbBmOZTjhgrDhfmf2nHlvJjqm3o6bCZ2zeNHCQpcROtFwjLSw4Xp19px5nyU6JvQPTz315JH5ebmvynCppqbmoGOOPe69RMeE/mHJ4oX7uZzQkY6M7Y5cT5GI3D7Gw85bsnjhUS4ntE/YMDa6g86/zr3wIpZ3x3f29YoVoyqrKtfXVlepdFPpLGP9+mUkuaM73LVwQbrjcma4HGdk2HC/z6zIHWNM7JwlixYNNRQ6yZAzKGy4X2MJXnSX9W+8bhqD8r+WJGf7tkmjj/wff6JjQv/w7N3eYa6QpshQXditz044167puNXAxXjYeYsfvn5vV8jZ23EZ2w2FXzn/9N82dNwKAAD0FyRgAQlWWlo6SlLTk6ajR4wYsSGR8QADGTcTgMRiTAR6B8ZDILEYD4HegzERSCzGRKB3YDwEAADoHFeiAwAAAAAAAAAAAAAAAACAvooELAAAAAAAAAAAAAAAAACIEQlYAAAAAAAAAAAAAAAAABAjErAAAAAAAAAAAAAAAAAAIEYkYAEAAAAAAAAAAAAAAABAjEjAAgAAAAAAAAAAAAAAAIAYkYA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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all_streams_io_name_df = pd.DataFrame(\n",
+    "#     block_output_data+\n",
+    "#     attn_out_data+mlp_out_data+\n",
+    "#     attn_value_out_data+mlp_act_data+\n",
+    "#     mlp_input_data+block_input_data+attn_input_data\n",
+    "# )\n",
+    "# all_streams_io_name_df.to_csv(\"./tutorial_data/all_streams_io_name_boundless_das_df.csv\")\n",
+    "all_streams_df = pd.read_csv(\"./tutorial_data/all_streams_io_name_boundless_das_df.csv\")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin(\n",
+    "        {\n",
+    "            \"block_output\",\n",
+    "            \"attention_input\",\n",
+    "            \"attention_output\",\n",
+    "            \"attention_value_output\",\n",
+    "        }\n",
+    "    )\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"block_output\": \"Block Output\",\n",
+    "    \"attention_input\": \"Attention Input\",\n",
+    "    \"attention_output\": \"Attention Output\\n(After Head Mixing)\",\n",
+    "    \"attention_value_output\": \"Attention Value Output\\n(Before Head Mixing)\",\n",
+    "}\n",
+    "df.loc[:, \"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df.loc[:, \"IIA_formatted\"] = df[\"IIA\"].map(custom_format)\n",
+    "other_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 1)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Correct IO Name (20) w/ Boundless DAS\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    other_locations_plot,\n",
+    "    filename=\"./tutorial_data/Boundless_DAS_IO_name_other_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "other_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1a271fa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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f9Q5LckxVvakPji3V5+/2fd4zSwSmegcneWK6yl/r2tfO1lo7PfPD03um+/uexIPTfeYk3fKq7+i335W599t4SGsp43+Tl1+wFQAAAAAAQE8FLIbume7LqVn/vF436pfs+c/M/xL1G+kqZp2U7ku0e6X78nyP/vHSqtrRWnvVhLe5frqw01XThXPema4Kyfnpwh73XeLaV2QumPG/6b64PznJVZLcNt0yhMPX8/LMXybsp/01n063PNs1ktwnc1UYfiHdl8S3aa39cLFBVNWN04UMhtVSzkoXDvtS3/c+SW6S7kv6myzQzenplqraK/MrOnw33XJE405ebDxrqV9S6w6DQx+ZdgnF1toFVfX2JL/fHzooXWhn2appU/qzzAVNDknyhHRhvbV0Zrrf/eeT/CDd39S1k9w585dVOyrd7/JvVtD372bu7/V/01UGOiXJvunCX7NV5K6Y5K1VddN0lV4e1x//XH/Nyeneq6PBNfsk2VpVt11qacY+aPTJdMsezvpmuupi304XyLtZumpgs22eVVXntdaGy6Su1vBz4FNr2O+46w22T1+i3S3H9hddCnLMeMWfW6b7OS6qtbatqk7L3M/3vklOnPB+i/V5WlV9I92Slkn3ubTY+/DwRY4tVtVq2P6LE1SxuVu6v+mkWyJzqmqOa+ROY/tLVtOpqqdnfhDl4iQfTDdvnp3k0CQPS1eFL+lCt8dV1R0nqRa0E9w03WfSVdN9fv17kq+kC8bcNMmjklyrb3undFX1lgvavDFzc2jSLb/7jnSfkRenC40eke53/vokr1n9y5ivqq6R7nPixoPDJ6erIvX1dK/vsHS/m9lw66PT/ZvhDxbp89ZJXpe5kN1F6d4Dx6dbarLS/VvpZumWrrzRAt2seV8b6A3p/vZnQ8hL/VttaMtg+32ttR8nP18C9F3p/uZm271ugv7OGNu/X+ZCXQAAAAAAAJcigMXQ3cf2P7YeN+mX7nlD5oevjk7yvNbaJYNjr6iqu6WrXnCN/tiLq+rDrbWvTHCrR/fPH07yqNbaD8bOH73EtX+U7ovLI1tr/7JEu1TVKPPDV5/q7/fdsaYvq6r7pPsiep90lSf+LotUY+irfRyT+eGr1yZ5ZmvtnEWuuW26cNDPf46ttT9N8qcLLEN3eGvtpKVe2zq7y9j+pJV+FvPhzAWwkuSuWacAVmvt41X1oXTLWibJn1XV6yddRmsZn0635NgHWms7FmrQBxjfkS5wkHTvi7e21n404T3+PF347vcXWE7sFX3lndnwx6FJXpauUs2O/po3jl3zyqr688wtA3mrdEGuYxcZ/15J3pq54M9P01VDe/3YZ8BsZbnXpwtiJclfVdV7W2urXg6qrxx2+8GhE1bb5yL3OXjsPkuFqm46tr9twtuMh25umkV+/mNOyNzv4Y4T3ms5x2V+AOvli7Q7vH++JHOBkcMXbHnpc8dNMI5fG2xv2PKDfdj0zweHTk7yiSXa3yrzw1enJ3nw+LJ6VfWcdFUD/6w/dI0kb6iqe7TW2lqMfRWenu53ujXJH7XWzhue7JeN/Y/MvS+eWFXPX+wzrKoelLnPgKRbpu5BrbXvjbU7Ot1n490yeeWklfjHzP1tt3T/jnjx+PJ9VfWX6T7Hn9ofelxVvau19sEF+vzjzAVqv5nkV5aam/tKZ09OFzpbz742RB+Y+nTm5tcbVdUBS1UnrKrrpKsgOestY03ekrkA1p2q6iatta8vM5T/Gdt/VVV9s7X2pWWuAwAAAAAALqMsQcjQsBrRT7NMhY5VeFC6Kj6zXtVa+6vx4EWStNY+2befPXf5JCtZhuwrSUYLhK8m8fsThK/2yvwqG/+b5D4LhK+SJK21j6SrjDHrt6rq5ot0/4R0VZxmvaS19oTFwld9/59vrT1s/EvpXdQvje1/bpX9jV9/hwVbrZ1npfsCPkmumfkhvGl9pLV2l9bazGLhqyRprX0zXTWO2QodV8z86h/LqSRPXiB8NevF6QIOs56U7kv9Jy0Qvpr1wnQV5mb91iLtkuT3Mv/3/8jW2usW+Qw4N8lvZ265ub2T/OUSfa/EDTJ/+dOvrlG/456W+fPtvy7Rdlgp65J0lckmcUoGwcssvrTkuGGY9bYTXrOc4wbbd11o+bWqOiBzQZYvpluONkluU1VXW6D9FTK/itRx420W8OuD7Z0awKqqK1fVzavqqHSv79D+1M+SPL61tlD1wVnPz9wytj9L8mvj4askaa1d3Fp7VrpQ0Ky7JfmNVQ5/LeyR5NjW2pHj4askaa2dle59PRv82TvJby7R37Dq3RlJHrjQPNcvYTdKV0lzTf+N2y9p/JDBoae31p47Hr7qx/HT1trT0i3zOmuxJXYPH2w/Y7lgdGvtxNba4xYJ/K5lXxtpPKS63LK2j83c7/usXHq50Y8kGQa4tkwwhg8l2T7YPyjJ56rqXVX121U10fLUAAAAAADAZYcAFkPXHmyfstCXimvk8YPtHyT5i6Uat9Y+la6KxqxfW8EXX3/SWvvJyoaXJDm+tfbmCdr9Zrol6JIujHNka+2ipS5orf1H5ocBHjfepg8sPGVw6MtJnj3BeDaTa43tT1rpZzHfyfwAyrUXa7gWWmufT7dk5qyjquqaq+xz4r/VPmjwd4NDv7JY2wV8ubX22iX6bukqVA2d0Fr7x4XaL3LNeMAuyc+rTg3/tt/RWvv3pQbbL2X41MGh3+irCq3WDcb2p1oCcylVdbvMf70npFsOdTHDQNi5ywR1fq4P7J0/OHTVxdqOGb7mQ/sKhat13GD7alk4PHH4WPvZa/ZMFyIad6d0AdykC+18coE2P9cv33rDfvfk1toXlmq/Cv9ZVW38kW55vBPTLWd7YN/2i0nu11obD4cMx32dzC1/myT/2FpbrpLfn6ZbsnTWH670RayDn6ULbi6qD5J+fHBosc+MO2f+8rrPW6oaUr/03LMmHunknjrYPr61Nsmys0elqxyYJLevqoXeC8NlWL815djWo6+NdNLY/jUWajSwZbD9jvF/v/ZzyDAM9+iFgqFj11yULgg/rCa3Z7r/KeAtSb5fVadU1bFV9cyq+uU+lA8AAAAAAFxGCWAxtN9g++z1uEFVXTHJPQeH3rZQdYwFDMMie2aysMkZ6SoYTGOS8FUyf/nAj7bWTpyi/3svcP72Sa472H/VpEGMTWS/sf1V/c31AaBhdbDx/tfDX2SugstVM7cU2M7y0cH27VZw3fjyTAsZD6xMcs3nB9vXq6q9F2hz68wPU7x6gn5nA2+zFar2zqWXTJ3Gdcb2T12DPn+uqvZN8vbMLfd7cZInLrM83FUG2ysNj164SD9LGVZ42TtzYaGp9eGYbwwO3XOBZocPtv8z80Nby7X/Ymttuc+LYfWr9y7Tdmf4SLqqc/+5TLv7Z24JuWR+dasF9YGjYbXGe/YVwzbSR1prk1RvG1Y6uvEibYZLSf40yZsm6Pff0lVCWhNVdfV0VQdnTfq5dXrmL6+70Hx/wWD7TgucX4m17Gsj/Xhsf9H5vF+qehimXWyuGh4/KPN/nwtqrb07XTXHxSqEHZSu4tyL0i0ffEpVvbyq1jUADgAAAAAA7JoEsBgaVkyZJBQ1jdtmLoyQJB+c5KK+AsgZg0MLVsoY89m+6sE0LrXc0yKGSylO9Fp6nx1s37SqxsMSdx3bP3YFfW8W4xV6zl+w1cpMUwFoaq21b2R+dbYn7ORliU4fbO+3gtDFZ5dvMq/vaa6pdNWPxt1lsH12Jn+vjY9hLZaY3Hdsf80+9/rw2TsyPxjwor6i31KGv8OVViEcVt+74oTXjL/mhX5n0zhusH34Audnj81Ws5q0/Xjfi9lZAaztSb69wOPkzA/E3SfJJ6rqPVW1VDWf4dx2WmvtSxOO4/2D7b2z/JJt6+1/Jmw3DADuu0ib4Xv9+AnCd7MV4Y6bcAyTuHO6z7RZ0873C31ufXGw/ddV9eC+UuA01rKvjTT+ubTUfH7kYPukJAt+xvZV8IZLrm6ZZCCttXckOSxdNbsfLtP8WumWI/5WVT1mkv4BAAAAAIDdhwAWQ8P/w//K63SPG47tf3kF156wRD8L+fYK+l7xtVV1YJL9B4e+toL+h0GVPXLpqjM3HWyf1Fo7M7uf8YoSa/E3N+xjsYoVa+2vMlep6ApJnrPaDqtqj6q6V1X9bVV9qqq2V9X5CyxzNr7E1KThmUWX7xoYD8SNB7ImuWah3+mtBtvfbK1dskCbxQzHsBZBt2FIaccKx7KoPvDwhnShm1kzSY6e4PJh1avLrfDWlx9sX7hoq/nG211phfdczHGD7bsOl/uqqgMyV+3oi621s1trP8jcZ+htqupqg/ZXyPxqPsO+L6WvVjQbjr0g8yvFrbXfbq0dtsDjOumqkN0qyd9mbnnUX0vy4QVCt7OGc9u08+N4Pxthks+YZP5nxmJzwI0G219ZpM1CJq1IOYnh59YZrbUfreDa5T63XjfYvlq6JUpP6j//H1JV+y9wzWLWsq+NNB64OmehRlV15SQPGxx62zIVBodVsH6j/6xYVmvtB621p6ereHWfJC9I97my2N/BVZO8sap2heVAAQAAAACAnUQAi6FhyGffdbrH+JddZyzYamHDtpN8abaaAM4k145XMXnfeEBmsUcuHXrYd4m+J/0ie7MZD5Xtu5rO+tDLPkv0vy5aa99P8veDQ1uq6kaLtV9OVf1SuqX8PprkSekqRh2YyYIxk1bAWunSdtNes1DlleHf9h0mfc/075s/HVy77xTjGbdjsL3noq1W7m+S/PZg/7gkvzVhRb5h5ZeVLiM3DJRNWs1rfJnIHQu2WrnjBtv7pKt+OOvwwfZwSb7Za/bI/CUm75S5cNlsxaylPCBzlRY/0lqb5m931Vprl7TWvtxae3K6Zcxm/WKS5y5y2XBum3Z+HO9nI6zV50Uy/72+mp/Jagw/t/Zf4efWcAnlfcc7bq39S5LXjx3+hXSf//+e5PSqOqGqXlRVNxm/fr362mD7ju0vNp8/LPOXW11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+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 1200
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1200 x 300)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_streams_df = pd.read_csv(\"./tutorial_data/all_streams_io_name_boundless_das_df.csv\")\n",
+    "all_streams_df[\"IIA\"] = all_streams_df[\"acc\"]\n",
+    "df = all_streams_df[\n",
+    "    all_streams_df[\"stream\"].isin({\"mlp_output\", \"mlp_input\", \"mlp_activation\"})\n",
+    "].copy()\n",
+    "stream_labels = {\n",
+    "    \"mlp_output\": \"MLP Output\",\n",
+    "    \"mlp_input\": \"MLP Input\",\n",
+    "    \"mlp_activation\": \"MLP Activations\",\n",
+    "}\n",
+    "df[\"stream\"] = df[\"stream\"].replace(stream_labels)\n",
+    "\n",
+    "\n",
+    "def custom_format(x):\n",
+    "    return f\"{x:.2f}\"\n",
+    "\n",
+    "\n",
+    "df[\"IIA_formatted\"] = df[\"IIA\"].apply(custom_format)\n",
+    "all_mlp_locations_plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"IIA\", color=\"stream\"))\n",
+    "    + geom_line()\n",
+    "    + geom_point(size=2)\n",
+    "    + geom_text(\n",
+    "        aes(label=\"IIA_formatted\"), nudge_y=0.01, size=8, va=\"bottom\", show_legend=False\n",
+    "    )\n",
+    "    + theme_minimal()\n",
+    "    + ylim(0, 0.72)\n",
+    "    + theme(figure_size=(12, 3))\n",
+    "    + ggtitle(\"Correct IO Name (20) w/ Boundless DAS\")\n",
+    ")\n",
+    "\n",
+    "ggsave(\n",
+    "    all_mlp_locations_plot,\n",
+    "    filename=\"./tutorial_data/Boundless_DAS_IO_name_all_mlp_locations_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "all_mlp_locations_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81ae49c5",
+   "metadata": {},
+   "source": [
+    "### DAS and Boundless DAS weight matrix and head importance\n",
+    "DAS-based methods learn a subspace that is a linear combination of axes in the original basis. Will the learned linear combination (i.e., the learned weights) interpretable by showing head's importance when applied on top of the attention value output stream before the head mixing? In this section, we investigate this question for both DAS as well as Boundless DAS when aligning with the name position variable as well as the IO name variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "97b3dc75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finding name position at: pos->17, layers->8, stream->attention_value_output\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, boundless_das_intervenable = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [8],\n",
+    "    \"attention_value_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    do_boundless_das=True,\n",
+    "    return_intervenable=True,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "b1f867f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       " <defs>\n",
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervention = boundless_das_intervenable.interventions[\n",
+    "    \"layer_8_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
+    "]\n",
+    "boundary_mask = sigmoid_boundary(\n",
+    "    intervention.intervention_population.repeat(1, 1),\n",
+    "    0.0,\n",
+    "    intervention.intervention_boundaries[0] * int(intervention.embed_dim),\n",
+    "    intervention.temperature,\n",
+    ")\n",
+    "learned_weights = (intervention.rotate_layer.weight * boundary_mask)[\n",
+    "    :, : math.ceil(intervention.intervention_boundaries[0] * 768)\n",
+    "]\n",
+    "headwise_learned_weights = torch.chunk(learned_weights, chunks=12, dim=0)\n",
+    "\n",
+    "sampled_weights = [w.cpu().data.flatten().numpy() for w in headwise_learned_weights]\n",
+    "\n",
+    "# Assuming your_list is your 2D list where each sublist is a NumPy array\n",
+    "df = pd.DataFrame(\n",
+    "    [\n",
+    "        (f\"Head {i}\", value)\n",
+    "        for i, sublist in enumerate(sampled_weights)\n",
+    "        for value in sublist\n",
+    "    ],\n",
+    "    columns=[\"Group\", \"Value\"],\n",
+    ")\n",
+    "df[\"Group\"] = pd.Categorical(\n",
+    "    df[\"Group\"], categories=[f\"Head {i}\" for i in range(12)], ordered=True\n",
+    ")\n",
+    "\n",
+    "# Plotting\n",
+    "boundless_das_weight_plot = (\n",
+    "    ggplot(df, aes(x=\"Value\", fill=\"Group\"))\n",
+    "    + geom_histogram(bins=30)\n",
+    "    + facet_wrap(\"~Group\", ncol=3)\n",
+    "    + theme(  # Arrange in a grid with 3 columns\n",
+    "        legend_position=\"none\", subplots_adjust={\"wspace\": 0.25, \"hspace\": 0.25}\n",
+    "    )\n",
+    "    + labs(x=\"Value\", y=\"Count\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    boundless_das_weight_plot,\n",
+    "    filename=\"./tutorial_data/Boundless_DAS_weight_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "print(boundless_das_weight_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "08306c23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finding name position at: pos->17, layers->8, stream->attention_value_output\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, das_intervenable = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [8],\n",
+    "    \"attention_value_output\",\n",
+    "    aligning_variable=\"position\",\n",
+    "    return_intervenable=True,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "ad25e21c",
+   "metadata": {},
+   "outputs": [
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+      "text/plain": [
+       "<Figure size 640x480 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervention = das_intervenable.interventions[\n",
+    "    \"layer_8_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
+    "]\n",
+    "learned_weights = intervention.rotate_layer.weight\n",
+    "headwise_learned_weights = torch.chunk(learned_weights, chunks=12, dim=0)\n",
+    "\n",
+    "sampled_weights = [w.cpu().data.flatten().numpy() for w in headwise_learned_weights]\n",
+    "\n",
+    "df = pd.DataFrame(\n",
+    "    [\n",
+    "        (f\"Head {i}\", value)\n",
+    "        for i, sublist in enumerate(sampled_weights)\n",
+    "        for value in sublist\n",
+    "    ],\n",
+    "    columns=[\"Group\", \"Value\"],\n",
+    ")\n",
+    "df[\"Group\"] = pd.Categorical(\n",
+    "    df[\"Group\"], categories=[f\"Head {i}\" for i in range(12)], ordered=True\n",
+    ")\n",
+    "\n",
+    "# Plotting\n",
+    "das_weight_plot = (\n",
+    "    ggplot(df, aes(x=\"Value\", fill=\"Group\"))\n",
+    "    + geom_histogram(bins=30)\n",
+    "    + facet_wrap(\"~Group\", ncol=3)\n",
+    "    + theme(  # Arrange in a grid with 3 columns\n",
+    "        legend_position=\"none\", subplots_adjust={\"wspace\": 0.25, \"hspace\": 0.25}\n",
+    "    )\n",
+    "    + labs(x=\"Value\", y=\"Count\")\n",
+    ")\n",
+    "ggsave(das_weight_plot, filename=\"./tutorial_data/DAS_weight_plot.pdf\", dpi=200)\n",
+    "print(das_weight_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "3991be4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finding name position at: pos->17, layers->9, stream->attention_value_output\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, boundless_das_intervenable = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [9],\n",
+    "    \"attention_value_output\",\n",
+    "    aligning_variable=\"name\",\n",
+    "    do_boundless_das=True,\n",
+    "    return_intervenable=True,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "e1dbc750",
+   "metadata": {},
+   "outputs": [
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+       "<Figure size 640x480 with 12 Axes>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
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+     "text": [
+      "\n"
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+   ],
+   "source": [
+    "intervention = boundless_das_intervenable.interventions[\n",
+    "    \"layer_9_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
+    "]\n",
+    "boundary_mask = sigmoid_boundary(\n",
+    "    intervention.intervention_population.repeat(1, 1),\n",
+    "    0.0,\n",
+    "    intervention.intervention_boundaries[0] * int(intervention.embed_dim),\n",
+    "    intervention.temperature,\n",
+    ")\n",
+    "learned_weights = (intervention.rotate_layer.weight * boundary_mask)[\n",
+    "    :, : math.ceil(intervention.intervention_boundaries[0] * 768)\n",
+    "]\n",
+    "headwise_learned_weights = torch.chunk(learned_weights, chunks=12, dim=0)\n",
+    "\n",
+    "sampled_weights = [w.cpu().data.flatten().numpy() for w in headwise_learned_weights]\n",
+    "\n",
+    "# Assuming your_list is your 2D list where each sublist is a NumPy array\n",
+    "df = pd.DataFrame(\n",
+    "    [\n",
+    "        (f\"Head {i}\", value)\n",
+    "        for i, sublist in enumerate(sampled_weights)\n",
+    "        for value in sublist\n",
+    "    ],\n",
+    "    columns=[\"Group\", \"Value\"],\n",
+    ")\n",
+    "df[\"Group\"] = pd.Categorical(\n",
+    "    df[\"Group\"], categories=[f\"Head {i}\" for i in range(12)], ordered=True\n",
+    ")\n",
+    "\n",
+    "# Plotting\n",
+    "boundless_das_weight_plot = (\n",
+    "    ggplot(df, aes(x=\"Value\", fill=\"Group\"))\n",
+    "    + geom_histogram(bins=30)\n",
+    "    + facet_wrap(\"~Group\", ncol=3)\n",
+    "    + theme(  # Arrange in a grid with 3 columns\n",
+    "        legend_position=\"none\", subplots_adjust={\"wspace\": 0.25, \"hspace\": 0.25}\n",
+    "    )\n",
+    "    + labs(x=\"Value\", y=\"Count\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    boundless_das_weight_plot,\n",
+    "    filename=\"./tutorial_data/Boundless_DAS_IO_name_weight_plot.pdf\",\n",
+    "    dpi=200,\n",
+    ")\n",
+    "print(boundless_das_weight_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "855cd01e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finding name position at: pos->17, layers->9, stream->attention_value_output\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, das_intervenable = find_variable_at(\n",
+    "    gpt2,\n",
+    "    tokenizer,\n",
+    "    [17],\n",
+    "    [9],\n",
+    "    \"attention_value_output\",\n",
+    "    low_rank_dimension=20,\n",
+    "    aligning_variable=\"name\",\n",
+    "    return_intervenable=True,\n",
+    "    debug=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "3a50ed9d",
+   "metadata": {},
+   "outputs": [
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervention = das_intervenable.interventions[\n",
+    "    \"layer_9_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
+    "]\n",
+    "learned_weights = intervention.rotate_layer.weight\n",
+    "headwise_learned_weights = torch.chunk(learned_weights, chunks=12, dim=0)\n",
+    "\n",
+    "sampled_weights = [w.cpu().data.flatten().numpy() for w in headwise_learned_weights]\n",
+    "\n",
+    "df = pd.DataFrame(\n",
+    "    [\n",
+    "        (f\"Head {i}\", value)\n",
+    "        for i, sublist in enumerate(sampled_weights)\n",
+    "        for value in sublist\n",
+    "    ],\n",
+    "    columns=[\"Group\", \"Value\"],\n",
+    ")\n",
+    "df[\"Group\"] = pd.Categorical(\n",
+    "    df[\"Group\"], categories=[f\"Head {i}\" for i in range(12)], ordered=True\n",
+    ")\n",
+    "\n",
+    "# Plotting\n",
+    "das_weight_plot = (\n",
+    "    ggplot(df, aes(x=\"Value\", fill=\"Group\"))\n",
+    "    + geom_histogram(bins=30)\n",
+    "    + facet_wrap(\"~Group\", ncol=3)\n",
+    "    + theme(  # Arrange in a grid with 3 columns\n",
+    "        legend_position=\"none\", subplots_adjust={\"wspace\": 0.25, \"hspace\": 0.25}\n",
+    "    )\n",
+    "    + labs(x=\"Value\", y=\"Count\")\n",
+    ")\n",
+    "ggsave(das_weight_plot, filename=\"./tutorial_data/DAS_IO_name_weight_plot.pdf\", dpi=200)\n",
+    "print(das_weight_plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d996329f",
+   "metadata": {},
+   "source": [
+    "**Findings:** DAS learned weights map very well to head importance! In the plot above, we can see that Heads 6 and 10 have many non-zero entries, indicating that these neurons contribute significantly to aligning with the name position information. Boundless DAS shows a less salient result, suggesting that it learns a more distributed representation."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.ipynb b/_sources/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.ipynb
new file mode 100644
index 00000000..aa7736c5
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a84a6b93",
+   "metadata": {},
+   "source": [
+    "## IOI with Masked Interventions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85342a5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"01/31/2024\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e15a59bf",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "This tutorial analyzes the IOI task with a new type of trainable intervention, masked interchange intervention"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d32b1cb3",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "213a933b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9decbfe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "380028cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "from tutorial_ioi_utils import *\n",
+    "import pyvene as pv\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    "    scale_y_reverse,\n",
+    "    scale_fill_cmap,\n",
+    "    geom_text,\n",
+    "    scale_fill_gradient,\n",
+    "    geom_point,\n",
+    "    geom_line,\n",
+    "    theme_minimal,\n",
+    "    ylim,\n",
+    "    ggtitle,\n",
+    "    ggsave,\n",
+    "    labs,\n",
+    "    scale_x_discrete,\n",
+    "    geom_histogram,\n",
+    "    scale_fill_manual,\n",
+    ")\n",
+    "\n",
+    "# please try not to do this, the plot somehow throw warnings though :(\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "config, tokenizer, gpt2 = pv.create_gpt2_lm()\n",
+    "_ = gpt2.eval().to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2616301d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_distribution = PromptDistribution(\n",
+    "    names=NAMES[: len(NAMES) // 2],\n",
+    "    objects=OBJECTS[: len(OBJECTS) // 2],\n",
+    "    places=PLACES[: len(PLACES) // 2],\n",
+    "    prefix_len=2,\n",
+    "    prefixes=PREFIXES,\n",
+    "    templates=TEMPLATES[:2],\n",
+    ")\n",
+    "\n",
+    "test_distribution = PromptDistribution(\n",
+    "    names=NAMES[len(NAMES) // 2 :],\n",
+    "    objects=OBJECTS[len(OBJECTS) // 2 :],\n",
+    "    places=PLACES[len(PLACES) // 2 :],\n",
+    "    prefix_len=2,\n",
+    "    prefixes=PREFIXES,\n",
+    "    templates=TEMPLATES[2:],\n",
+    ")\n",
+    "\n",
+    "aligning_variable = \"position\"\n",
+    "aligning_stream = \"block_output\"\n",
+    "aligning_pos = 17\n",
+    "aligning_layer = 8\n",
+    "\n",
+    "batch_size = 20\n",
+    "eval_every = 5\n",
+    "initial_lr = 1e-4\n",
+    "n_epochs = 50\n",
+    "\n",
+    "if aligning_variable == \"name\":\n",
+    "    # we hacky the distribution a little\n",
+    "    train_distribution = PromptDistribution(\n",
+    "        names=NAMES[:20],\n",
+    "        objects=OBJECTS[: len(OBJECTS) // 2],\n",
+    "        places=PLACES[: len(PLACES) // 2],\n",
+    "        prefix_len=2,\n",
+    "        prefixes=PREFIXES,\n",
+    "        templates=TEMPLATES[:2],\n",
+    "    )\n",
+    "\n",
+    "    test_distribution = PromptDistribution(\n",
+    "        names=NAMES[:20],\n",
+    "        objects=OBJECTS[len(OBJECTS) // 2 :],\n",
+    "        places=PLACES[len(PLACES) // 2 :],\n",
+    "        prefix_len=2,\n",
+    "        prefixes=PREFIXES,\n",
+    "        templates=TEMPLATES[2:],\n",
+    "    )\n",
+    "else:\n",
+    "    train_distribution = PromptDistribution(\n",
+    "        names=NAMES[: len(NAMES) // 2],\n",
+    "        objects=OBJECTS[: len(OBJECTS) // 2],\n",
+    "        places=PLACES[: len(PLACES) // 2],\n",
+    "        prefix_len=2,\n",
+    "        prefixes=PREFIXES,\n",
+    "        templates=TEMPLATES[:2],\n",
+    "    )\n",
+    "\n",
+    "    test_distribution = PromptDistribution(\n",
+    "        names=NAMES[len(NAMES) // 2 :],\n",
+    "        objects=OBJECTS[len(OBJECTS) // 2 :],\n",
+    "        places=PLACES[len(PLACES) // 2 :],\n",
+    "        prefix_len=2,\n",
+    "        prefixes=PREFIXES,\n",
+    "        templates=TEMPLATES[2:],\n",
+    "    )\n",
+    "\n",
+    "D_train = train_distribution.sample_das(\n",
+    "    tokenizer=tokenizer,\n",
+    "    base_patterns=[\"ABB\", \"BAB\"],\n",
+    "    source_patterns=[\"ABB\", \"BAB\"]\n",
+    "    if aligning_variable == \"position\"\n",
+    "    else [\"CDD\", \"DCD\"],\n",
+    "    labels=aligning_variable,\n",
+    "    samples_per_combination=50 if aligning_variable == \"position\" else 50,\n",
+    ")\n",
+    "D_test = test_distribution.sample_das(\n",
+    "    tokenizer=tokenizer,\n",
+    "    base_patterns=[\n",
+    "        \"ABB\",\n",
+    "    ],\n",
+    "    source_patterns=[\"BAB\"] if aligning_variable == \"position\" else [\"DCD\"],\n",
+    "    labels=aligning_variable,\n",
+    "    samples_per_combination=50,\n",
+    ") + test_distribution.sample_das(\n",
+    "    tokenizer=tokenizer,\n",
+    "    base_patterns=[\n",
+    "        \"BAB\",\n",
+    "    ],\n",
+    "    source_patterns=[\"ABB\"] if aligning_variable == \"position\" else [\"CDD\"],\n",
+    "    labels=aligning_variable,\n",
+    "    samples_per_combination=50,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "73234357",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:17<00:00,  2.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.58, 'kl_div': tensor(-77.9607, device='cuda:0')}\n",
+      "bits turned on:  tensor(534.7392, device='cuda:0', grad_fn=<SumBackward0>)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calculate_loss_with_mask(logits, labels, intervenable, coeff=1):\n",
+    "    loss = calculate_loss(logits, labels)\n",
+    "    for k, v in intervenable.interventions.items():\n",
+    "        mask_loss = coeff * torch.norm(v.mask, 1)\n",
+    "    loss += mask_loss\n",
+    "    return loss\n",
+    "\n",
+    "config = pv.IntervenableConfig({\n",
+    "    \"component\": aligning_stream, \"layer\": aligning_layer,\n",
+    "    \"intervention_type\": pv.SigmoidMaskIntervention,\n",
+    "    },\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, gpt2)\n",
+    "pv_gpt2.set_device(\"cuda\")\n",
+    "pv_gpt2.disable_model_gradients()\n",
+    "\n",
+    "total_step = 0\n",
+    "optimizer = torch.optim.Adam(\n",
+    "    pv_gpt2.get_trainable_parameters(), lr=initial_lr\n",
+    ")\n",
+    "target_total_step = int(len(D_train) / batch_size) * n_epochs\n",
+    "temperature_start = 1e-2\n",
+    "temperature_end = 1e-7\n",
+    "temperature_schedule = (\n",
+    "    torch.linspace(\n",
+    "        temperature_start, temperature_end, target_total_step\n",
+    "    ).to(torch.bfloat16).to(\"cuda\")\n",
+    ")\n",
+    "pv_gpt2.set_temperature(temperature_schedule[total_step])\n",
+    "scheduler = torch.optim.lr_scheduler.LinearLR(\n",
+    "    optimizer, end_factor=0.1, total_iters=n_epochs\n",
+    ")\n",
+    "\n",
+    "for epoch in tqdm(range(n_epochs)):\n",
+    "    torch.cuda.empty_cache()\n",
+    "    for batch_dataset in D_train.batches(batch_size=batch_size):\n",
+    "        # prepare base\n",
+    "        base_inputs = batch_dataset.base.tokens\n",
+    "        b_s = base_inputs[\"input_ids\"].shape[0]\n",
+    "        for k, v in base_inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                base_inputs[k] = v.to(gpt2.device)\n",
+    "        # prepare source\n",
+    "        source_inputs = batch_dataset.source.tokens\n",
+    "        for k, v in source_inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                source_inputs[k] = v.to(gpt2.device)\n",
+    "        # prepare label\n",
+    "        labels = batch_dataset.patched_answer_tokens[:, 0].to(\n",
+    "            gpt2.device\n",
+    "        )\n",
+    "\n",
+    "        assert all(x == 18 for x in batch_dataset.base.lengths)\n",
+    "        assert all(x == 18 for x in batch_dataset.source.lengths)\n",
+    "        _, counterfactual_outputs = pv_gpt2(\n",
+    "            {\"input_ids\": base_inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": source_inputs[\"input_ids\"]}],\n",
+    "            {\n",
+    "                \"sources->base\": aligning_pos\n",
+    "            },\n",
+    "        )\n",
+    "        eval_metrics = compute_metrics(\n",
+    "            [counterfactual_outputs.logits], [labels]\n",
+    "        )\n",
+    "        loss = calculate_loss_with_mask(\n",
+    "            counterfactual_outputs.logits, labels, pv_gpt2\n",
+    "        )\n",
+    "        loss_str = round(loss.item(), 2)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        scheduler.step()\n",
+    "        pv_gpt2.set_zero_grad()\n",
+    "        pv_gpt2.set_temperature(\n",
+    "            temperature_schedule[total_step]\n",
+    "        )\n",
+    "        total_step += 1\n",
+    "        \n",
+    "# eval\n",
+    "eval_labels = []\n",
+    "eval_preds = []\n",
+    "with torch.no_grad():\n",
+    "    torch.cuda.empty_cache()\n",
+    "    for batch_dataset in D_test.batches(batch_size=batch_size):\n",
+    "        # prepare base\n",
+    "        base_inputs = batch_dataset.base.tokens\n",
+    "        b_s = base_inputs[\"input_ids\"].shape[0]\n",
+    "        for k, v in base_inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                base_inputs[k] = v.to(gpt2.device)\n",
+    "        # prepare source\n",
+    "        source_inputs = batch_dataset.source.tokens\n",
+    "        for k, v in source_inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                source_inputs[k] = v.to(gpt2.device)\n",
+    "        # prepare label\n",
+    "        labels = batch_dataset.patched_answer_tokens[:, 0].to(gpt2.device)\n",
+    "\n",
+    "        assert all(x == 18 for x in batch_dataset.base.lengths)\n",
+    "        assert all(x == 18 for x in batch_dataset.source.lengths)\n",
+    "        _, counterfactual_outputs = pv_gpt2(\n",
+    "            {\"input_ids\": base_inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": source_inputs[\"input_ids\"]}],\n",
+    "            {\n",
+    "                \"sources->base\": aligning_pos\n",
+    "            },\n",
+    "        )\n",
+    "        eval_labels += [labels]\n",
+    "        eval_preds += [counterfactual_outputs.logits]\n",
+    "eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "for k, v in pv_gpt2.interventions.items():\n",
+    "    mask = v.mask\n",
+    "    temperature = v.temperature\n",
+    "    break\n",
+    "print(eval_metrics)\n",
+    "print(\n",
+    "    \"bits turned on: \", \n",
+    "    torch.sigmoid(mask / torch.tensor(temperature)).sum()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "9ba4df72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bit_map = torch.sigmoid(mask / torch.tensor(temperature)).tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "485e7a0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 800x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 800
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the list to a DataFrame\n",
+    "df = pd.DataFrame({'Value': bit_map, 'Index': range(len(bit_map))})\n",
+    "\n",
+    "# Create a barplot\n",
+    "plot = ggplot(df, aes(x='Index', weight='Value')) + geom_bar() + \\\n",
+    "       ggtitle('Bitmap Visualization of Intervening Dimensions') + \\\n",
+    "       theme(figure_size=(8,2))\n",
+    "\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "968049e5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/advanced_tutorials/Interventions_with_BLIP.ipynb b/_sources/tutorials/advanced_tutorials/Interventions_with_BLIP.ipynb
new file mode 100644
index 00000000..6f895660
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/Interventions_with_BLIP.ipynb
@@ -0,0 +1,1197 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "92f4cb49-4783-4df0-a4e8-e4b6f6604053",
+   "metadata": {},
+   "source": [
+    "# Intervening on Vision-Language Models\n",
+    "\n",
+    "This is a quick tutorial for running interventions on the decoder component of BLIP for visual question answering (support for the text/image encoders TBD). This is partially based on an earlier paper I was on, [\"Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP\" (Palit et al., 2023)](https://arxiv.org/abs/2308.14179)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5b0594d1-5c40-4c73-9e44-eaff19c2d683",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Aryaman Arora\"\n",
+    "__version__ = \"12/27/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f5c64d-5591-47b2-9ae7-e479470379c5",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "de032ff0-cbb7-4b9a-8f60-27e937e3f39c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a89edbd1-0821-4d1e-b1e1-3694451d3733",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    VanillaIntervention, Intervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")\n",
+    "from pyvene import create_blip\n",
+    "from pyvene.models.blip.modelings_blip import BlipWrapper\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    ")\n",
+    "from plotnine.scales import scale_y_reverse, scale_fill_cmap\n",
+    "from tqdm import tqdm\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "from functools import partial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "623307c7-3237-46eb-b74c-65e214203140",
+   "metadata": {},
+   "source": [
+    "## Load model and test inference\n",
+    "\n",
+    "We'll load BLIPForQuestionAnswering and use a special `BlipWrapper` to enable easy access of decoder logits; this doesn't modify the model's computations in any way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8d6ebae3-6916-40c9-8775-928e368043c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "BlipWrapper(\n",
+       "  (model_vis): BlipVisionModel(\n",
+       "    (embeddings): BlipVisionEmbeddings(\n",
+       "      (patch_embedding): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
+       "    )\n",
+       "    (encoder): BlipEncoder(\n",
+       "      (layers): ModuleList(\n",
+       "        (0-11): 12 x BlipEncoderLayer(\n",
+       "          (self_attn): BlipAttention(\n",
+       "            (dropout): Dropout(p=0.0, inplace=False)\n",
+       "            (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
+       "            (projection): Linear(in_features=768, out_features=768, bias=True)\n",
+       "          )\n",
+       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): BlipMLP(\n",
+       "            (activation_fn): GELUActivation()\n",
+       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "          )\n",
+       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (post_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+       "  )\n",
+       "  (model_text_enc): BlipTextModel(\n",
+       "    (embeddings): BlipTextEmbeddings(\n",
+       "      (word_embeddings): Embedding(30524, 768, padding_idx=0)\n",
+       "      (position_embeddings): Embedding(512, 768)\n",
+       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "      (dropout): Dropout(p=0.0, inplace=False)\n",
+       "    )\n",
+       "    (encoder): BlipTextEncoder(\n",
+       "      (layer): ModuleList(\n",
+       "        (0-11): 12 x BlipTextLayer(\n",
+       "          (attention): BlipTextAttention(\n",
+       "            (self): BlipTextSelfAttention(\n",
+       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (dropout): Dropout(p=0.0, inplace=False)\n",
+       "            )\n",
+       "            (output): BlipTextSelfOutput(\n",
+       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "              (dropout): Dropout(p=0.0, inplace=False)\n",
+       "            )\n",
+       "          )\n",
+       "          (crossattention): BlipTextAttention(\n",
+       "            (self): BlipTextSelfAttention(\n",
+       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (dropout): Dropout(p=0.0, inplace=False)\n",
+       "            )\n",
+       "            (output): BlipTextSelfOutput(\n",
+       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "              (dropout): Dropout(p=0.0, inplace=False)\n",
+       "            )\n",
+       "          )\n",
+       "          (intermediate): BlipTextIntermediate(\n",
+       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "            (intermediate_act_fn): GELUActivation()\n",
+       "          )\n",
+       "          (output): BlipTextOutput(\n",
+       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "            (dropout): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (model_text_dec): BlipTextLMHeadModel(\n",
+       "    (bert): BlipTextModel(\n",
+       "      (embeddings): BlipTextEmbeddings(\n",
+       "        (word_embeddings): Embedding(30524, 768, padding_idx=0)\n",
+       "        (position_embeddings): Embedding(512, 768)\n",
+       "        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "        (dropout): Dropout(p=0.0, inplace=False)\n",
+       "      )\n",
+       "      (encoder): BlipTextEncoder(\n",
+       "        (layer): ModuleList(\n",
+       "          (0-11): 12 x BlipTextLayer(\n",
+       "            (attention): BlipTextAttention(\n",
+       "              (self): BlipTextSelfAttention(\n",
+       "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (dropout): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (output): BlipTextSelfOutput(\n",
+       "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "                (dropout): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "            )\n",
+       "            (crossattention): BlipTextAttention(\n",
+       "              (self): BlipTextSelfAttention(\n",
+       "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (dropout): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (output): BlipTextSelfOutput(\n",
+       "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "                (dropout): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "            )\n",
+       "            (intermediate): BlipTextIntermediate(\n",
+       "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "              (intermediate_act_fn): GELUActivation()\n",
+       "            )\n",
+       "            (output): BlipTextOutput(\n",
+       "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "              (dropout): Dropout(p=0.0, inplace=False)\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (cls): BlipTextOnlyMLMHead(\n",
+       "      (predictions): BlipTextLMPredictionHead(\n",
+       "        (transform): BlipTextPredictionHeadTransform(\n",
+       "          (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "          (transform_act_fn): GELUActivation()\n",
+       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "        )\n",
+       "        (decoder): Linear(in_features=768, out_features=30524, bias=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
+    "config, processor, blip = create_blip(\n",
+    "    cache_dir=\"/nlp/scr/aryaman/.cache/huggingface/hub\"  # change to your local dir\n",
+    ")\n",
+    "blip = BlipWrapper(blip)\n",
+    "blip.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceacfe2a-6059-4b20-ab6a-a036340a4ae6",
+   "metadata": {},
+   "source": [
+    "Now testing some QA on a simple image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "21595cd9-c753-4acc-9129-590c817102d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=640x427>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img_url = \"http://images.cocodataset.org/train2017/000000458864.jpg\"\n",
+    "raw_image = Image.open(requests.get(img_url, stream=True).raw).convert(\"RGB\")\n",
+    "raw_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c94c1c2f-d051-4606-9a1f-9b2015fac033",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "what is the color of the shirt \n",
+      "----\n",
+      "torch.Size([1, 9])\n",
+      "torch.Size([1, 1, 30524])\n",
+      "red                        2417   10.64493     61.74%\n",
+      "white                      2317    9.11390     13.35%\n",
+      "pink                       5061    8.10488      4.87%\n",
+      "green                      2665    7.13710      1.85%\n",
+      "blue                       2630    6.71536      1.21%\n",
+      "gray                       3897    6.63115      1.12%\n",
+      "black                      2304    6.52738      1.01%\n",
+      "brown                      2829    6.42861      0.91%\n",
+      "maroon                    22222    6.03666      0.62%\n",
+      "multi                      4800    5.35350      0.31%\n",
+      "what is the color of the animal \n",
+      "----\n",
+      "torch.Size([1, 9])\n",
+      "torch.Size([1, 1, 30524])\n",
+      "brown                      2829   11.42929     88.94%\n",
+      "red                        2417    6.36936      0.56%\n",
+      "reddish                   14182    5.54194      0.25%\n",
+      "dark                       2601    5.49398      0.24%\n",
+      "tan                        9092    5.24055      0.18%\n",
+      "light                      2422    5.15968      0.17%\n",
+      "brownish                  19437    4.64057      0.10%\n",
+      "orange                     4589    4.52101      0.09%\n",
+      "rust                      18399    4.26794      0.07%\n",
+      "it                         2009    4.12371      0.06%\n"
+     ]
+    }
+   ],
+   "source": [
+    "def top_vals(tokenizer, res, n=10):\n",
+    "    \"\"\"Pretty print the top n values of a distribution over the vocabulary\"\"\"\n",
+    "    probs = res.softmax(-1)\n",
+    "    top_values, top_indices = torch.topk(res, n)\n",
+    "    for i in range(len(top_values)):\n",
+    "        tok = format_token(tokenizer, top_indices[i].item())\n",
+    "        print(\n",
+    "            f\"{tok:<20} {top_indices[i]:>10} {top_values[i]:>10.5f} {probs[top_indices[i]]:>10.2%}\"\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "question = \"what is the color of the shirt\"\n",
+    "print(question, \"\\n----\")\n",
+    "inputs = processor(raw_image, question, return_tensors=\"pt\").to(device)\n",
+    "print(inputs[\"input_ids\"].shape)\n",
+    "\n",
+    "out = blip(**inputs)\n",
+    "print(out[\"decoder_logits\"].shape)\n",
+    "top_vals(processor, out[\"decoder_logits\"][0, 0])\n",
+    "\n",
+    "question = \"what is the color of the animal\"\n",
+    "print(question, \"\\n----\")\n",
+    "inputs = processor(raw_image, question, return_tensors=\"pt\").to(device)\n",
+    "print(inputs[\"input_ids\"].shape)\n",
+    "\n",
+    "out = blip(**inputs)\n",
+    "print(out[\"decoder_logits\"].shape)\n",
+    "top_vals(processor, out[\"decoder_logits\"][0, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39385935-31a7-482a-8a80-955ca4184a3e",
+   "metadata": {},
+   "source": [
+    "## Vanilla intervention\n",
+    "\n",
+    "Let's see at what transformer blocks and position we can swap activations in order to make the model tell the colour of the shirt instead of the animal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "522ef638-f410-4f0d-b10d-9ec74522525e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simple_position_config(model_type, intervention_type, layer):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                layer,  # layer\n",
+    "                intervention_type,  # intervention type\n",
+    "                \"pos\",  # intervention unit\n",
+    "                1,  # max number of unit\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=VanillaIntervention,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "base = processor(raw_image, \"what is the color of the animal\", return_tensors=\"pt\").to(\n",
+    "    device\n",
+    ")\n",
+    "sources = [\n",
+    "    processor(raw_image, \"what is the color of the shirt\", return_tensors=\"pt\").to(\n",
+    "        device\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ada2d0a6-c33c-43be-8787-98fd2fbaff8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:15<00:00,  1.32s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = []\n",
+    "with torch.inference_mode():\n",
+    "    for layer_i in tqdm(range(12)):\n",
+    "        for pos_i in range(9):\n",
+    "            config = simple_position_config(\n",
+    "                type(blip), \"block_output\", layer_i\n",
+    "            )\n",
+    "            intervenable = IntervenableModel(config, blip)\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                base, sources, {\"sources->base\": ([[[pos_i]]], [[[pos_i]]])}\n",
+    "            )\n",
+    "            logits = counterfactual_outputs[\"decoder_logits\"][0, 0]\n",
+    "            # top_vals(processor, logits, n=1)\n",
+    "            logits = logits.softmax(-1)\n",
+    "            p_brown = logits[2829]\n",
+    "            p_red = logits[2417]\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"layer\": layer_i,\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"p(brown)\": p_brown.item(),\n",
+    "                    \"p(red)\": p_red.item(),\n",
+    "                }\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "43ecf650-b39b-448b-a6fd-76a09cf992b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data)\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"pos\"] = df[\"pos\"].astype(int)\n",
+    "df[\"p(brown)\"] = df[\"p(brown)\"].astype(float)\n",
+    "df[\"p(red)\"] = df[\"p(red)\"].astype(float)\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"pos\"))\n",
+    "    + scale_y_reverse()\n",
+    "    + geom_tile(aes(fill=\"p(red)\"))\n",
+    "    + scale_fill_cmap(\"Purples\")\n",
+    ")\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ab50f5-5720-4c89-8046-271de338fc87",
+   "metadata": {},
+   "source": [
+    "## Causal tracing\n",
+    "\n",
+    "This replicates the Gaussian interventions from [\"Locating and Editing Factual Associations in GPT\" (Meng et al., 2023)](https://arxiv.org/abs/2202.05262) that were also used on BLIP in the paper I linked at the start, i.e. this is a full port into pyvene of the original code for causal tracing on BLIP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "23d522e1-2d6c-4e0a-b67c-3208210a7df6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NoiseIntervention(Intervention):\n",
+    "    def __init__(self, embed_dim, **kwargs):\n",
+    "        super().__init__()\n",
+    "        self.interchange_dim = None\n",
+    "        self.embed_dim = embed_dim\n",
+    "        self.noise_level = 1.0\n",
+    "\n",
+    "    def set_interchange_dim(self, interchange_dim):\n",
+    "        self.interchange_dim = interchange_dim\n",
+    "\n",
+    "    def set_noise_level(self, noise_level):\n",
+    "        self.noise_level = noise_level\n",
+    "\n",
+    "    def forward(self, base, source):\n",
+    "        # sample gaussian noise\n",
+    "        mean = torch.zeros_like(base[..., : self.interchange_dim])\n",
+    "        stdev = torch.ones_like(base[..., : self.interchange_dim]) * self.noise_level\n",
+    "        noise = torch.normal(mean, stdev)\n",
+    "\n",
+    "        # interchange\n",
+    "        base[..., : self.interchange_dim] += noise\n",
+    "\n",
+    "        return base\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return f\"NoiseIntervention(embed_dim={self.embed_dim})\"\n",
+    "\n",
+    "\n",
+    "def make_noise_intervention(noise_level):\n",
+    "    def func(args, proj_dim, subspace_partition):\n",
+    "        intervention = NoiseIntervention(args)\n",
+    "        intervention.set_noise_level(noise_level)\n",
+    "        return intervention\n",
+    "\n",
+    "    return func\n",
+    "\n",
+    "\n",
+    "def corrupted_config(model_type, noise_level):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                0,  # layer\n",
+    "                \"block_input\",  # intervention type\n",
+    "                \"pos\",  # intervention unit\n",
+    "                1,  # max number of unit\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=make_noise_intervention(noise_level),\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "def restore_corrupted_config(model_type, layer, noise_level):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                0,  # layer\n",
+    "                \"block_input\",  # intervention type\n",
+    "                \"pos\",  # intervention unit\n",
+    "                1,  # max number of unit\n",
+    "            ),\n",
+    "            RepresentationConfig(\n",
+    "                layer,  # layer\n",
+    "                \"block_output\",  # intervention type\n",
+    "                \"pos\",  # intervention unit\n",
+    "                1,  # max number of unit\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=[\n",
+    "            make_noise_intervention(noise_level),\n",
+    "            VanillaIntervention,\n",
+    "        ],\n",
+    "        # mode='serial'\n",
+    "    )\n",
+    "    return config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f15d55-e975-4fcf-9b33-d58a5de1b648",
+   "metadata": {},
+   "source": [
+    "### Varying levels of noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a303239e-2d45-46fa-b319-ad9a23a25b17",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 88.95%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 59.44%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.79s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 11.18%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.79s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 5.78%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 4.00%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.79s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 5.99%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob: 4.40%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "base = inputs\n",
+    "data = []\n",
+    "with torch.inference_mode():\n",
+    "    for noise_level in [0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0]:\n",
+    "        for layer_i in tqdm(range(12)):\n",
+    "            for pos_i in range(9):\n",
+    "                config = restore_corrupted_config(\n",
+    "                    type(blip), layer_i, noise_level=noise_level\n",
+    "                )\n",
+    "                intervenable = IntervenableModel(config, blip)\n",
+    "                _, counterfactual_outputs = intervenable(\n",
+    "                    base,\n",
+    "                    [base, base],\n",
+    "                    {\n",
+    "                        \"sources->base\": (\n",
+    "                            [\n",
+    "                                [[0, 1, 2, 3, 4, 5, 6, 7, 8]],\n",
+    "                                [\n",
+    "                                    [\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                    ]\n",
+    "                                ],\n",
+    "                            ],\n",
+    "                            [\n",
+    "                                [[0, 1, 2, 3, 4, 5, 6, 7, 8]],\n",
+    "                                [\n",
+    "                                    [\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                    ]\n",
+    "                                ],\n",
+    "                            ],\n",
+    "                        )\n",
+    "                    },\n",
+    "                )\n",
+    "                logits = counterfactual_outputs[\"decoder_logits\"][0, 0].softmax(-1)\n",
+    "                p_brown = logits[2829]\n",
+    "                data.append(\n",
+    "                    {\n",
+    "                        \"layer\": layer_i,\n",
+    "                        \"pos\": pos_i,\n",
+    "                        \"p(brown)\": p_brown.item(),\n",
+    "                        \"noise_level\": noise_level,\n",
+    "                    }\n",
+    "                )\n",
+    "        df = pd.DataFrame(data)\n",
+    "        avg_p = df[df[\"noise_level\"] == noise_level][\"p(brown)\"].mean()\n",
+    "        print(f\"average prob: {avg_p:.2%}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "28b08515-7994-41bf-b665-a315e038881d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 640x480 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data)\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"pos\"] = df[\"pos\"].astype(int)\n",
+    "df[\"p(brown)\"] = df[\"p(brown)\"].astype(float)\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"pos\"))\n",
+    "    + scale_y_reverse()\n",
+    "    + geom_tile(aes(fill=\"p(brown)\"))\n",
+    "    + scale_fill_cmap(\"Purples\")\n",
+    "    + facet_wrap(\"noise_level\")\n",
+    ")\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99c57ecc-f4c4-459b-a391-7c8fde5818b4",
+   "metadata": {},
+   "source": [
+    "### Sampling noise and running repeatedly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4b729f90-4840-47c2-ac2e-1f5aa19b05bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 9.13%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 7.91%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 7.51%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 8.36%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.79s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 9.84%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 8.33%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 8.20%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 7.92%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 8.24%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21<00:00,  1.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average prob so far: 8.59%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "base = inputs\n",
+    "data = []\n",
+    "with torch.inference_mode():\n",
+    "    for i in range(10):\n",
+    "        for layer_i in tqdm(range(12)):\n",
+    "            for pos_i in range(9):\n",
+    "                config = restore_corrupted_config(\n",
+    "                    type(blip), layer_i, noise_level=1.0\n",
+    "                )\n",
+    "                intervenable = IntervenableModel(config, blip)\n",
+    "                _, counterfactual_outputs = intervenable(\n",
+    "                    base,\n",
+    "                    [base, base],\n",
+    "                    {\n",
+    "                        \"sources->base\": (\n",
+    "                            [\n",
+    "                                [[0, 1, 2, 3, 4, 5, 6, 7, 8]],\n",
+    "                                [\n",
+    "                                    [\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                    ]\n",
+    "                                ],\n",
+    "                            ],\n",
+    "                            [\n",
+    "                                [[0, 1, 2, 3, 4, 5, 6, 7, 8]],\n",
+    "                                [\n",
+    "                                    [\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                        pos_i,\n",
+    "                                    ]\n",
+    "                                ],\n",
+    "                            ],\n",
+    "                        )\n",
+    "                    },\n",
+    "                )\n",
+    "                logits = counterfactual_outputs[\"decoder_logits\"][0, 0].softmax(-1)\n",
+    "                p_brown = logits[2829]\n",
+    "                data.append(\n",
+    "                    {\n",
+    "                        \"layer\": layer_i,\n",
+    "                        \"pos\": pos_i,\n",
+    "                        \"p(brown)\": p_brown.item(),\n",
+    "                        \"iteration\": i,\n",
+    "                    }\n",
+    "                )\n",
+    "        df = pd.DataFrame(data)\n",
+    "        avg_p = df[df[\"iteration\"] == i][\"p(brown)\"].mean()\n",
+    "        print(f\"average prob so far: {avg_p:.2%}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a8e5abf3-fc91-4feb-9f9e-18844460011c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 640x480 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data)\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"pos\"] = df[\"pos\"].astype(int)\n",
+    "df[\"p(brown)\"] = df[\"p(brown)\"].astype(float)\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"pos\"))\n",
+    "    + scale_y_reverse()\n",
+    "    + geom_tile(aes(fill=\"p(brown)\"))\n",
+    "    + scale_fill_cmap(\"Purples\")\n",
+    "    + facet_wrap(\"iteration\")\n",
+    ")\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "796417fa-3192-4013-843e-2c20dc53b7c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     layer  pos  p(brown)  iteration\n",
+      "0        0    0  0.008152        4.5\n",
+      "1        0    1  0.015465        4.5\n",
+      "2        0    2  0.022314        4.5\n",
+      "3        0    3  0.003820        4.5\n",
+      "4        0    4  0.025465        4.5\n",
+      "..     ...  ...       ...        ...\n",
+      "103     11    4  0.193646        4.5\n",
+      "104     11    5  0.137404        4.5\n",
+      "105     11    6  0.209949        4.5\n",
+      "106     11    7  0.234223        4.5\n",
+      "107     11    8  0.048529        4.5\n",
+      "\n",
+      "[108 rows x 4 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MRhttlAsvvLDocVjFpZdemiS59tprc9999/XqvgVAAAAAgH7g1VdfzRe/+MUkyTnnnJPW1taCJ2JVRxxxRA477LBUq9V86lOf6tV9C4AAAAAA/cBXvvKVzJkzJ0OGDMnHP/7xosehE+eff36S5N57782vf/3rXtuvAAgAAABQcvPnz8+3vvWtJMk73vGObLzxxgVPRGfe+ta3ZuzYsUlWBNveIgACAAAAlNz111+f1157LUly5plnFjsMXWpqasr/+l//K0lyxx135Kmnnuqd/fbKXgAAAADoNb/97W877sZ70UUXJUmeeuqpnHPOOdlll10ybNiwtLa25qCDDsrXvva1tLW1rXV/3/ve95IkG2+8cY/v7jt79ux84QtfyH777ZfRo0dn2LBh2X333fPJT34yL7300lpfe9ZZZ3Ucx/PPP58k+a//+q+ccsop2XbbbTN48ODVnlvVT37yk7zrXe/Kdtttl5aWlowcOTK77bZbPvzhD+ePf/xjl+/5pS99qeM9b7/99k63efrppzu2qVQqueGGGzrdbsaMGWlqakqlUsk73vGO1Z57/vnnO16/8s69r732Wr74xS9mv/32y0YbbbTaz9WMGTPW+nO10qmnntrx+Pvf/363XrMuA3plLwAAAADUzH/+53/m7LPPzsKFCzu+t3Dhwtx///25//77c9VVV+XWW2/Ntttuu8Zrp0+fnv/+7/9OkhxyyCFpbm7u9vs+9thjmTBhQl544YXVvv/EE0/kiSeeyNVXX50bb7wxf/d3f7fOfS1ZsiSnnXZabr755rVuN3PmzJx66qm555571njuySefzJNPPpmrrroqf//3f59vfOMbaxzPUUcd1fH4N7/5Td7ylressZ/f/OY3a/z43e9+9xrb3XXXXalWq0mS8ePHr3XuP/3pTzn55JMzZcqU1b6/8ufquuuuyx133JFx48atdT/77LNPNtpoo7z22mv58Y9/3HHjlg0hAAIAAADUsT/+8Y+55JJLsnTp0rznPe/J0UcfnaFDh+bxxx/Pd7/73UyfPj1PPvlkjjrqqPz5z3/OqFGjVnv9qjeTOOigg7r9vnPmzMmJJ56YF154IYcffnhOO+20bLbZZpkyZUquv/76PPTQQ3nttddy0kkn5e67784BBxyw1v2de+65+dWvfpVtt902733ve7Prrrtm8eLFeeCBBzJ48OAkK65VePjhh+fJJ59MkmyyySZ53/vel7333jtLlizJ3XffnYkTJ2bp0qW58sorM3fu3Fx33XWrvc/++++fkSNHZu7cuWuEvpU6C4Dr2m5tAXDq1Kk5/vjjO+LlW97ylowePTrPP/98rrrqqkyePDkvv/xy3vWud+Whhx7KwIEDu9xXpVLJgQcemNtuuy1PPfVUnn/++Wy33XZdbt8dAiAAAABAHfv5z3+elpaW3HrrrWucvvtP//RPOf7443Pfffflueeey/nnn59vf/vbq21z//33dzx+05ve1O33feihh5KsOKX2U5/61GrPfeITn8i5556bK664IosXL85ZZ52VRx99NE1NXV9t7le/+lVOOumk3HDDDRkyZEjH988+++yOx+eff35H/Nt///1z6623ZsyYMR3Pn3XWWfnIRz6St7zlLZk9e3YmTpyYE044Ie985zs7tmlubs7hhx+en//85/njH/+YOXPmrBFFf/vb3yZJDj744PzhD3/Is88+mxdeeGGNFZR33XVXkmSrrbbKLrvs0uWx/eY3v8mIESNy11135fDDD1/tub//+7/PYYcdloceeiiTJk3KLbfcklNOOaXLfSXJm9/85tx2221Jkv/+7//e4ADoGoAAAAAAde6SSy7p9Np9G220UX70ox9l+PDhSVZc62/mzJmrbfPoo492PN5111179L6nnHLKGvEvWRHZLr/88rzxjW9MkkyaNCk///nP17qvrbbaKtddd91q8W9VM2fOzHe/+90kSUtLS26++ebV4t9K+++/f8cdjZMVPzevt3K1Xnt7e373u9+t9txjjz3WcT2+z3zmMx2r8V6/CvDFF1/M008/nWT104q7cvnll68R/5Jk+PDh+bd/+7eOH//qV79a57522223jsePPPLIOrdfFwEQAAAAoI5ttNFG+eAHP9jl81tttVVOP/30JElbW1tuueWW1Z5f9QYbo0eP7tF7dxb/Vmpqasp5553X8eObbrpprfs6++yzO0JlZ375y19m8eLFSZJ3vetdnV7PcKV3vvOd2XHHHZMkf/7zn/Pcc8+t9vyqp+t2dbrvsGHDMn78+Bx44IFr3e71++vMmDFjOu7e25mjjjoqAwasOBH3scceW+u+ktV/nTq7QUpPCYAAAAAAdezQQw/tctXcSsccc0zH4wceeGC151599dUkK1bVrWs/qxo5cmRHHFuf9329ww47bK3Pr3qq8rHHHrvWbSuVymrbrLzJyUp77bVXNt544yTJnXfeudpzK8PeYYcdloEDB3bEvZWn+75+u2TdAfCAAw7oCHydGTx4cMdqxtmzZ691X0k6Zk/+59dvQwiAAAAAAHVsp5126tE206ZNW+25tra2JMmIESN69L477rhjKpXKWrcZM2ZMNtpoo07f9/W23nrrtT4/ffr0jsc777zzOudbdZtVX5usCIQrT5l+/PHHO075Xb58ee6+++4k/xP1Vp7e+9e//jVPPfVUxz5WBsEddthhrasRk3R6qvLrrbzRycpVjmszcuTIjseLFi1a5/brIgACAAAA1LFhw4b1aJt58+at9tzK8DR37txef99Vt5s/f/5atxs6dOhan1917u6896qnE7/+mJP/CXzVarUj5v35z3/uWIG38vmDDz64Y2XkylV/zzzzTKZMmbLadmuztpufrI85c+Z0PF7Xz1t3CIAAAAAAdWzBggU92ub1K/1Wnk66aNGibq0+68n7rrrd2q7v1x2rzt2d9141OHa2unHVG3esPA14ZeBrbW3Nvvvum2RFID344INXe37V03+7cwOQ3vbKK690PO7pdRs7IwACAAAA1LHJkyf3aJstt9xytee23377jsc9uZ7cM888k2q1utZtXnnllbz22mudvm9PbbHFFh2PV959d23+8pe/dDzu7L132223jn2+PuwdeeSRq63aWxn5fvvb36Zarfbo+n+1sOqv03bbbbfB+xMAAQAAAOrYPffc03Edv67ccccdHY/f9KY3rfbcnnvu2fH4iSee6Pb7zp07d5039ljb+/bUqq+/7bbb1rn97bffvs73Xhn2nnnmmTzzzDO59957k6wZ9Vb+eNasWXnkkUfy29/+NsmKiLj55pt3/yB6yaRJkzoe77333hu8PwEQAAAAoI699tprufrqq7t8fvr06bn++uuTrDiddcKECas9f9BBB3U8XvVOu93xla98pcvnli9fnssuu6zjx6eddlqP9v16b3vb2zquxXfjjTfmhRde6HLbH/3oRx2rHvfdd9/VVjmuatXTdy+55JKO04ZfHwAPPPDAjlOYr7jiirz00kudbtdXVr2r8aq/futLAAQAAACoc+eff37H3WtXNXfu3Lzzne/suAnG+973vmyyySarbXPsscd23M23pwHwpptuWi3yrbR8+fL84z/+Y8cKwT322CNve9vberTv1xszZkze//73J0kWLlyY0047bbVr4a305z//OR/+8Ic7fvzP//zPXe5z1YD3ve99L8mKU41333331bYbMGBADjvssNW2e/3r+0q1Wu34ed1ll13WeQfi7hiwwXsAAAAAoGYmTJiQ22+/PePHj8//9//9fzn66KMzdOjQTJo0Kf/3//7fTJs2LcmKa/196UtfWuP1m222WQ455JDce++9uffee7N06dIMHDhwne+7zz77ZO7cuTnvvPPys5/9LKeddlo23XTTTJ06Nddff33+/Oc/J1mx6vCaa67plTvhXnLJJbnzzjvz5JNP5v/9v/+X3XbbLe9///uz1157ZcmSJbnnnnty3XXXZcmSJUmSM844I+94xzu63N8OO+yQbbfdNi+88EKWLVuWpOubehx11FH51a9+1bFdU1NTjjzyyA0+pp56+OGHO+4CfPLJJ/fKPgVAAAAAgDq2//7753/9r/+V973vfbn++us7Tvdd1S677JJbb701I0eO7HQf733ve3Pvvffm1VdfzS9+8YucdNJJ63zfUaNG5brrrssJJ5yQ3/3ud/nd737X6Tb/+Z//mQMOOKDHx9WZ4cOH5+67784pp5ySe++9NzNnzswll1yyxnaVSiUf/vCH8+///u/r3OdRRx3VrVV9r//+3nvv3St34O2pm2++uePxmWee2Sv7dAowAAAAQJ175zvfmT/96U/5yEc+kp122iktLS0ZNWpUDjzwwHz1q1/Nww8/vNa7xb7nPe9Ja2trkuTaa6/t9vuOGzcuf/7zn/P5z38+++67bzbaaKMMHTo0u+yyS84777w88cQT+bu/+7sNPbzVbLLJJrnnnnty88035x3veEe22WabDBkyJMOHD8/OO++cD33oQ3nggQdy5ZVXprm5eZ376+qGH6+37777dvwcrW27Wlq+fHmuu+66JMnRRx+dXXfdtVf2W6mu637O1I1Zs2YVPUKPtAzt/E8dWNOStmVFj9AQhg0fXPQIDcPfU92zdGl70SM0DJ+/7lm+fHnRIzSMRx59uegRGsKkR6cXPULD2H3PLYoeoWF8/e9/VvQIDWHpgqVFj9Awrrzn9KJHWE1ra2uam5vT3t6e2bNnr/H8mDFjCpiq53772992nKr62c9+NhdddNEG7/PCCy/MF77whQwaNCgvvvjiGtcKpHi//OUvO66l+Ktf/SrHHXdcr+zXCkAAAACAfuC8887LqFGjsmTJklx66aVFj0MnVl7D8ZBDDum1+JcIgAAAAAD9Qmtraz796U8nSb75zW/mpZdeKngiVnX33Xfn7rvvTqVS6fVAKwACAAAA9BOf+MQnsuuuu2bRokX54he/WPQ4rOKTn/xkkhU3bDn44IN7dd/uAgwAAADQTwwcODBPPPFE0WPQifvvv79m+7YCEAAAAABKzApAAAAAgDpz5JFHplqtFj0GJWEFIAAAAACUmAAIAAAAACXmFGAAAACAOjDp/qkZMLC56DEKsWDO4uxx8DYZNHhg0aOUkgAIAAAAULBnHnkp//Dm7xQ9RqFOOedN+djlbyt6jFISAAEAAAAKtnDeklQqlTWf6Ff3Aenk+OkVAiAAAABAPeisf73+e2UKgq8/Nv2vZgRAAAAAgIJVUu1e/+pyo3qsZz2rlfV4BGXRLwLgzJkzc9999+WRRx7J888/n1dffTUDBgzIJptskn322ScnnHBCNt988x7v9+WXX84HP/jBdW53/vnn55BDDlmf0QEAAIB+oJpK0tkpwA2tbMfTuEofAGfOnJkPfOADqVb/pzq3tLRkyZIlmTp1aqZOnZpf//rXOffcc3PooYeu9/uMHDkyTU1NnT43aNCg9d4vAAAAUH6VMva/Hurvx19LpQ+Ay5cvT5Lst99+GT9+fPbZZ5+MHDky7e3teeKJJ3LVVVfl+eefz2WXXZatt94622233Xq9z1e/+tVsttlmvTg5AAAA0K/0YQFb33eq7SUIFcBa6XzJWokMHz48X/va13LRRRfl8MMPz8iRI5Mkzc3NGTduXP71X/81o0aNyrJly/LTn/604GkBAACA/mrlKsC++Opxa/vba+pqJrqt9AFw2LBh2WGHHbp8vrW1Nfvvv3+S5JlnnumrsQAAAABWV+njr6bXfa1tm76Yh5op/SnA3bFyVWB7e3vBkwAAAAD9UiWpFHkRvGoKD3GFHn/JCYBJHnvssSTJtttuu977uPTSSzNt2rS0tbVl1KhR2XnnnXPMMcfkgAMO6K0xAQAAAHpNZWXt6yz8VVf+pbZX/aNvlP4U4HX57//+70yePDlJcvTRR6/3fp5++ulUq9U0NTXllVdeyX333ZfPf/7z+dKXvpSlS5f21rgAAABACVVSSaXSR19NK766c+rvym37Yq71WX04c+bMnHfeedlpp50ydOjQjBkzJscee2z+67/+a71+Hf7617/mK1/5St71rndl3Lhx2WSTTTJw4MCMHj06hxxySL70pS9l3rx567XvIvXrFYAzZ87MN7/5zSTJm970po5rAXbXoEGDcvzxx+ewww7L9ttvn5aWliTJlClTcvPNN+euu+7K73//+wwbNiwf/ehHe31+AAAAoByqSa+ffltZ40Fv7CyrrA4s1uOPP57x48dnxowZSZIRI0bktddey+23357bb789H/vYx3L55Zf3aJ/33HNPPvnJT3b8eNCgQRk2bFhmz56dP/zhD/nDH/6QK664Irfeemv22GOPXj2eWuq3KwDnz5+fz3/+85kzZ04233zzfOxjH+vxPlpbW/PhD384e+yxR0f8S5KxY8fmE5/4RE488cQkye23354XX3yx12YHAAAAyqWSai/dTXfVFX5/++rt2/X+bb9rrg7c8F13V1tbW97+9rdnxowZGTduXB566KHMnTs3c+fOzcUXX5xKpZJvfOMbueaaa3r06zB27Nh89rOfzR133JFZs2alra0tr732WhYsWJDrr78+m222WV588cWceuqpDXUviX65AnDRokX513/91zz//PMZPXp0Pve5z2XEiBG9/j6nn356fvWrX2XJkiV58MEHs/XWW691+4kTJ+YHP/hBl8+fdtppOfPMM3t7zJppW9w4HwQAAADWT2tra9EjrKapqanjr/U221r1tICtfFkNRumxLpYa9nyFYPeP5qqrrsqzzz6blpaW/OIXv8jYsWOTJC0tLbngggsyffr0fPOb38yFF16YM844IwMHDuzWfg8++OAcfPDBa3y/paUl73nPe7LpppvmLW95S5566qncd999OfTQQ7s9c5H6XQBsa2vL5z73uTz11FMZNWpUPv/5z2fzzTevyXsNGTIkY8eOzeTJk/Pyyy+vc/sFCxZ0LFvtzMKFC9Pc3NybI9bU4MF18Y+hhjBnzuKiR2gIw4YPLnqEhrFwkWuPdkdTk39OdVdb27KiR2gIy5b6w6/u2m7sqKJHaAivvrqw6BEaxt2/mVz0CA1juX9WdUu7n6duq9ffp1YqlbqdrStluwlujw+nBy+YOHFikuTd7353R/xb1ac+9alceeWVmTZtWu66664ce+yxPZ2mUwceeGDH47/+9a+9ss++0K8CYFtbWz7/+c/n8ccfz/Dhw/O5z30u22yzTdFjdRg2bFg23XTTLp9vaWlpqOWldfLnEAAAANRQvf0+tampKZVKJdVqNcuXL1/j+XqNgtVqda0FsEy/w+5qZeC8Vxd16/Xz58/Pgw8+mCQ57rjjOt1m7Nix2W233TJp0qTceeedvRYA//CHP3Q83mGHHXpln32h3wTApUuX5otf/GIeeeSRtLS05KKLLsr2229f0/dcvHhxpkyZkiTZbLPN1rn9GWeckTPOOKPL52fNmpXZs2f32ny11rrR6KJHAAAAoMbq7fepra2taW5uzvLlyzudbcyYMQVMtW7Lli4v3QrArnR1mAsXtHXr9U888cSKYJpk3LhxXW43bty4TJo0KZMmTerpiKtZunRpXnrppfzyl7/MhRdemCR585vfnAMOOGCD9tuX+kUAXLZsWS655JL8+c9/zpAhQ/KZz3wmO++88wbvt1qtrrhNdRduuOGGLFmyJJVKpaH+pgAAAAD61sDBzStu3NGPbbFt967ZOH369I7HW265ZZfbrXxu1e17Yp999snDDz+8xvePPfbYjlOQG0XpA2B7e3u+8pWv5MEHH8ygQYNy4YUXZvfdd+/26z/wgQ9kxowZGT9+fM4999zVnvv0pz+dfffdNwcccEDGjh3bsYx4ypQp+clPfpI777wzSfKWt7xlnTcAAQAAAPq57va/nt9do/50dqzdPP758+d3PG5paelyu5XPzZs3ryeTdRgzZkw222yzLFq0KHPnzk2SvPWtb82XvvSlbLLJJuu1z6KUPgA+8cQTHednV6vVfOUrX1nr9tdee2239z1z5sxMnDgxEydOTHNzc1paWrJkyZK0tf3PktUjjjgi//t//+/1Gx4AAADoFyqprPUsw9dt/D8aJQY24OLGO+64o+PxK6+8kh/84Af513/91+y77775+te/no9+9KMFTtczpQ+AK88JT1acs/3aa6/12r7POuusPPzww3n66acze/bszJs3L83Nzdliiy2y66675uijj85ee+3Va+8HAAAAlNfnbl2/G1V85rjbenmSDbe+x9Idw4cP73i8cOHCjBw5stPtFi5cmCQZMWLEBr/nxhtvnHPOOSeHHHJIDjzwwHz84x/PIYcckn333XeD990XSh8A99xzz/zsZz9b79dfffXVXT536KGH5tBDD13vfQMAAABsqLWtHKz2wRLBSh8v71v1un/Tpk3rMgBOmzYtSbLFFlv02nvvt99+OfTQQ/O73/0u3/3ud/Pv//7vvbbvWmoqegAAAACAfm9DGlql669KpVLzr07fu4Z23XXXjuj5+OOPd7ndyud6ci+I7thqq62SJM8880yv7reWSr8CEAAAAKARXPS2O9a9USe6fe3ATvRkhWBPVvqtz7FMeP/+2X//dW83fPjwHHjggbn//vtz66235tRTT11jmxdffDGTJk1Kkhx99NE9nmVtnn322Y45GoUVgAAAAAD1YC0r+Wr1tdpqvpX/68lKv97+6qbTTz89SXLDDTdk6tSpazx/6aWXplqtZsstt8xRRx3V7f0uW7Zsrc/ffffduf/++5Mkhx9+ePcHLpgACAAAAFAPCgiAq301/e2rqPfvgQ996EPZYYcdsmDBgkyYMCGPPPJIkmTRokW55JJLcsUVVyRJLr744gwcOHC112633XapVCo566yz1tjvYYcdli984Qt5/PHH097e3vH96dOn56tf/WomTJiQarWasWPHdvr6euUUYAAAAICiVTbsVN4y6MnhDx48OD/72c8yfvz4PPLII9l7770zcuTILFiwoCPcnXPOOXnf+97XoxmmT5+eCy+8MBdeeGEGDBiQUaNGZcmSJZk3b17HNrvsskt++tOfNtQpwAIgAAAAQD3og/63vnfs7Yu7CffUHnvskUcffTSXXHJJbrnllkydOjWjRo3Kfvvtl4985CM56aSTerzP73//+7n11ltzzz33ZMqUKZk5c2aSZJtttsk+++yTk08+Oe95z3syePDgXj6a2hIAAQAAAAq24izYGhTAXtrlGrPVpAf2fNhNN900l112WS677LJuv+b555/v8rkjjjgiRxxxRI/nqHcCIAAAAEA96I1Y11dnEb/+fXojCPbvM6BrSgAEAAAAKFxl/a8BWA/hrBeCYD0cRlkJgAAAAAD1oEwFbH2OpUzHX2cEQAAAAICCLV3a3mUAq8m1AQvU1Q1FXp76Wt8O0o8IgAAAAAAFGzioef1PAW4wXQXNYSOH9PEk/YcACAAAAFAHKk39IwB2ZUTr0KJHKC0BEAAAAKBglcqKr/6svx9/LQmAAAAAAPWg3xew/n78tSMAAgAAANQB/a/oAcpLAAQAAAAonHOA+/3x15AACAAAAFA0/c8CwBoSAAEAAADqQKWAAlhNtVvbVfoizymANSMAAgAAANSDAgJYn4Q9CicAAgAAANSBwk8BrlSSavdWBNbq7akNARAAAACgHvR1Aevs7Vadoc9boAJYKwIgAAAAQMEqfXETkJ6+wes3r/HqQCsAa0cABAAAAKgHvVzAej2ovW6Hvd4DBcCaEQABAAAA6kCjrYBrtHn7MwEQAAAAoHDrdw5wPTe4Hi8QVBRrRgAEAAAAKFpfXAOwj/X0cEp2+HVFAAQAAAAoWPvS9k4LYFmjWGerA//6/Ow+n6O/EAABAAAACjZgYHPpVgCuTWeHutk2I/t8jv5CAAQAAACoA5WmflQAOzFwkExVK35mAQAAAIpWSSo9WgLY41ts1JGujrN/B9BaEgABAAAA6kGP+lcJY1kJD6leCIAAAAAAdaA/XQOwM/388GtKAAQAAACoBwogNSIAAgAAANQB/Y9aEQABAAAAilapFFgAu7qhSB/P098LaA0JgAAAAAAFq6TI/lUn4a1OxigjARAAAACgYNWksAJY6fi/dCwG7GpNII1JAAQAAAAoWJ+fAdzVe1Ve93QflkBnANeOAAgAAABQD2oYwCrru/PXvaxqbWBDEgABAAAA6kClAZbArXdI7NbO6//4G5UACAAAAFAPNrB/1TTOdZMVgvVJAAQAAAAoWqUxVgCuy4ZEyBIcft0SAAEAAAAaQaMHsnUsDqxaPFgzAiAAAABAwdqXLl9zBWCjB7/Xe/3xvC74TZ/6Wl9N0u8IgAAAAAAFGzCoOZXmNW65W26vO9ytthtdzBz9gAAIAAAAULBqsuYKuXWsmCuFSheP6VUCIAAAAEDBKunGTUDKEATXcoj6X+0IgAAAAAD1oKcFrBGCYE+OSQGsGQEQAAAAoA6scwXgOnfwuh8XEQQ35BA29PjpkgAIAAAAULRKen8F3Kr7q1UM1OwaggAIAAAAULjKhq8AXPvuV7chQbBGY1oAWDsCIAAAAEDhqn27mq4nQVCYa3gCIAAAAEDBKrVeAbg21awe+YoKfpYA1owACAAAAFCwai2uAdgNlVS6fN9qXd5WmPUhAAIAAAAUrJJeuAtwL6v0cZGss8MvFQEQAAAAoB7UIIDVMuL1+gpBAbBmBEAAAACAOlBvKwDXpffjYmMdfyMRAAEAAACKtoHXAOzr03W7skGrAuvjEEpJAAQAAACoA422ArAzGxIi6yVilpEACAAAAFDvytLG1rJAcEnbsr6bo58RAAEAAAAK1r5s+ZorAMsS/Vb1+mNaJQi+9urCPh2lPxEAAQAAAAo2YGBzKs1lLH7dt9nWo4oeobQEQAAAAIB60K1rAG7ATTbqTv8Onn1JAAQAAACoA927B8jrNmqUHtiNYyvBPVDqlgAIAAAAUA/Wp4CtcU29OimC61XzFMBaEQABAAAAilbppRVwq+6kr1vghs6v/9WMAAgAAABQuN4qgKvvcjXVrp/qrjWaomjXEARAAAAAgDpQ82vgrSUIdnsXNZzRNQBrRwAEAAAAqAd9XcC6EwT7ciQFsGYEQAAAAICCVWpwBnCPVLMi9mlwpSQAAgAAABSsmvR5Aax0+YP/0af3EbECsGYEQAAAAICCVZJU6jCA9ekZwH34Xv2NAAgAAABQD2p8E+De1uurAxXAmhEAAQAAAApXqcsVgGvT29NWFMCaEQCpmbbFS4seoWGMGjWk6BEawqKFS4oeoWFs5O+pbmlv79MrmjS0pybPKnqEhtDqs9dtL0ydU/QIDWHiZ39T9AgN4x2fPrzoERrG09uMLHqEhrDVG7cqegT6m/W4AUe957Ie/9d2vR9QAxMAAQAAAOpAo60AXJceH025Dr+uCIAAAAAABatWq2sNYGVqY12tDJw3d3GfztGfCIAAAAAABasuL98KwK50dZTLXaKnZgRAAAAAgII1D6ik0tQ/AmBXNtq4pegRSksABAAAAKgD/WUFYFfcBbh2BEAAAACAolWSNK3naxvhzNnutL31PX7WSQAEAAAAKFxl/VcArvqyeomB63UoVgDWigAIAAAAUA96o3+9fh99GQQ3dH79r2YEQAAAAICCVVKjawDWMgj28rj6X+04uxoAAACgYNXkbxWwxl9Nr/vqq9d296uHZs6cmfPOOy877bRThg4dmjFjxuTYY4/Nf/3Xf/V8Z0nmzp2biRMn5r3vfW923333DBs2LEOGDMl2222X97znPbnnnnvWa79FswIQAAAAoGCVSkF3Ae7s+oEFLcXr6V2AH3/88YwfPz4zZsxIkowYMSKvvfZabr/99tx+++352Mc+lssvv7xH+9x///0zefLkjh8PGTIkzc3NeeGFF/LCCy/khhtuyD/90z/ly1/+co/2WzQrAAEAAADqQKVSKfar6W9fBb1/T/pfW1tb3v72t2fGjBkZN25cHnroocydOzdz587NxRdfnEqlkm984xu55pprevRrsHTp0uy11175xje+kcmTJ2fRokWZP39+nnrqqZxyyilJkq985Sv59re/3aP9Fk0ABAAAAKgHfXEK8LpO6631Kb69dArwVVddlWeffTYtLS35xS9+kb333jtJ0tLSkgsuuCD/8A//kCS58MILs3Tp0m7v99prr83DDz+cc845JzvuuGOSFWF25513zo9+9KMceeSRSWIFIAAAAAA91cer/Lqz2q8n2/bGVw8K4MSJE5Mk7373uzN27Ng1nv/Upz6VSqWSadOm5a677ur2fg8//PAun2tqasqZZ56ZJHn22Wcze/bsbu+3aAIgAAAAQNHq8aYfa9tXgSsA58+fnwcffDBJctxxx3W6zdixY7PbbrslSe68887u7bgbxowZ0/F42bJlvbbfWnMTEAAAAIA60Cs3AemrG3i8/n2qnW5VE0888USq1RVvOG7cuC63GzduXCZNmpRJkyb12nv/7ne/S5Jsttlmq8XAeicAAgAAANSDRj5PszfCYzf3MX369I7HW265ZZfbrXxu1e03xIsvvthx84+zzjqrmLs2rycBEAAAAKBglUovrQBsYN09/vnz53c8bmlp6XK7lc/NmzdvwwbLirsDv/vd7878+fOz7bbb5p//+Z83eJ99qdAA2N7enscffzzLli3LLrvskmHDhhU5Tt2rVCppamrkPw4AAACgbJqbm4seoUv1PNvrLVu2vMsVcD25OUajqHZyzvArry4sYJJ1q1ar+eAHP5h77703Q4YMyY033phRo0YVPVaP1CQALliwIL/+9a+TJG984xs7vRvLtddem/POOy+vvvpqkmTIkCH5+Mc/ni984Qv9vnh3ZejQoWst2/Vm4fy2okcAAACgxlpbW4seoVPNzc11O1tnBgxoyttPWb9ryt3yk1d6eZoNd8LJG9ds38OHD+94vHDhwowcObLT7RYuXBEUR4wYsUHv97GPfSzf//73M2DAgPzwhz/MQQcdtEH7K0JNAuBNN92U973vfWlubs6zzz67xvO33nprx7nSKy/auGjRonzpS1/KggULcvnll9dirIa3aNGitLU1TlQbPLBxYiUAAADrZ/bs2UWPsJqRI0emubk57e3tmTt37hrP128UXP/FUP1tIdWq1/2bNm1alwFw2rRpSZIttthivd/rn/7pn3LFFVekubk5EydOzAknnLDe+ypSTQLgHXfckSQ58MADs80226zx/Pnnn59kxRLKvffeO9tvv33uvPPOzJs3L9/85jdz9tlnZ++9967FaA2tWq2mvb296DG6b2DRAwAAAFBr9fz71HqerTf1s/6XXXfdtWNR2eOPP55dd9210+0ef/zxJMnuu+++Xu/z6U9/Ol/96ldTqVRy9dVX513vetd6z1y0mgTASZMmpVKp5PDDD1/juYceeiiPPvpoKpVKPvrRj3as9vvLX/6S/fffPwsXLsx3v/tdqwABAACAfqNSSX75s1fX+7X1Zn2O5YA3rbmIrDPDhw/PgQcemPvvvz+33nprTj311DW2efHFFzNp0qQkydFHH93jWS666KL827/9W5LkyiuvzFlnndXjfdSTmtxRYtasWUmSXXbZZY3nbrvttiTJgAED8pnPfKbj+zvvvHNOO+20VKvV/P73v6/FWAAAAAB1q1Kp9MrXitOJi/1ar9l7cBr06aefniS54YYbMnXq1DWev/TSS1OtVrPlllvmqKOO6vZ+k+SSSy7Jv/7rvyZJvva1r+XDH/5wj15fj2oaADs7B/vee+9Nkhx00EHZeOPVLwh54IEHJkmn1w0EAAAAYN0qleK/au1DH/pQdthhhyxYsCATJkzII488kmTF/RMuueSSXHHFFUmSiy++OAMHrn6Nsu222y6VSqXTVX2XX355/vmf/znJihB47rnn1vQ4+kpNTgFevnx5kmTOnDlrPHffffelUqnksMMOW+O5TTbZJEkyf/78WowFAAAAULfq8VTePtWD4x88eHB+9rOfZfz48XnkkUey9957Z+TIkVmwYEHHtR/POeecvO997+vRCJ/4xCdWjFKp5Gtf+1q+9rWvdbntj3/84xx88ME92n9RahIAN95440yfPj0vvPDCat9/6KGH8sorr6RSqeTNb37zGq9btGhRkmTQoEG1GAsAAACgbhV1N99qdd3b9MVoPX2PPfbYI48++mguueSS3HLLLZk6dWpGjRqV/fbbLx/5yEdy0kkn9XiG6t9+MqrVal5++eW1brtkyZIe778oNQmAe+21V6ZNm5Yf/ehHueiiizq+//3vfz9J0tTUlEMPPXSN102ZMiXJht2eGQAAAIDua+SVh5tuumkuu+yyXHbZZd1+zfPPP9/lc9Xu1NAGVJMAeOKJJ+bWW2/Nk08+mXe/+90588wz88c//jHf/OY3U6lUcswxx2TUqFFrvO7BBx9M0vnNQwAAAADKqq+unbeOKZKUM4D1dzUJgO973/ty2WWX5emnn84Pf/jD/PCHP0yyoqI2NzfnX/7lX9Z4zcKFC3PHHXekUql03AwEAAAAoP/o2wLYeXBc/Zt9uyCu8AJaWjW5C/CgQYPy61//Ovvuu2+q1WrHV0tLS7797W93eoHEG2+8MQsXLkySjB8/vhZjAQAAANSterxTbz3ORM/VZAVgsuKWyn/84x/zxz/+MZMnT86wYcNyyCGHpLW1tdPthwwZks9+9rOpVCoNcwcVAAAAgN7S+wGs1kWtd5cH6n+1U7MAuNL++++f/ffff53bvec976n1KAAAAAB1bMMSWN+voCvydGF6ouYBEAAAAIB1a/RTYDd4/gY//nrWpwHw5ZdfzvTp0zNv3ryMGDEiW265ZTbddNO+HAEAAACg/rgGnv5XQzUPgFOmTMnll1+eH//4x5kyZcoaz48dOzannXZaPvaxj2Wbbbap9TgAAAAAdWd5ezWdJbCyRsHOThd+5ZWFfT9IP1GTuwCvdM0112SPPfbI17/+9UyZMmW1OwKv/JoyZUouu+yy7L777vne975Xy3EAAAAA6lJzc1O/uituZ8faOrql6LFKq2YrAK+55pq8//3vT6VSSbVaTaVSyW677Zadd945w4cPz/z58/OXv/wlTz75ZKrVahYsWJD3v//9SZKzzjqrVmMBAAAA1KWmphIXv25obq7pOrV+rSYBcPr06TnnnHM6fvzhD384/+f//J+MHTt2jW2nTp2aSy65JP/xH/+R5cuX55xzzslxxx2XzTffvBajAQAAANSlnqz4a7Q77nbn2Mq84rFoNUmrV155ZRYuXJhKpZLvfOc7ufLKKzuNf0myzTbb5Jvf/GauvvrqJMnChQtz5ZVX1mIsAAAAgDpW6fZXpfL6r85Pq62Pr+4eF7VSkwD461//OpVKJccee2zOPvvsbr3mrLPOynHHHZdqtZpbb721FmMBAAAA1KXeiGz1+9XNYyj6F6HEahIAn3322STJSSed1KPXnXjiiau9HgAAAKC/KH6lXrFfCmDt1CQAzps3L0kyevToHr1u5fbz58/v9ZkAAAAAoD+qyU1ANt5447z88st57rnnevS6559/PknPwyEAAABAo1txrbz+q2IJYM3UZAXguHHjUq1Wc91112X58uXdek17e3uuu+66VCqVjBs3rhZjAQAAANStok/BLfpL/6udmgTAt7/97UmSSZMm5R/+4R9SXce9qavVaj7ykY/kscceS/I/1wIEAAAA6A+Kjm/1ckdhaqMmAfADH/hAtt566yTJd77zney33365/vrrM2PGjNW2mzlzZq6//vrsv//++c53vpNKpZKtt946H/jAB2oxFgAAAEBdqlZXLoGr7Vf37tS75vN9MRu1U5NrAA4ZMiQ333xzxo8fn4ULF+aRRx7Je9/73iTJiBEjMmzYsCxYsKDjZiHJilWAw4YNy49//OMMHjy4FmMBAAAA1KVar4Db0H2vfP06TvKkTtVkBWCSHHDAAfn973+f3XffPdVqteNr7ty5eemllzJ37tzVvr/nnnvmD3/4Q/bff/9ajQQAAABQt2p3Sm8tVxA6BbgR1GQF4Ep77bVXHnnkkfziF7/Ij3/849x///2ZPn165s2blxEjRmSLLbbIm970ppx66qk5/vjj09/vdgMAAAD0Z+vXRYrNKZ2/+fqtFNSFaqWmATBZUZwnTJiQCRMm1PqtAAAAABpWmdZFrc+xlOjw607NAyAAAAAA69CtU2AbPZGtfVlgdR3Ps/76JAC+8sorueWWW/LAAw9k2rRpHacAb7nllnnTm96UCRMmZOONN+6LUQAAAADqzvL25f3g0mhrP745ry3qozn6n5oGwHnz5uX888/P9773vbS1tXW6zX/8x39k8ODBOfvss3PJJZdk+PDhtRwJAAAAoO40Nzf1gwC4dq2jhxU9QmnVLABOmTIl48ePz3PPPZfqOq78uHjx4nzrW9/Kr3/96/zmN7/JNttsU6uxAAAAAOpSP+9//f74a6kmAXDJkiU57rjj8uyzzyZJhg8fntNPPz3HHHNMdtpppwwbNiwLFizI5MmTc8cdd+T666/PvHnz8swzz+S4447LQw89lIEDB9ZiNAAAAID6JIBRIzUJgFdeeWWefPLJVCqVHHTQQfnRj36ULbfcco3t9tprr5xyyin5l3/5l7zzne/M73//+zz55JO58sor8/GPf7wWowEAAADUoUoq/b4A9vfjr52mWuz0P//zP5MkW2yxRX71q191Gv9WtcUWW+SXv/xlx3Y33nhjLcYCAAAAqEuVVFP5252Ae+srffjVazNTEzUJgE899VQqlUrOPvvsjBw5sluvGTFiRN7//venWq3mqaeeqsVYAAAAAHWp2tv1r1JJpQ+/1L/6VrNrACbJHnvs0aPX7b777kmSpUuX9vpMAAAAAPWqEg2svx9/LdVkBeDWW2+dJFm0aFGPXrdy+6222qrXZwIAAACoZ3181u5qX/UwF7VTkwD4lre8JdVqNb/5zW969Lo777wzlUolxx57bC3GAgAAAKhfNTgNuDdOF+6zOWTAmqlJADznnHMydOjQ3HDDDbnnnnu69Zp77rknN954Y1paWnLOOefUYiwAAACA+lRI91vXtf36fiZqoyYBcOedd84111yTAQMG5Pjjj8+VV17ZcV3A11u6dGm+9a1v5W1ve1sGDhyYa665JjvttFMtxgIAAACoS5VqUumL//Uo8L1u+xr/j9qpyU1APve5zyVZcSrwz3/+85xzzjn5l3/5lxx66KHZaaedMmzYsCxYsCCTJ0/OPffck9deey1JMmHChEyaNKnj9Z35zGc+U4uRAQAAAIpTwzNge21l3d/2U6320v662D+9ryYB8KKLLlpxjnjS8dfZs2fn5z//+RrbVqvVjm1+/vOfd7rNqgRAAAAAoIx6J9TVvqJ1PeeGlUH9r3ZqEgCTFWGvO99b2/dfr+JkcAAAAKCkGr97bOD8DX/89asmAfCuu+6qxW4BAAAASOO0spqdLkyP1CQAHnHEEbXYLQAAAEA5dXkX3AYpfV3oyenCy5a213SW/qxmpwADAAAA0D3Ll1dLcApwT6x5rG2LlxYwR/8gAAIAAAAUrKmpKZWmTgJgfzmFtpKMGDW06ClKSwAEAAAAKFglXZwu+/rvlSUIdnKs/WoBZB8TAAEAAADqQKU71/srdSQr9cEVSgAEAAAAKFqXNwHpmXpZILg+hyL/1Y4ACAAAAFAPeqEANnREa+jh65sACAAAAFCwLq8BWNN37Kl6WV9ITwmAAAAAAIWrpFL3d8Go7Xz1f/yNSwAEAAAAKFqlWvwpsJ0t8Ct6JnqFAAgAAABQuIJXAFbzt/OQixvBAsDaEQABAAAA6kAR1wCsrP7DDtVOHtWcAFgzAiAAAABAwVYsvuuDAtbNt6i8/lEfdMA+Of5+SgCkZuYvWFL0CA1jQHNT0SM0hEWLlxU9QsMYMmRg0SM0hOdemF30CA1j261HFT1CQ/DPqe4bt9umRY/QEA4+fc+iR2gYd//syaJHaBjL/LOqW8bttXnRI9DPVJNeXwFXSaX39vn6/VRX/sWdgRuBAAgAAABQsEqlF++C2xcL6Sor/9KLKwQtAKwZARAAAACgDvS8/9VRMet0lJ5VwTo6mtIRAAEAAAAKV1lnAWy8QLb6xOvMgW4DXDMCIAAAAEDBqtXqWvpXOcLY/xxF5ylw8eKlfTVKvyMAAgAAABSsV68BWPc6P87+c/x9TwAEAAAAKFilUklTU+cBrFryG+2u7H5Dhw4sdpASEwABAAAA6piFcWwoARAAAACgDvT3U2D7+/HXkgAIAAAAUAf0L2pFAAQAAACoCwogtSEAAgAAANSB/r4CsL8ffy0JgAAAAAAFq1QqBV0Drye3GK71fApgrQiAAAAAAHWgmBVw9RPdrACsHQEQAAAAoJ9bNb5Ve7IokIYgAAIAAADUgb4+Bbirt1v5/b4OgVYA1o4ACAAAAFCwSqWvAlj332TNeSwNbFQCIAAAAEDBVqy2670CWJuYuOZOe3eVoCWAtSIAAgAAABSst1YA9vVptL15urBTgGtHAAQAAACoA319DcDe1DujN+7x1zsBEAAAAKAONHD/6xX9/fhrSQAEAAAAKNjy5dV0tQKujGGss1OGFy5c0veD9BMCIAAAAEDBmpubShn6utLZsQ4e3Nz3g/QTAiAAAABAHWhq6roA9u7dduvLyhg4cKBMVSt+ZgEAAADqXH9aHUjvEwABAAAA6oDIR600FT0AAAAAAEmlUumVrxU3Eynua/1n7/nP2cyZM3Peeedlp512ytChQzNmzJgce+yx+a//+q+e7yzJsmXLcuedd+bSSy/NO9/5zuy4444d81100UXrtc96YAUgAAAAQMF6c/Xf2vZVrZZnpeHjjz+e8ePHZ8aMGUmSESNG5LXXXsvtt9+e22+/PR/72Mdy+eWX92ifL774Yo455phajFsoKwABAAAA6kClUvuvpqa+eZ/1+eqJtra2vP3tb8+MGTMybty4PPTQQ5k7d27mzp2biy++OJVKJd/4xjdyzTXX9PjXYcSIETn88MPziU98IhMnTswb3vCGHu+j3lgBCAAAAFAHKgUtzevOHYb7YrSeHP9VV12VZ599Ni0tLfnFL36RsWPHJklaWlpywQUXZPr06fnmN7+ZCy+8MGeccUYGDhzYrf2OHTs2c+bMWW2WL3/5yz07kDokAPaCO++8s1tLSidOnJiRI0f2wUQAAAAA3dOIpwRPnDgxSfLud7+7I/6t6lOf+lSuvPLKTJs2LXfddVeOPfbYbu23qamcJ8sKgL2oqalprYGvqJIPAAAA1L9is8HKN+/GcsCCzZ8/Pw8++GCS5Ljjjut0m7Fjx2a33XbLpEmTcuedd3Y7AJaVANiLxowZk6uvvrroMQAAAICGU6mThUPFzdDdw3/iiSdS/dt5y+PGjetyu3HjxmXSpEmZNGlSb4zX0ARAAAAAgIL1zTX2Nnwf3bleYK1Nnz694/GWW27Z5XYrn1t1+/5KAAQAAACoA70dAWuxonDVXVZ7uQZ2d9z58+d3PG5paelyu5XPzZs3b4PmKgMBEAAAAKAO1McpwN3X+/M21vE3EgGwF82ZMyfnnntu/vrXvyZJNt5444wbNy4TJkzIdtttV+xwAAAAQF0bMGDGer2uvX3TXp5kwzU39/xYXnllRrbbbv91bjd8+PCOxwsXLuzyhqwLFy5MkowYMaLHs5SNANiL2tra8txzz2XYsGFZvHhxpk2blmnTpuWOO+7ImWeemZNPPrnoEQEAAICSabSVgxtq1ev+TZs2rcsAOG3atCTJFlts0Sdz1TMBsBeMHj067373u3PwwQdnyy23zMCBA7Ns2bJMmjQp1157bf7yl7/kmmuuyejRo3PEEUcUPS4AAABQZzbkenr9rP9l1113TaVSSbVazeOPP55dd9210+0ef/zxJMnuu+/el+PVpaaiByiDfffdN+9+97uz7bbbZuDAgUmSAQMGZK+99sq//du/ZZdddkmSfP/738/y5cuLHBUAAACoQ01NlVSrm63XVz1an+PYeOPtu7Xv4cOH58ADD0yS3HrrrZ1u8+KLL2bSpElJkqOPPrp3DqqBWQFYYwMHDswZZ5yRf/mXf8msWbPy7LPP5g1veEOn206cODE/+MEPutzXaaedljPPPLNWo/a6V2YtLHoEAAAAaqy1tbXoEVbT1NTU8dd6m21dmprWvpSvl2+6W6jOVi02N3d/ndrpp5+e+++/PzfccEM+85nPZJtttlnt+UsvvTTVajVbbrlljjrqqA0dt+EJgH1g5QrAJHnppZe6DIALFizIjBldXyRz4cKFaW5u7vX5AAAAYH3V6+9TK5VK3c62vvrbqb5r86EPfShf//rX8+yzz2bChAm57rrrstdee2XRokW5/PLLc8UVVyRJLr744o6zNVfabrvt8sILL+TMM8/M9773vTX2PWfOnCxdurTjx+3t7UlWdJlZs2Z1fL+lpSUtLS01OLreJwDWkWHDhmXTTbu+c09LS0vH33QAAABQD+rt96lNTU0d14fr7DJc9RwFN+xmHvW6PLD7x9ST4x88eHB+9rOfZfz48XnkkUey9957Z+TIkVmwYEHH35PnnHNO3ve+9/V44hNPPDG/+93v1vj+l7/85Xz5y1/u+PFnP/vZXHTRRT3efxEEwD7w1FNPdTzebLOuz80/44wzcsYZZ3T5/KxZszJ79uxena22Bhc9AAAAADVWb79PbW1tTXNzc5YvX97pbGPGjClgqu7ZsBV+q7+4yNOF+2ql4h577JFHH300l1xySW655ZZMnTo1o0aNyn777ZePfOQjOemkk/pmkAYgAG6garW61kK9bNmyXH/99UmSjTfeODvuuGNfjQYAAAD0U/3ldOFNN900l112WS677LJuv+b5559f6/O//e1vN2yoOiQAbqAZM2bky1/+ct7ylrdkn3326Vjh197enieeeCLXXnttnnzyySTJmWee2XExUgAAAICVKpXKBp4C3FMbskSwNnP2l2hZBAGwF/zlL3/JX/7ylyTJoEGDMmTIkCxcuDDLli1LkgwYMCBnnnlmjjzyyAKnBAAAAOpZ3waw/3mzdZ0uLMw1PgFwA2200Ub50Ic+lCeeeCLPPfdc5syZkwULFmTw4MHZZpttsueee+atb31rttpqq6JHBQAAAFiDwFd+AuAGGjx4cCZMmJAJEyYUPQoAAADQwIoNcau+eTF3EBEia0cABAAAAKgDfXsNwLUpao56Of7yEQABAAAASq632uK6rhdIfRIAAQAAAApWqfT2KbB9dafe3iuCdbMAsoQEQAAAAIA6UD+nAPdE783ckIffIJqKHgAAAAAAqB0rAAEAAADqwLpXwJVhiVzXpwwvX+4Cg7UiAAIAAAAUrFpt1FOAe6rrY1y2bHkfztG/CIAAAAAABev9m4A0nsGDZapa8TMLAAAAULDK3/63NmU7Qfb1R7uu42f9CYAAAAAARatknZf4K30eK/0BFkcABAAAAChadcP7Vz2uENT06oMACAAAAFC0bqwA7M4uulbd8DegYQmAAAAAAHWg1tfA680VgpVOHvXePultAiAAAABAPahpAWuAW2zU/YCNSwAEAAAAqAP9vX/19+OvJQEQAAAAoB4UXsAKvk5g4cdfXgIgAAAAQMFW3AOkjwvYGm/3um/0+W2FFcBaEQABAAAAitYLdwHuyVv1ZMM+74D0OgEQAAAAoGjVGvW/yoaf1rvmq/+WBKu9O7H1f7UjAAIAAAAUrZJUKrVIYDXcZ2/vWgGsGQEQAAAAoNHVUzxzznDdEQABAAAA6kBNFgAWYT2PoyyHX48EQAAAAIDCreMuIGWpY2tZHbh8ed+N0d8IgAAAAAAFq1ar5VkBuDZrOcb29va+m6OfEQABAAAACtbUVOkfAXAtBg2SqWrFzywAAABAXejnBZCaEQABAAAA6kB/XwHY34+/lgRAAAAAgJLp65a2lnt7UAcEQAAAAIA6UJMVcH1U5iod/7ehO6EWBEAAAACAulCDArbaLqudf7ub1myJvT2vAlgrAiAAAABAHaj9NfBe9wY9XB1Y6/nkv9oRAAEAAAD6I8Wt3xAAAQAAAApWiR5H7QiAAAAAAAWrJn1aACudPFpddZX/7yMKaM0IgNTMiBGDix6hYSxcsKToERrC5puPKHoESmbbsRsVPULDWLqkvegRGsLAAU1Fj9AwFi5aWvQIDeH4t+9e9AgN45lxmxc9QsO48dJ7ih6hITz4u+eKHqFhHHa4z19vWLECsJ4KWGWV/++rd6yn4y8XARAAAACgaDU4B7gvclqfrhBkvQmAAAAAAHWgEde/NeLM/ZEACAAAAFAPeljT6j2+9Xh1YL0fUAMTAAEAAADqQNmugdfToynX0dcXARAAAACgjlVKVsaqXSwNbF+2vG8H6UcEQAAAAICiVaulC31d6eo4K01uKVIrAiAAAABAwSqVSr8JgF1pbm4ueoTSEgABAAAACleJq+D19+OvHQEQAAAAoGi90P9WnkDbsBmtYQevfwIgAAAAQB3Y0P7V6P2s0eevZwIgAAAAQD1QwKgRARAAAACgLvRFAayu9qjn76hSNiIBEAAAAKAO9M1dgCurP6p2ueHaXlob2mLNCIAAAAAABSusfYlu/UJT0QMAAAAAALVjBSAAAABAHeibU4BXe8d1PN+T84M3nMWItSMAAgAAANSF2iawngfG1V9QrXkPlABrRQAEAAAAKFqliBWAPVPz+er8+BuZawACAAAANLhq+vqE3c5noD5ZAQgAAABQBzZkhV09LJ7b0Bnq4RjKSgAEAAAAqAsSGLUhAAIAAAAUrFqt/2sA1try5cuLHqG0XAMQAAAAoGD9Pf6t4CqCtWIFIAAAAEDhKqmstQKWOY6tOO7mAc0Fz1FeAiAAAABA3bNEkPXnFGAAAAAAKDErAAEAAAAKVkmN1vj1xcLBXjo72RrH2hEAAQAAAOpBoxawRp27HxEAAQAAAOpCfy9p/f34a0cABAAAAKgDdZG/Kin3DYf7KQEQAAAAoGg1uwjgeihqjno5/hJyF2AAAACAfq6e+iO9zwpAAAAAgKJVk0pfJbi1vE2XT/XBacECZO0IgAAAAABl1htl7fX7cJ3AhiIAAgAAABStBufg1nRF3d92rgM2BgEQAAAAoA404imwjThzf+QmIAAAAABQYlYAAgAAANSDytrX0zX6art1ni68juNn/QmAAAAAAEWrNn7gW5d1HV+1fXmS5r4Ypd9xCjAAAABA0cpe/7qh0uQnoVasAAQAAAAoWCU9OQO2UUPZ2k8CbnIKcM0IgAAAAACFq6Rxw153lf346pcACAAAAFC0De1/67zDRkF6ckz6YM0IgAAAAAB1YIP6l3jGWrgJCAAAAACUmBWAAAAAAIWr9u0qvtVOGV7bG3dybnGt5rSKsWYEQAAAAID+ZrXYVq8XEKS3CIAAAAAAhauk0s+XwPX3468lARAAAACgP3t9d7MgsHQEQAAAAIB60AcL4Lr1Fl1spAs2LgEQAAAAoKR6symuui8xsLEIgAAAAAAFq6SXFwBWarucsGPvVSmwEQiAAAAAAPVgvZpdwTfO6DI0rkcYdA+QmhEAAQAAAOpC9wtY/beyFRNaH1gfBEAAAACAolW6EfXqv/qtYY2R11YE1cKaEQABAAAA6kEDBr4eW8sxVpr6boz+RgAEAAAAKFilUknzgP5QACmCtgoAAAAAJSYAAgAAAECJCYAAAAAAUGKuAdhAKpVKmpo0WwAAAOpHc3Nz0SN0qZ5ng74kADaQoUOHpqWlpegxum3RwiVFjwAAAECNtba2Fj1Cp5qbm+t2NuhrAmADWbRoUdra2ooeo9uGDB5W9AgAAADU2OzZs4seYTUjR45Mc3Nz2tvbM3fu3DWeFwXpjwTABlKtVtPe3l70GAAAANChnn+fWs+zQV9yQTkAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACixAUUPQHktaWsveoSGMWLEkKJHaAhL2pYVPULDGDTYP967o6mpUvQIDWP+giVFj9AQRrcOLXqEhjFkaLXoERqCf05139QX5xY9QsM46ORdix6hIfzptmeKHgGg11gBCAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiA4oeoC+8/e1v7/a2H//4x3P00Ud3e/uXX345H/zgB9e53fnnn59DDjmk2/sFAAAAgN7QLwLgRhtttNbnFy9enMWLFydJ3vCGN6z3+4wcOTJNTZ0vqhw0aNB67xcAAAAA1le/CIDXXnvtWp//zGc+k4ceeihveMMbsu222673+3z1q1/NZptttt6vBwAAAIDe1u+vAThr1qw88sgjSdKjU38BAAAAoBH0+wD4m9/8JsuXL8/AgQNzxBFHFD0OAAAAAPSqfh8A77rrriTJgQcemOHDhxc8DQAAAAD0rn5xDcCuPPHEE/nrX/+aJDnmmGM2eH+XXnpppk2blra2towaNSo777xzjjnmmBxwwAEbvG8AAAAAWB/9OgDeeeedSZLRo0dnn3322eD9Pf3002lpaUlTU1NeeeWV3HfffbnvvvtyyCGH5B//8R8zcODADX4PAAAAAOiJfhsA29racu+99yZJjjrqqDQ3N6/XfgYNGpTjjz8+hx12WLbffvu0tLQkSaZMmZKbb745d911V37/+99n2LBh+ehHP9pr8wMAAABAd/TbawDed999WbhwYZINu/tva2trPvzhD2ePPfboiH9JMnbs2HziE5/IiSeemCS5/fbb8+KLL27Y0AAAAADQQ/12BeDK03932WWXbL311jV7n9NPPz2/+tWvsmTJkjz44INrfa+JEyfmBz/4QZfPn3baaTnzzDNrMWZNzJ+7pOgRAAAAqLHW1taiR1hNU1NTx1/rbTYoSr8MgDNnzsyjjz6aZMNW/3XHkCFDMnbs2EyePDkvv/zyWrddsGBBZsyY0eXzCxcuXO9TlYswcFDjzFq0Rx57qegRGsKN336g6BEaxgnv3bfoERrC1OdeKXqEhnH4kTsWPUJDWLx4adEjUDKDh7iGdHeN3Xpk0SM0jN13GVP0CA3hxJPHFT1Cw6jX36dWKpW6nQ36Wr8MgHfddVeWL1+eQYMG5bDDDit6nA7Dhg3Lpptu2uXzLS0taW9v78OJAAAAYO3q7fepTU1NqVQqqVarWb58+RrPi4L0R/0yAK48/feggw7KsGHDavpeixcvzpQpU5Ikm2222Vq3PeOMM3LGGWd0+fysWbMye/bsXp2vllqG+lNYAACAsqu336e2tramubk5y5cv73S2MWOsgqX/6Xc3AZk0aVKmT5+epHdO/61Wq2t9/oYbbsiSJUtSqVRywAEHbPD7AQAAAEBP9LsVgCtX/40ZMyZ77713t17zgQ98IDNmzMj48eNz7rnnrvbcpz/96ey777454IADMnbs2I6lxFOmTMlPfvKTjvd7y1veUtObjQAAAABAZ/pVAGxra8vvf//7JMn48eM77gy0IWbOnJmJEydm4sSJaW5uTktLS5YsWZK2traObY444oj87//9vzf4vQAAAACgp/pVAPzDH/6QhQsXJlkRAHvDWWedlYcffjhPP/10Zs+enXnz5qW5uTlbbLFFdt111xx99NHZa6+9euW9AAAAAKCn+lUAPOqoo3LUUUf1+HVXX311l88deuihOfTQQzdkLAAAAAComX53ExAAAAAA6E8EQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhMAAQAAAKDEBEAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAACgxARAAAAAACgxARAAAAAASkwABAAAAIASEwABAAAAoMQEQAAAAAAosUq1Wq0WPQTdM2vWrKJHaHitra1pbm5Oe3t7Zs+eXfQ49EBzc3NaW1sze/bstLe3Fz0OPeSz19h8/hqbz1/j8tlrbD57jc3nr7Gt6/M3ZsyYAqaCYlkBCAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGIDih6A7qtUKmlq0mx7S3Nzc9Ej0AMrf738ujU+v4aNx+evPPwaNhafvfLwa9h4fP7Kw68hrFCpVqvVooegexYuXJiWlpaixwAAAACggVgB2EAWLVqUtra2osdoaCNHjkxzc3Pa29szd+7cosehB5qbmzNy5MjMnTs37e3tRY9DD/nsNTafv8bm89e4fPYam89eY/P5a2zr+vy1trYWMBUUSwBsINVq1b98epGfy8bU3t7u167B+fVrXD5/jc+vX2Py2Wt8fv0al89f4/PrByu4oBwAAAAAlJgACAAAAAAlJgACAAAAQIkJgAAAAABQYgIgAAAAAJSYAAgAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQmAAAAAAFBiAiAAAAAAlFilWq1Wix4C+srEiROzYMGCDBs2LGeccUbR40C/4bMHxfH5g2L47EFxfP5gTQIg/crxxx+fGTNmZNNNN80vf/nLoseBfsNnD4rj8wfF8NmD4vj8wZqcAgwAAAAAJSYAAgAAAECJCYAAAAAAUGICIAAAAACUmAAIAAAAACUmAAIAAABAiQ0oegDoS+95z3uyYMGCDBs2rOhRoF/x2YPi+PxBMXz2oDg+f7CmSrVarRY9BAAAAABQG04BBgAAAIASEwABAAAAoMQEQAAAAAAoMQEQAAAAAEpMAAQAAACAEhtQ9ABQa3PmzMlNN92UBx54IK+88koGDx6cHXfcMccff3wOOuigoseDUpo8eXIeeOCBPP3005k2bVrmzp2btra2jBgxIjvssEMOP/zwHHHEEWlq8udQUEuvvfZafv7zn+fBBx/MjBkzsnTp0rS2tmb77bfPm970phx99NFFjwilUq1W8/vf/z633357nnnmmSxcuDAbbbRRxo0blxNPPDE77rhj0SNCQ5o/f34ee+yxTJ48Oc8880wmT56cOXPmJEm+8IUvZM899+z0de3t7XnooYfypz/9KU888USmT5+exYsXZ/jw4dlhhx1y5JFH+m9S+o1KtVqtFj0E1MqUKVNywQUXdPzLYejQoWlra8vy5cuTJCeccEI++MEPFjkilNKVV16ZW2+9tePHQ4YMSZIsXry443vjxo3LhRdemJaWlj6fD/qD+++/P1//+tezYMGCJMmgQYPS3NycRYsWJUk233zzXHXVVUWOCKWybNmyfOUrX8kf/vCHJElTU1NaWlqyYMGCVKvVNDc35+///u9z7LHHFjwpNJ4777wzl19+eafPrS0AXnHFFbnttts6ftzc3JzBgwdn4cKFHd/bc889c+GFF2bo0KG9OzTUGSsAKa2lS5fm4osvzpw5c7LtttvmH//xH7P99tunra0tP/3pT3P99dfnlltuyfbbb59jjjmm6HGhVHbZZZdstdVW2X333bPVVlt1RL7XXnstt99+e66//vo89thj+e53v5uPfvSjBU8L5fPQQw/lS1/6UpYtW5ajjjoqp556asaOHZtkxSqKp556Kk8++WTBU0K5XHvttfnDH/6QpqamnHnmmXnrW9+aIUOG5LXXXsvEiRNz22235corr8zYsWOz6667Fj0uNJzW1tbsuOOOecMb3pAtt9wyl1122Tpfs2zZsrS2tuboo4/OwQcfnB122CFNTU2ZO3dufvazn+Wmm27Ko48+miuuuCKf/OQn++AooDhWAFJaP//5z3PVVVdl8ODBufLKK7PJJpus9vy3v/3t/PKXv8zo0aNz9dVXZ8AAPRz6ysSJE/PDH/4wgwYNyo033ujzB71o0aJF+chHPpJZs2bllFNOyVlnnVX0SFB6c+bMydlnn52lS5fmpJNOytlnn73GNp/+9Kfz2GOPZffdd88ll1xSwJTQuNrb29Pc3Nzx4/nz5+c973lPkrWvAHzqqaey/fbbZ9CgQZ0+/4Mf/CA33nhjkuT//t//u8bvGaFMnOhOaf32t79Nkhx++OGd/oP81FNPTaVSyauvvppHH320j6eD/m2nnXZKkixZsiTz5s0reBoolzvvvDOzZs3KxhtvnNNPP73ocaBfePjhh7N06dIkycknn9zpNieddFKSZNKkSXnppZf6ajQohVXjX0/ssssuXca/JKtdC3fy5Mnr9R7QKARASmnRokV5+umnkyT77bdfp9tssskm2XrrrZOs+I82oO+sPPVwyJAh2WijjYodBkpm5R+AHXzwwRk4cGCxw0A/MXPmzCTJsGHD0tra2uk2K/+7M1lxmj5QvJEjR3Y8bm9vL3ASqD3nXFFKL774Ylae3b7tttt2ud22226bqVOnZurUqX01GvRbbW1tmTlzZu6666785Cc/SZK87W1vS6VSKXgyKI8lS5bk2WefTZLsuOOOefHFF/Of//mfefjhhzN//vy0trZmzz33zCmnnNJxTUCg96y80dy6npsyZUpfjAOsw2OPPdbxeG2/b4QyEAAppVdffbXj8ejRo7vcbuVzs2fPrvlM0B+ten2WVQ0YMCATJkzIGWecUcBUUF4zZszIsmXLkiTTpk3Lt771rbS1tWXQoEEZNGhQZs6cmd/85je555578olPfCKHHnpowRNDOWy66aZJVpyFMnPmzE4vP7Nq9Fv1v1WBYrS3t+eGG25IsuJU4W222abgiaC2nAJMKS1evLjj8eDBg7vcbuVzixYtqvlM0B81NTVlo402ykYbbdRx/ZVKpZIJEybk1FNPXe/ruQCdmz9/fsfjm266KS0tLfnMZz6TH/7wh7nxxhvz9a9/PW94wxuydOnSfP3rX8+0adMKnBbKY8899+y4odWPfvSjNZ6vVqu5+eabO37svz2heNddd10mT56cAQMG5EMf+lDR40DNCYAA1ExLS0uuvfbaXHvttfnRj36U73znOznhhBNyyy235KMf/WgmTZpU9IhQKisvf5GsON3w3HPPzRvf+MY0Na34T74ddtghF154YYYMGZIlS5bkZz/7WVGjQqlstNFGeetb35okufXWW3PNNddk5syZWbZsWV544YX827/9W55++umOSOjyF1Cs22+/PT/+8Y+TJGeeeWbHDeqgzJwCTCkNGTKk43FbW1taWlo63a6trS1JMnTo0D6ZC/qzSqWSzTbbLB/4wAey6aab5uqrr86Xv/zlfPvb317rSl2g+1b999k222yTfffdd41tRo8encMPPzy33Xabm2BBLzrrrLPy8ssv54EHHshPfvKTjuvdrnTcccdl8uTJmTx5coYNG1bQlMA999yTb37zm0mSU089NSeeeGLBE0HfsAKQUlr1un9ru8bKyue6ulsbUBvHHXdcBg4cmFdeeSV//OMfix4HSmPVf/+tesfR11v53Mo7lwIbbuDAgbngggty/vnn56CDDsqWW26ZzTbbLPvtt1/+z//5P/mHf/iHzJkzJ0my1VZbFTwt9E///d//ncsuuyzLly/P2972tpx55plFjwR9xgpASmnrrbdOpVJJtVrNlClTuvxN0MqLMbvgK/StQYMGZcSIEXn11Vczffr0oseB0hg5cmRaW1u7fXMrpyFC76pUKjnkkENyyCGHrPHc3LlzO6L7Lrvs0tejQb/34IMP5tJLL017e3uOOeYY1/2j37ECkFIaOnRox3Uc/vSnP3W6zaxZszJ16tQkyd57791nswErLn4+d+7cJE7Bh962zz77JElefPHFLrdZ+dzKO5cCtXf33XcnWXHmycrPKdA3/vSnP+WSSy7JsmXLcsQRR+SjH/2oPwSj3xEAKa0jjzwyyYr/2OrsFKcf//jHqVarGT16dPbcc88+ng7Kq729fbUbEXTmpz/9aZYtW5Yk2WOPPfpiLOg3xo8fnySZOnVqp38I9uqrr3aEiDe+8Y19Ohv0VzNmzMiNN96YJDnllFPS3Nxc8ETQfzzyyCP54he/mKVLl+bggw/Oueee23FzLOhP/F1Paf3d3/1dNt988yxevDif//zn89xzzyVZceOPm266Kb/4xS+SJGeccUbHHdmADTdr1qx84hOfyG233bZafK9Wq5k6dWq+/e1v54YbbkiSvPnNb862225b1KhQSnvvvXf233//JMnll1+eP/7xj1m+fHmS5LnnnssXvvCFLF68OCNGjHDhc+hFjzzySH7yk59k2rRpaW9vT7Jixftdd92V888/P3Pnzs2ee+6ZE044oeBJoTHNnTu342v+/Pkd31+wYMFqz638Q+YkeeKJJ3LxxRdnyZIlOfDAA/NP//RPAjz9VqW6rmUa0MCmTJmSCy64oOOCyy0tLVm8eHHHb4QmTJjg2g/Qy15++eV88IMf7PjxoEGDMmTIkCxevDhLlizp+P4BBxyQT37yk6vdtRvoHfPnz8+FF16YZ599NsmKz+GAAQOycOHCJMnw4cPz6U9/OuPGjStyTCiVO++8M5dffnmSpKmpKS0tLVmwYEHHqvg3vvGN+dSnPuXfe7Ce3v72t3druy984QsdZ3hdcMEFefTRR5MkI0aMWGv8O/nkk3PyySdv+KBQpyx7otTGjh2bf//3f8/NN9+cBx54ILNmzcqwYcOyww475G1ve1sOOuigokeE0hk9enQ+9alP5ZFHHslf/vKXzJ49O3Pnzs3AgQOz1VZbZeedd84RRxyR/fbbr+hRobSGDx+eL3/5y/nFL36Ru+++O3/961+zbNmybLXVVtl///1z8sknZ+ONNy56TCiV3XbbLSeeeGIef/zxzJgxIwsXLkxra2t22mmnjB8/Pm9+85uLHhH6nVXXO82bN2+t2y5atKjW40ChrAAEAAAAgBJzDUAAAAAAKDEBEAAAAABKTAAEAAAAgBITAAEAAPj/27vbWJ/rhw/g79MRuWmMRUPKnKhZN8islIbWaloSRps5R3OTplZ50BM1NjWbZWltjZYhlSm21sJQI6GhB6FyynTmrhbS5K7MXA+s37ico8P/ui5dX6/Xo8/5fm6/59HZ+3w/nw8ABSYABAAAAIACEwACAAAAQIEJAAEAAACgwASAAAAAAFBgAkAAAAAAKDABIAAAAAAUmAAQAAAAAApMAAgAAAAABSYABAAAAIACEwACAAAAQIEJAAEAAACgwASAAAAAAFBgAkAA4P+1mpqalJWVpaysLFVVVZd7OQAA8K8jAAQAAACAAhMAAgAAAECBCQABAAAAoMAEgAAAAABQYAJAAAAAACgwASAAUHg//PBDZsyYkUGDBuXmm29Os2bN0rBhw7Ru3Tp9+vTJ1KlTc+DAgTr7Dx06tHTT8Lp16+o1Z9++fUt9vv/++zrbffLJJxk5cmQqKipy7bXXpkmTJunYsWNGjBiRVatWXXCO1atXl+aYPHlykuTHH3/MxIkT07Vr17Ro0eKcOgAArkwNLvcCAAD+N82fPz+VlZW11u3fvz/79+/P2rVrM3369Lz//vsZMGDAee3Gjx+fjz76KEkye/bs9O7d+4JzVldXZ/Xq1UmSPn365NZbbz2vze7duzNs2LBs2LDhvLqamprU1NTkvffey+DBgzN//vw0adLkn141CxYsyNixY3P8+PF/bAsAwJVDAAgAFNqxY8dSVlaWO+64I3369Mktt9ySli1bJkn27NmTVatWZfny5Tl8+HAGDx6c9evXp3v37ueM0a9fv3Tp0iXV1dX58MMPM3PmzLRo0aLOOWfPnl0qjxs37rz63bt3p1evXvn555+TJN26dctjjz2WioqKXHXVVamurs78+fOzc+fOLF68OEePHs3SpUtTVlZW55zr16/PK6+8krKyslRWVua+++5L06ZNs2PHjnTo0OFifmUAABRM2enTp09f7kUAAFyqmpqadOzYMUlSWVmZuXPnnlP/7bffplGjRqmoqKhzjFWrVmXgwIE5duxY+vfvX+vW29dffz3PP/98kuSNN97IM888U+tYf/75Z9q1a5eDBw+mVatW2bt3bxo1alSqP336dHr37p0NGzakvLw8b731VsaMGVPrOFVVVVm4cGGS5O23387o0aPPabN69er07du39HPr1q2zcuXK3H777XW+KwAAVx5nAAIAhda1a9cLhn9J8sADD+SFF15Iknz22WfZu3fveW2qqqpK23DP/sLvv1u8eHEOHjxY6nN2+JecOfPv722/kydPrjX8S5JGjRpl3rx5uemmm5Ikr7322gXfIUlmzZol/AMA4DwCQACAJPfee2+p/NVXX51X36JFiwwfPjxJsm3btqxfv77Wcc4OB8eOHXte/bx585KcCfieffbZC66pYcOGeeKJJ5Ik27dvz65du+pse+ONN2bgwIEXHA8AgCuTMwABgCvCl19+mQ8++CAbN27Mzp0788cff+TkyZO1tt2zZ0+tz8ePH585c+YkORP03XPPPefUV1dXZ82aNUnO3ALcuXPn88b44osvkiRt2rTJ559//o/rPnToUKn83Xff1XmeX+/evS94RiAAAFcuASAAUGhHjhzJiBEj8vHHH9e7z+HDh2t9ftddd6Vnz57ZtGlTFi1alJkzZ6Z58+al+n+6/OPo0aM5cOBAkmTXrl0ZNGhQvdeUJL/99ludde3bt7+osQAAuHIIAAGAQhs2bFiWLl2aJGnatGkGDBiQbt26pW3btmnSpEkaNDjz59C2bdvy0ksvJUlOnTpV53jjx4/Ppk2bcvz48bz77ruZMGFCkjOXdvy9vfe6666rNdz7/fff/6N3+euvv+qsa9y48X80NgAAxSUABAAKa926daXw77bbbsuKFSty/fXX19r26quvrteYw4cPz8SJE3Po0KHMnj27FACeffnHqFGj0rBhw/P6NmvWrFTu3r17vv7664t6HwAAuBQuAQEACmvFihWl8quvvlpn+JckP/30U73GbNy4caqqqpIkW7duLd3oO2vWrCRJWVlZrZd/JEnz5s1LIWBd5wwCAMD/NAEgAFBYv/zyS6lcUVFxwbbLli2r97hPPfVU6cKN2bNnZ/v27aXLPfr3759OnTrV2ff+++9Pkvz666++AAQA4P+EABAAKKymTZuWyjt27Kiz3YYNGy4qAOzcuXP69euXJFm0aFGmT59eqqvt8o+zVVZWlsqTJk3K6dOn6z0vAABcCgEgAFBYPXv2LJWnTJmSEydOnNdmy5YtGTJkyEUHcU8//XSS5NixY5kzZ06SpE2bNhk4cOAF+w0ZMiS9evVKkixfvjwjR47MkSNH6mx/6tSpLF++PFOnTr2o9QEAwN9cAgIAFNbjjz+eDh06ZNeuXdm8eXO6dOmS0aNHp6KiIseOHcuaNWuycOHCnDx5MpWVlaVbfOvj0UcfTdu2bbNv377SsyeffPIfLxMpKyvL4sWLc/fdd2f37t1ZsGBBPv300wwdOjQ9evRIy5Ytc+LEiezbty/ffPNNVq5cmf3796d///6ZNGnSJf8uAAC4cgkAAYDCatSoUZYsWZKHHnooBw4cyK5du/Lyyy+f06a8vDzTpk1Lr169LioAbNCgQcaMGZMpU6YkORPsjRkzpl5927Vrl82bN6eqqirLli0r3Sh8Ie3bt6/32gAA4Gy2AAMAhdajR49s2bIlEydOTJcuXXLNNdekWbNm6dy5c8aNG5eNGzfmxRdfvKSxH3zwwXPKHTt2rHff1q1bZ+nSpdmwYUMmTJiQO++8M61atUp5eXmaNm2aTp065ZFHHsm0adOybdu2zJ0795LWCAAAZaedPA0AcEmee+65zJw5M0myZMmSDBo06DKvCAAAzicABAC4BEePHs0NN9yQQ4cOpV27dqmpqUmDBk5XAQDg38cWYACASzBjxowcOnQoSTJhwgThHwAA/1q+AAQAqIe9e/dm69atOX78eNasWZM333wzp06dSps2bbJjx440a9bsci8RAABq5V/VAAD1sHLlyowaNeqcZ+Xl5XnnnXeEfwAA/KvZAgwAcJHatGmThx9+OGvXrs2AAQMu93IAAOCCbAEGAAAAgALzBSAAAAAAFJgAEAAAAAAKTAAIAAAAAAUmAAQAAACAAhMAAgAAAECBCQABAAAAoMAEgAAAAABQYAJAAAAAACgwASAAAAAAFJgAEAAAAAAKTAAIAAAAAAUmAAQAAACAAhMAAgAAAECBCQABAAAAoMAEgAAAAABQYAJAAAAAACgwASAAAAAAFJgAEAAAAAAKTAAIAAAAAAUmAAQAAACAAhMAAgAAAECBCQABAAAAoMAEgAAAAABQYAJAAAAAACgwASAAAAAAFNh/ARNsEv41G/hDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data)\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"pos\"] = df[\"pos\"].astype(int)\n",
+    "df[\"p(brown)\"] = df[\"p(brown)\"].astype(float)\n",
+    "df = df.groupby([\"layer\", \"pos\"]).mean().reset_index()\n",
+    "print(df)\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"pos\"))\n",
+    "    + scale_y_reverse()\n",
+    "    + geom_tile(aes(fill=\"p(brown)\"))\n",
+    "    + scale_fill_cmap(\"Purples\")\n",
+    ")\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "245a1a0c-e393-41e0-8790-051d5a00eb63",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/advanced_tutorials/MQNLI.ipynb b/_sources/tutorials/advanced_tutorials/MQNLI.ipynb
new file mode 100644
index 00000000..ba6980ab
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/MQNLI.ipynb
@@ -0,0 +1,1794 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nested Hierarchical Structure with MQNLI\n",
+    "\n",
+    "In this notebook, we use `pyvene` to analyze language models on a complex heirarchical task: multiply-quantified natural language inference (MQNLI). \n",
+    "We begin by describing the causal structure that generates our data through semantic composition. Then, we analyze whether models that learn this task employ the same compositional structure to solve it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Amir Zur\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set-Up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import pyvene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json \n",
+    "import random\n",
+    "import copy\n",
+    "import itertools\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm, trange\n",
+    "from collections import Counter\n",
+    "\n",
+    "import torch\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.utils.data import DataLoader\n",
+    "from transformers import TrainingArguments, Trainer\n",
+    "from datasets import Dataset\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "from pyvene import CausalModel\n",
+    "# from pyvene.models.configuration_intervenable_model import RepresentationConfig\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    RotatedSpaceIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig\n",
+    ")\n",
+    "\n",
+    "from pyvene.models.gpt2.modelings_intervenable_gpt2 import create_gpt2_lm\n",
+    "# from pyvene.models.gpt_neox.modelings_intervenable_gpt_neox import create_gpt_neox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TRAIN_DIR = 'mqnli_factual'\n",
+    "DAS_DIR = 'mqnli_das'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x169a1e510b0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seed = 42\n",
+    "np.random.seed(seed)\n",
+    "random.seed(seed)\n",
+    "torch.manual_seed(seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The MQNLI Task\n",
+    "\n",
+    "Multiply-quantified natural language inference (MQNLI) is a variant of natural language inference (NLI) with a highly structured composition. Each datapoint is a pair of sentences, and each label is the logical relation between them (e.g., entailment, reverse entailment, no relation). Crucially, the logical relation can be computed compositionally: first compute the relation between the two sentences' nouns, then their determiners, and then combine these intermediate computations to find the relation between the sentences' noun phrases (NPs). \n",
+    "\n",
+    "In this section, we walk through the high-level causal structure of MQNLI with examples from the MQNLI dataset. We encourage first-time readers to read the original paper, [Geiger, Cases, Karttunen, and Potts (2018)](https://arxiv.org/pdf/1810.13033.pdf), before getting started."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# JSON files generated from adapting MQNLI codebase\n",
+    "# https://github.com/atticusg/MultiplyQuantifiedData\n",
+    "\n",
+    "class Hashabledict(dict):\n",
+    "    def __hash__(self):\n",
+    "        return hash(frozenset(self))\n",
+    "\n",
+    "with open('tutorial_data/mqnli_q_projectivity.json') as f:\n",
+    "    determiner_signatures = json.load(f)\n",
+    "    determiner_signatures = Hashabledict({\n",
+    "        q1: Hashabledict({\n",
+    "            q2: Hashabledict({\n",
+    "                r1: Hashabledict({\n",
+    "                    r2: determiner_signatures[q1][q2][r1][r2]  \n",
+    "                    for r2 in determiner_signatures[q1][q2][r1]\n",
+    "                })  \n",
+    "                for r1 in determiner_signatures[q1][q2]\n",
+    "            })\n",
+    "            for q2 in determiner_signatures[q1]\n",
+    "        })\n",
+    "        for q1 in determiner_signatures\n",
+    "    })\n",
+    "\n",
+    "with open('tutorial_data/mqnli_neg_signature.json') as f:\n",
+    "    negation_signature = Hashabledict(json.load(f))\n",
+    "\n",
+    "with open('tutorial_data/mqnli_empty_signature.json') as f:\n",
+    "    emptystring_signature = Hashabledict(json.load(f))\n",
+    "\n",
+    "with open('tutorial_data/mqnli_cont_signature.json') as f:\n",
+    "    compose_contradiction_signature = Hashabledict(json.load(f))\n",
+    "\n",
+    "with open('tutorial_data/mqnli_neg_cont_signature.json') as f:\n",
+    "    compose_neg_contradiction_signature = Hashabledict(json.load(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parents = {\n",
+    "    \"N_P_O\": [],\n",
+    "    \"N_H_O\": [],\n",
+    "    \"N_O\": [\"N_P_O\", \"N_H_O\"],\n",
+    "    \"Adj_P_O\": [],\n",
+    "    \"Adj_H_O\": [],\n",
+    "    \"Adj_O\": [\"Adj_P_O\", \"Adj_H_O\"],\n",
+    "    \"NP_O\": [\"Adj_O\", \"N_O\"],\n",
+    "    \"Q_P_O\": [],\n",
+    "    \"Q_H_O\": [],\n",
+    "    \"Q_O\": [\"Q_P_O\", \"Q_H_O\"],\n",
+    "    \"V_P\": [],\n",
+    "    \"V_H\": [],\n",
+    "    \"V\": [\"V_P\", \"V_H\"],\n",
+    "    \"Adv_P\": [],\n",
+    "    \"Adv_H\": [],\n",
+    "    \"Adv\": [\"Adv_P\", \"Adv_H\"],\n",
+    "    \"VP\": [\"Adv\", \"V\"],\n",
+    "    \"QP_O\": [\"Q_O\", \"NP_O\", \"VP\"],\n",
+    "    \"Neg_P\": [],\n",
+    "    \"Neg_H\": [],\n",
+    "    \"Neg\": [\"Neg_P\", \"Neg_H\"],\n",
+    "    \"NegP\": [\"Neg\", \"QP_O\"],\n",
+    "    \"N_P_S\": [],\n",
+    "    \"N_H_S\": [],\n",
+    "    \"N_S\": [\"N_P_S\", \"N_H_S\"],\n",
+    "    \"Adj_P_S\": [],\n",
+    "    \"Adj_H_S\": [],\n",
+    "    \"Adj_S\": [\"Adj_P_S\", \"Adj_H_S\"],\n",
+    "    \"NP_S\": [\"Adj_S\", \"N_S\"],\n",
+    "    \"Q_P_S\": [],\n",
+    "    \"Q_H_S\": [],\n",
+    "    \"Q_S\": [\"Q_P_S\", \"Q_H_S\"],\n",
+    "    \"QP_S\": [\"Q_S\", \"NP_S\", \"NegP\"]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EMPTY = \"\"\n",
+    "IND = \"independence\"\n",
+    "EQV = \"equivalence\"\n",
+    "ENT = \"entails\"\n",
+    "REV = \"reverse entails\"\n",
+    "CON = \"contradiction\"\n",
+    "ALT = \"alternation\"\n",
+    "COV = \"cover\"\n",
+    "# all possible relation values from the original paper \n",
+    "# (https://arxiv.org/pdf/1810.13033.pdf)\n",
+    "RELATIONS = [IND, EQV, ENT, REV, CON, ALT, COV]\n",
+    "\n",
+    "Q_VALUES = [\n",
+    "    determiner_signatures[q1][q2]\n",
+    "    for q1 in determiner_signatures for q2 in determiner_signatures[q1]\n",
+    "]\n",
+    "\n",
+    "values = {\n",
+    "    \"N_P_O\": [\"tree\", \"rock\"],\n",
+    "    \"N_H_O\": [\"tree\", \"rock\"],\n",
+    "    \"N_O\": [EQV, IND],\n",
+    "    \"Adj_P_O\": [\"happy\", \"sad\", EMPTY],\n",
+    "    \"Adj_H_O\": [\"happy\", \"sad\", EMPTY],\n",
+    "    \"Adj_O\": [EQV, IND, ENT, REV],\n",
+    "    \"NP_O\": [EQV, IND, ENT, REV],\n",
+    "    \"Q_P_O\": [\"some\", \"every\"],\n",
+    "    \"Q_H_O\": [\"some\", \"every\"],\n",
+    "    \"Q_O\": Q_VALUES,\n",
+    "    \"V_P\": [\"climbed\", \"threw\"],\n",
+    "    \"V_H\": [\"climbed\", \"threw\"],\n",
+    "    \"V\": [EQV, IND],\n",
+    "    \"Adv_P\": [\"energetically\", \"joyfully\", EMPTY],\n",
+    "    \"Adv_H\": [\"energetically\", \"joyfully\", EMPTY],\n",
+    "    \"Adv\": [EQV, IND, ENT, REV],\n",
+    "    \"VP\": [EQV, IND, ENT, REV],\n",
+    "    \"QP_O\": [EQV, IND, ENT, REV],\n",
+    "    \"Neg_P\": [\"not\", EMPTY],\n",
+    "    \"Neg_H\": [\"not\", EMPTY],\n",
+    "    \"Neg\": [\n",
+    "        negation_signature, \n",
+    "        emptystring_signature, \n",
+    "        compose_contradiction_signature, \n",
+    "        compose_neg_contradiction_signature\n",
+    "    ],\n",
+    "    \"NegP\": RELATIONS,\n",
+    "    \"N_P_S\": [\"child\", \"dog\"],\n",
+    "    \"N_H_S\": [\"child\", \"dog\"],\n",
+    "    \"N_S\": [EQV, IND],\n",
+    "    \"Adj_P_S\": [\"little\", \"cute\", EMPTY],\n",
+    "    \"Adj_H_S\": [\"little\", \"cute\", EMPTY],\n",
+    "    \"Adj_S\": [EQV, IND, ENT, REV],\n",
+    "    \"NP_S\": [EQV, IND, ENT, REV],\n",
+    "    \"Q_P_S\": [\"some\", \"every\"],\n",
+    "    \"Q_H_S\": [\"some\", \"every\"],\n",
+    "    \"Q_S\": Q_VALUES,\n",
+    "    \"QP_S\": RELATIONS\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# adapted from original code for MQNLI:\n",
+    "# https://github.com/atticusg/MultiplyQuantifiedData/blob/master/natural_logic_model.py\n",
+    "\n",
+    "def adj_merge(adj_p, adj_h):\n",
+    "    if adj_p == adj_h:\n",
+    "        return EQV\n",
+    "    if adj_p == EMPTY:\n",
+    "        return REV\n",
+    "    if adj_h == EMPTY:\n",
+    "        return ENT\n",
+    "    return IND\n",
+    "\n",
+    "adv_merge = adj_merge\n",
+    "\n",
+    "def noun_phrase(adj, noun):\n",
+    "    #merges a noun relation with an adjective relation\n",
+    "    #or a verb relation with an adverb relation\n",
+    "    # makes sense: if the objects are the same, then adjective's relation holds\n",
+    "    # otherwise, they're independent\n",
+    "    if noun == EQV:\n",
+    "        return adj\n",
+    "    return IND\n",
+    "\n",
+    "verb_phrase = noun_phrase\n",
+    "\n",
+    "def negation_merge(neg_p, neg_h):\n",
+    "    #merges negation\n",
+    "    if neg_p == neg_h and neg_p == EMPTY:\n",
+    "        return Hashabledict(emptystring_signature)\n",
+    "    if neg_p == neg_h and neg_p != EMPTY:\n",
+    "        return Hashabledict(negation_signature)\n",
+    "    if neg_p == EMPTY:\n",
+    "        return Hashabledict(compose_contradiction_signature)\n",
+    "    if neg_p != EMPTY:\n",
+    "        return Hashabledict(compose_neg_contradiction_signature)\n",
+    "\n",
+    "negation_phrase = lambda neg, qp: neg[qp]\n",
+    "\n",
+    "noun_merge = lambda n_p, n_h: EQV if n_p == n_h else IND\n",
+    "verb_merge = noun_merge\n",
+    "\n",
+    "quantifier_merge = lambda q_p, q_h: determiner_signatures[q_p][q_h]\n",
+    "\n",
+    "quantifier_phrase = lambda q, np, vp: q[np][vp]\n",
+    "\n",
+    "functions = {\n",
+    "    \"N_P_O\": lambda: \"tree\",\n",
+    "    \"N_H_O\": lambda: \"tree\",\n",
+    "    \"N_O\": noun_merge,\n",
+    "    \"Adj_P_O\": lambda: \"happy\",\n",
+    "    \"Adj_H_O\": lambda: \"happy\",\n",
+    "    \"Adj_O\": adj_merge,\n",
+    "    \"NP_O\": noun_phrase,\n",
+    "    \"Q_P_O\": lambda: \"some\",\n",
+    "    \"Q_H_O\": lambda: \"some\",\n",
+    "    \"Q_O\": quantifier_merge,\n",
+    "    \"V_P\": lambda: \"climbed\",\n",
+    "    \"V_H\": lambda: \"climbed\",\n",
+    "    \"V\": verb_merge,\n",
+    "    \"Adv_P\": lambda: \"energetically\",\n",
+    "    \"Adv_H\": lambda: \"energetically\",\n",
+    "    \"Adv\": adv_merge,\n",
+    "    \"VP\": verb_phrase,\n",
+    "    \"QP_O\": quantifier_phrase,\n",
+    "    \"Neg_P\": lambda: \"not\",\n",
+    "    \"Neg_H\": lambda: \"not\",\n",
+    "    \"Neg\": negation_merge,\n",
+    "    \"NegP\": negation_phrase,\n",
+    "    \"N_P_S\": lambda: \"dog\",\n",
+    "    \"N_H_S\": lambda: \"dog\",\n",
+    "    \"N_S\": noun_merge,\n",
+    "    \"Adj_P_S\": lambda: \"cute\",\n",
+    "    \"Adj_H_S\": lambda: \"cute\",\n",
+    "    \"Adj_S\": adj_merge,\n",
+    "    \"NP_S\": noun_phrase,\n",
+    "    \"Q_P_S\": lambda: \"some\",\n",
+    "    \"Q_H_S\": lambda: \"some\",\n",
+    "    \"Q_S\": quantifier_merge,\n",
+    "    \"QP_S\": quantifier_phrase\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# coordinates to display the MQNLI tree\n",
+    "pos = {\n",
+    "    \"N_P_O\": (32, 0.3),\n",
+    "    \"N_H_O\": (30, 0.7),\n",
+    "    \"N_O\": (31, 1.3),\n",
+    "    \"Adj_P_O\": (28, 0),\n",
+    "    \"Adj_H_O\": (26, 0.5),\n",
+    "    \"Adj_O\": (27, 1),\n",
+    "    \"NP_O\": (29, 2),\n",
+    "    \"Q_P_O\": (24, 1.3),\n",
+    "    \"Q_H_O\": (22, 1.7),\n",
+    "    \"Q_O\": (23, 2.5),\n",
+    "    \"V_P\": (21, 0),\n",
+    "    \"V_H\": (19, 0.5),\n",
+    "    \"V\": (20, 1),\n",
+    "    \"Adv_P\": (17, -0.3),\n",
+    "    \"Adv_H\": (15, 0.2),\n",
+    "    \"Adv\": (16, 0.7),\n",
+    "    \"VP\": (18, 2),\n",
+    "    \"QP_O\": (25, 3),\n",
+    "    \"Neg_P\": (13, 2.5),\n",
+    "    \"Neg_H\": (11, 3),\n",
+    "    \"Neg\": (12, 3.5),\n",
+    "    \"NegP\": (14, 4),\n",
+    "    \"N_P_S\": (9, 2.2),\n",
+    "    \"N_H_S\": (7, 2.8),\n",
+    "    \"N_S\": (8, 3.3),\n",
+    "    \"Adj_P_S\": (5, 1.5),\n",
+    "    \"Adj_H_S\": (3, 2),\n",
+    "    \"Adj_S\": (4, 2.5),\n",
+    "    \"NP_S\": (6, 4.3),\n",
+    "    \"Q_P_S\": (2, 3.2),\n",
+    "    \"Q_H_S\": (0, 3.5),\n",
+    "    \"Q_S\": (1, 4),\n",
+    "    \"QP_S\": (10, 5)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variables = list(parents.keys())  # pretty sure this preserves order?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mqlni_model = CausalModel(variables, values, parents, functions, pos=pos)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by visualizing the high-level structure of the MQNLI task. All leaf nodes consist of text entries (e.g., Adj_P_O might be \"fun\", N_H_O might be \"child\"), with one copy corresponding to the premise sentence (P) and the other corresponding to the hypothesis (H). All other nodes take on relation values (e.g., entailment, reverse entailment, no relation), or mappings between relations (e.g., (entailment, entailment) --> no entailment)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mqlni_model.print_structure()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example MQNLI Input\n",
+    "\n",
+    "Here we walk through an example MQNLI input, presented below.\n",
+    "```json\n",
+    "Premise: Every dog climbed some tree.\n",
+    "Hypothesis: Some dog did not climb every tree.\n",
+    "```\n",
+    "\n",
+    "The output for this sentence pair is `contradiction`, because the premise entails that there cannot be a dog who climbed no tree. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = {\n",
+    "    # premise\n",
+    "    \"Q_P_S\": \"every\",\n",
+    "    \"Adj_P_S\": EMPTY,\n",
+    "    \"N_P_S\": \"dog\",\n",
+    "    \"Neg_P\": EMPTY,\n",
+    "    \"Adv_P\": EMPTY,\n",
+    "    \"V_P\": \"climbed\",\n",
+    "    \"Q_P_O\": \"some\",\n",
+    "    \"Adj_P_O\": EMPTY,\n",
+    "    \"N_P_O\": \"tree\",\n",
+    "    # hypothesis\n",
+    "    \"Q_H_S\": \"some\",\n",
+    "    \"Adj_H_S\": EMPTY,\n",
+    "    \"N_H_S\": \"dog\",\n",
+    "    \"Neg_H\": \"not\",\n",
+    "    \"Adv_H\": EMPTY,\n",
+    "    \"V_H\": \"climbed\",\n",
+    "    \"Q_H_O\": \"some\",\n",
+    "    \"Adj_H_O\": EMPTY,\n",
+    "    \"N_H_O\": \"tree\"\n",
+    "}\n",
+    "\n",
+    "setting = mqlni_model.run_forward(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "every  dog   climbed some  tree\n",
+      "some  dog not  climbed some  tree\n",
+      "contradiction\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_premise(setting):\n",
+    "    print(\n",
+    "        setting[\"Q_P_S\"],\n",
+    "        setting[\"Adj_P_S\"],\n",
+    "        setting[\"N_P_S\"],\n",
+    "        setting[\"Neg_P\"],\n",
+    "        setting[\"Adv_P\"],\n",
+    "        setting[\"V_P\"],\n",
+    "        setting[\"Q_P_O\"],\n",
+    "        setting[\"Adj_P_O\"],\n",
+    "        setting[\"N_P_O\"]\n",
+    "    )\n",
+    "\n",
+    "def print_hypothesis(setting):\n",
+    "    print(\n",
+    "        setting[\"Q_H_S\"],\n",
+    "        setting[\"Adj_H_S\"],\n",
+    "        setting[\"N_H_S\"],\n",
+    "        setting[\"Neg_H\"],\n",
+    "        setting[\"Adv_H\"],\n",
+    "        setting[\"V_H\"],\n",
+    "        setting[\"Q_H_O\"],\n",
+    "        setting[\"Adj_H_O\"],\n",
+    "        setting[\"N_H_O\"]\n",
+    "    )\n",
+    "\n",
+    "print_premise(setting)\n",
+    "print_hypothesis(setting)\n",
+    "\n",
+    "print(setting[\"QP_S\"]) # the output is in the root node, QP_S"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We display the structure of the MQNLI example below. Note that the intermediate relation values compose with each other to produce the final output. For instance, since `N_O` and `Adj_O` both take on the value `equivalence`, then `NP_O` is also an equivalence relation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display = {v: not isinstance(values[v][0], dict) for v in variables}\n",
+    "mqlni_model.print_setting(setting, display=display)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MQNLI with an Intervention\n",
+    "\n",
+    "Below is a run of the MQNLI logic model where we intervene on the object quantifier (`QP_O`) and swap its relation value from `equivalence` to `contradiction`. Note that this changes the final output from `contradiction` to `entailment`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "every  dog   climbed some  tree\n",
+      "some  dog not  climbed some  tree\n",
+      "entails\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "intervention = {**inputs, 'QP_O': 'contradiction'}\n",
+    "setting = mqlni_model.run_forward(intervention)\n",
+    "print_premise(setting)\n",
+    "print_hypothesis(setting)\n",
+    "print(setting[\"QP_S\"])\n",
+    "\n",
+    "mqlni_model.print_setting(setting, display=display)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training a Model on MQNLI\n",
+    "\n",
+    "In this section, we train a language model (GPT-2) on the MQNLI task. Importantly, we do not need to access the original MQNLI dataset to do this -- having set up our causal model earlier in this notebook, we can generate datapoints by sampling from it directly! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = mqlni_model.generate_factual_dataset(100, sampler=mqlni_model.sample_input_tree_balanced, return_tensors=False)\n",
+    "\n",
+    "X = [example['input_ids'] for example in dataset]\n",
+    "y = [example['labels'] for example in dataset]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "some little child  energetically climbed some happy tree\n",
+      "every  child not energetically climbed some happy tree\n",
+      "alternation\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "\n",
+    "print_premise(X[i])\n",
+    "print_hypothesis(X[i])\n",
+    "print(y[i]['QP_S'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to make sure that we indeed sampled evenly from the causal structure. For now, we can just verify that every possible output value (i.e., the values of the root node `QP_S`) has enough datapoints. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'alternation': 16,\n",
+       "         'contradiction': 18,\n",
+       "         'reverse entails': 15,\n",
+       "         'independence': 14,\n",
+       "         'entails': 16,\n",
+       "         'equivalence': 10,\n",
+       "         'cover': 11})"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Counter([n['QP_S'] for n in y])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to train a language model!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, model = create_gpt2_lm()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def premise_to_string(setting):\n",
+    "    return \\\n",
+    "        setting[\"Q_P_S\"] + ' ' + \\\n",
+    "        setting[\"Adj_P_S\"] + ' ' + \\\n",
+    "        setting[\"N_P_S\"] + ' ' + \\\n",
+    "        setting[\"Neg_P\"] + ' ' + \\\n",
+    "        setting[\"Adv_P\"] + ' ' + \\\n",
+    "        setting[\"V_P\"] + ' ' + \\\n",
+    "        setting[\"Q_P_O\"] + ' ' + \\\n",
+    "        setting[\"Adj_P_O\"] + ' ' + \\\n",
+    "        setting[\"N_P_O\"]\n",
+    "\n",
+    "def hypothesis_to_string(setting):\n",
+    "    return \\\n",
+    "        setting[\"Q_H_S\"] + ' ' + \\\n",
+    "        setting[\"Adj_H_S\"] + ' ' + \\\n",
+    "        setting[\"N_H_S\"] + ' ' + \\\n",
+    "        setting[\"Neg_H\"] + ' ' + \\\n",
+    "        setting[\"Adv_H\"] + ' ' + \\\n",
+    "        setting[\"V_H\"] + ' ' + \\\n",
+    "        setting[\"Q_H_O\"] + ' ' + \\\n",
+    "        setting[\"Adj_H_O\"] + ' ' + \\\n",
+    "        setting[\"N_H_O\"]\n",
+    "\n",
+    "def preprocess_input(setting):\n",
+    "    return f'Premise: {premise_to_string(setting)}\\nHypothesis: {hypothesis_to_string(setting)}\\nRelation: '\n",
+    "\n",
+    "def preprocess_output(setting):\n",
+    "    return setting['QP_S']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IGNORE_INDEX = -100\n",
+    "MAX_LENGTH = 64\n",
+    "\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "\n",
+    "def preprocess(X, y):\n",
+    "    examples = [preprocess_input(x) for x in X]\n",
+    "    labels = [preprocess_output(y) for y in y]\n",
+    "\n",
+    "    examples = tokenizer(\n",
+    "        examples, \n",
+    "        padding='max_length', \n",
+    "        max_length=MAX_LENGTH, \n",
+    "        truncation=True, \n",
+    "        return_tensors='pt'\n",
+    "    )\n",
+    "    labels = tokenizer(\n",
+    "        labels, \n",
+    "        padding='max_length', \n",
+    "        max_length=MAX_LENGTH, \n",
+    "        truncation=True, \n",
+    "        return_tensors='pt'\n",
+    "    )['input_ids'][:, 0] # get first token of label\n",
+    "    \n",
+    "    # put label at the last index\n",
+    "    examples['labels'] = torch.full_like(examples['input_ids'], IGNORE_INDEX)\n",
+    "    examples['labels'][:, -1] = labels\n",
+    "\n",
+    "    return examples\n",
+    "\n",
+    "train_dataset = preprocess(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the wandb project where this run will be logged\n",
+    "os.environ[\"WANDB_PROJECT\"]=TRAIN_DIR\n",
+    "\n",
+    "# save your trained model checkpoint to wandb\n",
+    "os.environ[\"WANDB_LOG_MODEL\"]=\"false\"\n",
+    "\n",
+    "def accuracy_metric(x):\n",
+    "    labels = x.label_ids[:, -1]\n",
+    "    # predictions = x.predictions[0].argmax(axis=-1)[:, -2]  # uncomment for gpt-neox\n",
+    "    predictions = x.predictions.argmax(axis=-1)[:, -2]\n",
+    "    return {\n",
+    "        'accuracy': accuracy_score(y_true=labels, y_pred=predictions),\n",
+    "    }\n",
+    "\n",
+    "train_ds = Dataset.from_dict(train_dataset)\n",
+    "\n",
+    "batch_size = 8\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "    output_dir=TRAIN_DIR,\n",
+    "    overwrite_output_dir=True,\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    learning_rate=1e-05,\n",
+    "    num_train_epochs=1,\n",
+    "    per_device_train_batch_size=batch_size,\n",
+    "    per_device_eval_batch_size=batch_size,\n",
+    "    report_to=\"wandb\", # optional, remove if you don't want to log to wandb\n",
+    "    use_cpu=True,\n",
+    ")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=train_ds,\n",
+    "    eval_dataset=train_ds,\n",
+    "    compute_metrics=accuracy_metric\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train the model and log it in a Weights & Biases run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "wandb version 0.16.4 is available!  To upgrade, please run:\n",
+       " $ pip install wandb --upgrade"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.16.3"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>c:\\Users\\amirz\\Source\\NLP\\clones\\pyvene\\tutorials\\advanced_tutorials\\wandb\\run-20240314_140315-cwmxs9sx</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx' target=\"_blank\">derby-crumble-2</a></strong> to <a href='https://wandb.ai/amirzur1212/mqnli_factual' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/amirzur1212/mqnli_factual' target=\"_blank\">https://wandb.ai/amirzur1212/mqnli_factual</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx' target=\"_blank\">https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b3008cdd19b4d1f80ef096f3d2c97ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fd69497aae04b8c8a7be79b77196a5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'eval_loss': 4.242105484008789, 'eval_accuracy': 0.17, 'eval_runtime': 12.9558, 'eval_samples_per_second': 7.719, 'eval_steps_per_second': 1.003, 'epoch': 1.0}\n",
+      "{'train_runtime': 52.4216, 'train_samples_per_second': 1.908, 'train_steps_per_second': 0.248, 'train_loss': 5.367249708909255, 'epoch': 1.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import wandb \n",
+    "\n",
+    "_ = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.save_model(TRAIN_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We evaluate the model on the training dataset and a newly-sampled test dataset. The model generalizes, meaning it learned the MQNLI task!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "test_examples = mqlni_model.generate_factual_dataset(100, sampler=mqlni_model.sample_input_tree_balanced, return_tensors=False)\n",
+    "X = [example['input_ids'] for example in test_examples]\n",
+    "y = [example['labels'] for example in test_examples]\n",
+    "test_dataset = preprocess(X, y)\n",
+    "test_ds = Dataset.from_dict(test_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8c92d344b674837bc7733b012829136",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'eval_loss': 4.242105484008789,\n",
+       " 'eval_accuracy': 0.17,\n",
+       " 'eval_runtime': 12.6987,\n",
+       " 'eval_samples_per_second': 7.875,\n",
+       " 'eval_steps_per_second': 1.024,\n",
+       " 'epoch': 1.0}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = trainer.evaluate(train_ds)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce841fd20aad4b9fa81e502ee8fe8328",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'eval_loss': 4.242105484008789,\n",
+       " 'eval_accuracy': 0.17,\n",
+       " 'eval_runtime': 12.532,\n",
+       " 'eval_samples_per_second': 7.98,\n",
+       " 'eval_steps_per_second': 1.037,\n",
+       " 'epoch': 1.0}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = trainer.evaluate(test_ds)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc294f9a84f24cbaa44ceeb6dc1e379c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
+       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
+       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
+       "    </style>\n",
+       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▁▁</td></tr><tr><td>eval/loss</td><td>▁▁▁</td></tr><tr><td>eval/runtime</td><td>█▄▁</td></tr><tr><td>eval/samples_per_second</td><td>▁▅█</td></tr><tr><td>eval/steps_per_second</td><td>▁▅█</td></tr><tr><td>train/epoch</td><td>▁▁▁▁</td></tr><tr><td>train/global_step</td><td>▁▁▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.17</td></tr><tr><td>eval/loss</td><td>4.24211</td></tr><tr><td>eval/runtime</td><td>12.532</td></tr><tr><td>eval/samples_per_second</td><td>7.98</td></tr><tr><td>eval/steps_per_second</td><td>1.037</td></tr><tr><td>train/epoch</td><td>1.0</td></tr><tr><td>train/global_step</td><td>13</td></tr><tr><td>train/total_flos</td><td>3266150400000.0</td></tr><tr><td>train/train_loss</td><td>5.36725</td></tr><tr><td>train/train_runtime</td><td>52.4216</td></tr><tr><td>train/train_samples_per_second</td><td>1.908</td></tr><tr><td>train/train_steps_per_second</td><td>0.248</td></tr></table><br/></div></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run <strong style=\"color:#cdcd00\">derby-crumble-2</strong> at: <a href='https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx' target=\"_blank\">https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: <code>.\\wandb\\run-20240314_140315-cwmxs9sx\\logs</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wandb.finish()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpreting Our Model\n",
+    "\n",
+    "In this final section, we use distributed alignment search DAS from [Geiger*, Wu*, Potts, Icard, and Goodman (2024)](https://arxiv.org/pdf/2303.02536.pdf) to find an alignment between the high-level causal structure of MQNLI and our low-level language model trained on the MQNLI dataset.\n",
+    "\n",
+    "In this section, we won't search for an alignment over the entire causal structure. Instead, we will search for an alignment between the low-level model representations and the `NegP` token. Efficiently searching for a full alignment over a complex, nested causal structure is an open problem that is worth pursuing! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, model = create_gpt2_lm(name=TRAIN_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we set up our alignment. We will search for a subspace with a dimension of 128 (a fourth of our model's hidden dimension size) in the residual stream of the 10th layer of the model (out of 12 overall transformer layers)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = IntervenableConfig(\n",
+    "    model_type=type(model),\n",
+    "    representations=[\n",
+    "        RepresentationConfig(\n",
+    "            10,  # layer\n",
+    "            \"block_output\",  # intervention type\n",
+    "            \"pos\",  # intervention unit is now aligned with tokens\n",
+    "            1,  # max number of unit\n",
+    "            subspace_partition=[[0, 128]], \n",
+    "            # intervention_link_key=0,\n",
+    "        )\n",
+    "    ],\n",
+    "    intervention_types=RotatedSpaceIntervention,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.\n",
+      "\n",
+      "In case multiple location tags are passed only the first one will be considered\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable = IntervenableModel(config, model, use_fast=True)\n",
+    "# intervenable.set_device('cuda')\n",
+    "intervenable.disable_model_gradients()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Crucially, our interpretability experiments rely on a counterfactual dataset: for a given input, we want to consider what might happen had the value of `NegP` changed while all else remained the same. Fortunately, this is precisely what our causal model can provide us with! We can generate a counterfactual dataset by sampling inputs that only vary from each other on the `NegP` node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample_intervention(*args, **kwargs):\n",
+    "    return {\n",
+    "        'NegP' : random.choice(mqlni_model.values['NegP'])\n",
+    "    }\n",
+    "\n",
+    "def intervention_id(*args, **kwargs):\n",
+    "    return 0\n",
+    "\n",
+    "batch_size = 2 # specifies how many inputs we want per intervention that is sampled\n",
+    "\n",
+    "dataset = mqlni_model.generate_counterfactual_dataset(\n",
+    "    100, intervention_id, batch_size, \n",
+    "    sampler=mqlni_model.sample_input_tree_balanced, intervention_sampler=sample_intervention, return_tensors=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alternation\n",
+      "independence\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset[0]['base_labels']['QP_S'])\n",
+    "print(dataset[0]['labels']['QP_S'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'alternation': 23,\n",
+       "         'reverse entails': 10,\n",
+       "         'cover': 15,\n",
+       "         'equivalence': 15,\n",
+       "         'contradiction': 9,\n",
+       "         'independence': 14,\n",
+       "         'entails': 14})"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check that base labels are diverse (should be guaranteed by sampling from balanced input tree)\n",
+    "Counter([d['base_labels']['QP_S'] for d in dataset])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'independence': 47,\n",
+       "         'reverse entails': 10,\n",
+       "         'entails': 9,\n",
+       "         'alternation': 20,\n",
+       "         'cover': 10,\n",
+       "         'contradiction': 3,\n",
+       "         'equivalence': 1})"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check that counterfactuals labels are diverse (note that this could be skewed by the node's effect on the final output)\n",
+    "Counter([d['labels']['QP_S'] for d in dataset])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IGNORE_INDEX = -100\n",
+    "MAX_LENGTH = 64\n",
+    "\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "\n",
+    "def preprocess_input(setting):\n",
+    "    return f'Premise: {premise_to_string(setting)}\\nHypothesis: {hypothesis_to_string(setting)}\\nRelation: '\n",
+    "\n",
+    "def preprocess_output(setting):\n",
+    "    return setting['QP_S']\n",
+    "\n",
+    "def tokenize(x):\n",
+    "    return tokenizer(\n",
+    "        x, \n",
+    "        padding='max_length', \n",
+    "        max_length=MAX_LENGTH, \n",
+    "        truncation=True, \n",
+    "        return_tensors='pt'\n",
+    "    )\n",
+    "\n",
+    "def preprocess_counterfactual(data):\n",
+    "    preprocessed_data = []\n",
+    "    for d in data:\n",
+    "        base = preprocess_input(d['input_ids'])\n",
+    "        sources = [preprocess_input(d['source_input_ids'][0])]\n",
+    "        label = preprocess_output(d['labels'])\n",
+    "        base_label = preprocess_output(d['base_labels'])\n",
+    "\n",
+    "        preprocessed = {}\n",
+    "        preprocessed['input'] = tokenize(base)\n",
+    "        preprocessed['source'] = [tokenize(sources[0])]\n",
+    "        # place label at last index\n",
+    "        label = tokenize(label)['input_ids'][:, 0]\n",
+    "        preprocessed['label'] = torch.full_like(preprocessed['input']['input_ids'], IGNORE_INDEX)\n",
+    "        preprocessed['label'][:, -1] = label\n",
+    "        # repeat for base label\n",
+    "        base_label = tokenize(base_label)['input_ids'][:, 0]\n",
+    "        preprocessed['base_label'] = torch.full_like(preprocessed['input']['input_ids'], IGNORE_INDEX)\n",
+    "        preprocessed['base_label'][:, -1] = base_label\n",
+    "        preprocessed['intervention_id'] = torch.tensor(d['intervention_id'])\n",
+    "        preprocessed_data.append(preprocessed)\n",
+    "    return preprocessed_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset = preprocess_counterfactual(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = DataLoader(train_dataset, batch_size=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up our optimizer, loss function, and accuracy metric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epochs = 3\n",
+    "batch_size = 8\n",
+    "gradient_accumulation_steps = 1\n",
+    "\n",
+    "optimizer_params = []\n",
+    "for k, v in intervenable.interventions.items():\n",
+    "    optimizer_params += [{\"params\": v.rotate_layer.parameters()}]\n",
+    "    break\n",
+    "optimizer = torch.optim.Adam(optimizer_params, lr=0.001)\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_preds, eval_labels):\n",
+    "    accuracy = accuracy_score(\n",
+    "        y_pred=eval_preds[..., -2].squeeze().clone().detach().cpu().numpy(), \n",
+    "        y_true=eval_labels[..., -1].squeeze().clone().detach().cpu().numpy()\n",
+    "    )\n",
+    "    return {\n",
+    "        \"accuracy\": accuracy\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def compute_loss(outputs, labels):\n",
+    "    # Shift so that tokens < n predict n\n",
+    "    shift_logits = outputs[..., :-1, :].contiguous()\n",
+    "    shift_labels = labels[..., 1:].contiguous()\n",
+    "    # Flatten the tokens\n",
+    "    loss_fct = CrossEntropyLoss()\n",
+    "    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run DAS to find an alignment between our high-level causal model and the language model trained on MQNLI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "intervention trainable parameters:  589824\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch: 0: 100%|██████████| 12/12 [00:59<00:00,  4.93s/it, loss=6.88, acc=0]    \n",
+      "\n",
+      "Epoch: 1: 100%|██████████| 12/12 [00:58<00:00,  4.91s/it, loss=5.86, acc=0]   \n",
+      "\n",
+      "Epoch: 2: 100%|██████████| 12/12 [00:59<00:00,  4.98s/it, loss=6.37, acc=0]    \n",
+      "\n",
+      "Epoch: 100%|██████████| 3/3 [02:57<00:00, 59.28s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable.model.train()  # train enables drop-off but no grads\n",
+    "print(\"intervention trainable parameters: \", intervenable.count_parameters())\n",
+    "train_iterator = trange(0, int(epochs), desc=\"Epoch\")\n",
+    "\n",
+    "total_step = 0\n",
+    "for epoch in train_iterator:\n",
+    "    epoch_iterator = tqdm(\n",
+    "        DataLoader(\n",
+    "            train_dataset,\n",
+    "            batch_size=batch_size,\n",
+    "            drop_last=True\n",
+    "        ),\n",
+    "        desc=f\"Epoch: {epoch}\",\n",
+    "        position=0,\n",
+    "        leave=True\n",
+    "    )\n",
+    "    for batch in epoch_iterator:\n",
+    "        inputs = {k: v.to(intervenable.get_device()) for k, v in batch['input'].items()}\n",
+    "        sources = [{k: v.to(intervenable.get_device()) for k, v in s.items()} for s in batch['source']]\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            inputs,\n",
+    "            sources,\n",
+    "            {\"sources->base\": ([[[MAX_LENGTH - 2]] * batch_size], [[[MAX_LENGTH - 2]] * batch_size])},\n",
+    "            subspaces=[[[0]] * batch_size],\n",
+    "        )\n",
+    "\n",
+    "        eval_metrics = compute_metrics(\n",
+    "            counterfactual_outputs.logits.argmax(-1), batch[\"label\"].to(intervenable.get_device())\n",
+    "        )\n",
+    "\n",
+    "        # loss and backprop\n",
+    "        loss = compute_loss(\n",
+    "            counterfactual_outputs.logits, batch[\"label\"].to(intervenable.get_device())\n",
+    "        )\n",
+    "\n",
+    "        epoch_iterator.set_postfix({\"loss\": loss.item(), \"acc\": eval_metrics[\"accuracy\"]})\n",
+    "\n",
+    "        if gradient_accumulation_steps > 1:\n",
+    "            loss = loss / gradient_accumulation_steps\n",
+    "        loss.backward()\n",
+    "        if total_step % gradient_accumulation_steps == 0:\n",
+    "            optimizer.step()\n",
+    "            intervenable.set_zero_grad()\n",
+    "        total_step += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory 'mqnli_das' already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable.save(DAS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate DAS Alignment\n",
+    "\n",
+    "Lastly, we evaluate the accuracy of the alignment found by DAS. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, model = create_gpt2_lm(name=TRAIN_DIR)\n",
+    "intervenable = IntervenableModel.load(DAS_DIR, model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we evaluate the model's performance on the MQNLI factual task. As before, we expect our model to complete this task with nearly perfect accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "examples = mqlni_model.generate_factual_dataset(100, sampler=mqlni_model.sample_input_tree_balanced, return_tensors=False)\n",
+    "X = [example['input_ids'] for example in examples]\n",
+    "y = [example['labels'] for example in examples]\n",
+    "test_factual_dataset = preprocess(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy_metric(x):\n",
+    "    labels = x.label_ids[:, -1]\n",
+    "    # predictions = x.predictions[0].argmax(axis=-1)[:, -2]  # take one index before label\n",
+    "    predictions = x.predictions.argmax(axis=-1)[:, -2]\n",
+    "    return {\n",
+    "        'accuracy': accuracy_score(y_true=labels, y_pred=predictions),\n",
+    "    }\n",
+    "\n",
+    "test_factual_ds = Dataset.from_dict(test_factual_dataset)\n",
+    "\n",
+    "batch_size = 8\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"test_mqnli_trainer\",\n",
+    "    overwrite_output_dir=True,\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    learning_rate=1e-05,\n",
+    "    num_train_epochs=1,\n",
+    "    per_device_train_batch_size=batch_size,\n",
+    "    per_device_eval_batch_size=batch_size,\n",
+    "    report_to=\"none\",\n",
+    "    use_cpu=True,\n",
+    ")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=intervenable.model,\n",
+    "    args=training_args,\n",
+    "    compute_metrics=accuracy_metric\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba33bc2aaff448c1bd8ef2e97ed4f74b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'eval_loss': 4.302060127258301,\n",
+       " 'eval_accuracy': 0.17,\n",
+       " 'eval_runtime': 18.8671,\n",
+       " 'eval_samples_per_second': 5.3,\n",
+       " 'eval_steps_per_second': 0.689}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.evaluate(test_factual_ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we evaluate the DAS alignment on a newly-sampled counterfactual dataset. If our alignment generalizes, then we should expect a high interchange intervention accuracy (IIA) on the counterfactual dataset. We compute IIA by comparing the model's output with an interchange-intervention to the causal model's output with that same intervention applied to its high-level variables (i.e., we verify that an intervention changes the model's output in the direction that our causal model predicts). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 8\n",
+    "\n",
+    "dataset = mqlni_model.generate_counterfactual_dataset(\n",
+    "    1000, intervention_id, 1, \n",
+    "    sampler=mqlni_model.sample_input_tree_balanced, intervention_sampler=sample_intervention, return_tensors=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_cf_dataset = preprocess_counterfactual(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_metrics(eval_preds, eval_labels):\n",
+    "    accuracy = accuracy_score(\n",
+    "        y_pred=eval_preds[..., -2].squeeze().clone().detach().cpu().numpy(), \n",
+    "        y_true=eval_labels[..., -1].squeeze().clone().detach().cpu().numpy()\n",
+    "    )\n",
+    "    return {\n",
+    "        \"accuracy\": accuracy\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intervenable.set_device('cuda')\n",
+    "# intervenable.disable_intervention_gradients()\n",
+    "# intervenable.disable_model_gradients()\n",
+    "# intervenable.model.eval()  # train enables drop-off but no grads\n",
+    "print(\"intervention trainable parameters: \", intervenable.count_parameters())\n",
+    "\n",
+    "base_labels = None\n",
+    "counterfactual_labels = None\n",
+    "base_preds = None\n",
+    "counterfactual_preds = None\n",
+    "\n",
+    "batch_size = 8\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    for batch in tqdm(DataLoader(\n",
+    "        test_cf_dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        drop_last=True\n",
+    "    )):\n",
+    "        inputs = {k: v.to(intervenable.get_device()) for k, v in batch['input'].items()}\n",
+    "        sources = [{k: v.to(intervenable.get_device()) for k, v in s.items()} for s in batch['source']]\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            inputs,\n",
+    "            sources,\n",
+    "            {\"sources->base\": ([[[MAX_LENGTH - 2]] * batch_size], [[[MAX_LENGTH - 2]] * batch_size])},\n",
+    "            subspaces=[[[0]] * batch_size],\n",
+    "        )\n",
+    "\n",
+    "        if base_labels is None:\n",
+    "            # base_preds = base_outputs.logits.argmax(-1).clone().detach()\n",
+    "            # base_labels = batch['base_label']\n",
+    "            counterfactual_preds = counterfactual_outputs.logits.argmax(-1).clone().detach()\n",
+    "            counterfactual_labels = batch['label']\n",
+    "        else:\n",
+    "            # base_preds = torch.cat((base_preds, base_outputs.logits.argmax(-1).clone().detach()))\n",
+    "            # base_labels = torch.cat((base_labels, batch['base_label']))\n",
+    "            counterfactual_preds = torch.cat((counterfactual_preds, counterfactual_outputs.logits.argmax(-1).clone().detach()))\n",
+    "            counterfactual_labels = torch.cat((counterfactual_labels, batch['label']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'counterfactual_accuracy': 0.125}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    # 'base_accuracy': compute_metrics(base_preds, base_labels)['accuracy'],\n",
+    "    'counterfactual_accuracy': compute_metrics(counterfactual_preds, counterfactual_labels)['accuracy']\n",
+    "}"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyvene",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/tutorials/advanced_tutorials/Probing_Gender.ipynb b/_sources/tutorials/advanced_tutorials/Probing_Gender.ipynb
new file mode 100644
index 00000000..1a741ded
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/Probing_Gender.ipynb
@@ -0,0 +1,7253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Causal Evaluation of Probes\n",
+    "\n",
+    "This is a simple tutorial showing you to collect activations from intervention-points in a model. We'll compare 1D DAS IIA on each layer and position for `block_output` in pythia-70M with logistic regression probing accuracy. The task we'll look at is gender prediction, where gendered names are used in templates like \"[name] walked because\", which elicits the associated gendered pronoun \"he\" or \"she\" as the next-token prediction for this model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/advance_tutorials/Probing_Gender.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Aryaman Arora\"\n",
+    "__version__ = \"01/10/2024\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene as pv\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from transformers import (\n",
+    "    AutoModelForCausalLM,\n",
+    "    AutoTokenizer,\n",
+    "    get_linear_schedule_with_warmup,\n",
+    ")\n",
+    "import torch\n",
+    "import random\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import f1_score\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    "    geom_line,\n",
+    "    geom_point,\n",
+    "    geom_text,\n",
+    "    ggtitle, xlab, ylab, \n",
+    "    ggsave\n",
+    ")\n",
+    "from plotnine.scales import scale_y_reverse, scale_fill_cmap\n",
+    "from tqdm import tqdm\n",
+    "from collections import namedtuple"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load model and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model = \"EleutherAI/pythia-70m\" # \"EleutherAI/pythia-6.9B\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "gpt = AutoModelForCausalLM.from_pretrained(\n",
+    "    model,\n",
+    "    revision=\"main\",\n",
+    "    torch_dtype=torch.bfloat16 if model == \"EleutherAI/pythia-6.9b\" else torch.float32,\n",
+    ").to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a list of 100 names for each gender, and we'll filter for names that are one token in length. We'll further filter for examples the model agrees with our labels for, since some of these names might be ambiguous or the model might not have the expected behaviour. This ensures that baseline IIA is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "47 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "Example = namedtuple(\"Example\", [\"base\", \"src\", \"base_label\", \"src_label\"])\n",
+    "\n",
+    "names = {\n",
+    "    \"he\": [\n",
+    "        \"James\",\n",
+    "        \"Robert\",\n",
+    "        \"John\",\n",
+    "        \"Michael\",\n",
+    "        \"David\",\n",
+    "        \"William\",\n",
+    "        \"Richard\",\n",
+    "        \"Joseph\",\n",
+    "        \"Thomas\",\n",
+    "        \"Christopher\",\n",
+    "        \"Charles\",\n",
+    "        \"Daniel\",\n",
+    "        \"Matthew\",\n",
+    "        \"Anthony\",\n",
+    "        \"Mark\",\n",
+    "        \"Donald\",\n",
+    "        \"Steven\",\n",
+    "        \"Andrew\",\n",
+    "        \"Paul\",\n",
+    "        \"Joshua\",\n",
+    "        \"Kenneth\",\n",
+    "        \"Kevin\",\n",
+    "        \"Brian\",\n",
+    "        \"George\",\n",
+    "        \"Timothy\",\n",
+    "        \"Ronald\",\n",
+    "        \"Jason\",\n",
+    "        \"Edward\",\n",
+    "        \"Jeffrey\",\n",
+    "        \"Ryan\",\n",
+    "        \"Jacob\",\n",
+    "        \"Gary\",\n",
+    "        \"Nicholas\",\n",
+    "        \"Eric\",\n",
+    "        \"Jonathan\",\n",
+    "        \"Stephen\",\n",
+    "        \"Larry\",\n",
+    "        \"Justin\",\n",
+    "        \"Scott\",\n",
+    "        \"Brandon\",\n",
+    "        \"Benjamin\",\n",
+    "        \"Samuel\",\n",
+    "        \"Gregory\",\n",
+    "        \"Alexander\",\n",
+    "        \"Patrick\",\n",
+    "        \"Frank\",\n",
+    "        \"Raymond\",\n",
+    "        \"Jack\",\n",
+    "        \"Dennis\",\n",
+    "        \"Jerry\",\n",
+    "        \"Tyler\",\n",
+    "        \"Aaron\",\n",
+    "        \"Jose\",\n",
+    "        \"Adam\",\n",
+    "        \"Nathan\",\n",
+    "        \"Henry\",\n",
+    "        \"Zachary\",\n",
+    "        \"Douglas\",\n",
+    "        \"Peter\",\n",
+    "        \"Kyle\",\n",
+    "        \"Noah\",\n",
+    "        \"Ethan\",\n",
+    "        \"Jeremy\",\n",
+    "        \"Walter\",\n",
+    "        \"Christian\",\n",
+    "        \"Keith\",\n",
+    "        \"Roger\",\n",
+    "        \"Terry\",\n",
+    "        \"Austin\",\n",
+    "        \"Sean\",\n",
+    "        \"Gerald\",\n",
+    "        \"Carl\",\n",
+    "        \"Harold\",\n",
+    "        \"Dylan\",\n",
+    "        \"Arthur\",\n",
+    "        \"Lawrence\",\n",
+    "        \"Jordan\",\n",
+    "        \"Jesse\",\n",
+    "        \"Bryan\",\n",
+    "        \"Billy\",\n",
+    "        \"Bruce\",\n",
+    "        \"Gabriel\",\n",
+    "        \"Joe\",\n",
+    "        \"Logan\",\n",
+    "        \"Alan\",\n",
+    "        \"Juan\",\n",
+    "        \"Albert\",\n",
+    "        \"Willie\",\n",
+    "        \"Elijah\",\n",
+    "        \"Wayne\",\n",
+    "        \"Randy\",\n",
+    "        \"Vincent\",\n",
+    "        \"Mason\",\n",
+    "        \"Roy\",\n",
+    "        \"Ralph\",\n",
+    "        \"Bobby\",\n",
+    "        \"Russell\",\n",
+    "        \"Bradley\",\n",
+    "        \"Philip\",\n",
+    "        \"Eugene\",\n",
+    "    ],\n",
+    "    \"she\": [\n",
+    "        \"Mary\",\n",
+    "        \"Patricia\",\n",
+    "        \"Jennifer\",\n",
+    "        \"Linda\",\n",
+    "        \"Elizabeth\",\n",
+    "        \"Barbara\",\n",
+    "        \"Susan\",\n",
+    "        \"Jessica\",\n",
+    "        \"Sarah\",\n",
+    "        \"Karen\",\n",
+    "        \"Lisa\",\n",
+    "        \"Nancy\",\n",
+    "        \"Betty\",\n",
+    "        \"Sandra\",\n",
+    "        \"Margaret\",\n",
+    "        \"Ashley\",\n",
+    "        \"Kimberly\",\n",
+    "        \"Emily\",\n",
+    "        \"Donna\",\n",
+    "        \"Michelle\",\n",
+    "        \"Carol\",\n",
+    "        \"Amanda\",\n",
+    "        \"Melissa\",\n",
+    "        \"Deborah\",\n",
+    "        \"Stephanie\",\n",
+    "        \"Dorothy\",\n",
+    "        \"Rebecca\",\n",
+    "        \"Sharon\",\n",
+    "        \"Laura\",\n",
+    "        \"Cynthia\",\n",
+    "        \"Amy\",\n",
+    "        \"Kathleen\",\n",
+    "        \"Angela\",\n",
+    "        \"Shirley\",\n",
+    "        \"Brenda\",\n",
+    "        \"Emma\",\n",
+    "        \"Anna\",\n",
+    "        \"Pamela\",\n",
+    "        \"Nicole\",\n",
+    "        \"Samantha\",\n",
+    "        \"Katherine\",\n",
+    "        \"Christine\",\n",
+    "        \"Helen\",\n",
+    "        \"Debra\",\n",
+    "        \"Rachel\",\n",
+    "        \"Carolyn\",\n",
+    "        \"Janet\",\n",
+    "        \"Maria\",\n",
+    "        \"Catherine\",\n",
+    "        \"Heather\",\n",
+    "        \"Diane\",\n",
+    "        \"Olivia\",\n",
+    "        \"Julie\",\n",
+    "        \"Joyce\",\n",
+    "        \"Victoria\",\n",
+    "        \"Ruth\",\n",
+    "        \"Virginia\",\n",
+    "        \"Lauren\",\n",
+    "        \"Kelly\",\n",
+    "        \"Christina\",\n",
+    "        \"Joan\",\n",
+    "        \"Evelyn\",\n",
+    "        \"Judith\",\n",
+    "        \"Andrea\",\n",
+    "        \"Hannah\",\n",
+    "        \"Megan\",\n",
+    "        \"Cheryl\",\n",
+    "        \"Jacqueline\",\n",
+    "        \"Martha\",\n",
+    "        \"Madison\",\n",
+    "        \"Teresa\",\n",
+    "        \"Gloria\",\n",
+    "        \"Sara\",\n",
+    "        \"Janice\",\n",
+    "        \"Ann\",\n",
+    "        \"Kathryn\",\n",
+    "        \"Abigail\",\n",
+    "        \"Sophia\",\n",
+    "        \"Frances\",\n",
+    "        \"Jean\",\n",
+    "        \"Alice\",\n",
+    "        \"Judy\",\n",
+    "        \"Isabella\",\n",
+    "        \"Julia\",\n",
+    "        \"Grace\",\n",
+    "        \"Amber\",\n",
+    "        \"Denise\",\n",
+    "        \"Danielle\",\n",
+    "        \"Marilyn\",\n",
+    "        \"Beverly\",\n",
+    "        \"Charlotte\",\n",
+    "        \"Natalie\",\n",
+    "        \"Theresa\",\n",
+    "        \"Diana\",\n",
+    "        \"Brittany\",\n",
+    "        \"Doris\",\n",
+    "        \"Kayla\",\n",
+    "        \"Alexis\",\n",
+    "        \"Lori\",\n",
+    "        \"Marie\",\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "# filter names that are > 1 token\n",
+    "names = {\n",
+    "    key: [name for name in names[key] if len(tokenizer.tokenize(name)) == 1]\n",
+    "    for key in names\n",
+    "}\n",
+    "print(len(names[\"he\"]), len(names[\"she\"]))\n",
+    "\n",
+    "\n",
+    "def sample_example(tokenizer):\n",
+    "    # sample labels (not matching)\n",
+    "    base_label = random.choice(list(names.keys()))\n",
+    "    src_label = [key for key in names if key != base_label][0]\n",
+    "\n",
+    "    # sample names\n",
+    "    base_name = random.choice(names[base_label])\n",
+    "    src_name = random.choice(names[src_label])\n",
+    "\n",
+    "    # make pair\n",
+    "    base = tokenizer(f\"<|endoftext|>{base_name} walked because\", return_tensors=\"pt\")\n",
+    "    src = tokenizer(f\"<|endoftext|>{src_name} walked because\", return_tensors=\"pt\")\n",
+    "    base_label = tokenizer.encode(\" \" + base_label)[0]\n",
+    "    src_label = tokenizer.encode(\" \" + src_label)[0]\n",
+    "    return Example(base, src, base_label, src_label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Example(base={'input_ids': tensor([[    0, 37376,  7428,   984]]), 'attention_mask': tensor([[1, 1, 1, 1]])}, src={'input_ids': tensor([[    0, 44305,  7428,   984]]), 'attention_mask': tensor([[1, 1, 1, 1]])}, base_label=344, src_label=703)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_example(tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_n_doable_examples(n, model, tokenizer):\n",
+    "    examples = []\n",
+    "    iterator = tqdm(range(n))\n",
+    "    while len(examples) < n:\n",
+    "        ex = sample_example(tokenizer)\n",
+    "        for k, v in ex.base.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                ex.base[k] = v.to(model.device)\n",
+    "        for k, v in ex.src.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                ex.src[k] = v.to(model.device)\n",
+    "        logits_base = model(**ex.base).logits[0, -1]\n",
+    "        logits_src = model(**ex.src).logits[0, -1]\n",
+    "        if (\n",
+    "            logits_base[ex.base_label] > logits_base[ex.src_label]\n",
+    "            and logits_src[ex.src_label] > logits_src[ex.base_label]\n",
+    "        ):\n",
+    "            examples.append(ex)\n",
+    "            iterator.update(1)\n",
+    "    return examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+      "100%|██████████| 100/100 [00:05<00:00, 17.51it/s]\n",
+      "100%|██████████| 50/50 [00:02<00:00, 19.85it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# make dataset\n",
+    "total_steps = 100\n",
+    "trainset = generate_n_doable_examples(total_steps, gpt, tokenizer)\n",
+    "evalset = generate_n_doable_examples(50, gpt, tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DAS\n",
+    "\n",
+    "This is the usual 1D DAS setup, training on batch size of 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def intervention_config(intervention_site, layer, num_dims=1):\n",
+    "    config = pv.IntervenableConfig([\n",
+    "        {\n",
+    "            \"layer\": layer,\n",
+    "            \"component\": intervention_site,\n",
+    "            \"intervention_type\": pv.LowRankRotatedSpaceIntervention,\n",
+    "            \"low_rank_dimension\": num_dims,\n",
+    "        }\n",
+    "    ])\n",
+    "    return config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loss function\n",
+    "loss_fct = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "def calculate_loss(logits, label):\n",
+    "    \"\"\"Calculate cross entropy between logits and a single target label (can be batched)\"\"\"\n",
+    "    shift_labels = label.to(logits.device)\n",
+    "    loss = loss_fct(logits, shift_labels)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "layer: 0, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:09<00:00, 10.66it/s, loss=4.355]\n",
+      "100%|██████████| 50/50 [00:03<00:00, 13.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 0, position: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:09<00:00, 10.97it/s, loss=1.268]\n",
+      "100%|██████████| 50/50 [00:03<00:00, 13.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 98.000%\n",
+      "layer: 0, position: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:09<00:00, 10.93it/s, loss=4.130]\n",
+      "100%|██████████| 50/50 [00:03<00:00, 13.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 0, position: 3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:09<00:00, 10.83it/s, loss=4.276]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 1, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:09<00:00, 10.94it/s, loss=4.355]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 1, position: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 100.000%\n",
+      "layer: 1, position: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:09<00:00, 10.94it/s, loss=4.422]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 1, position: 3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 2, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:08<00:00, 11.26it/s, loss=4.355]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 2, position: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 98.000%\n",
+      "layer: 2, position: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 16.000%\n",
+      "layer: 2, position: 3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 10.000%\n",
+      "layer: 3, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 0.000%\n",
+      "layer: 3, position: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 52.000%\n",
+      "layer: 3, position: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iia: 6.000%\n",
+      "layer: 3, position: 3\n"
+     ]
+    },
+    {
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+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:08<00:00, 11.44it/s, loss=1.113]\n",
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+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# intervention settings\n",
+    "stats = []\n",
+    "num_layers = gpt.config.num_hidden_layers\n",
+    "\n",
+    "# loop over layers and positions\n",
+    "for layer in range(num_layers):\n",
+    "    for position in range(4):\n",
+    "        print(f\"layer: {layer}, position: {position}\")\n",
+    "\n",
+    "        # set up intervenable model\n",
+    "        config = intervention_config(\"block_output\", layer, 1)\n",
+    "        intervenable = pv.IntervenableModel(config, gpt)\n",
+    "        intervenable.set_device(device)\n",
+    "        intervenable.disable_model_gradients()\n",
+    "\n",
+    "        # set up optimizer\n",
+    "        optimizer_params = []\n",
+    "        for k, v in intervenable.interventions.items():\n",
+    "            try:\n",
+    "                optimizer_params.append({\"params\": v.rotate_layer.parameters()})\n",
+    "            except:\n",
+    "                pass\n",
+    "        optimizer = torch.optim.Adam(optimizer_params, lr=1e-3)\n",
+    "        scheduler = get_linear_schedule_with_warmup(\n",
+    "            optimizer,\n",
+    "            num_warmup_steps=int(0.1 * total_steps),\n",
+    "            num_training_steps=total_steps,\n",
+    "        )\n",
+    "\n",
+    "        # training loop\n",
+    "        iterator = tqdm(trainset)\n",
+    "        for example in iterator:\n",
+    "            # forward pass\n",
+    "            _, counterfactual_outputs = intervenable(\n",
+    "                example.base,\n",
+    "                [example.src],\n",
+    "                {\"sources->base\": position},\n",
+    "            )\n",
+    "\n",
+    "            # loss\n",
+    "            logits = counterfactual_outputs.logits[:, -1]\n",
+    "            loss = calculate_loss(logits, torch.tensor([example.src_label]).to(device))\n",
+    "            iterator.set_postfix({\"loss\": f\"{loss.item():.3f}\"})\n",
+    "\n",
+    "            # backward\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            scheduler.step()\n",
+    "\n",
+    "        # eval\n",
+    "        with torch.no_grad():\n",
+    "            iia = 0\n",
+    "            iterator = tqdm(evalset)\n",
+    "            for example in iterator:\n",
+    "                # forward\n",
+    "                _, counterfactual_outputs = intervenable(\n",
+    "                    example.base,\n",
+    "                    [example.src],\n",
+    "                    {\"sources->base\": position},\n",
+    "                )\n",
+    "\n",
+    "                # calculate iia\n",
+    "                logits = counterfactual_outputs.logits[0, -1]\n",
+    "                if logits[example.src_label] > logits[example.base_label]:\n",
+    "                    iia += 1\n",
+    "\n",
+    "            # stats\n",
+    "            iia = iia / len(evalset)\n",
+    "            stats.append({\"layer\": layer, \"position\": position, \"iia\": iia})\n",
+    "            print(f\"iia: {iia:.3%}\")\n",
+    "df = pd.DataFrame(stats)\n",
+    "df.to_csv(f\"./tutorial_data/pyvene_gender_das.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And this is the plot of IIA. In layers 2 and 3 it seems the gender is represented across positions 1-3, and entirely in position 3 in later layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:718: PlotnineWarning: Saving 5 x 3 in image.\n",
+      "/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Filename: ./tutorial_data/pyvene_gender_das.pdf\n"
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+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(f\"./tutorial_data/pyvene_gender_das.csv\")\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"pos\"] = df[\"position\"].astype(int)\n",
+    "df[\"IIA\"] = df[\"iia\"].astype(float)\n",
+    "\n",
+    "custom_labels = [\"EOS\", \"<name>\", \"walked\", \"because\"]\n",
+    "breaks = [0, 1, 2, 3]\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"pos\"))    \n",
+    "    + geom_tile(aes(fill=\"IIA\"))\n",
+    "    + scale_fill_cmap(\"Purples\") + xlab(\"layers\")\n",
+    "    + scale_y_reverse(\n",
+    "        limits = (-0.5, 3.5), \n",
+    "        breaks=breaks, labels=custom_labels) \n",
+    "    + theme(figure_size=(5, 3)) + ylab(\"\") \n",
+    "    + theme(axis_text_y  = element_text(angle = 90, hjust = 1))\n",
+    "    + ggtitle(\"Trained Intervention (DAS)\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    plot, filename=f\"./tutorial_data/pyvene_gender_das.pdf\", dpi=200\n",
+    ")\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Probing\n",
+    "\n",
+    "We'll define a dummy intervention `CollectActivation` to collect activations and train a simple probe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probing_config(intervention_site, layer):\n",
+    "    \"\"\"Generate intervention config.\"\"\"\n",
+    "\n",
+    "    # init\n",
+    "    config = pv.IntervenableConfig([{\n",
+    "        \"layer\": layer,\n",
+    "        \"component\": intervention_site,\n",
+    "        \"intervention_type\": pv.CollectIntervention,\n",
+    "    }])\n",
+    "    return config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the training loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "layer: 0, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:11<00:00,  8.98it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 50.000%, f1: 0.000\n",
+      "layer: 0, position: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.12it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 100.000%, f1: 1.000\n",
+      "layer: 0, position: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.12it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 99.000%, f1: 0.990\n",
+      "layer: 0, position: 3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.23it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 88.000%, f1: 0.875\n",
+      "layer: 1, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:11<00:00,  9.09it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  8.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 50.000%, f1: 0.000\n",
+      "layer: 1, position: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.13it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 100.000%, f1: 1.000\n",
+      "layer: 1, position: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.45it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 97.000%, f1: 0.971\n",
+      "layer: 1, position: 3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.50it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 96.000%, f1: 0.962\n",
+      "layer: 2, position: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.33it/s]\n",
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+      "layer: 2, position: 1\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:10<00:00,  9.19it/s]\n",
+      "100%|██████████| 50/50 [00:05<00:00,  9.43it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 100.000%, f1: 1.000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# intervention settings\n",
+    "stats = []\n",
+    "num_layers = gpt.config.num_hidden_layers\n",
+    "\n",
+    "# 344 = \" he\", 703 = \" she\"\n",
+    "label_mapping = {344: 0, 703: 1}\n",
+    "\n",
+    "# loop over layers and positions\n",
+    "with torch.no_grad():\n",
+    "    for layer in range(num_layers):\n",
+    "        for position in range(4):\n",
+    "            print(f\"layer: {layer}, position: {position}\")\n",
+    "\n",
+    "            # set up intervenable model\n",
+    "            config = probing_config(\"block_output\", layer)\n",
+    "            intervenable = pv.IntervenableModel(config, gpt)\n",
+    "            intervenable.set_device(device)\n",
+    "            intervenable.disable_model_gradients()\n",
+    "\n",
+    "            # training loop\n",
+    "            activations, labels = [], []\n",
+    "            iterator = tqdm(trainset)\n",
+    "            for example in iterator:\n",
+    "                # forward pass\n",
+    "                base_outputs, _ = intervenable(\n",
+    "                    example.base,\n",
+    "                    unit_locations={\"base\": position},\n",
+    "                )\n",
+    "                base_activations = base_outputs[1][0]\n",
+    "\n",
+    "                src_outputs, _ = intervenable(\n",
+    "                    example.src,\n",
+    "                    unit_locations={\"base\": position},\n",
+    "                )\n",
+    "                src_activations = src_outputs[1][0]\n",
+    "                \n",
+    "                # collect activation\n",
+    "                activations.extend(\n",
+    "                    [base_activations.detach()[0].cpu().numpy(), src_activations.detach()[0].cpu().numpy()]\n",
+    "                )\n",
+    "                labels.extend([example.base_label, example.src_label])\n",
+    "            labels = [label_mapping[label] for label in labels]\n",
+    "            \n",
+    "            # train logistic regression\n",
+    "            lr = LogisticRegression(random_state=42, max_iter=1000).fit(\n",
+    "                activations, labels\n",
+    "            )\n",
+    "\n",
+    "            # eval\n",
+    "            activations, labels = [], []\n",
+    "            iterator = tqdm(evalset)\n",
+    "            for example in iterator:\n",
+    "                # forward pass\n",
+    "                base_outputs, _ = intervenable(\n",
+    "                    example.base,\n",
+    "                    unit_locations={\"base\": position},\n",
+    "                )\n",
+    "                base_activations = base_outputs[1][0]\n",
+    "\n",
+    "                src_outputs, _ = intervenable(\n",
+    "                    example.src,\n",
+    "                    unit_locations={\"base\": position},\n",
+    "                )\n",
+    "                src_activations = src_outputs[1][0]\n",
+    "                \n",
+    "                # collect activation\n",
+    "                activations.extend(\n",
+    "                    [base_activations.detach()[0].cpu().numpy(), src_activations.detach()[0].cpu().numpy()]\n",
+    "                )\n",
+    "                labels.extend([example.base_label, example.src_label])\n",
+    "            labels = [label_mapping[label] for label in labels]\n",
+    "\n",
+    "            # stats\n",
+    "            acc = lr.score(activations, labels)\n",
+    "            f1 = f1_score(labels, lr.predict(activations))\n",
+    "            stats.append({\"layer\": layer, \"position\": position, \"acc\": acc, \"f1\": f1})\n",
+    "            print(f\"acc: {acc:.3%}, f1: {f1:.3f}\")\n",
+    "df = pd.DataFrame(stats)\n",
+    "df.to_csv(f\"./tutorial_data/pyvene_gender_probe.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the probe accuracy plot is below. Note the extremely high accuracy at all positions at and after the name! Early layers at later positions are better but it saturates much before the IIA for DAS. This shows how unreliable probes are for tracing causal effect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:718: PlotnineWarning: Saving 5 x 3 in image.\n",
+      "/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Filename: ./tutorial_data/pyvene_gender_probe.pdf\n"
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+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(f\"./tutorial_data/pyvene_gender_probe.csv\")\n",
+    "df[\"layer\"] = df[\"layer\"].astype(int)\n",
+    "df[\"pos\"] = df[\"position\"].astype(int)\n",
+    "df[\"ACC\"] = df[\"acc\"].astype(float)\n",
+    "\n",
+    "custom_labels = [\"EOS\", \"<name>\", \"walked\", \"because\"]\n",
+    "breaks = [0, 1, 2, 3]\n",
+    "\n",
+    "plot = (\n",
+    "    ggplot(df, aes(x=\"layer\", y=\"pos\", fill=\"ACC\"))    \n",
+    "    + geom_tile()\n",
+    "    + scale_fill_cmap(\"Reds\") + xlab(\"layers\")\n",
+    "    + scale_y_reverse(\n",
+    "        limits = (-0.5, 3.5), \n",
+    "        breaks=breaks, labels=custom_labels) \n",
+    "    + theme(figure_size=(5, 3)) + ylab(\"\") \n",
+    "    + theme(axis_text_y  = element_text(angle = 90, hjust = 1))\n",
+    "    + ggtitle(\"Trained Linear Probe\")\n",
+    ")\n",
+    "ggsave(\n",
+    "    plot, filename=f\"./tutorial_data/pyvene_gender_probe.pdf\", dpi=200\n",
+    ")\n",
+    "print(plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/tutorials/advanced_tutorials/Voting_Mechanism.ipynb b/_sources/tutorials/advanced_tutorials/Voting_Mechanism.ipynb
new file mode 100644
index 00000000..56a45cbf
--- /dev/null
+++ b/_sources/tutorials/advanced_tutorials/Voting_Mechanism.ipynb
@@ -0,0 +1,1557 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a35493d6",
+   "metadata": {},
+   "source": [
+    "## An Exploration on Voting Mechanisms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aab5efc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu and Kevin Du and Tiago Pimentel\"\n",
+    "__version__ = \"02/13/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6633e9fa",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "Through our offline discussions, we found a strange behavior in the Boundless DAS paper: there are multiple \"good\" aligning representations for a single causal variable. If these aligning representations take on different values, how will the NN behave? Will it trust more on certain location?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b39c8f0",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "f5ff4f76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "850bf6fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm, trange\n",
+    "from datasets import Dataset\n",
+    "from torch.utils.data import DataLoader\n",
+    "from transformers import get_linear_schedule_with_warmup\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from tutorial_price_tagging_utils import *\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from plotnine import ggplot, aes, geom_boxplot, geom_point, geom_errorbar, labs, theme\n",
+    "\n",
+    "import pyvene as pv\n",
+    "from pyvene import create_llama\n",
+    "from pyvene import set_seed, count_parameters\n",
+    "\n",
+    "# You can define your custom compute_metrics function.\n",
+    "def compute_metrics(eval_preds, eval_labels):\n",
+    "    total_count = 0\n",
+    "    correct_count = 0\n",
+    "    for eval_pred, eval_label in zip(eval_preds, eval_labels):\n",
+    "        actual_test_labels = eval_label[:, -1]\n",
+    "        pred_test_labels = torch.argmax(eval_pred[:, -1], dim=-1)\n",
+    "        correct_labels = actual_test_labels == pred_test_labels\n",
+    "        total_count += len(correct_labels)\n",
+    "        correct_count += correct_labels.sum().tolist()\n",
+    "    accuracy = round(correct_count / total_count, 2)\n",
+    "    return {\"accuracy\": accuracy}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "75d4d2e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
+      "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e43d3570ec941adb21f8203bbf61b4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/34 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/sailhome/wuzhengx/.local/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, llama = create_llama()\n",
+    "_ = llama.to(\"cuda\")\n",
+    "_ = llama.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1499039a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_prealign = factual_sampler(tokenizer, 5000, game=\"pricing_tag\")\n",
+    "prealign_dataset = Dataset.from_dict(\n",
+    "    {\"input_ids\": raw_prealign[0], \"labels\": raw_prealign[1]}\n",
+    ")\n",
+    "prealign_dataset.set_format(\"torch\", columns=[\"input_ids\", \"labels\"])\n",
+    "prealign_dataloader = DataLoader(prealign_dataset, batch_size=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f13b8ed9",
+   "metadata": {},
+   "source": [
+    "#### Display some training task instances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fac2e331",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<s>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nPlease say yes only if it costs between 1.60 and 4.11 dollars, otherwise no.\\n\\n### Input:\\n0.32 dollars\\n\\n### Response:\\n'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer.decode(raw_prealign[0][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c599652d",
+   "metadata": {},
+   "source": [
+    "#### creating alignment datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "18d62623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "set_seed(42)\n",
+    "\n",
+    "###################\n",
+    "# data loaders\n",
+    "###################\n",
+    "raw_data = bound_alignment_sampler(\n",
+    "    tokenizer, 10000, [lower_bound_alignment_example_sampler]\n",
+    ")\n",
+    "\n",
+    "raw_train = (\n",
+    "    raw_data[0][:8000],\n",
+    "    raw_data[1][:8000],\n",
+    "    raw_data[2][:8000],\n",
+    "    raw_data[3][:8000],\n",
+    ")\n",
+    "raw_eval = (\n",
+    "    raw_data[0][8000:9000],\n",
+    "    raw_data[1][8000:9000],\n",
+    "    raw_data[2][8000:9000],\n",
+    "    raw_data[3][8000:9000],\n",
+    ")\n",
+    "raw_test = (\n",
+    "    raw_data[0][9000:],\n",
+    "    raw_data[1][9000:],\n",
+    "    raw_data[2][9000:],\n",
+    "    raw_data[3][9000:],\n",
+    ")\n",
+    "train_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_train[0],\n",
+    "        \"source_input_ids\": raw_train[1],\n",
+    "        \"labels\": raw_train[2],\n",
+    "        \"intervention_ids\": raw_train[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "train_dataloader = DataLoader(\n",
+    "    train_dataset,\n",
+    "    batch_size=16,\n",
+    ")\n",
+    "eval_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_eval[0],\n",
+    "        \"source_input_ids\": raw_eval[1],\n",
+    "        \"labels\": raw_eval[2],\n",
+    "        \"intervention_ids\": raw_eval[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "eval_dataloader = DataLoader(\n",
+    "    eval_dataset,\n",
+    "    batch_size=16,\n",
+    ")\n",
+    "test_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_test[0],\n",
+    "        \"source_input_ids\": raw_test[1],\n",
+    "        \"labels\": raw_test[2],\n",
+    "        \"intervention_ids\": raw_test[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "test_dataloader = DataLoader(\n",
+    "    test_dataset,\n",
+    "    batch_size=16,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff11ff48",
+   "metadata": {},
+   "source": [
+    "#### where are we aligning in LLaMA?\n",
+    "\n",
+    "you need to run this multiple times, and save different interventions into your disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "07976f76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = 15\n",
+    "token_position = 78"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "471a4c00",
+   "metadata": {},
+   "source": [
+    "set-up training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "99dd2bb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pv_config = pv.IntervenableConfig(\n",
+    "    representations=[\n",
+    "        pv.RepresentationConfig(\n",
+    "            layer, \"block_output\"\n",
+    "        )],\n",
+    "    intervention_types=pv.BoundlessRotatedSpaceIntervention,\n",
+    ")\n",
+    "pv_llama = pv.IntervenableModel(pv_config, llama)\n",
+    "pv_llama.set_device(\"cuda\")\n",
+    "pv_llama.disable_model_gradients()\n",
+    "\n",
+    "epochs = 1\n",
+    "gradient_accumulation_steps = 4\n",
+    "total_step = 0\n",
+    "t_total = int(len(train_dataloader) * epochs)\n",
+    "temperature_start = 50.0\n",
+    "temperature_end = 0.1\n",
+    "temperature_schedule = (\n",
+    "    torch.linspace(\n",
+    "        temperature_start, temperature_end, t_total\n",
+    "    )\n",
+    "    .to(torch.bfloat16)\n",
+    "    .to(\"cuda\")\n",
+    ")\n",
+    "pv_llama.set_temperature(temperature_schedule[total_step])\n",
+    "\n",
+    "warm_up_steps = 0.1 * t_total\n",
+    "optimizer_params = []\n",
+    "for k, v in pv_llama.interventions.items():\n",
+    "    optimizer_params += [\n",
+    "        {\"params\": v.rotate_layer.parameters()}]\n",
+    "    optimizer_params += [\n",
+    "        {\"params\": v.intervention_boundaries, \"lr\": 1e-2}]\n",
+    "optimizer = torch.optim.Adam(optimizer_params, lr=1e-3)\n",
+    "scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer, num_warmup_steps=warm_up_steps,\n",
+    "    num_training_steps=t_total\n",
+    ")\n",
+    "\n",
+    "def calculate_loss(logits, labels):\n",
+    "    shift_logits = logits[..., :, :].contiguous()\n",
+    "    shift_labels = labels[..., :].contiguous()\n",
+    "    # Flatten the tokens\n",
+    "    loss_fct = CrossEntropyLoss()\n",
+    "    shift_logits = shift_logits.view(-1, config.vocab_size)\n",
+    "    shift_labels = shift_labels.view(-1)\n",
+    "    # Enable model parallelism\n",
+    "    shift_labels = shift_labels.to(shift_logits.device)\n",
+    "    loss = loss_fct(shift_logits, shift_labels)\n",
+    "\n",
+    "    for k, v in pv_llama.interventions.items():\n",
+    "        boundary_loss = 1.0 * v.intervention_boundaries.sum()\n",
+    "    loss += boundary_loss\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "c8c31a14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llama trainable parameters:  0\n",
+      "intervention trainable parameters:  16777218\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [13:24<00:00,  1.61s/it, loss=0.12, acc=0.75]\n",
+      "Epoch: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [13:24<00:00, 804.32s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pv_llama.model.train()  # train enables drop-off but no grads\n",
+    "print(\"llama trainable parameters: \", count_parameters(pv_llama.model))\n",
+    "print(\"intervention trainable parameters: \", pv_llama.count_parameters())\n",
+    "train_iterator = trange(0, int(epochs), desc=\"Epoch\")\n",
+    "for epoch in train_iterator:\n",
+    "    epoch_iterator = tqdm(\n",
+    "        train_dataloader, \n",
+    "        desc=f\"Epoch: {epoch}\", position=0, leave=True\n",
+    "    )\n",
+    "    for step, inputs in enumerate(epoch_iterator):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(\"cuda\")\n",
+    "        b_s = inputs[\"input_ids\"].shape[0]\n",
+    "        _, counterfactual_outputs = pv_llama(\n",
+    "            {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": inputs[\"source_input_ids\"]}],\n",
+    "            {\"sources->base\": token_position},\n",
+    "        )\n",
+    "        eval_metrics = compute_metrics(\n",
+    "            [counterfactual_outputs.logits], [inputs[\"labels\"]]\n",
+    "        )\n",
+    "\n",
+    "        # loss and backprop\n",
+    "        loss = calculate_loss(counterfactual_outputs.logits, inputs[\"labels\"])\n",
+    "        loss_str = round(loss.item(), 2)\n",
+    "        epoch_iterator.set_postfix({\"loss\": loss_str, \"acc\": eval_metrics[\"accuracy\"]})\n",
+    "\n",
+    "        if gradient_accumulation_steps > 1:\n",
+    "            loss = loss / gradient_accumulation_steps\n",
+    "        loss.backward()\n",
+    "        if total_step % gradient_accumulation_steps == 0:\n",
+    "            if not (gradient_accumulation_steps > 1 and total_step == 0):\n",
+    "                optimizer.step()\n",
+    "                scheduler.step()\n",
+    "                optimizer.zero_grad()\n",
+    "                pv_llama.set_temperature(\n",
+    "                    temperature_schedule[total_step])\n",
+    "        total_step += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "31915697",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:45<00:00,  1.38it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.64}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluation on the test set\n",
+    "eval_labels = []\n",
+    "eval_preds = []\n",
+    "with torch.no_grad():\n",
+    "    epoch_iterator = tqdm(test_dataloader, desc=f\"Test\")\n",
+    "    for step, inputs in enumerate(epoch_iterator):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(\"cuda\")\n",
+    "        b_s = inputs[\"input_ids\"].shape[0]\n",
+    "        _, counterfactual_outputs = pv_llama(\n",
+    "            {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": inputs[\"source_input_ids\"]}],\n",
+    "            {\"sources->base\": 75}\n",
+    "        )\n",
+    "        eval_labels += [inputs[\"labels\"]]\n",
+    "        eval_preds += [counterfactual_outputs.logits]\n",
+    "eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "print(eval_metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "de8abd35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(\n",
+    "    pv_llama.interventions[\n",
+    "        f\"layer_{layer}_comp_block_output_unit_pos_nunit_1#0\"].state_dict(), \n",
+    "    f\"./tutorial_data/layer.{layer}.pos.{token_position}.bin\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a7b22ad",
+   "metadata": {},
+   "source": [
+    "#### Loading back saved DAS params and intervene on single or multiple sites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "9fa49db7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "layer = 15\n",
+    "pv_config = pv.IntervenableConfig(\n",
+    "    representations=[\n",
+    "        pv.RepresentationConfig(layer, \"block_output\"), \n",
+    "        pv.RepresentationConfig(layer, \"block_output\")],\n",
+    "    intervention_types=pv.BoundlessRotatedSpaceIntervention,\n",
+    ")\n",
+    "pv_llama = pv.IntervenableModel(pv_config, llama)\n",
+    "pv_llama.set_device(\"cuda\")\n",
+    "pv_llama.disable_model_gradients()\n",
+    "pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#0'].load_state_dict(\n",
+    "    torch.load('./tutorial_data/layer.15.pos.75.bin'))\n",
+    "pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#1'].load_state_dict(\n",
+    "    torch.load('./tutorial_data/layer.15.pos.80.bin'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "26659165",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[control ]intervening location:  78\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:41<00:00,  1.51it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "intervening location:  75\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:42<00:00,  1.50it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "intervening location:  80\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:43<00:00,  1.46it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "intervening location:  [75, 80]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:09<00:00,  1.10s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for loc in [78, 75, 80, [75, 80]]:\n",
+    "    if loc == 78:\n",
+    "        print(\"[control] intervening location: \", loc)\n",
+    "        pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#0'].load_state_dict(\n",
+    "            torch.load('./tutorial_data/layer.15.pos.78.bin'))\n",
+    "    else:\n",
+    "        print(\"intervening location: \", loc)\n",
+    "        pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#0'].load_state_dict(\n",
+    "            torch.load('./tutorial_data/layer.15.pos.75.bin'))\n",
+    "    # evaluation on the test set\n",
+    "    collected_probs = []\n",
+    "    eval_labels = []\n",
+    "    eval_preds = []\n",
+    "    with torch.no_grad():\n",
+    "        epoch_iterator = tqdm(test_dataloader, desc=f\"Test\")\n",
+    "        for step, inputs in enumerate(epoch_iterator):\n",
+    "            for k, v in inputs.items():\n",
+    "                if v is not None and isinstance(v, torch.Tensor):\n",
+    "                    inputs[k] = v.to(\"cuda\")\n",
+    "            b_s = inputs[\"input_ids\"].shape[0]\n",
+    "            if loc == 75 or loc == 78:\n",
+    "                input_sources = [{\"input_ids\": inputs[\"source_input_ids\"]}, None]\n",
+    "            elif loc == 80:\n",
+    "                input_sources = [None, {\"input_ids\": inputs[\"source_input_ids\"]}]\n",
+    "            else:\n",
+    "                input_sources = [{\"input_ids\": inputs[\"source_input_ids\"]}]\n",
+    "            _, counterfactual_outputs = pv_llama(\n",
+    "                {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "                input_sources, {\"sources->base\": loc}\n",
+    "            )\n",
+    "            correct_label = inputs[\"labels\"][:,-1]\n",
+    "            incorrect_label = \\\n",
+    "                ((correct_label == 8241)*3782 + \\\n",
+    "                (correct_label == 3782)*8241).long()\n",
+    "            norm_prob = torch.softmax(\n",
+    "                counterfactual_outputs.logits[:, -1], dim=-1)\n",
+    "            correct_prob = norm_prob[torch.arange(b_s),correct_label].detach().cpu()\n",
+    "            incorrect_prob = norm_prob[torch.arange(b_s),incorrect_label].detach().cpu()\n",
+    "            diff_prob = torch.abs(correct_prob - incorrect_prob)\n",
+    "            collected_probs += [[correct_prob, incorrect_prob, diff_prob]]\n",
+    "            eval_labels += [inputs[\"labels\"]]\n",
+    "            eval_preds += [counterfactual_outputs.logits]\n",
+    "    correct_probs = torch.cat([p[0] for p in collected_probs])\n",
+    "    incorrect_probs = torch.cat([p[1] for p in collected_probs])\n",
+    "    diff_probs = torch.cat([p[2] for p in collected_probs])\n",
+    "    eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "\n",
+    "    list1 = correct_probs.tolist()\n",
+    "    list2 = incorrect_probs.tolist()\n",
+    "    list3 = diff_probs.tolist()\n",
+    "    acc = eval_metrics['accuracy']\n",
+    "    df = pd.DataFrame({'Group': ['Correct']*len(list1) + ['Incorrect']*len(list2) + ['Abs(Diff)']*len(list2),\n",
+    "                       'Value': list1 + list2 + list3})\n",
+    "    means = df.groupby('Group')['Value'].mean().reset_index()\n",
+    "    means['ymin'] = means['Value'] - df.groupby('Group')['Value'].std().reset_index()['Value']\n",
+    "    means['ymax'] = means['Value'] + df.groupby('Group')['Value'].std().reset_index()['Value']\n",
+    "    plot = (ggplot(df, aes('Group', 'Value')) +\n",
+    "            geom_boxplot(fill='lightgray', alpha=0.5) +\n",
+    "            geom_point(means, aes('Group', 'Value'), color='blue', size=3) +\n",
+    "            geom_errorbar(means, aes('Group', ymin='ymin', ymax='ymax'), width=0.2) +\n",
+    "            labs(title=f'w/ Acc={acc}', x='Group', y='Prob (Softmax)') +\n",
+    "            theme(figure_size=(4, 2)))\n",
+    "    print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73312a7b",
+   "metadata": {},
+   "source": [
+    "#### Now, what if we set two aligning intervention sites with contradicting values?\n",
+    "\n",
+    "We need to write new data generator since we know want two source examples and these two examples have to result in contrasting behaviors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "df4bacaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lower_bound_alignment_example_sampler_multisource_non_contradict(\n",
+    "    tokenizer, amount=None, lower_bound=None, bound_width=None\n",
+    "):\n",
+    "    (\n",
+    "        base_lower_bound_sample,\n",
+    "        base_upper_bound_sample,\n",
+    "        _,\n",
+    "    ) = pricing_tag_game_config_sampler(amount, lower_bound, bound_width)\n",
+    "    (\n",
+    "        source_lower_bound_sample,\n",
+    "        source_upper_bound_sample,\n",
+    "        _,\n",
+    "    ) = pricing_tag_game_config_sampler(amount, lower_bound, bound_width)\n",
+    "    (\n",
+    "        source_lower_bound_sample_extra,\n",
+    "        source_upper_bound_sample_extra,\n",
+    "        _,\n",
+    "    ) = pricing_tag_game_config_sampler(amount, lower_bound, bound_width)\n",
+    "    ctf_label_str = random.choice([\"Yes\", \"No\"])\n",
+    "    if ctf_label_str == \"Yes\":\n",
+    "        ctf_label = tokenizer.convert_tokens_to_ids(\"Yes\")\n",
+    "        base_source_regions = [\n",
+    "            [1, 2],\n",
+    "            [1, 3],\n",
+    "            [2, 2],\n",
+    "            [2, 3],\n",
+    "        ]\n",
+    "    elif ctf_label_str == \"No\":\n",
+    "        ctf_label = tokenizer.convert_tokens_to_ids(\"No\")\n",
+    "        base_source_regions = [[1, 1], [2, 1], [3, 1], [3, 2], [3, 3]]\n",
+    "    base_source_region = random.choice(base_source_regions)\n",
+    "    base_region = base_source_region[0]\n",
+    "    source_region = base_source_region[1]\n",
+    "\n",
+    "    base_amount_sample = sample_with_region(\n",
+    "        base_region, base_lower_bound_sample, base_upper_bound_sample\n",
+    "    )\n",
+    "    source_amount_sample = sample_with_region(\n",
+    "        source_region, source_lower_bound_sample, source_upper_bound_sample\n",
+    "    )\n",
+    "    source_amount_sample_extra = sample_with_region(\n",
+    "        source_region, source_lower_bound_sample_extra, source_upper_bound_sample_extra\n",
+    "    )\n",
+    "    return (\n",
+    "        base_lower_bound_sample,\n",
+    "        base_upper_bound_sample,\n",
+    "        source_lower_bound_sample,\n",
+    "        source_upper_bound_sample,\n",
+    "        source_lower_bound_sample_extra, # extra bound\n",
+    "        source_upper_bound_sample_extra, # extra bound\n",
+    "        base_amount_sample,\n",
+    "        source_amount_sample,\n",
+    "        source_amount_sample_extra,      # extra amount\n",
+    "        ctf_label,\n",
+    "        ctf_label_str,\n",
+    "        ctf_label,     # extra label\n",
+    "        ctf_label_str, # extra label\n",
+    "    )\n",
+    "\n",
+    "def lower_bound_alignment_example_sampler_multisource_contradict(\n",
+    "    tokenizer, amount=None, lower_bound=None, bound_width=None\n",
+    "):\n",
+    "    (\n",
+    "        base_lower_bound_sample,\n",
+    "        base_upper_bound_sample,\n",
+    "        _,\n",
+    "    ) = pricing_tag_game_config_sampler(amount, lower_bound, bound_width)\n",
+    "    (\n",
+    "        source_lower_bound_sample,\n",
+    "        source_upper_bound_sample,\n",
+    "        _,\n",
+    "    ) = pricing_tag_game_config_sampler(amount, lower_bound, bound_width)\n",
+    "    (\n",
+    "        source_lower_bound_sample_extra,\n",
+    "        source_upper_bound_sample_extra,\n",
+    "        _,\n",
+    "    ) = pricing_tag_game_config_sampler(amount, lower_bound, bound_width)\n",
+    "    ctf_label_str = random.choice([\"Yes\", \"No\"])\n",
+    "    if ctf_label_str == \"Yes\":\n",
+    "        ctf_label = tokenizer.convert_tokens_to_ids(\"Yes\")\n",
+    "        base_source_regions = [\n",
+    "            [1, 2],\n",
+    "            [1, 3],\n",
+    "            [2, 2],\n",
+    "            [2, 3],\n",
+    "        ]\n",
+    "        \n",
+    "    elif ctf_label_str == \"No\":\n",
+    "        ctf_label = tokenizer.convert_tokens_to_ids(\"No\")\n",
+    "        base_source_regions = [[1, 1], [2, 1]]\n",
+    "        \n",
+    "    base_source_region = random.choice(base_source_regions)\n",
+    "    base_region = base_source_region[0]\n",
+    "    source_region = base_source_region[1]\n",
+    "    if ctf_label_str == \"Yes\":\n",
+    "        source_extra_region = 1 # flip the left label\n",
+    "        ctf_label_extra = tokenizer.convert_tokens_to_ids(\"No\")\n",
+    "        ctf_label_str_extra = \"No\"\n",
+    "    elif ctf_label_str == \"No\":\n",
+    "        source_extra_region = random.choice([2,3]) # flip the left label\n",
+    "        ctf_label_extra = tokenizer.convert_tokens_to_ids(\"Yes\")\n",
+    "        ctf_label_str_extra = \"Yes\"\n",
+    "\n",
+    "    base_amount_sample = sample_with_region(\n",
+    "        base_region, base_lower_bound_sample, base_upper_bound_sample\n",
+    "    )\n",
+    "    source_amount_sample = sample_with_region(\n",
+    "        source_region, source_lower_bound_sample, source_upper_bound_sample\n",
+    "    )\n",
+    "    source_amount_sample_extra = sample_with_region(\n",
+    "        source_region, source_lower_bound_sample_extra, source_upper_bound_sample_extra\n",
+    "    )\n",
+    "    return (\n",
+    "        base_lower_bound_sample,\n",
+    "        base_upper_bound_sample,\n",
+    "        source_lower_bound_sample,\n",
+    "        source_upper_bound_sample,\n",
+    "        source_lower_bound_sample_extra, # extra bound\n",
+    "        source_upper_bound_sample_extra, # extra bound\n",
+    "        base_amount_sample,\n",
+    "        source_amount_sample,\n",
+    "        source_amount_sample_extra,      # extra amount\n",
+    "        ctf_label,\n",
+    "        ctf_label_str,\n",
+    "        ctf_label_extra,     # extra label\n",
+    "        ctf_label_str_extra, # extra label\n",
+    "    )\n",
+    "\n",
+    "def bound_alignment_sampler_multisource(\n",
+    "    tokenizer,\n",
+    "    max_n_training_examples,\n",
+    "    bound_functors,\n",
+    "    amount=None,\n",
+    "    lower_bound=None,\n",
+    "    bound_width=None,\n",
+    "):\n",
+    "    all_base_input_ids = []\n",
+    "    all_source_input_ids = []\n",
+    "    all_ctf_output_ids = []  # this one does not have input ids, etc..\n",
+    "    all_intervention_ids = []\n",
+    "    \n",
+    "    all_second_source_input_ids = []\n",
+    "    all_second_ctf_output_ids = []\n",
+    "    \n",
+    "    for _ in range(max_n_training_examples):\n",
+    "        bound_functor = random.choice(bound_functors)\n",
+    "        (\n",
+    "            base_lower_bound_sample,\n",
+    "            base_upper_bound_sample,\n",
+    "            source_lower_bound_sample,\n",
+    "            source_upper_bound_sample,\n",
+    "            source_lower_bound_sample_extra, # extra bound\n",
+    "            source_upper_bound_sample_extra, # extra bound\n",
+    "            base_amount_sample,\n",
+    "            source_amount_sample,\n",
+    "            source_amount_sample_extra,      # extra amount\n",
+    "            ctf_label,\n",
+    "            ctf_label_str,\n",
+    "            ctf_label_extra,     # extra label\n",
+    "            ctf_label_str_extra, # extra label\n",
+    "        ) = bound_functor(\n",
+    "            tokenizer,\n",
+    "            amount,\n",
+    "            lower_bound,\n",
+    "            bound_width,\n",
+    "        )\n",
+    "\n",
+    "        base_amount_str = \"%.2f dollars\" % base_amount_sample\n",
+    "        source_amount_str = \"%.2f dollars\" % source_amount_sample\n",
+    "        source_amount_str_extra = \"%.2f dollars\" % source_amount_sample_extra\n",
+    "        base_lower_bound_str = \"%.2f\" % base_lower_bound_sample\n",
+    "        base_upper_bound_str = \"%.2f\" % base_upper_bound_sample\n",
+    "        source_lower_bound_str = \"%.2f\" % source_lower_bound_sample\n",
+    "        source_upper_bound_str = \"%.2f\" % source_upper_bound_sample\n",
+    "        source_lower_bound_str_extra = \"%.2f\" % source_lower_bound_sample_extra\n",
+    "        source_upper_bound_str_extra = \"%.2f\" % source_upper_bound_sample_extra\n",
+    "        # print(f\"base: [{base_lower_bound_str}, {base_upper_bound_str}], {base_amount_str}\")\n",
+    "        # print(f\"source: [{source_lower_bound_str}, {source_upper_bound_str}], {source_amount_str}\")\n",
+    "        # print(f\"ctf label: {ctf_label_str}\")\n",
+    "\n",
+    "        base_instruction = f\"Please say yes only if it costs between {base_lower_bound_str} and {base_upper_bound_str} dollars, otherwise no.\"\n",
+    "        source_instruction = f\"Please say yes only if it costs between {source_lower_bound_str} and {source_upper_bound_str} dollars, otherwise no.\"\n",
+    "        source_instruction_extra = f\"Please say yes only if it costs between {source_lower_bound_str_extra} and {source_upper_bound_str_extra} dollars, otherwise no.\"\n",
+    "        \n",
+    "        base_alpaca_prompt = alpaca_prompt_template % (\n",
+    "            base_instruction,\n",
+    "            base_amount_str,\n",
+    "        )\n",
+    "        source_alpaca_prompt = alpaca_prompt_template % (\n",
+    "            source_instruction,\n",
+    "            source_amount_str,\n",
+    "        )\n",
+    "        source_alpaca_prompt_extra = alpaca_prompt_template % (\n",
+    "            source_instruction_extra,\n",
+    "            source_amount_str_extra,\n",
+    "        )\n",
+    "        \n",
+    "        base_input_ids = tokenizer(base_alpaca_prompt, return_tensors=\"pt\").input_ids[0]\n",
+    "        source_input_ids = tokenizer(\n",
+    "            source_alpaca_prompt, return_tensors=\"pt\"\n",
+    "        ).input_ids[0]\n",
+    "        source_input_ids_extra = tokenizer(\n",
+    "            source_alpaca_prompt_extra, return_tensors=\"pt\"\n",
+    "        ).input_ids[0]\n",
+    "        \n",
+    "        base_input_ids = base_input_ids.tolist()\n",
+    "        source_input_ids = source_input_ids.tolist()\n",
+    "        source_input_ids_extra = source_input_ids_extra.tolist()\n",
+    "        \n",
+    "        ctf_output_ids = (torch.ones(len(base_input_ids)) * -100).long().tolist()\n",
+    "        ctf_output_ids[-1] = ctf_label\n",
+    "        ctf_output_ids_extra = (torch.ones(len(base_input_ids)) * -100).long().tolist()\n",
+    "        ctf_output_ids_extra[-1] = ctf_label_extra\n",
+    "        intervention_id = 0 if bound_functor == bound_functors[0] else 1\n",
+    "\n",
+    "        all_base_input_ids += [base_input_ids]\n",
+    "        all_source_input_ids += [source_input_ids]\n",
+    "        all_second_source_input_ids += [source_input_ids_extra]\n",
+    "\n",
+    "        all_ctf_output_ids += [ctf_output_ids]\n",
+    "        all_second_ctf_output_ids += [ctf_output_ids_extra]\n",
+    "        all_intervention_ids += [intervention_id]\n",
+    "\n",
+    "        assert len(base_input_ids) == 82\n",
+    "        assert len(source_input_ids) == 82\n",
+    "\n",
+    "    return (\n",
+    "        all_base_input_ids,\n",
+    "        all_source_input_ids,\n",
+    "        all_second_source_input_ids,\n",
+    "        all_ctf_output_ids,\n",
+    "        all_second_ctf_output_ids,\n",
+    "        all_intervention_ids,\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "daa4becb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "multisource_non_contradict_test_data = bound_alignment_sampler_multisource(\n",
+    "    tokenizer, 1000, [lower_bound_alignment_example_sampler_multisource_non_contradict]\n",
+    ")\n",
+    "multisource_contradict_test_data = bound_alignment_sampler_multisource(\n",
+    "    tokenizer, 1000, [lower_bound_alignment_example_sampler_multisource_contradict]\n",
+    ")\n",
+    "multisource_non_contradict_test_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": multisource_non_contradict_test_data[0],\n",
+    "        \"source_input_ids\": multisource_non_contradict_test_data[1],\n",
+    "        \"second_source_input_ids\": multisource_non_contradict_test_data[2],\n",
+    "        \"labels\": multisource_non_contradict_test_data[3],\n",
+    "        \"second_labels\": multisource_non_contradict_test_data[4],\n",
+    "        \"intervention_ids\": multisource_non_contradict_test_data[5],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "multisource_non_contradict_test_dataloader = DataLoader(multisource_non_contradict_test_dataset, batch_size=16)\n",
+    "\n",
+    "multisource_contradict_test_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": multisource_contradict_test_data[0],\n",
+    "        \"source_input_ids\": multisource_contradict_test_data[1],\n",
+    "        \"second_source_input_ids\": multisource_contradict_test_data[2],\n",
+    "        \"labels\": multisource_contradict_test_data[3],\n",
+    "        \"second_labels\": multisource_contradict_test_data[4],\n",
+    "        \"intervention_ids\": multisource_contradict_test_data[5],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "multisource_contradict_test_dataloader = DataLoader(multisource_contradict_test_dataset, batch_size=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "bcb96a3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[control] swapping two sources with the same counterfactual behavior\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:04<00:00,  1.02s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "swapping contradicting sources, and evaluate with matching location 75\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:07<00:00,  1.07s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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HoeXzlylMaa9+/vDhQ63WmStXrkAQhCL/aZ7P5cuXcfPmzQJ1a3ahMcUAYs36cnJyCnRFKk88PT21nusaE1EYzbL56zFGWFgYgoKCpOeTJk2SuljpM3z4cNSpU0d6vmfPHr3l5XI5li1bhnv37mHp0qUYNmwYOnTogBYtWqB///749NNPcffuXXz33XewsrLCkydPpH29vLyKf2JERESVEKfhrWAKWw8kJiZGGv/Rrl07rW+2XVxc0Lx5c9y6dQshISFITEyEjY1NscZ/nD17tlRXP9+4caM0s1FJbNiwAUuXLtV6rXr16tLj+Ph4ZGdnl6gblmZ9QN7YGw8PD6PrUzPHOiC1a9dGlSpVpOl4izMNsGbZohaCNMS1a9e0nuefYlqf9u3b4+nTpwCAW7duGbSPh4cH5syZgzlz5ugsk5WVhdDQUOl5x44dDY6JiIioMmMCUsFoJgspKSn4999/tRZIKyyZ8PHxwa1bt6RxIFWqVCnW+I/S7H4liiL8/Pyk546OjnBycjJ4/+TkZGl9h61bt2Lx4sVaK2c3b95cepyVlYXr16+jXbt2RserWR8A/PvvvyZJQMyxDggAtGrVCufOnQMAXLhwwaD6IiIipFXQ1XWUVFpamtbz4qwYr9m1zpSzVJ09e1Ya9wQAvr6+JqubiIioImMXrAqmsHEgRXWnyj8O5EUa/3HixAmt7jwbNmzA/fv3Df63b98+ad/nz59LXcXU8k/lq7mGhzHatGmj1Q2rpPWZ28CBA6XH9+/fx6NHj4rcRz1DlNqgQYNKHEf+RRCjoqIM3jcyMlJ6XJzktSjbtm2THnt4eJh85jciIqKKiglIBaQrobC0tCy0NUNfAlJU96vSXv1cc/B41apV8dJLLxVrf29vb60WiPyD0V1cXNCrVy/p+dq1a/W2CBTFwsJCqzvTjh078PjxY6PrUzPHOiBAwXVM1q1bV2Ss69evlx67uLjA29vb2NOWNGzYUOt5/iRHl+TkZK3uhI0aNSpxLEDe+15zIcKpU6dyADoREZGB+BezAsqfUGiO/8i/bgEAODs7S12HQkJCijX+Q7P1w9SDz5OTk7F7927p+fDhw42aVWrMmDHS4wMHDhSYKveDDz6QHj9//hzvvPNOicaczJ49W3qcmZmJN998s9wORm/RooXWtM3Lli3TOxh9165dUpctAHjvvff0fjB/8803tSYO0NXC0qhRI60V5f/44w9pOlx9vvrqK61uV5rT5horKSkJ48aNQ3Z2NgCgQYMGWu8hIiIi0o8JSAWkmTRofvjSl0xorgdSnPEfBw4ckB6buvuVv7+/VvyaiURxaO6XlZWFrVu3am0fNmyY1srt27Ztw+jRo/V283nw4AGmT58uLb6oqU2bNnjvvfek5wEBARgwYIDWWJz8IiMj8dFHH2kttviiWLRokfQ4NTUVw4YN0+rWpHbmzBlMmTJFeu7s7KyVjJXUrFmzpMfZ2dkYOHAgLl68WGjZnJwcfPnll/jxxx+l1+zt7fH222/rrH/nzp04cOCA1riO/P7991/4+Pjg33//BZA3Je/q1atNttAiERFRZSCIppheiF44TZs2lVo+1P755x+dXZh27NiB0aNHa73Wvn17vatHJyQkwMXFBTk5OVAoFIiLizPpAoRdu3aVBj5Xr14dkZGRRs9Q1bhxY6mrWNu2bbUWaQSA2NhYdO3aVSoD5A10HjRoEDp16oTq1asjPT0dDx8+xNmzZ6UpYU+ePFloYpeZmYnevXtrtSZZW1ujX79+6N69O1xcXKBUKhEeHo4LFy7gzJkzUKlU2LBhg94uUeby6aef4ttvv5WeOzg4YMKECWjVqhUyMzNx+vRp7N27V1qnw8LCAgcPHsSAAQP01vvmm29qTTIQFham1dKhKSsrCz4+PlqD4QVBgK+vL3x9fVGrVi0olUrcvXsXe/bsKdD17bfffsO0adN0xvLRRx/h+++/h5OTE3x9feHl5QVXV1fk5OTg2bNnOHbsGAIDA6VzFAQBGzdu1FpBnYiIiAwgUoU0bdo0EYD0z9LSUkxJSdFZPjo6Wqs8AHHOnDl6j7F161ap7LBhw0wa/507d7RimTp1aonq+/TTT7XqCwkJKVAmOjpa7Ny5c4HroO/fyZMndR4zLS1NHDp0aLHq27BhQ4nOs7SoVCpx+vTpBp2DXC43+DwmTpyotW9YWJje8nFxcaKPj0+xrqmVlZW4dOnSImP58MMPDa6zevXqor+/v0HnSERERNrYBauCyv+tfPv27fWu9O3s7IwWLVrorSM/zfEfml2YTCH/YHFju1/p2r+wldGdnZ1x/vx5rF+/Ho0bN9ZbX8OGDbFw4UK9g+4VCgX27duH3bt3Fzm1b506dTBnzpwiWwzMRRAErFq1Crt379Y5ra5MJkO/fv1w+fLlUmvFcXJywvHjx7Fu3Tq0adNGb1lra2uMHTsWQUFBetfzUOvRowd69Oiht5XN2dkZ//nPf3D79u0SvyeJiIgqK3bBIqPk5OTA2dkZiYmJEAQBT58+NfkChOZ2//59XLp0CVFRUUhNTYWDgwPc3d3h5eVl1OreERERuHDhAp4/f46kpCTY2dmhTp06aNWqFZo1a1YKZ1B6rl+/jmvXruHZs2ewtrZGnTp10LVrV9SuXbtM44iIiMClS5fw9OlTJCcnw9LSEo6OjmjSpAk6dOhg0Grp+aWlpSEwMBD3799HfHw8LCwsUKtWLXh6eqJLly6wsLAohTMhIiKqPJiAkFECAgKk2ZE6dOiAS5cumTkiIiIiIioP2AWLjFKaiw8SERERUcXFBISMUprjP4iIiIio4mIXLCIiIiIiKjNsASEiIiIiojLDBISIiIiIiMoMExAiIiIiIiozTECIiIiIiKjMMAEhIiIiIqIywwSEiIiIiIjKDBMQIiIiIiIqM0xAiIiIiIiozDABISIiIiKiMsMEhIiIiIiIygwTECIiIiIiKjNMQIiIiIiIqMwwASEiIiIiojLDBISIiIiIiMoMExAiIiIiIiozTECIiIiIiKjMMAEhIiIiIqIywwSEiIiIiIjKDBMQIiIiIiIqM5bmDoBKJjY21twhkJnY2NhAJpNBpVIhMzPT3OEQlWu8n4hMi/dU5VajRg2925mAEJVTtra2sLCwQG5uLn+5E5UQ7yci0+I9RfqwCxYREREREZUZJiBERERERFRmmIAQEREREVGZYQJCRERERERlhgkIERERERGVGSYgRERERERUZpiAEBERERFRmWECQkREREREZYYJCBERERERlRkmIEREVKndvHkTnTt3RocOHRASEmLucIiIKjwmIEREREREVGYsTVVRVlYWEhMTkZGRAVtbWzg6OsLKyspU1RMRERERUQVgdAISFBSEQ4cOITAwENeuXcPz588hiqK0XRAEuLq6ok2bNvD29saAAQPQqVMnkwRNRERERETlU7ESkCdPnuC3337D5s2b8fTpU61tmsmH+nlkZCQiIyNx6NAhLFy4EHXq1MGECRMwbdo0uLu7lzx6IiIiIiIqVwwaA3Lv3j2MGzcODRo0wOLFixEREQFRFKV/crkctWvXRqtWrdCtWze0bNkStWrVglwu1yoXERGBxYsXo2HDhhg3bhxCQ0NL+/yIKiQOmiUiIqLySm8LSEJCAhYsWIB169YhNzdXauVo3bo1fHx84O3tjS5dusDDw0NnHWFhYQgMDERgYCBOnz6NkJAQ5OTkwN/fHzt27MCUKVPw7bffwtHR0bRnRkRERERELxy9CUjjxo0RHx8PURRRu3ZtvPXWW3jjjTfQsGFDgw/g6ekJT09PvPbaawCABw8ewM/PD7///juePXuG1atXY+fOnYiOji7ZmRARERER0QtPbxesuLg4NGjQAOvXr8ejR4/w1VdfFSv5KEyDBg3w1Vdf4dGjR1i/fj0aNGiAuLi4EtVJRERERETlg94E5Ndff8Xt27cxadIkWFqabMZeAIClpSUmTZqE27dvY+XKlSatm4iIiIiIXkx6s4p33nmn1AOwsLAok+MQEREREZH5cSV0IiIiIiIqM0xAiIiIiIiozBiVgJw6dcroA+bk5GDOnDlG709EREREROWXUQlI3759sXDhwgKrnxfl/v376Ny5M3755RdjDktEREREROWcUQlIbm4uvv76a/j4+ODp06cG7ePn54d27drh6tWrxhySiIiIiIgqAKPm1q1VqxYiIyNx9uxZtGnTBuvWrcPw4cMLLZuamorp06fjjz/+kFpMRo8ebXTApE0QBMhkHMpT2eT/mVtYWJgpEqLyj/cTUeniPUX5GZWAXLt2DRMnTsTff/+N+Ph4jBw5EtOnT8dPP/0EuVwulbt06RJee+01hIWFQRRFKBQKLFu2DJMnTzbZCVR2tra2UCgU5g6DypiDg4P0WCaTwdHR0YzREJVvvJ+ISo+FhQXvKSrAqASkevXqOHDgAH7++Wd8/PHHyMrKwm+//YazZ8/C398fzZo1w+LFi/HFF18gJycHoiiidevW8Pf3R9OmTU19DpVaRkYGlEqlucOgMpaSkiI9VqlUSEhIMGM0ROUb7yci06tSpQosLCyQm5uL5ORkc4dDZayopLNEy5vPnj0bvXr1wtixY3H//n1cv34dHTt2RMuWLXHp0iWpy9V7772HpUuXwtrauiSHo0KIoojc3Fxzh0FlTKVSaT3ne4DIeLyfiEoX7ynKr8SDB9QDyydMmAAASE9Pl5KPGjVqYO/evVi+fDmTDyIiIiIiMs1ChDY2NnB1dQWQNyha/X+rVq3QtWtXUxyCiIiIiIgqgBInII8ePUL37t2xdOlSCIIACwsLVKlSBaIoIiAgAG3atMHJkydNESsREREREZVzJUpA/P390bZtWwQFBUEURbi7u+PUqVO4du0aunbtClEU8ezZM/Tr1w8LFiwo0M+WiIiIiIgqF6MSkIyMDEyePBnjxo1DUlISRFHEyJEjERwcjC5duqBu3bo4ffo0FixYAJlMBpVKhcWLF6NHjx4IDw839TkQEREREVE5YVQC0rZtW2zcuBGiKMLW1ha//fYbduzYgapVq/6vYpkMX3/9NY4dO4Y6depAFEUEBgaiTZs2+PPPP012AkREREREVH4YlYCEhoZCFEW0aNECQUFBePvtt3WW9fHxQUhICIYMGQJRFJGUlITXX3/d6ICJiIiIiKj8MnodEPXK5zY2NkWWdXJywr59+7BixQrMnz8fWVlZxh6WiIgIFy9eNFldYWFhSE1NBQAEBwcjMjLSZHV37tzZZHUREVUURiUgO3fuxIgRI4q934wZM9CzZ0+89tprxhyWiIgIADBkyJBSqXfy5MkmrS8mJsak9RERVQRGdcEyJvlQa9OmDS5fvmz0/kREREREVH4Z3QWrJBQKhTkOS0REFcSBAwdMVtfx48fx888/AwA++ugjdO/e3WR1E1U2KpUKwcHBuHr1KlJSUuDg4IC2bdvCy8sLMplJ1r+mCsAsCQgREVFJmHJsRVhYmPS4UaNGHLdBZKS7d+9iyZIliIiI0Hp9x44dcHNzw7x589CkSRMzRUcvEpMmICkpKUhOTkZubm6RZd3d3U15aCIiIiIyk7t372L+/PlQKpWFbo+IiMD8+fPxww8/MAmhkiUgKpUK/v7+8PPzQ1BQEJKTkw3aTxAE5OTklOTQRERERPQCUKlUWLJkic7kQ02pVGLJkiVYs2YNu2NVckYnINHR0RgxYgQuXLgAABBF0WRBERFR8aWkpCA8PNzcYZQ7mtPuRkRE4ObNm2aMpvxyd3eHg4ODucMgMwgODi7Q7UqXiIgIBAcHo127dqUcFb3IjEpARFHEiBEjcP78eQBAvXr14O3tDX9/fwiCAB8fH1SvXh1hYWEICQlBTk4OBEFA//794erqatITICKivORj4sSJSE9PN3co5Y56DRAA2LZtG/bt22fGaMovhUIBPz8/JiGVUGBgYLHLMwGp3IxKQHbv3o3z589DEAS8++67+OWXX2BhYQF/f38AwMyZMzFs2DAAwPPnz/Hll19i9erVuH79Or777jt4eXmZ7ATUkpKSsHPnTgQFBSEuLg5yuRwNGjTAoEGD4O3tXez6PvnkE9y4ccOgsn369MHMmTO1Xvvll19w4sQJvfu5u7tj5cqVxY6NiCi/8PBwJh9kVunp6QgPD0eLFi3MHQqVsZSUlFItTxWPUQnI9u3bAQANGjTAsmXL9Pbjc3V1xapVq9CwYUPMmzcPI0eOxNWrV1GlShXjIi5EeHg4FixYgKSkJACAra0t0tLSEBwcjODgYAwdOhRTp04tVp329vaoVq2azu05OTnSt2YNGjTQWc7a2lrntMOmvAZERGpTpkyBh4eHucMoN27evIl58+YBAN599100atTIzBGVL48fP8a6devMHQaZUXFbvdhKRkYlIJcuXYIgCBg1alShyUdh40HmzJmD7du3499//8WaNWswd+5cYw5dQHZ2Nr755hskJSXBw8MDH3zwATw9PaFUKrF3715s3boV+/fvh6enJ/r27WtwvZ988one7X/++Se2bNkCKysr9OrVS2e57t27Y9asWQYfl4iopDw8PNC0aVNzh1FuaHbBqlevHq8dUTF5e3sXq+uiMT1TqGIxagqCmJgYAECzZs20XhcEAQCQmZlZ6H5jxoyBKIrYvXu3MYct1OHDh/H8+XPI5XJ8/vnn8PT0BADI5XKMHj0aAwcOBABs2bLFpDNvnTx5EgDQsWNHZvJERERUaXl5ecHNzc2gsnXr1i2VrvhUvhiVgGRnZwMA7OzstF63t7cHAMTGxha6n3rtj4cPHxpz2EIFBAQAAHr27AlnZ+cC20eOHAlBEBAfH4/r16+b5Ji3b9/G06dPAaBYrSpEREREFY1MJsO8efMgl8v1lpPL5Zg7dy6n4CXjEpDq1asDKDiIqGbNmgCA0NDQQveLiooCACQmJhpz2AIyMjJw7949ANA5m4Kzs7OUlYeEhJjkuMePHwcAODk5oW3btiapk4iIiKi8atKkCX744QedLSF169blIoQkMWoMSOPGjREVFYWwsDCt11u1aoX79+/j0KFDhe6nft3JycmYwxYQEREhjTfRN+DSw8MDT548wZMnT0p8TKVSiXPnzgEAfHx8YGFhobf8tWvXMG3aNMTExMDa2hq1atVC+/btMXjwYDg6OpY4HiIiIqIXQZMmTbBmzRoEBwfj6tWrSE1Nhb29Pdq2bYu2bdtKXfWJjEpAOnfujNOnT+Pff//Ven3o0KHYs2cP7t+/jwULFuCbb76R3mw//fQT/v77bwiCgC5dupQ8cgDx8fHSY31JjXpbQkJCiY8ZGBiItLQ0AHnT7xYlNjYWFhYWsLW1RXp6Oh48eIAHDx7gn3/+wfz589GmTZsSx0RERET0IpDJZGjXrh369OkDCwsL5ObmmuTzF1UsRnXB6tevH4C88ReaA87HjBkjNb0tXrwYtWvXRteuXVGzZk1pikMAeP/990sSs0Tz2Pr6Haq3ZWRklPiYx44dA5DXClS3bl2d5Ro0aIDp06dj/fr12LVrF7Zt24Zt27bhgw8+gJOTE1JSUrBo0SJpLAkRERERUWVgVALSu3dv+Pj4oEWLFtJq6EDe+hv+/v6ws7ODKIqIiorCxYsXERMTI3WV+uKLL/ROW/sii4mJkQayF9X6MXToUAwaNAjOzs7SYCuFQgEfHx/88MMPsLe3R0ZGBv74449Sj5uIiIiI6EVhVBcsCwsLnat8d+3aFdeuXcO3336Lo0eP4vnz57Czs0PHjh0xa9YsvPTSSyUKWJONjY30WKlU6lzwT6lUAshLkEri5MmTUKlUsLa2Ro8ePYyux8XFBYMHD8b27dtx+fJlqFQqnTNCbNmyBdu2bdNZ16uvvoqJEycaHQuVT5pTP8tkMo4nIq33hFwuL/Hvu8pEswVdEAReu2LSvH4ODg78fUQAIH2u4d8oKoxRCUhR6tWrh7Vr15ZG1Vo0x33Ex8frTEDUY0VKegOok67OnTtLUw4bq3HjxgCA9PR0pKSkoGrVqoWWS0tLQ3R0tM560tPTixwITxVP/oSV7wHSfE8IgsDBnsWQ/1rx2hWP5vWSyWT8fURaBEHge4IKKJUEpKy4ublBEASIoojw8HCdU7+Fh4cDgN4xG0W5desWnj17BqBs1/6ws7ODi4uLzu0KhQK5ubllFg+9GFQqldZzvgdI8z0hiqLU7ZWKlv9a8doVj+b1UqlU/H1EAPKSUfVntPx/s6jiKyrpLNcJiK2tLRo1aoTQ0FBcuXIFXbt2LVAmNjZWmn63JDNOqdf+qFGjhklmrlKvlWJra6t3JfXx48dj/PjxOrfHxsZydolKSHMNHpVKxfcAab0nlEqlSSbdqCzU3XSBvA/TvHbFo3n9UlJS+PuIAOT1OrGwsODfqEqqRo0aereX+6UofXx8AACnT59GTExMge27d++GKIpwcnJCq1atjDqG5tofvXv3LnIFz6K+PYuJicHff/8NAOjQoQNXBCUiIiKiSqPELSA5OTm4ePEibt26hYSEBK2pcfX5/PPPS3poAMCAAQOwb98+PH/+HF9//TVmz54NT09PKJVK7N+/HwcPHgSQ15Jgaal9ulOmTEF0dDR8fX0xa9Ysncc4f/480tPTARi29kdAQAACAwPRu3dvNG/eHFWqVAGQNw1wUFAQ/Pz8kJKSAltbW7z22mtGnjkRERERUfljdAKSlZWFRYsWYeXKlUY1rZkqAbGyssKnn36KBQsW4NGjR5g5cyYUCgUyMzOlPodDhgwp0bgN9eDzZs2aoXbt2kWWV6lUuHDhAi5cuAAgr5uVpaUl0tLSpJiqVq2KefPm6Ry3QkRERERUERmVgGRnZ2PgwIEICAgwarCeqWcYcXd3x4oVK7Br1y4EBQUhNjYWdnZ2qF+/PgYPHgxvb2+j6y7O2h9qrVq1wvjx43H79m08ffoUycnJSE9Ph52dHerWrYsOHTpgwIABesd+EBERERFVREYlIMuXL8fJkycB5K3FMW7cOPTo0QOurq56VyQvTdWqVcPkyZMxefJkg/dZt25dkWWcnZ3x119/FSsWFxcXjB49ulj7EBERERFVBkYlIFu2bAGQtw7HmTNn0KxZM5MGRURERETl082bNzFnzhwAwJo1a+Du7m7miOhFY9T0S/fu3YMgCJgxYwaTDyIiIiIiMphRCYiNjQ0AoEWLFiYNhoiIqKzk5ACHDinw66/VpNe++84eK1ZUw/PnXLmZiKi0GNUFq379+vj333+RlJRk6niIiIhKVWKiDJs2VYG/vwOeP7cEoJC2BQbKERjoiOXLq6Ffv3RMmpSEjh2VuisjIqJiM6oFZOzYsRBFEUePHjV1PERERKXmwQMrDBtWG7/84vj/yUfhcnMFHDpkhzFjamPVqqowYsJHIiLSwagWkKlTp+K3337Dzp07cfz4cYOnpyWqzC5evGiyusLCwpCamgoACA4ORmRkpMnq7ty5s8nqIvP46quvTD7deUWQkVEXN26sRk6OVbH2W7LECdu2/QV396JnTqyMjJmOn4gqN6MSEAcHBxw4cAB9+/bFsGHD8OWXX2LatGlc14JIjyFDhpRKvcWZetoQMTExJq2Pyl52dra5Q3jh5Oba4vbtpcjJqWbU/k+fvgVr6wdwcjps2sCIiCoho1dCb9y4Ma5fv46+ffviww8/xCeffIImTZqgevXqkMn09+wSBAHHjx839tBERETFEhc3GEpl3RLV8ezZ23B0PAJB4Df+REQlYXQC8vTpU0yYMAFXrlwBAOTk5ODWrVtF7ieKIrsGUKV04MABk9V1/Phx/PzzzwCAjz76CN27dzdZ3UQVjSgCMTGjSlyPUumBlJROqFLFdN0piYgqI6MSkPj4ePTq1QthYWFafT/ZD5RIN1OOrQgLC5MeN2rUiOM2SIuVlRW/6NGQlOSFzMwGJqkrNnYMatS4apK6KgpRFNntj4iKxagE5Mcff8TDhw8hCAJatmwpfQPr6uoKa2trU8dIRETF8Pnnn6Np06bmDuOF8eOPjjCggd4gmZndsXXrVtNUVkHcuXMHn332mbnDIKJyxKgE5K+//gIANGvWDIGBgVAoFPp3ICIiMpOEBKNmnC9URoYMSqUAuZwt/kRExjIqAXn06BEEQcDbb7/N5IOMlpKSgvDwcHOHUS5pTrsbERGBmzdvmjGa8snd3Z0z91USKpVp68vNNW19RESVjVEJSJUqVZCZmYnatWubOh6qJFJSUjBx4kSkp6ebO5RySb0GCABs27YN+/btM2M05ZNCoYCfnx+TkEqgShXTZSBWViJsbdn6QURUEka1S7do0QJA3kxYRMYIDw9n8kFmlZ6ezha4SsLLS2myutq0UYLj+4mISsaoFpA33ngDJ06cwJ9//omZM2eaOiaqZKZMmQIPDw9zh1Gu3Lx5E/PmzQMAvPvuu2jUqJGZIyo/Hj9+jHXruKJ1ZdKnTzpq1sxBVJTRM89Lxo1LNkFERESVm9EJyNatW3H06FF8/fXXnP2CSsTDw4Mz9hSTZhesevXq8foR6WFlBYwdm4JlyxxLVE/16rl46aU0E0VFRFR5GT01yO7duzFq1CgsXLgQAwcOxMGDBxEXF2fK2IiIiEzi9ddTYG9fsrEgkyYlQS43UUBERJWYUS0gFhYW0mNRFHHkyBEcOXLE4P0FQUBOTo4xhyYiIio2Z+dcrFoVhbfeckV2dvEHcbz0UhqmT08qhciIiCofo1pARFGU/uV/bug/IiKistStWyZ++y0KCkXxWkKGDEnFzz/HQGa65USIiCo1o1pAevbsCYHTgBARUTnTu3cGdux4hmXLHHHsmAIqle6/ZR4e2Zg0KQnjx6cw+SAiMiGjEpCAgAATh0HGEgQBsnL4l1EzZplMVi7PwZzyXy9eP8Plf+9pdiktz3hPGa5Fi1ysWROLyEgL/PGHPfbuzcbjx3nbXF1z4OWVhtdfT0X37pn/n3jwWupTUe8pMl7+3z98T1B+JZ+TkMzK1ta2XK5Gr7n4m7W1NWxsbMwYTfljbW0tPRYEgdevGDSvnYODAxwdSzYz0ouC91TxeXoCn3yiRN++KRg2LO+1NWuS0amTepY5XkNDVNR7ioyn+ftIJpPxPUEFMAEp5zIyMqBUmm6RrbKSkpIiPc7KykJmZqYZoyl/srKypMeiKPL6FYPmtUtJSUFCQoIZozEd3lPG4/1UMhX1niLjaf4+UqlUfE9UQkUlnUYlIL6+vhAEAV9//TW6du1q8H6XLl3Chx9+CEEQcPz4cWMOTfmIoojc3Fxzh1FsKpVK67Hmcypa/uvF62e4/O+98nj/FIb3lPF4P5VMRb2nyHj57yG+Jyg/o8eACIKA2NjYYu0XHx8v7UtERERERJUPR9YREREREVGZKdMEJDs7GwBgZWVVloclIiIiIqIXRJkmIHfv3gVQ9MAUIiIiIiKqmIocA5KcnIzExMRCt0VHRyM8PFzv/qIoIi0tDVeuXMHSpUshCAJatmxpVLBERERERFS+FZmA/Pzzz/jqq68KvC6KIqZNm1asg4miCEEQ8NprrxVrPyIiIiIiqhgMmgVLFMViva7P+PHj8dZbbxV7PyIiIiIiKv+KTEC8vLwwceJErdf8/PwgCAJ8fHzg7u6ud3+ZTAZ7e3t4enqib9++7H5FRERERFSJFZmAvPzyy3j55Ze1XvPz8wMAzJw5E8OGDSudyIiIiIiIqMLRm4Cou0q9//778PLykl6fOHEiBEEosvWDiIiIiIhIk95peDdu3Ag/P78CM10FBAQgICCg2CuhExERERFR5WbQIPT8Hj9+DEEQkJ6ebup4iIiIiIioAtObgMjlcmRlZSE5Obms4iEiohJ6/PixuUMoVzSv16NHj2BlZWXGaMofvt+IqLj0JiCurq4IDw9HUFAQxo8fX1YxERFRCaxbt87cIZQrqamp0uP//ve/sLe3N2M0REQVn94EpFu3bnj8+DF+++03ZGVloV27drCxsZG2nzhxQucq6UV54403jNqPiIgKcnd3h0KhYNdYMhuFQsHJaYjIIHoTkJkzZ2L79u3Izc3F2rVrtbaJoogVK1YYdVBBEJiAEBGZkIODQ6GThlDRjhw5gkWLFgEAXn/9dfTq1cvMEZVP7u7ucHBwMHcYRFQO6E1AOnbsiC1btuD9999HTExMge3GrIReWpKSkrBz504EBQUhLi4OcrkcDRo0wKBBg+Dt7V3s+qKiojB16tQiy3344Yfo1q2bzu0PHz7Enj17cP36dSQnJ6Nq1apo2bIlRowYAU9Pz2LHRUSki4ODA1q0aGHuMMqd69evS4/d3Nx4DYmISlmRs2CNGTMGr7zyCi5duoQnT55AqVRi0qRJEAQB7733Htq1a1cWceoVHh6OBQsWICkpCQBga2uLtLQ0BAcHIzg4GEOHDjUomdClSpUqkMkKn7HY2tpa536nTp3CsmXLkJOTAwCws7NDXFwcTp06hXPnzmH27Nno0aOH0XEREREREZU3Bk3Da21trfUt/6RJkwAAffr0MftK6NnZ2fjmm2+QlJQEDw8PfPDBB/D09IRSqcTevXuxdetW7N+/H56enujbt69Rx/jxxx9Rs2bNYu0THh4uJR/du3fHlClT4OTkhPj4eKxduxbnzp3DL7/8Ak9PT7i5uRkVFxERERFReaN3IUJ9XpTuV4cPH8bz588hl8vx+eefS92a5HI5Ro8ejYEDBwIAtmzZIrVElIWtW7ciJycHnp6emDNnDpycnAAATk5OmDt3Ljw9PZGdnY2tW7eWWUxEREREROZmVAKiUqmgUqnM3voB5K3KDgA9e/aEs7Nzge0jR46EIAiIj4/X6udbmtLS0nDp0iUAwPDhw2FhYaG13cLCAsOHDwcABAUFcdYaIiIiIqo0jG4BeRFkZGTg3r17AKBzLIqzs7PUxSkkJKRM4rp165bU2qIrLvXr2dnZuH37dpnERURERERkbgaNASlKTk4Ozp07h8DAQERGRiIlJQUODg6oXbs2OnfujG7dusHS0iSH0hIRESF1BfPw8NBZzsPDA0+ePMGTJ0+MOs4PP/yAZ8+eQalUomrVqmjcuDH69u2Ljh07FlpefZxq1aqhatWqhZapWrUqqlatiqSkJISHh6N9+/ZGxUZEREREVJ6UKCsQRRE//vgjfvrpJ0RFReks5+rqijlz5mD27NkQBKEkh9QSHx8vPVaPsSiMeltCQoJRx7l37x4UCgVkMhni4uJw4cIFXLhwAd26dcMHH3wAKysrrfLq4+iLSb09KSnJ6LiIiCqrixcvmqyuhw8fSo/v3btn0ro7d+5ssrqIiCoKoxOQjIwMDBkyRBqDoW9QemRkJObNm4e///4bBw4c0FpNvSQyMzOlx3K5XGc59baMjAyD67a2tsagQYPQo0cPeHp6QqFQAMib3WrXrl04efIkzp07Bzs7O7z33nta+6qPoy8mY+MiIiJgyJAhpVLv4sWLTVpfYWtoERFVdkYnIBMmTMDJkycB5K1s7uvriwEDBqBx48awt7dHamoqQkNDcfjwYZw8eRKiKOLkyZOYMGECduzYYbITKC2Ojo6YPn16gdfd3d0xe/ZsVKlSBXv37sXRo0cxfPhwTqVLRERERGQAoxKQEydOYPfu3RAEAe7u7vD399fZzDxv3jwEBQXhtddeQ1hYGHbv3o2TJ0+id+/eJQocgFZLilKplFop8lMqlQDyFig0lXHjxuGff/5BVlYWLl26pJWAqI+jPq4uhsS1ZcsWbNu2Tef2V199FRMnTixO6C8EBwcH6bFcLjfpz6Yy0GxdEwSB168YNK+dg4MDHB0dzRgNGevUqVMmq+v+/fv44YcfAAAfffQR6tevb7K6+f6iykjzb7xMJuN9QAUYlYBs2rQJAGBvb4+AgAC9A8ABoFOnTjh+/DjatGmD1NRU+Pn5mSQB0RxjER8frzMBUY8VMeUNYGNjA3d3d9y/f7/A+Bd1XJpjVIyNKy0tDdHR0Tq3p6enF5jmtzzQXFleEASTjg2qDPJfL14/w2leK5lMVi7vH8qbet1UqlSpAnt7ewCAl5cXvLy8TFY3UWWk+TceAH/PUgFGJSDnzp2DIAiYNGlSkcmHWr169TBp0iQsX74cZ8+eNeawBbi5uUEQBIiiiPDwcJ3doMLDwwEAdevWNclxi6I+TmJiIpKTk1GlSpUCZZKSkpCUlAQgr1uXLnZ2dnBxcdG5XaFQIDc3t4QRlz2VSiU9FkXxhVnYsrzIf714/Qynea1UKlW5vH/ItDR/HwHge4KohHhPUVFJp1EJSGRkJIC8lo3iUJfXN2NWcdja2qJRo0YIDQ3FlStX0LVr1wJlYmNjpWlx27RpY5LjAnkD4NWJTc2aNbW2NW/eHJaWlsjJycGVK1fg4+NTYP+rV68CAKysrNCsWTOdxxk/fjzGjx+vc3tsbGy5nEUrJSVFeqxUKjkQv5g0u/eJosjrVwya1y4lJaVc3j9kWpq/j1QqFd8TRCXEe4pq1Kihd7tRCxGquzDkz3CLUhrf0qo/3J8+fbrQ2UZ2794NURTh5OSEVq1aGVxvUbH+8ccfyMrKgiAIBdYDUSgU0mt79+4tkPnn5uZi7969APKSMl1dx4iIiIiIKhqjEhBXV1cAwKVLl4q1X1BQkNb+pjBgwAC4uroiMzMTX3/9NcLCwgDkfcu5c+dOHDx4EEBeS0L+xRCnTJmCYcOG4ZdffilQ7yeffII///wTYWFhWglEeHg4li1bhj179gAA+vXrV2jXr3HjxsHS0hIPHjzATz/9JGX/CQkJ+Omnn/DgwQNYWVlh3LhxJrkORERERETlgVFdsLp3744HDx5g48aNmDNnjt4xDGqPHz/Ghg0bIAgCunfvbsxhC2VlZYVPP/0UCxYswKNHjzBz5kwoFApkZmZKLTRDhgxB3759i1VvTEwMtmzZgi1btsDCwgIKhQJZWVla3Td69eqFadOmFbq/u7s7Zs6ciWXLluHMmTM4e/YsFAoF0tLSAACWlpaYOXMmp++lYsvNBWJi/vfdQVKSUd8jEBEREZmFUQnIG2+8AT8/P6SmpqJ3797Yvn07OnTooLP85cuXMXbsWKSmpkIQBJNPG+vu7o4VK1Zg165dCAoKQmxsLOzs7FC/fn0MHjwY3t7exa7zzTffREhICO7du4eEhASkpKTAwsICtWrVQtOmTdGnTx+0bt1abx29evVC3bp1sXv3bty4cQPJyclSV7ARI0bA09PT2FOmSigy0gL+/g7Yvt0B0dHPpNffeMMRLVvWxrhxyRg2LA22thyQTkRERC8uoxKQ3r1745VXXsGePXvw6NEjeHt7w8fHB/3790fjxo1hZ2eHtLQ03Lt3D0eOHJEWIhQEAa+88kqhg7JLqlq1apg8eTImT55s8D7r1q3Tua179+4maampX78+5s6dW+J6qPJKTRXwxRfVsW+fPXJzC59u98YNOT7+2BnffeeE995LxOTJyeDMvERERPQiMnol9C1btmDgwIE4ffq0tMq5emX0/NQDunv16oXNmzcbe0iiSicuToY333TFzZvyogsDSE62wKJF1fHwoRW++SYOMvbOIiIioheM0R9PbG1tceLECXz//fdwdXWV1nIo7F+tWrWwZMkSHD9+nCs2ExkoI0PAlCk1DU4+NPn7V8EPP3DlWSIiInrxGN0CAuStdDlv3jzMnj0b58+fx8WLFxEZGYmUlBQ4ODigVq1a6Ny5M7p27VpgBioi0m/ZsmoICbExev81a6qhZ88MdO2aacKoiIiIiErGJFmBpaUlevbsiZ49e5qiOqJKLzNTwPbtDiWuZ9OmKkxAiIiI6IXCHuJEL6ADB+yQlGRR4nqOHVMgMrLk9RARERGZChMQoheQKVo/AEClErBzp2nqIiIiIjIFkw/MePjwIb7//nscPXoUkZGRsLe3R6tWrfDGG2/gzTffNPXhqAL46quvIHDOWC0hIQcBGD/+Q9PmzRcRGPiVSeqqCNSz8hEREZF5FJmA5OTkYMKECcjNzYWXlxc++eQTnWUPHTqEUaNGIT09Xfojr1QqcerUKZw6dQrbt2/H3r17YW1tbbozoHIvOzvb3CG8cHJz7UxWV3a2AllZWSarj4iIKpaLFy+atL6wsDCkpqYCAIKDgxEZGWmyujt37myyush8ikxALly4gO3bt0MQBAwePFhnufDwcIwdOxZpaWkFvs1WJyNHjhzBBx98gJUrV5YwbKKKTSZTIjfXNIm6TMZB6EREpNuQIUNKre7iLBBtiJiYGJPWR+ZR5BiQ06dPAwCsra3x6quv6iy3cOFCJCcnQxAEyOVyLFq0CEFBQbh69Sq++OILyOVyiKKI1atX4+HDh6Y7A6IKyMoq9oWsi4iIiKikimwBuXLlCgCgZ8+esLMrvFtIamoqtm/fLj0/cOAAfH19pedt2rRB06ZN8dprr0GlUsHf319vVy6qXKysrDgGJJ8aNU4gIsI03xq5uJxkt0cNoiiy2x8RkYYDBw6YtL7jx4/j559/BgB89NFH6N69u0nrp/KvyAQkNDQUgiCgU6dOOsucOHECGRkZEAQBvr6+WsmH2pgxY7B06VJcuXIFZ8+eLVnUVKF8/vnnaNq0qbnDeKE8f26BHj1E5OaWLDFr0UKJvXs/AfO7/7lz5w4+++wzc4dBRPTCMPW4irCwMOlxo0aNOG6DCiiyC9bTp08BAA0bNtRZ5ty5c9LjESNG6Cw3aNAgiKKI27dvFydGokrH1TUX/fql//8z42dtGj8+mckHERERvVCKTEDUsxg4OOheS+DSpUvS427duuks16hRIwBAfHy8wQESVVbz5sWjWrVcAMZlEO3bZ+KVV1JNGxQRERFRCRWZgKj7jqenp+ssc/XqVQCAXC5HixYtdJZTKBRF1kVEeTw9c7B6dRTs7FTF3rdx4yysWRMFDv0gIiKiF02RCYiTkxOAvLEghQkNDUVSUhIEQUCbNm1gYWGhs66kpCQA/0tEiEi/jh2V8PePhKtrjsH7dOmSge3bI+HoWPzEhYiIiKi0FZmAtG7dGqIoYt++fYVu15w5oWvXrnrrCg8PBwC4uLgUJ0aiSq1FiywcPx6B77+PQcuWykLLWFiIGDAgDZs3R2LLlueoWpXJBxEREb2YikxABgwYAAC4fv061qxZo7UtMTERK1askJ4PHTpUb11BQUEA9A9oJ6KCbG1FjBqVin37nmHv3qeYMiVR2vb++6k4ffoJVq2KRrdumRx0TkRERC+0IqfhHT9+PL744gskJSXhnXfeQUBAAHr16oWoqCj4+fnh8ePHEAQBjRo1go+Pj8560tPTcerUKQiCgA4dOpjyHIgqlVatsqBUpmPdurznffooUatWrnmDIiKqYFJSUqSeG1Q8kZGR0uOIiAjcvHnTjNGUT+7u7nongCrvikxAHB0d8csvv+DNN9+EIAjYvn271qKDakuWLNFbz549e5Ceng5BENCzZ0/jIyYiIiIqRSkpKZg4cSInzTGSegZVANi2bZvObvykm0KhgJ+fX4VNQorsggUAb7zxBlatWgVbW1uIoqj1z9raGr/88kuR3a/UK2La29vrbSkhIiIiMqfw8HAmH2RW6enpFboFrsgWELVp06Zh+PDh2L17N+7cuYOcnBw0bNgQI0eOhLu7u959Y2Ji0K9fP/Tr1w8NGzaElZVViQOnPIIgQCYzKI98oWjGLJPJyuU5mFP+68XrZ7j87z19M/dR5ZD//uF7gjTfE1OnTkW9evXMF0w5dPPmTcyZMwcA8O6776JJkyZmjqj8ePToEdauXQugYv+NMjgBAYCaNWvinXfeKfZBnJ2d8d133xV7Pyqara1tuZzWWLNJ0draGjY2NmaMpvyx1ljgQxAEXr9i0Lx2Dg4OcHR0NGM09CLQ/H0kk8n4niCt90Tjxo3RvHlzM0ZT/mRlZUmPPT094eXlZb5gypnK8jeqWAkIvXgyMjKgVBY+NeuLLCUlRXqclZWFzMxMM0ZT/mj+chdFkdevGDSvXUpKChISEswYDb0INH8fqVQqvieIf6NKiH+jjFdR/kYVlTgxASnnRFFEbm75mwFJpVJpPdZ8TkXLf714/QyX/71XHu8fMq389w/fE8S/USXDv1HGqyx/o9hxnIiIiIiIyozeBGTUqFF4+PBhqQZw/fp1DB8+vFSPQURERERELwa9CciuXbvQrFkzTJo0Cbdv3zbpgW/cuIExY8agbdu22L9/v0nrJiIiIiKiF5PeBMTX1xfZ2dnYtGkTWrZsCR8fH2zcuNHoATGxsbFYvnw5OnTogDZt2mDnzp1QqVTw9fU1qj4iIiIiIipf9A5CP3bsGP788098/PHHCAsLw5kzZ3DmzBm8/fbbaNGiBby9vdG5c2c0bdoUTk5OqF69OhwcHJCcnIz4+HjEx8fj9u3buHjxIi5evIibN28iNzcXoigCABo0aIBFixZh1KhRZXKyRERERERkXkXOgjV69GiMGDECv//+O3788Ufcu3cPOTk5uHbtGq5du4Y1a9YYfDB14tGkSRPMnTsXEydOhKUlJ+IiIiIiIqosDJoFy9LSEm+//Tbu3LmDQ4cOYezYsbC3t4coigb/q1KlCsaPH48jR47g9u3bmDx5MpMPIiIiIqJKplgZgCAI6N+/P/r374/s7GycP38eFy9exPXr1/Ho0SPEx8dDqVRCLpejevXq8PT0RKtWreDt7Y0uXbpU2OXkiYiIiIjIMEY3QVhZWaFXr17o1auXKeMhIiIiIqIKjAsREhERERFRmWECQkREREREZYYJCBERERGVmFIJHDmiwP79dtJrAQHWSEkRzBgVvYg4DRURERERGe3pU0v88YcDtm93QFycBYCq0raff3bAmjXuePnlVIwfn4ymTbPNFyi9MNgCQkRERERG2bLFAb17u+G//632/8lHQWlpMmzbVgWDBrlh0SInqFRlHCS9cJiAEBEREVGxrVxZDZ9/XgM5OYZ3sVq3rirmzHFmElLJMQEhIiIiomLZvdseP/3kaNS+e/faY8kS4/aliqHCjAFJSkrCzp07ERQUhLi4OMjlcjRo0ACDBg2Ct7d3setLT0/HxYsXERwcjPv37yM6OhoqlQqOjo5o2rQpBg4ciBYtWujc/5dffsGJEyf0HsPd3R0rV64sdmxERERE5pKZKeCbb5xKVMeaNVXx2mspcHfPMVFUVJ5UiAQkPDwcCxYsQFJSEgDA1tYWaWlpCA4ORnBwMIYOHYqpU6cWq87Zs2cjMjJSem5tbQ2ZTIbo6GhER0fj9OnTeOWVVzBp0iS99VhbW0OhUBS6rUqVKsWKiYiIiMjcDh60Q2Ji4eM9DCWKArZtc8BHHyWYKCoqT8p9ApKdnY1vvvkGSUlJ8PDwwAcffABPT08olUrs3bsXW7duxf79++Hp6Ym+ffsaXG9ubi7q1auH/v37o3379qhVqxZEUcSzZ8+wadMmXLhwAXv27IGrqysGDhyos57u3btj1qxZJjhTIiIiIvPbssXBJPXs2OGA2bMTIZeLJqmPyo9yPwbk8OHDeP78OeRyOT7//HN4enoCAORyOUaPHi0lB1u2bEFOjuHNfLNmzcLy5csxZMgQ1KpVCwAgCALq1KmDDz/8EK1atQIA7Nmzx8RnRERERPRievrUEiEhNiapKyHBAhcumKYuKl/KfQISEBAAAOjZsyecnZ0LbB85ciQEQUB8fDyuX79ucL0tW7bUuU0mk8HX1xcA8Pz5c6SmphYvaCIiIqJyKCamZF2v8ouNNW19VD6YPAFJTEzEkydPkJiYaOqqC8jIyMC9e/cAAO3atSu0jLOzM9zc3AAAISEhJju25viN3Nxck9VLRERE9KLKzDTtquYZGVwlvTIq8RiQ1NRUbNy4ETt37sS///6L9PR0aZtCoUCHDh0watQovPHGG7C3ty/p4bRERERAFPP6DXp4eOgs5+HhgSdPnuDJkycmO/aNGzcAANWqVdM7mPzatWuYNm0aYmJiYG1tjVq1aqF9+/YYPHgwHB05BR0RERGVHw4Opl3Ao0oVLghSGZWoBWT//v1o1KgRZs6ciTNnziAtLQ2iKEr/0tLScPr0acyYMQONGjXCgQMHTBU3ACA+Pl567OSkezo49baEBNPMtBAbG4tDhw4BAPr06QNB0J29x8bGIjo6GjY2NsjMzMSDBw/w559/4r333jNpiwwRERFRaatbNwdWVqYbNO7pmW2yuqj8MLoFZNOmTZg8eTJUKpXUCuHg4IAGDRrAzs4OaWlpePjwIZKTkwEAUVFRGD58ODZs2IAJEyaYJPjMzEzpsVwu11lOvS0jI6PEx8zJycHSpUuRkZEBFxcXvPrqq4WWa9CgARo3boyOHTuievXqkMlkSE9PR1BQEDZu3Ij4+HgsWrQIP/30E+rUqVPiuIiIiMj0vvrqK71fNFZGVat+gdjYASWuR6EIxeLFb4KX93/Un6krOqMSkPv372P69OnS2IdXXnkF8+fPR+fOnQuUDQoKwpIlS7Br1y6oVCpMmzYNXbt2RYMGDUoWuRmIooiVK1fi1q1bsLa2xty5c2FnZ1do2aFDhxZ4TaFQwMfHB82bN8esWbOQmpqKP/74A3Pnzi3t0ImIiMgI2dn8hj6/6tX/NEkCUqPGn8jOzjJBRFTeGJWA/Pzzz8jMzIQgCPj+++/1foDu1KkTduzYgZ9++glz586FUqnEzz//bJIVwG1s/jd1m1Kp1Lngn1KpBJC3QGFJrFmzBidOnICFhQXmz5+Ppk2bGlWPi4sLBg8ejO3bt+Py5ctQqVSQyQrvDbdlyxZs27ZNZ12vvvoqJk6caFQc5uTg8L85xOVyeYl/NpWNZoufIAi8fsWgee0cHBw4Fou0fh/JZDK+J0jrPUEF2dmFQKG4jfT0ZgBEAMVvwrCwSIST0yGTx1aRVOS/UUYlIEeOHIEgCOjZs6fB395/8MEH2L9/P06dOoXDhw8bc9gCNMd9xMfH60xA1GNFSvJD/P3333Hw4EHIZDJ88MEH6NSpk9F1AUDjxo0BAOnp6UhJSUHVqlULLZeWlobo6Gid9aSnp8PCovxNYaeZcAmCwObtYsp/vXj9DKd5rWQyWbm8f8i08n8BxPcE6fpSkPIIAuDp+Snu3FmP3NxqRtSQg/r1P4GFRWbRRSuxivw3yqgE5OnTpwCgc/yDLq+++ipOnTol7V9Sbm5uEAQBoigiPDxcmm43v/DwcABA3bp1jTrOpk2b8Ndff0EQBMyYMQM9evQwOubisrOzg4uLi87tCoWiXE4DrFL9b9YL9aQFZLj814vXz3Ca10qlUpXL+4dMS/P3EcCp1Un7PWFlZcWEpBBy+XM0bz4Pt28vQU5ONYP3E4QsNGr0FWrUCAGge/xuZaVSqaRuf+X5b1RRiZNRCYi9vT2USiVq1qxZrP3UH6RNNR2vra0tGjVqhNDQUFy5cgVdu3YtUCY2NlaafrdNmzbFPsa2bduwc+dOAMD06dPRp0+fkgX9/0JDQwHknYO+pt7x48dj/PjxOrfHxsaabHavspSSkiI9ViqVJpkgoDJRdysE8j5Q8/oZTvPapaSklMv7h0xL8/eRSqXie4K03hOff/650V2uK4OHD1PwySdyBAUV3RW4UaMsfP11LDp1mgxgcukHVw7duXMHn332GYDy/TeqRo0aercblYA0bNgQcXFxUsuCodSJQKNGjYw5bKF8fHwQGhqK06dPY8yYMQVWQ9+9ezdEUYSTkxNatWpVrLp37twJf39/AMDkyZMxcOBAg/YTRVFvl5iYmBj8/fffAIAOHTpU+m9WHj9+bO4Qyh3Na/bo0SNYWVmZMZryhe83IiLTqV8/B/7+z3H7tjW2bHHA3r320FgSDhYWIgYMSMWECSno1CmTM14RACMTkDFjxiAwMBDbtm3D7NmzDep/Looitm7dCkEQMHbsWGMOW6gBAwZg3759eP78Ob7++mvMnj0bnp6eUCqV2L9/Pw4ePAggryXB0lL7dKdMmYLo6Gj4+vpi1qxZWtv27duHTZs2AQAmTpyIl19+2eCYAgICEBgYiN69e6N58+bSQoUZGRkICgqCn58fUlJSYGtri9dee60EZ18xrFu3ztwhlDupqanS4//+978mX+STiIioOJo1y8K338bhiy/icPLkc7zzTt7r27bFo2PHGPMGRy8coxKQ6dOnY8OGDbh69Spmz56NX375pch9PvjgA1y9ehVeXl6YNm2aMYctlJWVFT799FMsWLAAjx49wsyZM6FQKJCZmSn14RwyZAj69u1brHrXr18PIG/A6t69e7F3716dZT/++GM0a9ZMeq5SqXDhwgVcuHABQF43K0tLS6SlpUkxVa1aFfPmzdM5bqWic3d3h0KhQLrm1yREZUihUMDd3d3cYRARVSjW1kD16v8bQ6MxYSmRxKgERC6X4+DBgxg1ahRWrFiBwMBAzJ07F3369NGaaSoxMRFHjx7FTz/9hKCgIHTp0gU7d+6EtbW1yU4AyPswu2LFCuzatQtBQUGIjY2FnZ0d6tevj8GDB8Pb27vYdaoHqoqiiMTERL1lc3JytJ63atUK48ePx+3bt/H06VMkJycjPT0ddnZ2qFu3Ljp06IABAwZU6mn+HBwc4OfnV+xufJTnyJEjWLRoEQDg9ddfR69evcwcUfnj7u5eqe9BIiIic9GbgNSvX1/vztnZ2RBFEZcuXcKYMWMA5E11q14JXT1wRj0mIjw8HN26dYMgCHjw4IGJTiFPtWrVMHnyZEyebPigJn1df/bt22d0LC4uLhg9erTR+1cWDg4OaNGihbnDKJeuX78uPXZzc+N1JCIionJDbwLy6NEjaZrbwmiu36AuEx8fL627oVkOAJ49e1bkAG0iIiIiIqq49CYg7u7uTBaIiIiIiMhkimwBISIiqshatGiBixcvwsLCArm5ueV23n0iovKici9AQUREREREZYoJCBERERERlRkmIEREREREVGaMWgckv9jYWBw8eBCBgYGIjIxESkoKHBwcULt2bXTu3BmDBw9GjRo1THEoonLr4sWLJqvr4cOH0uN79+6ZtO7OnTubrC4iIiKi/EqUgKSnp2P+/Pn4/fffoVQqCy2zevVqyOVyTJkyBd9//z1sbW1LckiicmvIkCGlUu/ixYtNWl9MTIxJ6yMiIiLSZHQXrNjYWHTs2BGrVq1CZmYmRFHU+S8zMxO//vorOnbsiLi4OFPGT0RERERE5YjRLSAjR47E7du3AQC2trZ47bXXMGDAADRu3Bj29vZITU1FaGgoDh8+DH9/f6Snp+PWrVsYOXIkAgICTBU/Ublx4MABk9UVFhaGlStXAgDmz5+PmjVrmqxuIiIiotJkVAKyZ88enDlzBoIgwMvLC7t374aHh0eBcm3atMGoUaPw2Wef4dVXX8W///6LM2fOYO/evXj55ZdLHDxReWLKsRX29vawt7cHAHh5ecHd3d1kdRMRERGVJqO6YPn7+wMAnJ2dcfTo0UKTD00eHh44dOgQXFxcAADbtm0z5rBERERERFTOGdUCcvHiRQiCgLfeegtOTk4G7VO9enVMnjwZ3333nUln7CEiIjKWSqVCcHAwrl69Ks3g2LZtW3h5eUEm40z1RESlwagEJDo6GgDQunXrYu3XqlUrrf2JiIjM5e7du1iyZAkiIiK0Xt+xYwfc3Nwwb948NGnSxEzRERFVXEZ9vWNtbQ0AyMrKKtZ+6vJWVlbGHJaIiMgk7t69i/nz5xdIPtQiIiIwf/583L17t4wjIyKq+IxKQGrXrg0AOHPmTLH2O336NACgTp06xhyWiIioxFQqFZYsWaJz/So1pVKJJUuWQKVSlVFkRESVg1FdsHx8fHDnzh1s3rwZ7733Htq0aVPkPsHBwdiyZQsEQYCPj48xhyUiIiqx4OBgnS0f+UVERCA4OBjt2rUr5ajoRfX48WNzh1DuaF6zR48esedLMVSW95tRCciUKVOwevVqZGdno2/fvli9ejVGjBihs/zu3bsxbdo0ZGVlQSaTYerUqUYHTEREVBKBgYHFLs8EpPJat26duUMod1JTU6XH//3vf6Vp44nUjEpA2rVrh+nTp2PVqlWIj4/HqFGjUL9+ffTr1w+NGzeGnZ0d0tLScO/ePRw9ehQPHjyAKIoQBAHTp09H27ZtTX0eREREBklJSSnV8lT+ubu7Q6FQID093dyhUCWlUCgq9BpfRq+EvmLFCiQnJ2Pr1q0AgIcPH2L16tWFlhVFEQAwbtw4LF++3NhDEhERlZiDg0Oplqfyz8HBAX5+fggPDzd3KOXSkSNHsGjRIgDA66+/jl69epk5ovLH3d29Qv/uMToBkclk2Lx5M4YNG4bvv/8eV65c0Vm2ffv2+OijjzBy5EhjD0dERGQS3t7e2LdvX7HKU+Xj4OCAFi1amDuMcun69evSYzc3N15HKsDoBERt1KhRGDVqFMLDw3Hx4kVERkZKiznVqlULnTt3rtBNSOYmCAIXy6qE8v/MLSwszBQJUfnTvn171K1bF0+ePCmybN26ddG+fXv+nqUKrbjjoooSFhYmPb537x4uXbpksrr5hUDFIIjq/lHFsGnTJgCAq6sr+vfvb/KgyHDp6elQKBTmDoPKWHBwMKZMmQIgb4Ckl5eXeQMiKmdu3ryJadOmITMzU2cZGxsbrF69mt/eUoUnCIK5QzCYER9b6QVkVAvIm2++CUEQsGDBAiYgZpaRkVHkXPZU8WgOilWpVEhISDBjNETlT+3atbF06VJ8//33hbaE1K1bFx9++CFq167N+4voBcL7sXxwdHTUu92oBMTe3h5paWlo3ry5UUGR6YiiiNzcXHOHQWUs/8JofA8QFV/Dhg2xevVqBAcH4+rVq0hNTYW9vT3atm2Ltm3bQhAE3ltUKRw4cMCk9YWFhWHlypUAgPnz56NmzZomq5v3ZMVgVAJSq1Yt3L9/H9nZ2aaOh4iIqMzIZDK0a9cOffr0gYWFBXJzc/kNK1U6nTt3Nml99vb20tofXl5eHAtMBRg1qq53794AYNJBRUREREREVPEZlYBMmzYNMpkMfn5+ePr0qaljIiIiIiKiCsqoBKRt27b49ttvkZKSgn79+uHatWumjouIiIiIiCogo8aAbNq0Ca6urhg4cCD++ecftGvXDt27d0ePHj3g5uYGW1vbIut44403jDk0ERERERGVYyWahhfImztapVLhzJkzOHPmjEH7C4LABISIiIiIqBIyeiX0/AvBcGEYIiIiIiIqilEJyIYNG0wdBxERERERVQJGJSATJ040dRxERERERFQJGDULFhERERERkTGK3QLy9OlTXLt2DUlJSahatSpatWoFNze30oiNiIiIiIgqGIMTkKCgIMyePRuBgYEFtnl7e+Pnn39Gp06dTBocERERERFVLAZ1wTpy5Ah8fHwQGBgIURQL/Ltw4QJ69eqFw4cPl3a8RERERERUjhWZgKSkpGDixInIzMyUptpt2LAhunbtioYNG0rllEolJk6ciOTk5NKLloiIiIiIyrUiE5DNmzcjKioKgiCgQ4cOuHnzJkJDQ3H27FmEhobi1q1bUtermJgYbN68udSDJiIiIiKi8qnIMSD//PMPAKBGjRo4fPgwHB0dtbY3bdoU//zzD5o1a4aYmBj8888/+M9//lM60ZZDSUlJ2LlzJ4KCghAXFwe5XI4GDRpg0KBB8Pb2Nnd4RERERERlqsgWkGvXrkEQBLzxxhsFkg81R0dHvPHGGxBFEdevXzd5kOVVeHg43nvvPezduxeRkZGwsLBAWloagoODsWjRIqxdu9bcIRIRERERlakiW0Di4+MBAF5eXnrLtWnTBgAQFxdX8qgqgOzsbHzzzTdISkqCh4cHPvjgA3h6ekKpVGLv3r3YunUr9u/fD09PT/Tt29fc4RIRERERlYkiW0DS0tIAAA4ODnrL2dvbAwAyMjJMEFb5d/jwYTx//hxyuRyff/45PD09AQByuRyjR4/GwIEDAQBbtmxBTk6OOUMlIiIiIiozXAm9lAQEBAAAevbsCWdn5wLbR44cCUEQEB8fz25rRERERFRpMAEpBRkZGbh37x4AoF27doWWcXZ2llaQDwkJKbPYiIiIiIjMyeAERBCE0oyjQomIiJDWTPHw8NBZTr3tyZMnZRIXEREREZG5FTkIXW348OEGlRNFERYWFnrLCIJQocc9qAfuA4CTk5POcuptCQkJpR4TEREREdGLwOAEBID0rX5hBEGQWkn0lasMMjMzpcdyuVxnOfU2DtwnIiKiiqJFixa4ePEiLCwskJubyy9aqQCDEhBDEorKnnQQEREREVHRikxAVCpVWcRRodjY2EiPlUolFApFoeWUSiUAwNbWVmddW7ZswbZt23Ruf/XVVzFx4kQjI6XySnNabJlMpnORUCIyjEwmk/7n/URUcrynSJ9idcEiw2iO+4iPj9eZgKjHiui7MdPS0hAdHa1ze3p6epFjbqjiadeuHS5fvmzuMIgqHEEQ+DuVyIR4T1FhmICUAjc3NwiCAFEUER4eLk23m194eDgAoG7dujrrsrOzg4uLi87tCoUCubm5JQuYyiWZTCa9z9hSSVQyvJ+ITIv3VOVWVNLJBKQU2NraolGjRggNDcWVK1fQtWvXAmViY2Ol6XfbtGmjs67x48dj/PjxOrfHxsZycFcl5ejoCAsLC6hUKr4HiEqI9xORafGeqtxq1KihdzsXIiwlPj4+AIDTp08jJiamwPbdu3dDFEU4OTmhVatWZRwdEREREZF5MAEpJQMGDICrqysyMzPx9ddfIywsDEDewPOdO3fi4MGDAPJaOCwt2RBFRERERJWDIHL+3FITHh6OBQsWICkpCUDeeI3MzEypL+SQIUPw9ttvl+gYsbGxJY6Tyid18zbnWCcqOd5PRKbFe6pyK6oLFr96L0Xu7u5YsWIFdu3ahaCgIMTGxsLOzg7169fH4MGD4e3tbe4QiYiIiIjKFFtAyjm2gFRe/HaJyHR4PxGZFu+pyo2D0ImIiIiI6IXBBISIiIiIiMoMExAiIiIiIiozTECIiIiIiKjMcBA6UTm1ZcsWpKWlwc7ODuPHjzd3OETlGu8nItPiPUX6MAEhKqcGDRqE6OhouLi44O+//zZ3OETlGu8nItPiPUX6sAsWERERERGVGSYgRERERERUZpiAEBERERFRmWECQkREREREZYYJCBERERERlRkmIEREREREVGYszR0AERnn9ddfl+ZYJ6KS4f1EZFq8p0gfrgNCRERERERlhl2wiIiIiIiozDABISIiIiKiMsMEhIiIiIiIygwTECIiIiIiKjOcBYuoGKKiojB16lQAwNq1a1GzZs0yPf7KlStx5MgRfPjhh+jWrVupHGPbtm3w9/dHy5YtsWjRogLbMzIy4O/vj8DAQMTGxiI7OxsAsG/fPqlMSEgIdu3ahfv37yMtLQ2iKGLs2LF4/fXXMX/+fISGhmL58uVwd3cvlXOgF1d2djYCAgJw+fJlPHjwAMnJyVCpVHBwcICnpyfatWuHXr16wcHBwdyhvhD27t2LtLQ0eHt7o379+uYOh0rZL7/8ghMnTuj8/UtlIzAwEA8fPkT9+vXh7e1t7nAqJCYgVOnduXMH8+fPBwA4Oztj7dq1kMlevMbBJ0+e4NixY3B3d0fXrl21tmkmRprkcjns7Ozg5OSE+vXro1WrVujSpQusra2NjmPx4sW4evUqAMDGxqbAFIu3bt3CF198AZVKBZlMhipVqkAQBNja2gIAxowZgy+//BJ+fn747LPPjI6Dyp+rV69ixYoViI2NlV6ztraGlZUV4uLiEBcXh8uXL2Pz5s2YPHky+vfvb8ZoXwz79+9HdHQ0atasyQSEqIwEBgbixIkT8PX1ZQJSSpiAUKV3/Phx6XFMTAyuXbsGLy8v8wWkw6ZNm6BSqTB69GgIgqCznL29PSwt827t3NxcJCYmIj4+Hvfv38eRI0dgb2+PCRMmYODAgYXuX6VKFdSpUwfOzs4FtoWHh0vJx0cffVQgEQLyPjCpVCo0b94cn376Kezt7bW2t2/fHg0bNsSlS5dw8+ZNtGjRwuBrQOXXqVOn8MsvvyA3Nxeurq4YNWoUOnToAEdHRwCAUqnEjRs3cOzYMZw/fx6XLl1iAkJEVEExAaFKTalU4uzZsxAEAf3798fhw4dx/PjxFy4BefbsGS5evAgHB4dCP/Rr+vjjj9GqVSvpuSiKePr0Ka5du4b9+/fj6dOnWLVqFe7fv48ZM2YU2H/IkCEYMmRIoXWHh4cDgN441GW6d+9eIPlQ69+/P+7fv4+9e/cyAakEwsPDsWLFCuTm5qJt27b4+OOPYWNjo1VGLpejffv2aN++Pe7cuYPTp0+bKVoiIiptTECoUgsMDERaWhqaNWuG0aNH48iRIwgMDER6ejoUCoW5w5McPXoUANCtWzepdcNQgiDAzc0Nbm5u6NevH3799VecOHECR48eRaNGjfDSSy8ZXJdSqQQAqTuVsWW6deuGNWvW4NKlS0hISJC+BaeKacuWLcjKyoKTkxPmzZtXIPnIr2nTpmjcuHGB1y9fvoxDhw4hNDQUqampsLe3R+PGjTFw4EC0b9++0Lo++eQT3LhxA2PHjsWoUaOwZ88enDlzBtHR0cjIyMC2bdtgb2+PYcOGAQC+/fZb1K5dG3/++SeuXLmCuLg41K1bF8uWLZPqVCqVOHToEM6dO4eIiAhkZmaiWrVqaNmyJV5++WU0aNBA57llZWXhyJEjOH/+PB4/foyMjAxUq1YNderUQZcuXdCrVy/Y2dlJY7HUli1bphWDi4sL1q1bp/c6UsWhHhvi6+uLWbNm4dSpUzh48CAeP34MAPDw8MDLL79c5NjAGzdu4NChQ7h9+zYSExNhY2ODGjVqwMvLC71790a9evUK7BMeHo49e/bg+vXrSEhIgLW1Ndzd3eHj44P+/fvDwsKiwD75xxIGBATg8OHDCA8PR0pKCj755BN4e3sbfH+qnT9/HsePH8e9e/eQmpoKhUKBhg0bYsCAAejSpYvec1d3qwoNDUVycjLs7Ozg4uKC9u3bw9fXF66urrh+/ToWLFgg7XPixAmcOHFCqx5zjP+siJiAUKWm7n7l4+MDZ2dntGzZEtevX8fZs2cN6v4RHh6OP//8E9evX0dqaiqcnZ3Rs2dPjBw5EnK5vNB9Ll26hEOHDuH+/ftITk6GjY0NqlatCg8PD7Rr1w79+/fX6mKlUqmkX4A9evQo0flaWVlhxowZCA8Px/3797F9+3b06dMHVlZWUpnCBqHn/zAUHR0tfWADgJkzZ2p9OAK0PzDl/7Dk4OAALy8vXL58GSdPnsSIESNKdF704oqLi8PFixcBAEOHDtXZKpaf5jgsURSxatUqHDp0SNpma2uL5ORkBAUFISgoCIMGDcK0adN0dk/Mzs7Gxx9/jNDQUFhaWuq8P58+fYrvv/8eycnJkMvlBRL+Z8+e4auvvsKzZ8+kWORyOWJjYxEQEIDTp0/jnXfewYABAwqt++uvv9ba187ODomJiYiNjUVISAicnJzg7e0NW1tbVKtWTRqkr1AotMZuValSxaDrSBXPf//7Xxw6dAgymQw2NjZIT0/HnTt3cOfOHbz99tuFtmDn5uZi1apVOHLkiPSaQqFATk4OwsLCEBYWhmfPnml9+AbyPoCrWy8BwM7ODkqlErdv38bt27dx8uRJfP7553rv6zVr1uDAgQOQyWRQKBSFjrEs6v7MzMzEkiVLcOnSJa34k5OTceXKFVy5cgV9+vTB+++/X+B3QEZGBpYuXaq1r/o87t27h3v37iEtLQ1Tp06FpaUlqlWrhvT0dGRlZcHa2rrAl5Ev4hjR8ogJCFVa6vEelpaW6N69O4C8ROT69es4duxYkQnInTt38OuvvyIzMxMKhQKiKOLZs2fw9/dHUFAQvvnmmwK/lLdu3Yrt27dLz21sbJCTk4Nnz57h2bNnuHDhAvr27av1jdKjR4+QkJAACwuLQr8VLi4LCwu8+uqrWLx4MeLi4nDjxg20bdtW7z7qD0NZWVlIT0+XBperWVtbo1q1agBQ6Aemwj4sNWvWDJcvX8bVq1eZgFRgN27cgCiKAIBOnToZVcfevXul5GPkyJEYOXIk7O3tkZKSgh07duCvv/7C33//jTp16mDo0KGF1vH3339DJpNh3rx58Pb2hpWVFaKjowu0xmzYsAEuLi5YsGABmjVrBgBSwpCeno4vv/wSkZGR6NSpE8aOHQtPT09YWFggLi4Ou3fvxv79+7Fq1SrUq1cPTZo0kepNTU3FwoULERUVBScnJ7z11lvo3Lkz5HI5cnNz8fjxY5w6dUpqOXzllVfwyiuvYMqUKYiOjsbUqVPRp08fo64fVRxBQUHIysrCO++8A19fX8jlcjx//hzLli3DzZs3sXHjxkJnkduwYQOOHDkCQRAwfPhwDB06FDVq1AAAJCQk4NKlS4iKitLaJzQ0VEo+2rdvj7fffhu1atVCdnY2Tp06hdWrV+POnTtYvnw5Pvnkk0LjffDgAW7evInXX38dQ4YMgb29vfThXlNR9+fy5ctx6dIleHh4YMKECWjTpg3kcjnS09MREBAAPz8/HD9+HO7u7njllVe06v75559x6dIlWFpa4vXXX0e/fv1QtWpVAHkTuAQGBkplmzVrhk2bNkktTt27d8esWbOK/4OiIjEBoUrr5MmTUKlU6NChg/TLulu3btIv1WfPnqF27do69//tt99Qt25dvPfee/D09EROTg5Onz6NVatW4eHDh1i1ahXmzZsnlY+KisKOHTsA5H2Ievnll7U+tIeGhuLkyZMFjnPr1i0AgLu7u85vbYurXbt2kMlkUKlUuHnzZpEJiPrD0PHjx7Fs2TLUqFGjQPcPdeuMoR+Y1MnU3bt3kZOTU+yuZVQ+PHnyBEBe65ubm1ux91cqlVLSPmTIEEycOFHa5uDggLfeegsZGRk4fPgw/P390b9//0Lvk4yMDHz55Zda73UXF5cC5WQyGb766iutboHq3wN//fUXIiMj4e3tjY8//ljrm9bq1atj6tSpyM7OxqFDh7Bjxw58+umn0vZdu3YhKioKdnZ2WLx4MVxdXaVtFhYWqF+/Pme5oiKlpqbigw8+gI+Pj/Saq6sr5s6di6lTpyIrKwuXLl2Cr6+vtD0iIgL79+8HAEyaNAnDhw/XqtPR0bHQL9y2bNmC3NxcNGjQAAsWLJB+R1tZWaFv376wsrLCjz/+iMDAQNy9e1cr4VbLyMjAq6++irFjx0qvKRSKAq0K+u5Pda+EmjVr4ttvv9X6QkuhUGDQoEGwt7fH0qVLsXPnTgwdOlSK9cqVK1KCMXfu3AJjF2vWrImXX365QNxU+tiORJWWZvcrNYVCIX1Lqzk7VmEsLS2xcOFCeHp6Ss99fX3x7rvvAgDOnDkjDcgGgHv37kGlUqFOnTqYOHGilHwAeS0EHTp0wLx58wr0p7179y4AFNo311g2NjbSB6D833qVFfV1y8zMlPoxU8WTnJwMIG92Nn2zt+ly9epVpKWlQSaTYdSoUYWWGTt2LGQyGVJSUhAcHFxomXr16hWZaAOAr6+vzjFJx44dAwAMHz5c57n06tULAHDt2jWp2woA6cuFoUOHaiUfRMXh7Owsvcc0Va9eXfpSJ//v0+PHj0MURdSsWVOr66w+qampCAkJAQC8+uqrhX5B1KtXL9SpUwcAcPbs2ULrkclkBRKewui7P9X3Xd++fXV2PezWrRusrKyQkpKC+/fvS6+r/463aNGiyAlcqGzxK0eqlG7duoXIyEjY2dkV6BbSu3dvnD17FidPnsS4ceN09vccOHBgoYul+fj4YOvWrYiOjsa5c+ekxfbUXSsyMjKgVCoNbs1ISEgAYPo+3+ruYSkpKSat11AODg5SK0x8fLzegbtUeak/TNStW1dnYlC9enW4ubkhPDwcDx48QOfOnQuUadq0qUHHK+xbXACIjY2V1i/57rvvdCYgKpUKQF5inZKSgmrVqiEqKgrx8fEAgA4dOhgUB1FhGjZsqPO9V716dQBAWlqa1uvqL7HULd+GePDggdR1snXr1jrLtWnTBk+fPtX60K+pVq1aBv3t0nd/3r59G0BeC+TBgwd1llMn/JrrDKnPnffdi4cJCFVK6m9FCluUr127dqhatao0KFTXtzItW7Ys9HVBENCiRQtER0fjwYMH0utNmjSBg4MD4uPjMW/ePAwcOBBeXl6oVauW3lg1v0GuSARBgJ2dHVJSUqRzpIpH/eEjNTUVoigWuxVE/d5Qf7jSpXr16ggPD0diYqLeOIqi7huen/qLAABISkoyqC71jHCaMRW2vg6RofTNLqieTCQnJ0frdfX7r7Auh7qo3+PW1taFftGmph5Houue0HU/5afv/lTfe/kTK13U9x1g3LlT2WACQpWOeu0PQLv7lZqFhQV69OiBAwcO4Pjx4zoTEH0fiJycnABo/1K2t7fHBx98gJ9++gmPHj3CqlWrAOT9gm7dujV69+5d6Lc02dnZAKA1U5UppKamAoDePy6lTX1Omn8wqGKpW7cugLz3cUREhPS8rBn6za+ucprdqfz8/Dh1NJEBSnrfAf9rVZw7dy569uxpkrjI/JiAUKVz/vx5ZGRkAIDWINHCqNcJsbOzM8mx27dvj7Vr1+LcuXMICQnB7du3ERMTgzNnzuDMmTPo2LEjFixYoPXLWN3yoU4YTCEzMxPPnz8HALPOZ64+J04pWnG1bNkSgiBAFEUEBQUVOwFRvzfi4uL0llNv1xxbZUqa9cbExBQrAdEsW9x9iUrK0dERERERiI6ONngfdctFVlYWUlJSdH5Rpe7uZGhLhzGqVauGmJgYxMTEGLVvdHR0sc6dygYHoVOlox7QZoisrCycOXOm0G3qPt36thX2S1mhUKBfv36YO3cu1q9fj99++02ahUO9RogmzS4spnLlyhXpWyVzrUSelZUlTcXIBKTiql69ujTO6sCBAwa/j9Xvz4YNGwLIm01LsxuUpri4OERERABAqY0lcnV1lRIHzfUEDOHi4iK1il6+fLlY+6q7rKn74xMVl3pc07///ivdV0Vp0KCB9N5TD0YvzLVr1wD87z4tDerxIcW97zT35X334mECQpVKVFQUbty4ASBvIOm2bdt0/lPPuJN/FVQ1dT35iaKImzdvAjDsw1Dt2rUxefJkdOzYEUDelIOa1FOXmmq2qtzcXOzcuRNAXv9dXWNZSpvm+RgzPSuVH+PHj4e1tTXi4uKwZMmSIrvc3blzR5rmuW3btrC3t4dKpZKmsc7P398fKpVKWuCytPTt2xdAXiKlXhtEl/yJlnpa1P3790utj4ZQT1dqaP93ovx8fX0hCAKio6Oxb98+g/axt7eX7qVdu3YVGFcCAKdOnZIS/5IukquP+r67deuWztm21PLfd+qp4G/evInz588bfEzed6WPCQhVKidOnJCmI2zRogXs7e11/lP3Nb1z5470S1bTP//8U+i3uadPn5aae7t16ya9rh7LoYt6MHz+cs2bNwcAnbOMFEd2djZWrFgh1TV27FiTjy0xVGhoKIC8WVLU3w5TxeTh4YH//Oc/kMlkuHr1Kt5//30cO3ZMa3C2UqnEv//+ix9++AEffvih1N1CLpdjzJgxAPI++Pv5+Un3XUpKCjZs2IDDhw8DAF577TWTrZVTmJEjR6JOnTpIS0vDRx99hOPHjyM9PV3anpiYiLNnz2LhwoXYuHGj1r4jRoyAi4sL0tLS8PHHH+PMmTNSIqZSqXD//n2sWrWqwDTC6ln0Lly4wA9DZBQ3NzdpdfQNGzZgw4YNWjNFJSQkYN++fQXes+PGjYOFhQUePHiARYsWITIyEkDe35Fjx45h5cqVAPImczHFIrm6tG3bVkpwfvrpJ2zbtk2rS2ZGRgZCQkKwYsUKfPTRRwX2Vc+Kt3TpUuzatUtr0pOoqChs374de/bs0drPw8MDQN4MXEV92UDG4RgQqjREUZRaMwyZD9zDwwN16tTB06dPcfz4ca0F0IC8X8ILFy7EjBkz4OHhgZycHJw5c0YaXN6jRw/pwwOQ9y1SaGgoevXqhdatW0vdOdLT03HixAlcuHABQN44EU3NmjWT1jgoanFEXef97NkzhISEYP/+/Xj69CkAYMCAAUWu9l6a7t27B0D3bGJUsfTu3RtVqlTBypUrERkZieXLlwPISzAsLS21Plzb2dmhS5cu0vNhw4bh6dOnOHToEHbt2oU9e/ZAoVAgPT1d6lIyaNAgDB48uFTPQaFQ4Msvv8S3336LsLAwLFu2DCtWrICdnR2ys7ORmZkplc1/b9nb22PhwoX48ssvERUVhSVLlkAmk8HOzg4ZGRnSN8z5J73o378/Tp8+jVu3bmH8+PGoWrUqLC0tUb16dXz//feler5UcUyaNAmpqak4efIk9uzZgz179sDOzg6iKEpJdP7pqxs3bowZM2ZgxYoVuHz5Mi5fvgw7OzsolUrp/dq0aVPMmDGj1ON///33IQgCTp8+DX9/f/j7+0tjM9PT06WuUoXNKjl79mx8//33uHr1Kvz8/LBp0ybY2dkhJydHumeHDh2qtY+3tzf8/PyQkpKCd955B1WqVJG+3Fi8eLE0+xcZjwkIVRo3btyQuv1otkzo07VrV+zYsQMBAQGYMGGC1rbp06fj119/xYwZM2BnZ4esrCyp9aJ+/fp45513tMqrVCrplziQtxighYWF1gevzp07F/jgUqVKFbRp0wZXr17F5cuXi1xI6rvvvpMWjcrNzUVaWppWv18HBwdMmDABL730kkHXoLSor0P37t3NGgeVnfbt22PNmjU4efIkLl++jAcPHiApKQlZWVmoUaMG6tWrh44dO6Jnz55aEz8IgoB3330XHTt2xKFDhxAaGorU1FQ4ODigSZMmGDhwYIHEvbS4uLjgxx9/REBAAM6ePYsHDx4gNTUVVlZWqFOnDho2bIh27dppJVBqbm5uWLlyJf7++29cuHABT548QWZmJpycnFC7dm106dKlwJoLrVq1woIFC7B3716EhYUhMTHR4H78RGqWlpaYPXs2fHx8cPjwYdy5cwfJycmws7ND/fr14eXlpbV6upqvry8aNmyIPXv24Nq1a0hISIBcLkfDhg3h4+OD/v37F7pIoanJ5XLMnTsX/fr1w9GjR3H79m2pBbVGjRrw8PBAmzZtCv17olAosHDhQml9r3v37iEtLQ0ODg5wd3dHhw4d0Lt3b619qlSpgu+++w5//PEHbt++jaSkJGlWS80Z8ch4gsgRNlRJ/PLLLzhx4gScnZ2xfv16g/Z58OABZs+eDQBYuHAh6tSpg6lTpwIA1q5dC6VSie3bt+PGjRtISUmBi4sLevbsiZEjRxboChIXF4dLly4hJCQE4eHhiI+PR2ZmJhwcHNCgQQP07t0bPXr0KHSdhHPnzuH7779H48aNsXTp0gLbo6KipLg0WVtbQ6FQoHr16mjQoAFat26NLl266O12tW3bNvj7+6Nly5ZYtGiR1rbjx49j2bJlcHFxkfro5zdlyhRER0dj5syZUv/b/G7fvo0PP/wQLi4uWLt2rVErZBMREVH5xASEqBzIycnB5MmTkZCQgN9++63Y3bBeNL/99hv+/vtvjB8/HqNHjzZ3OERERFSGOAidqBywtLSUBuLmHyxX3iQlJeH48eOoWrWqNDCSiIiIKg8mIETlxIABA1C7dm0cP35cawaT8uavv/6CUqnE6NGjpakOiYiIqPJgFyyicuTWrVsIDg5G27Zt0axZM3OHY5S//voLmZmZGDlypNmmACYiIiLzYQJCRERERERlhl2wiIiIiIiozDABISIiIiKiMsMEhIiIiIiIygwTECIiIiIiKjNMQIiIiIiIqMwwASEiIiIiojLDBISIiIiIiMoMExAiIiqxjRs3QhAECIKAjRs3mjscIiJ6gTEBISIqxx49eiR98C/pvzfffNPcp1Pu1KtXzyTXvl69euY+FSKiMsMEhIiIiIiIyoyluQMgIiLjubi4YM+ePTq337hxA5999hkAoEWLFvjmm290lnV3dzd5fBXdmjVrkJ6ernP7K6+8Ij3W93NSKBQmjYuI6EXGBISIqBxTKBQYPny4zu3VqlWTHteoUUNvWSq+/v37G1yW156IKA+7YBERERERUZlhAkJERMjIyMDKlSvRr18/1KpVC9bW1qhevTo6duyITz/9FM+ePTPJce7du4cGDRpIg6+/+uqrQsudPn0ab7/9Npo1a4Zq1arBxsYGdevWxciRI7Fr1y6IoqjzGJoD89UD6xMTE7Fo0SK0a9cO1apVg52dHZo3b4558+YhOjraJOdWHDk5OahduzYEQUC1atX0duNSS01NRZUqVSAIAtzc3JCbmyttCwgIkM554cKFAIC7d+9ixowZaNKkCezs7ODo6Ahvb2/8/PPPUCqVBsdakp8FEVGhRCIiqrBOnjwpAhABiL169Sq0TFBQkFi3bl2pXGH/FAqFuH79ep3H2bBhg1R2w4YNOo/j7OwsAhAtLCzEtWvXFiiTkJAgDhkyRG8sAMSePXuKMTExhR4nLCxMKjdx4kTx33//Fd3d3XXWVbNmTfH69etFXktjaB4nv88++0za9vvvvxdZ1+rVq6Xyn3/+udY2zZ/zF198Ifr7+4sKhULnOTdt2lR89OiR3uOZ4mdBRFQYjgEhIqrErl27ht69eyMtLQ0A0Lx5c0yYMAGenp6Ij4/HX3/9hSNHjiA9PR2TJ0+GKIqYPHlysY/zzz//YNSoUUhLS4OtrS22b9+OoUOHapVJTk5Gt27dcOvWLQBAo0aNMGrUKDRr1gzW1tZ4+PAh/vjjD1y7dg2nT59G3759ERgYCBsbG53HffLkCQYNGoSYmBiMHDkS/fr1g5OTEx49eoQ1a9bg/v37iIqKwpgxYxAcHAwrK6tin5uxpk6dikWLFiE3Nxdr1qzBpEmT9JZfvXo1AMDCwgJTpkzRWe7ff//F4sWLkZ2djddffx19+vSBra0tbt68id9//x2RkZG4c+cOevfujatXr6Jq1aoF6iiNnwURkcTcGRAREZUefS0gubm5YsuWLaXtU6ZMEbOzswvUsW7dOlEQBKklJCwsrEAZfS0gGzduFC0tLUUAopOTk3j+/PlCYx07dqxUx8KFC8WcnJwCZXJzc8U5c+ZI5RYsWFCgjGYLCADRwcFBPHXqVIFyKSkpopeXl1Ru165dhcZVEppxFObll1+Wtl+7dk1nPZcvX5bKDRkypMB2zZ+z+ud08uTJAuUSEhLELl26SOWmTZtW6PFM9bMgIioMExAiogpMXwKyb98+aVvr1q0L/ZCpNn36dKnsrFmzCmzXlYAsWrRIet3d3V28detWofWHhIRI5SZPnlzkeXXr1k0EIFatWlXMzMzU2pY/AdHXvemff/7RSsBMragE5PDhw9L29957T2c9U6dOlcrt37+/wPb8Ccjy5ct11hURESHa29uLAES5XC5GR0drbTflz4KIqDAchE5EVEnt3r1bejxnzhxYWFjoLPvRRx9BEIQC++miUqkwY8YMfPLJJwCAVq1a4fz582jWrFmh5f38/KTH8+fPL7L+N954AwCQlJSEixcv6ixXo0YNTJgwQef23r17w9IyrzfyjRs3ijyuqfXr1w8NGzYEAGzZsgUZGRkFyqSkpOCPP/4AANStWxcDBw7UW2e1atUwdepUndvr1KmDcePGAQCUSiX279+vtb20fhZERGocA0JEVElpflgsaj0LDw8PNG3aFLdv30Z4eDgiIyNRq1atQssqlUqMGTMGO3fuBAD07NkT+/btK3Ssgdrp06cBADY2Nrh165Y09kCXp0+fSo9v3bqFnj17FlquY8eOUoJRGLlcjho1auD58+dISEjQe8zSIAgCpk2bhnnz5iExMRHbt2+XZu5S27ZtG1JTUwEAU6ZM0ZsoAkD37t2LHIvRt29faUxJUFAQ3nrrLWlbaf0siIjUmIAQEVVSkZGRAAAHBwe4uroWWb5x48a4ffu2tK+uBOTDDz9EUlISAGDEiBHYtm0b5HK53rofPXoEAMjMzNRaPdwQ8fHxOrfVqFGjyP3VsWVmZhbruKby1ltv4bPPPkNmZibWrFlTIAFZs2YNgLzB54ZMANCoUaNilck/xXJp/SyIiNTYBYuIqJJKSUkBANjZ2RlU3t7evsC+hcnJyZEep6amQqVSFVl3YmKiQTEUJisrS+c2mezF/zPn5OSE0aNHAwAuXLig1RXs8uXLuHLlCgBgyJAhqFOnTpH1GfLz1CyT/2dZWj8LIiK1F/83MxERlQoHBwcAkKbgLYq6G5DmvoX57rvv0L17dwDAkSNHMHTo0CIX2lMnN05OThDzJkgx+J964b3y7J133pEeq1s88j+eNm2aQXUZ8vPULJP/Z1nZfxZEVPqYgBARVVLqLlQpKSmIiooqsnxoaKj0uHbt2jrLOTg44NChQ/Dx8QEAHD9+HIMHD9b7wdjNzQ1A3rfvmolOZeHt7Y22bdsCADZv3oyMjAytweceHh4YMGCAQXXdv3+/WGXy/ywr+8+CiEofExAiokqqc+fO0uMjR47oLRseHo47d+4AANzd3YscM2JnZ4e///4bffv2BQAEBATgpZde0tl1q1evXgDyZs86fPiwwedQkahbQRITE/Hnn39qDT6fOnWqwd3Jzpw5A6VSqbfMsWPHpMea7wOAPwsiKn1MQIiIKqmRI0dKj3/88Ufk5ubqLPv9999DFMUC++lja2uL/fv3S9/cnz17FgMGDEBycnKBsuqpXAHgq6++MtuAcHN6/fXXpZnC1qxZI3W/srS01JqlqiiJiYlYt26dzu2RkZHYunUrgLwB+EOGDNHazp8FEZU2JiBERJXUoEGD0KpVKwBASEgI3nnnHa0B5GobN27Eb7/9BgBQKBSYOXOmwcewsbHB3r17pQ+5Fy5cQL9+/QoMdO7UqRNGjRoFALh27RpefvllxMTE6KxXFEWcO3cOc+fONTiWF52dnZ304f/8+fPS4PNhw4bpnHFMlw8//FCaTldTcnIyRo8eLbVETZo0Cc7Ozlpl+LMgotLGaXiJiCopmUyGLVu2oGvXrkhLS8PatWtx4cIFTJgwAfXq1UN8fDz27t2LQ4cOSfssX74cHh4exTqOXC7Hrl27MGbMGPz1118ICgpCnz59cPToUTg5OUnl1q9fj9DQUISEhODIkSOoV68eRo4cCW9vbzg7OyM7OxtRUVG4du0ajh07hoiICDRo0ABLly412TUxt+nTp2PFihVarxk6+FxtyJAhOHr0KHx9fTF27Fj06dMHtra2uHXrFtavXy9Nu+vp6Ynvv/++0Dr4syCiUlVGK64TEZEZnDx5UgQgAhB79epVaJmgoCDRzc1NKlfYP4VCIa5bt07ncTZs2CCV3bBhQ6FlsrKyxFdffVUq16ZNGzEmJkarTEpKijhhwgRREAS98eg7p7CwMGn7xIkTi7xGHh4eIgDRw8OjyLLFpRmroXr16iXtU79+fVGlUhW5j+bP+YsvvhC3b98uKhQKndetSZMmYlhYmN46TfGzICIqDLtgERFVch07dkRoaCiWL1+OPn36oGbNmrCysoKjoyPat2+PTz75BPfu3TNoETx9rKys8Mcff+C1114DkNfty9fXF9HR0VIZe3t7bNq0CTdu3MC8efPQqVMnODs7w9LSEgqFAh4eHujfvz8WLlyIixcvIiAgoEQxvYg0V6WfOnUqBEEodh2jR4/GlStX8J///AeNGjWCQqFA1apV0alTJ/z4448ICQlBvXr19NbBnwURlRZBFP9/VCERERGZnZeXF0JCQmBlZYUnT56gZs2aRe4TEBCA3r17AwC++OILrsdBRC80toAQERG9IC5cuICQkBAAwIgRIwxKPoiIyhsmIERERC8AURTx2WefSc9nzZplvmCIiEoRZ8EiIiIyk+vXr+Pp06dISEjA1q1bcfz4cQDA4MGD4e3tbeboiIhKBxMQIiIiM/nxxx/h5+en9ZqLiwv++9//mikiIqLSxy5YREREZmZhYYF69erhrbfewqVLl+Du7m7ukIiISg1nwSIiIiIiojLDFhAiIiIiIiozTECIiIiIiKjMMAEhIiIiIqIywwSEiIiIiIjKDBMQIiIiIiIqM0xAiIiIiIiozDABISIiIiKiMsMEhIiIiIiIygwTECIiIiIiKjNMQIiIiIiIqMwwASEiIiIiojLDBISIiIiIiMoMExAiIiIiIiozTECIiIiIiKjMMAEhIiIiIqIy838gbnubQWGalAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "swapping contradicting sources, and evaluate with matching location 80\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:09<00:00,  1.10s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"[control] swapping two sources with the same counterfactual behavior\")\n",
+    "for testing_label in [\"labels\"]:\n",
+    "    collected_probs = []\n",
+    "    eval_labels = []\n",
+    "    eval_preds = []\n",
+    "    with torch.no_grad():\n",
+    "        epoch_iterator = tqdm(multisource_non_contradict_test_dataloader, desc=f\"Test\")\n",
+    "        for step, inputs in enumerate(epoch_iterator):\n",
+    "            for k, v in inputs.items():\n",
+    "                if v is not None and isinstance(v, torch.Tensor):\n",
+    "                    inputs[k] = v.to(\"cuda\")\n",
+    "            b_s = inputs[\"input_ids\"].shape[0]\n",
+    "            input_sources = [\n",
+    "                {\"input_ids\": inputs[\"source_input_ids\"]},\n",
+    "                {\"input_ids\": inputs[\"second_source_input_ids\"]}\n",
+    "            ]\n",
+    "            _, counterfactual_outputs = pv_llama(\n",
+    "                {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "                input_sources, {\"sources->base\": [75, 80]}\n",
+    "            )\n",
+    "            correct_label = inputs[testing_label][:,-1]\n",
+    "            incorrect_label = \\\n",
+    "                ((correct_label == 8241)*3782 + \\\n",
+    "                (correct_label == 3782)*8241).long()\n",
+    "            norm_prob = torch.softmax(\n",
+    "                counterfactual_outputs.logits[:, -1], dim=-1)\n",
+    "            correct_prob = norm_prob[torch.arange(b_s),correct_label].detach().cpu()\n",
+    "            incorrect_prob = norm_prob[torch.arange(b_s),incorrect_label].detach().cpu()\n",
+    "            diff_prob = torch.abs(correct_prob - incorrect_prob)\n",
+    "            collected_probs += [[correct_prob, incorrect_prob, diff_prob]]\n",
+    "            eval_labels += [inputs[testing_label]]\n",
+    "            eval_preds += [counterfactual_outputs.logits]\n",
+    "    correct_probs = torch.cat([p[0] for p in collected_probs])\n",
+    "    incorrect_probs = torch.cat([p[1] for p in collected_probs])\n",
+    "    diff_probs = torch.cat([p[2] for p in collected_probs])\n",
+    "    eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "\n",
+    "    list1 = correct_probs.tolist()\n",
+    "    list2 = incorrect_probs.tolist()\n",
+    "    list3 = diff_probs.tolist()\n",
+    "    acc = eval_metrics['accuracy']\n",
+    "    df = pd.DataFrame({'Group': ['Correct']*len(list1) + ['Incorrect']*len(list2) + ['Abs(Diff)']*len(list2),\n",
+    "                       'Value': list1 + list2 + list3})\n",
+    "    means = df.groupby('Group')['Value'].mean().reset_index()\n",
+    "    means['ymin'] = means['Value'] - df.groupby('Group')['Value'].std().reset_index()['Value']\n",
+    "    means['ymax'] = means['Value'] + df.groupby('Group')['Value'].std().reset_index()['Value']\n",
+    "    plot = (ggplot(df, aes('Group', 'Value')) +\n",
+    "            geom_boxplot(fill='lightgray', alpha=0.5) +\n",
+    "            geom_point(means, aes('Group', 'Value'), color='blue', size=3) +\n",
+    "            geom_errorbar(means, aes('Group', ymin='ymin', ymax='ymax'), width=0.2) +\n",
+    "            labs(title=f'w/ Acc={acc}', x='Token Type', y='Prob (Softmax)') +\n",
+    "            theme(figure_size=(4, 2)))\n",
+    "    print(plot)\n",
+    "    \n",
+    "for testing_label in [\"labels\", \"second_labels\"]:\n",
+    "    if testing_label == \"labels\":\n",
+    "        print(\"swapping contradicting sources, and evaluate with matching location 75\")\n",
+    "    elif testing_label == \"second_labels\":\n",
+    "        print(\"swapping contradicting sources, and evaluate with matching location 80\")\n",
+    "    collected_probs = []\n",
+    "    eval_labels = []\n",
+    "    eval_preds = []\n",
+    "    with torch.no_grad():\n",
+    "        epoch_iterator = tqdm(multisource_contradict_test_dataloader, desc=f\"Test\")\n",
+    "        for step, inputs in enumerate(epoch_iterator):\n",
+    "            for k, v in inputs.items():\n",
+    "                if v is not None and isinstance(v, torch.Tensor):\n",
+    "                    inputs[k] = v.to(\"cuda\")\n",
+    "            b_s = inputs[\"input_ids\"].shape[0]\n",
+    "            input_sources = [\n",
+    "                {\"input_ids\": inputs[\"source_input_ids\"]},\n",
+    "                {\"input_ids\": inputs[\"second_source_input_ids\"]}\n",
+    "            ]\n",
+    "            _, counterfactual_outputs = pv_llama(\n",
+    "                {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "                input_sources, {\"sources->base\": [75, 80]}\n",
+    "            )\n",
+    "            correct_label = inputs[testing_label][:,-1]\n",
+    "            incorrect_label = \\\n",
+    "                ((correct_label == 8241)*3782 + \\\n",
+    "                (correct_label == 3782)*8241).long()\n",
+    "            norm_prob = torch.softmax(\n",
+    "                counterfactual_outputs.logits[:, -1], dim=-1)\n",
+    "            correct_prob = norm_prob[torch.arange(b_s),correct_label].detach().cpu()\n",
+    "            incorrect_prob = norm_prob[torch.arange(b_s),incorrect_label].detach().cpu()\n",
+    "            diff_prob = torch.abs(correct_prob - incorrect_prob)\n",
+    "            collected_probs += [[correct_prob, incorrect_prob, diff_prob]]\n",
+    "            eval_labels += [inputs[testing_label]]\n",
+    "            eval_preds += [counterfactual_outputs.logits]\n",
+    "    correct_probs = torch.cat([p[0] for p in collected_probs])\n",
+    "    incorrect_probs = torch.cat([p[1] for p in collected_probs])\n",
+    "    diff_probs = torch.cat([p[2] for p in collected_probs])\n",
+    "    eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "\n",
+    "    list1 = correct_probs.tolist()\n",
+    "    list2 = incorrect_probs.tolist()\n",
+    "    list3 = diff_probs.tolist()\n",
+    "    acc = eval_metrics['accuracy']\n",
+    "    df = pd.DataFrame({'Group': ['Correct']*len(list1) + ['Incorrect']*len(list2) + ['Abs(Diff)']*len(list2),\n",
+    "                       'Value': list1 + list2 + list3})\n",
+    "    means = df.groupby('Group')['Value'].mean().reset_index()\n",
+    "    means['ymin'] = means['Value'] - df.groupby('Group')['Value'].std().reset_index()['Value']\n",
+    "    means['ymax'] = means['Value'] + df.groupby('Group')['Value'].std().reset_index()['Value']\n",
+    "    plot = (ggplot(df, aes('Group', 'Value')) +\n",
+    "            geom_boxplot(fill='lightgray', alpha=0.5) +\n",
+    "            geom_point(means, aes('Group', 'Value'), color='blue', size=3) +\n",
+    "            geom_errorbar(means, aes('Group', ymin='ymin', ymax='ymax'), width=0.2) +\n",
+    "            labs(title=f'w/ Acc={acc}', x='Token Type', y='Prob (Softmax)') +\n",
+    "            theme(figure_size=(4, 2)))\n",
+    "    print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2105ac9f",
+   "metadata": {},
+   "source": [
+    "#### The results above show if two intervention sites take on contradicting values, it will trust the earlier site. \n",
+    "It is hard to say if this would hold till more tests. However, we can see the neural network shows some voting mechanism by trusting some location over another during interventions when these locations take different values. If they have the same value, it seems to working well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d2deee7",
+   "metadata": {},
+   "source": [
+    "##### Minor: make sure other control models cannot find good alignments\n",
+    "We test this by training Boundless DAS with the best locations in the paper: layer 15 and last token (81th)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e5a9c190",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "control_sampler = bracket_alignment_sampler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5cb5aa40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "set_seed(42)\n",
+    "\n",
+    "###################\n",
+    "# data loaders\n",
+    "###################\n",
+    "raw_data = control_sampler(\n",
+    "    tokenizer, 10000,\n",
+    ")\n",
+    "\n",
+    "raw_train = (\n",
+    "    raw_data[0][:8000],\n",
+    "    raw_data[1][:8000],\n",
+    "    raw_data[2][:8000],\n",
+    "    raw_data[3][:8000],\n",
+    ")\n",
+    "raw_eval = (\n",
+    "    raw_data[0][8000:9000],\n",
+    "    raw_data[1][8000:9000],\n",
+    "    raw_data[2][8000:9000],\n",
+    "    raw_data[3][8000:9000],\n",
+    ")\n",
+    "raw_test = (\n",
+    "    raw_data[0][9000:],\n",
+    "    raw_data[1][9000:],\n",
+    "    raw_data[2][9000:],\n",
+    "    raw_data[3][9000:],\n",
+    ")\n",
+    "train_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_train[0],\n",
+    "        \"source_input_ids\": raw_train[1],\n",
+    "        \"labels\": raw_train[2],\n",
+    "        \"intervention_ids\": raw_train[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "train_dataloader = DataLoader(\n",
+    "    train_dataset,\n",
+    "    batch_size=16,\n",
+    ")\n",
+    "eval_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_eval[0],\n",
+    "        \"source_input_ids\": raw_eval[1],\n",
+    "        \"labels\": raw_eval[2],\n",
+    "        \"intervention_ids\": raw_eval[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "eval_dataloader = DataLoader(\n",
+    "    eval_dataset,\n",
+    "    batch_size=16,\n",
+    ")\n",
+    "test_dataset = Dataset.from_dict(\n",
+    "    {\n",
+    "        \"input_ids\": raw_test[0],\n",
+    "        \"source_input_ids\": raw_test[1],\n",
+    "        \"labels\": raw_test[2],\n",
+    "        \"intervention_ids\": raw_test[3],  # we will not use this field\n",
+    "    }\n",
+    ").with_format(\"torch\")\n",
+    "test_dataloader = DataLoader(\n",
+    "    test_dataset,\n",
+    "    batch_size=16,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0f4d8e82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = 15\n",
+    "token_position = 81"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "de0d63ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pv_config = pv.IntervenableConfig(\n",
+    "    representations=[\n",
+    "        pv.RepresentationConfig(\n",
+    "            layer, \"block_output\"\n",
+    "        )],\n",
+    "    intervention_types=pv.BoundlessRotatedSpaceIntervention,\n",
+    ")\n",
+    "pv_llama = pv.IntervenableModel(pv_config, llama)\n",
+    "pv_llama.set_device(\"cuda\")\n",
+    "pv_llama.disable_model_gradients()\n",
+    "\n",
+    "epochs = 1\n",
+    "gradient_accumulation_steps = 4\n",
+    "total_step = 0\n",
+    "t_total = int(len(train_dataloader) * epochs)\n",
+    "temperature_start = 50.0\n",
+    "temperature_end = 0.1\n",
+    "temperature_schedule = (\n",
+    "    torch.linspace(\n",
+    "        temperature_start, temperature_end, t_total\n",
+    "    )\n",
+    "    .to(torch.bfloat16)\n",
+    "    .to(\"cuda\")\n",
+    ")\n",
+    "pv_llama.set_temperature(temperature_schedule[total_step])\n",
+    "\n",
+    "warm_up_steps = 0.1 * t_total\n",
+    "optimizer_params = []\n",
+    "for k, v in pv_llama.interventions.items():\n",
+    "    optimizer_params += [\n",
+    "        {\"params\": v.rotate_layer.parameters()}]\n",
+    "    optimizer_params += [\n",
+    "        {\"params\": v.intervention_boundaries, \"lr\": 1e-2}]\n",
+    "optimizer = torch.optim.Adam(optimizer_params, lr=1e-3)\n",
+    "scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer, num_warmup_steps=warm_up_steps,\n",
+    "    num_training_steps=t_total\n",
+    ")\n",
+    "\n",
+    "def calculate_loss(logits, labels):\n",
+    "    shift_logits = logits[..., :, :].contiguous()\n",
+    "    shift_labels = labels[..., :].contiguous()\n",
+    "    # Flatten the tokens\n",
+    "    loss_fct = CrossEntropyLoss()\n",
+    "    shift_logits = shift_logits.view(-1, config.vocab_size)\n",
+    "    shift_labels = shift_labels.view(-1)\n",
+    "    # Enable model parallelism\n",
+    "    shift_labels = shift_labels.to(shift_logits.device)\n",
+    "    loss = loss_fct(shift_logits, shift_labels)\n",
+    "\n",
+    "    for k, v in pv_llama.interventions.items():\n",
+    "        boundary_loss = 1.0 * v.intervention_boundaries.sum()\n",
+    "    loss += boundary_loss\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8a996118",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llama trainable parameters:  0\n",
+      "intervention trainable parameters:  16777218\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [13:18<00:00,  1.60s/it, loss=0.73, acc=0.75]\n",
+      "Epoch: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [13:18<00:00, 798.36s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pv_llama.model.train()  # train enables drop-off but no grads\n",
+    "print(\"llama trainable parameters: \", count_parameters(pv_llama.model))\n",
+    "print(\"intervention trainable parameters: \", pv_llama.count_parameters())\n",
+    "train_iterator = trange(0, int(epochs), desc=\"Epoch\")\n",
+    "for epoch in train_iterator:\n",
+    "    epoch_iterator = tqdm(\n",
+    "        train_dataloader, \n",
+    "        desc=f\"Epoch: {epoch}\", position=0, leave=True\n",
+    "    )\n",
+    "    for step, inputs in enumerate(epoch_iterator):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(\"cuda\")\n",
+    "        b_s = inputs[\"input_ids\"].shape[0]\n",
+    "        _, counterfactual_outputs = pv_llama(\n",
+    "            {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": inputs[\"source_input_ids\"]}],\n",
+    "            {\"sources->base\": token_position},\n",
+    "        )\n",
+    "        eval_metrics = compute_metrics(\n",
+    "            [counterfactual_outputs.logits], [inputs[\"labels\"]]\n",
+    "        )\n",
+    "\n",
+    "        # loss and backprop\n",
+    "        loss = calculate_loss(counterfactual_outputs.logits, inputs[\"labels\"])\n",
+    "        loss_str = round(loss.item(), 2)\n",
+    "        epoch_iterator.set_postfix({\"loss\": loss_str, \"acc\": eval_metrics[\"accuracy\"]})\n",
+    "\n",
+    "        if gradient_accumulation_steps > 1:\n",
+    "            loss = loss / gradient_accumulation_steps\n",
+    "        loss.backward()\n",
+    "        if total_step % gradient_accumulation_steps == 0:\n",
+    "            if not (gradient_accumulation_steps > 1 and total_step == 0):\n",
+    "                optimizer.step()\n",
+    "                scheduler.step()\n",
+    "                optimizer.zero_grad()\n",
+    "                pv_llama.set_temperature(\n",
+    "                    temperature_schedule[total_step])\n",
+    "        total_step += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "32e2894a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:43<00:00,  1.46it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.68}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluation on the test set\n",
+    "eval_labels = []\n",
+    "eval_preds = []\n",
+    "with torch.no_grad():\n",
+    "    epoch_iterator = tqdm(test_dataloader, desc=f\"Test\")\n",
+    "    for step, inputs in enumerate(epoch_iterator):\n",
+    "        for k, v in inputs.items():\n",
+    "            if v is not None and isinstance(v, torch.Tensor):\n",
+    "                inputs[k] = v.to(\"cuda\")\n",
+    "        b_s = inputs[\"input_ids\"].shape[0]\n",
+    "        _, counterfactual_outputs = pv_llama(\n",
+    "            {\"input_ids\": inputs[\"input_ids\"]},\n",
+    "            [{\"input_ids\": inputs[\"source_input_ids\"]}],\n",
+    "            {\"sources->base\": 75}\n",
+    "        )\n",
+    "        eval_labels += [inputs[\"labels\"]]\n",
+    "        eval_preds += [counterfactual_outputs.logits]\n",
+    "eval_metrics = compute_metrics(eval_preds, eval_labels)\n",
+    "print(eval_metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18a61a22",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/basic_tutorials/Add_Activations_to_Streams.ipynb b/_sources/tutorials/basic_tutorials/Add_Activations_to_Streams.ipynb
new file mode 100644
index 00000000..bf30ce7e
--- /dev/null
+++ b/_sources/tutorials/basic_tutorials/Add_Activations_to_Streams.ipynb
@@ -0,0 +1,418 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88fe760f",
+   "metadata": {},
+   "source": [
+    "## Activation Addition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcef5dfe",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/basic_tutorials/Add_Activations_to_Streams.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "682d1fdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"10/06/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3deec495",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "Interventions have many types: (1) activation swapping, (2) activation addition, or (3) any other kind of operations that modify the activation. In this tutorial, we show how we ca do activation addition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17476fc2",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c34ae314",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-11 00:31:07,569] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e5cb9eb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "from pyvene.models.basic_utils import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    AdditionIntervention,\n",
+    "    SubtractionIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")\n",
+    "from pyvene.models.gpt2.modelings_intervenable_gpt2 import create_gpt2\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d4487e4",
+   "metadata": {},
+   "source": [
+    "### Factual recall with our intervenable module directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1fc15f36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "def activation_addition_position_config(model_type, intervention_type, n_layer):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                i,                 # layer\n",
+    "                intervention_type, # component\n",
+    "                \"pos\",             # intervention unit\n",
+    "                1,                 # max number of unit\n",
+    "            )\n",
+    "            for i in range(n_layer)\n",
+    "        ],\n",
+    "        intervention_types=AdditionIntervention,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "config, tokenizer, gpt = create_gpt2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "151ded21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The capital of Spain is\n",
+      "_Madrid              0.10501234978437424\n",
+      "_the                 0.0949699655175209\n",
+      "_Barcelona           0.0702790841460228\n",
+      "_a                   0.04010068252682686\n",
+      "_now                 0.02824278175830841\n",
+      "_in                  0.02759990654885769\n",
+      "_Spain               0.022991720587015152\n",
+      "_Catalonia           0.018823225051164627\n",
+      "_also                0.018689140677452087\n",
+      "_not                 0.01735665090382099\n",
+      "\n",
+      "The capital of Italy is\n",
+      "_Rome                0.15734916925430298\n",
+      "_the                 0.07316355407238007\n",
+      "_Milan               0.046878915280103683\n",
+      "_a                   0.03449810668826103\n",
+      "_now                 0.03200329467654228\n",
+      "_in                  0.02306535840034485\n",
+      "_also                0.02274816483259201\n",
+      "_home                0.01920313946902752\n",
+      "_not                 0.01640527881681919\n",
+      "_Italy               0.01577090471982956\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = activation_addition_position_config(\n",
+    "    type(gpt), \"mlp_output\", gpt.config.n_layer\n",
+    ")\n",
+    "\n",
+    "intervenable = IntervenableModel(config, gpt)\n",
+    "\n",
+    "base = \"The capital of Spain is\"\n",
+    "source = \"The capital of Italy is\"\n",
+    "inputs = [tokenizer(base, return_tensors=\"pt\"), tokenizer(source, return_tensors=\"pt\")]\n",
+    "print(base)\n",
+    "res = intervenable(inputs[0])[0]\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
+    "print()\n",
+    "print(source)\n",
+    "res = intervenable(inputs[1])[0]\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79cb7ebc",
+   "metadata": {},
+   "source": [
+    "### We add a word embedding to all MLP streams at the last position\n",
+    "In other tutorials, we often pass in `sources` where each of the example is drawn from the training data. Another way to do patching is, instead of passing in real input example, we pass in activations. These activations can be designed off-line in some particular ways."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0481a874",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can patch mlp with the rome word embedding\n",
+    "rome_token_id = tokenizer(\" Rome\")[\"input_ids\"][0]\n",
+    "rome_embedding = (\n",
+    "    gpt.wte(torch.tensor(rome_token_id)).clone().unsqueeze(0).unsqueeze(0)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78d9a0be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_Rome                0.4558262228965759\n",
+      "_Madrid              0.2788238823413849\n",
+      "_Barcelona           0.10828061401844025\n",
+      "_Valencia            0.015630871057510376\n",
+      "_Lisbon              0.008415448479354382\n",
+      "_the                 0.006678737234324217\n",
+      "_Santiago            0.006526812445372343\n",
+      "_Naples              0.0041163465939462185\n",
+      "_Florence            0.003120437264442444\n",
+      "_Athens              0.0028584974352270365\n"
+     ]
+    }
+   ],
+   "source": [
+    "base = \"The capital of Spain is\"\n",
+    "\n",
+    "_, counterfactual_outputs = intervenable(\n",
+    "    base=tokenizer(base, return_tensors=\"pt\"),\n",
+    "    unit_locations={\n",
+    "        \"sources->base\": 4\n",
+    "    },  # last position\n",
+    "    source_representations=rome_embedding,\n",
+    ")\n",
+    "distrib = embed_to_distrib(gpt, counterfactual_outputs.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e6e5ff6",
+   "metadata": {},
+   "source": [
+    "If you are interested by this work, you can simply think token embeddings at each layer are moved toward the token `_Rome` via the activation addition. Obviouosly, the LM head (which is tied with the embedding matrix) is going to pick out the most similar vectors, which are `_Rome` at the end, and some other countries since they are close to `_Rome`.\n",
+    "\n",
+    "You can also read more about this in this paper: [Language Models Implement Simple Word2Vec-style Vector Arithmetic](https://arxiv.org/abs/2305.16130)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0784203e",
+   "metadata": {},
+   "source": [
+    "### Let's have a more systematic analysis of the addition effect of MLP and MHA streams\n",
+    "We add the word embedding till the `i`-th layer of these streams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fe8195d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should finish within 1 min with a standard 12G GPU\n",
+    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "\n",
+    "data = []\n",
+    "for till_layer_i in range(gpt.config.n_layer):\n",
+    "    config = activation_addition_position_config(\n",
+    "        type(gpt), \"mlp_output\", till_layer_i + 1\n",
+    "    )\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base,\n",
+    "            unit_locations={\"sources->base\": pos_i},\n",
+    "            source_representations=rome_embedding,\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"mlp_o{till_layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"mlp_output\",\n",
+    "                }\n",
+    "            )\n",
+    "\n",
+    "    config = activation_addition_position_config(\n",
+    "        type(gpt), \"attention_output\", till_layer_i + 1\n",
+    "    )\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base,\n",
+    "            unit_locations={\n",
+    "                \"sources->base\": pos_i\n",
+    "            },\n",
+    "            source_representations=rome_embedding,\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"attn_o{till_layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"attention_output\",\n",
+    "                }\n",
+    "            )\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "81604a1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
+    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
+    "nodes = []\n",
+    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
+    "    nodes.append(f\"mlp_o{l}\")\n",
+    "    nodes.append(f\"attn_o{l}\")\n",
+    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
+    "\n",
+    "g = (\n",
+    "    ggplot(df)\n",
+    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
+    "    + facet_wrap(\"~token\")\n",
+    "    + theme(axis_text_x=element_text(rotation=90))\n",
+    ")\n",
+    "print(g)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/basic_tutorials/Basic_Intervention.ipynb b/_sources/tutorials/basic_tutorials/Basic_Intervention.ipynb
new file mode 100644
index 00000000..35d9b598
--- /dev/null
+++ b/_sources/tutorials/basic_tutorials/Basic_Intervention.ipynb
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "df3b00a9",
+   "metadata": {},
+   "source": [
+    "## Interchange Intervention"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89f31a38",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/basic_tutorials/Basic_Intervention.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d4afb217",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Aryaman Arora and Zhengxuan Wu\"\n",
+    "__version__ = \"10/05/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1488cea4",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "This tutorial aims to reproduce some of the results in this [notebook](https://github.com/aryamanarora/nano-causal-interventions/blob/main/The%20capital%20of%20France%20is.ipynb) for path patching or causal scrubbing. This library could potentially support other kinds of interventions that were not originally supported by previous works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f10fade",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "474750ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-11 01:18:09,028] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9c684415",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pyvene\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import RepresentationConfig, IntervenableConfig, IntervenableModel\n",
+    "from pyvene import VanillaIntervention\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aaf70de7",
+   "metadata": {},
+   "source": [
+    "### Factual recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "56cc896c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "The capital of Spain is\n",
+      "_Madrid              0.10501234978437424\n",
+      "_the                 0.0949699655175209\n",
+      "_Barcelona           0.0702790841460228\n",
+      "_a                   0.04010068252682686\n",
+      "_now                 0.02824278175830841\n",
+      "_in                  0.02759990654885769\n",
+      "_Spain               0.022991720587015152\n",
+      "_Catalonia           0.018823225051164627\n",
+      "_also                0.018689140677452087\n",
+      "_not                 0.01735665090382099\n",
+      "\n",
+      "The capital of Italy is\n",
+      "_Rome                0.15734916925430298\n",
+      "_the                 0.07316355407238007\n",
+      "_Milan               0.046878915280103683\n",
+      "_a                   0.03449810668826103\n",
+      "_now                 0.03200329467654228\n",
+      "_in                  0.02306535840034485\n",
+      "_also                0.02274816483259201\n",
+      "_home                0.01920313946902752\n",
+      "_not                 0.01640527881681919\n",
+      "_Italy               0.01577090471982956\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, gpt = pyvene.create_gpt2()\n",
+    "\n",
+    "base = \"The capital of Spain is\"\n",
+    "source = \"The capital of Italy is\"\n",
+    "inputs = [tokenizer(base, return_tensors=\"pt\"), tokenizer(source, return_tensors=\"pt\")]\n",
+    "print(base)\n",
+    "res = gpt(**inputs[0])\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
+    "print()\n",
+    "print(source)\n",
+    "res = gpt(**inputs[1])\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d532a8e8",
+   "metadata": {},
+   "source": [
+    "### Patch Patching on Position-aligned Tokens\n",
+    "We path patch on two modules on each layer:\n",
+    "- [1] MLP output (the MLP output will be from another example)\n",
+    "- [2] MHA input (the self-attention module input will be from another module)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "73c4ade3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simple_position_config(model_type, component, layer):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                layer,              # layer\n",
+    "                component,          # component\n",
+    "                \"pos\",              # intervention unit\n",
+    "                1,                  # max number of unit\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=VanillaIntervention,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3cc24ea1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should finish within 1 min with a standard 12G GPU\n",
+    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
+    "\n",
+    "data = []\n",
+    "for layer_i in range(gpt.config.n_layer):\n",
+    "    config = simple_position_config(type(gpt), \"mlp_output\", layer_i)\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base, sources, {\"sources->base\": pos_i}\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"f{layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"mlp_output\",\n",
+    "                }\n",
+    "            )\n",
+    "\n",
+    "    config = simple_position_config(type(gpt), \"attention_input\", layer_i)\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base, sources, {\"sources->base\": pos_i}\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"a{layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"attention_input\",\n",
+    "                }\n",
+    "            )\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b1cfab3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
+    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
+    "nodes = []\n",
+    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
+    "    nodes.append(f\"f{l}\")\n",
+    "    nodes.append(f\"a{l}\")\n",
+    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
+    "\n",
+    "g = (\n",
+    "    ggplot(df)\n",
+    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
+    "    + facet_wrap(\"~token\")\n",
+    "    + theme(axis_text_x=element_text(rotation=90))\n",
+    ")\n",
+    "print(g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3a191190",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgweaUDmB2tFqt9PXN/ry33W7P+jmnU8392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/ZmmO33Ym9+71vdbrdbOoKZt2nTpgwMDJTOAAAAgGmx6q1nl06YlCM/dXXphGlVy/ueNO+9nwp3APaI0dHRjI2Nzcq5BgcH02630+l0MjIyMivnnE4192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115O7f0zYXh4eMbP4X2fWFPf+6Ghod0+xgCwR3S73XQ6nVk/b4lzTqea+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n9v7pMtvvg/f9f/Pe/28eAgIAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAAN5inAu2HDhg255pprcuutt+bBBx/M3Llzc8QRR+TVr351jj/++N1er9Pp5I477siqVauyatWq/OxnP8u9996bJDn77LPzpje9abpfAgAAAAA9xgBwklavXp0LL7wwGzZsSJL09/dn48aNWb58eZYvX54zzjgjb3/723drzXXr1uWiiy6aiVwAAAAASGIAOCmPPfZYPvjBD2bDhg057LDDcsEFF2Tx4sUZGxvLddddl6uuuirXX399Fi9enFNOOWW31u7v78/hhx+eI488MkcccUQ+97nP5Z577pmhVwIAAACPe+g/nT/9a077io/b/0MfmaGVoTcYAE7CTTfdlHvvvTdz587NxRdfnAULFiRJ5s6dm7POOivr16/Pl7/85Vx55ZVZsmRJ5syZ3Nu6YMGCXH311Wm1WuN/94UvfGFGXgMAvW26f8F/aFpX+9/8cg/+H3IAYPp5CMgk3HzzzUmSk08+eXz490RveMMb0mq1sn79+tx+++2TXrevr2+74R8AAAAATDd3AD6J0dHRrFy5Mkly3HHHTbjPggULsnDhwqxZsya33XZbjj322NlMhCJquZsocXfC3sR1A9B8tfy7fqJ/z9fSnvi/UwDsHgPAJ3H33Xen2+0mSQ477LCd7nfYYYdlzZo1WbNmzWylTRu/6JRTy3vftPcdAAAAeokB4JNYv379+J8POOCAne63bdvw8PCMNwG9zeAYAGDv4XczoAYGgE9i8+bN43+eO3fuTvfbtm10dHTGmwAAAJrEEA1gZhkAUjVPyWNPuG4AJqfm/4e8lvbEv+uZHn6/AWBXDACfxLx588b/PDY2loGBgQn3GxsbS5L09/fPStevuvLKK/PZz352p9vf+MY35i1vecuE2x6aoaaZMDQ0tN3PD5XJ2CO/2p7U019ze+K6KaXm9mTi/unW19c3/t8zfb6HZnT16dO0930muG521LR/3zxUJmOP1Pze19yeuG5K0V5Ozf1N+/3moRldfXo17b2fCgPAJ/HE7/1bv379TgeA274rsNQ/7I0bN+b+++/f6fZNmzal3W7PYtHMqPk1aC+n5n7t5fxq/6q3nl2oZPcd+amrSyfssdm8blqtVtXXae3906n296Hmfu3l1NyvvYya25O6+/1+U473/n8zAHwSCxcuTKvVSrfbzerVq7Nw4cIJ91u9enWS5NBDD53NvHHz58/PgQceuNPtAwMD6XQ6s1g0M2p+DdrLqblfezk192vftb6+vvH/275169YZP990q71/JtR8zSd192svp+Z+7WXU3J7U3e/3m3Ka+t7vyaDRAPBJ9Pf356ijjsqKFSvyve99LyeeeOIO+6xbty5r1qxJkrzgBS+Y7cQkyTnnnJNzzjlnp9vXrVvXiCcU1/watJdTc7/2cmru175rQ0NDabfb2bp164yfbya+k2um1PydXDVf80nd/drLqblfexk1tyd190/UXsvvCDX/fpA073fLbZ7+9Kfv9jF9M9DROEuWLEmS3HLLLXnggQd22H7ttdem2+3mgAMOyDHHHDPLdQAAAACwcwaAk3DqqafmoIMOyubNm/OBD3wgd955Z5LHH/xxzTXX5MYbb0zy+F14c+Zsf1PleeedlzPPPDMf/vCHJ1x748aNGRkZGf/PtttFx8bGtvv7bQ8ZAQAAAIDd4SPAk7DPPvvk/e9/fy688MLcddddec973pOBgYFs3rx5fGB3+umn55RTTtnttf/iL/4id9xxxw5//4UvfCFf+MIXxn8+++yz86Y3vWnPXwQAAAAAPckAcJIWLVqUyy+/PJ///Odz6623Zt26dZk/f34OP/zwnHbaaTn++ONLJ+5Sq9UafzR1zfbmJ+o8Ge3l1NyvvZya+7Xvvefbm9X8XtTcntTdr72cmvu1l1Fze1J3v/Zy/G75vxkA7ob9998/5557bs4999xJH/OJT3xil9svvfTSqWZNSn9/fwYGBibc9uCsFEyPoaGh7X6uuT2pp7/m9sR1U0rN7YnrppSJ2mdKu92e8fPV8r4nzbtuamlP/PumlJrbE9dNKdrLqblfezlN+91yKgwAe8To6GgjvkewaU9+qkXN7Und/drLqblf+64NDg6m3W6n0+lkZGRkxs9XC9dNOTX3ay+n5n7tZdTcntTdr72cpv5uuSeDRgPAHtHtdtPpdEpnTFnNr0F7OTX3ay+n5v6mtT/0n86f1nPM5P9qvf+HPjKDq8+spl03Nam5X3s5NfdrL6Pm9qTufu3lzHb/3vx+1f+lcAAAAADAThkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDeQpwj2i1Wunrq3/e2263SyfsMe3l1NyvvZya+7WXU3O/9nJq7tdeTs392suouT2pu197ObPdvze/XwaAPaK/vz8DAwMTbntwllumYmhoaLufa25P6umvuT1x3ZRSc3viuiml5vbEdVNKze2J66aUmtsT100p2supuV97ORP1z5R2uz2r59tdBoA9YnR0NGNjY6Uzpmx4eLh0wh7TXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk7r7tZczG/2Dg4Npt9vpdDoZGRmZ8fMlezbYNADsEd1uN51Op3TGlNX8GrSXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vak7n7t5UzU/9B/On9azzFTd0Tu/6GPTOt69X8pHAAAAACwUwaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgngLcI1qtVvr66p/3ttvt0gl7THs5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/q7tdeTs39091uANgj+vv7MzAwMOG2mXpk9UwYGhra7uea25N6+mtuT1w3pdTcnrhuSqm5PXHdlFJze+K6KaXm9sR1U4r2cmru115Ozf0TtU+FAWCPGB0dzdjYWOmMKRseHi6dsMe0l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pO5+7eXU3L+r9j0ZDhoA9ohut5tOp1M6Y8pqfg3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXUXN7Une/9nJq7p/u9vq/FA4AAAAA2CkDQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGiwOaUDmB2tVit9ffXPe9vtdumEPaa9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcndfdrL6fm/uluNwDsEf39/RkYGJhw24Oz3DIVQ0ND2/1cc3tST3/N7YnrppSa2xPXTSk1tyeum1Jqbk9cN6XU3J64bkrRXk7N/drLqbl/ovapMADsEaOjoxkbGyudMWXDw8OlE/aY9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcntTdr72cmvt31b4nw0EDwB7R7XbT6XRKZ0xZza9Bezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT+ru115Ozf3T3V7/l8IBAAAAADtlAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA02p3QAs6PVaqWvr/55b7vdLp2wx7SXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vak7n7t5dTcP93tBoA9or+/PwMDAxNue3CWW6ZiaGhou59rbk/q6a+5PXHdlFJze+K6KaXm9sR1U0rN7YnrppSa2xPXTSnay6m5X3s5NfdP1D4VBoA9YnR0NGNjY6Uzpmx4eLh0wh7TXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk7r7tZdTc/+u2vdkOGgA2CO63W46nU7pjCmr+TVoL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supuX+62+v/UjgAAAAAYKcMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDB5pQOYHa0Wq309dU/722326UT9pj2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+6e73QCwR/T392dgYGDCbQ/OcstUDA0Nbfdzze1JPf01tyeum1Jqbk9cN6XU3J64bkqpuT1x3ZRSc3viuilFezk192svp+b+idqnwgCwR4yOjmZsbKx0xpQNDw+XTthj2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau7fVfueDAcNAHtEt9tNp9MpnTFlNb8G7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pam7X3s5NfdPd3v9XwoHAAAAAOyUASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANNic0gHMjlarlb6++ue97Xa7dMIe015Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O6+7WXU3P/dLcbAPaI/v7+DAwMTLjtwVlumYqhoaHtfq65Pamnv+b2xHVTSs3tieumlJrbE9dNKTW3J66bUmpuT1w3pWgvp+Z+7eXU3D9R+1QYAPaI0dHRjI2Nlc6YsuHh4dIJe0x7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7XXk7N/btq35PhoAFgj+h2u+l0OqUzpqzm16C9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcndfdrL6fm/ulur/9L4QAAAACAnTIABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABptTOoDZ0Wq10tdX/7y33W6XTthj2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau6f7nYDwB7R39+fgYGBCbc9OMstUzE0NLTdzzW3J/X019yeuG5Kqbk9cd2UUnN74roppeb2xHVTSs3tieumFO3l1NyvvZya+ydqnwoDwB4xOjqasbGx0hlTNjw8XDphj2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5f1ftezIcNADsEd1uN51Op3TGlNX8GrSXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vak7n7t5dTcP93t9X8pHAAAAACwUwaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgc0oHzKYNGzbkmmuuya233poHH3wwc+fOzRFHHJFXv/rVOf744/d43S1btuSGG27I0qVLs3bt2iTJIYcckpe+9KU57bTTMmfOxG/zsmXL8sMf/jArV67MunXrsmHDhnS73QwNDeXZz352XvWqV+Xoo4/e4y4AAAAA6JkB4OrVq3PhhRdmw4YNSZL+/v5s3Lgxy5cvz/Lly3PGGWfk7W9/+26vOzo6mosuuigrVqxIkuy7775JklWrVmXVqlX55je/mUsuuSTz5s3b4dhPf/rT+eUvfzn+8/z58zM2Npb77rsv9913X5YuXZrXvOY1Offcc/fkJQMAAABAbwwAH3vssXzwgx/Mhg0bcthhh+WCCy7I4sWLMzY2luuuuy5XXXVVrr/++ixevDinnHLKbq19xRVXZMWKFZk/f37e/e53j99JuGzZsvzN3/xNfvKTn+SjH/1o3ve+9+1w7EknnZQDDzwwz3nOc/KMZzwj++yzT7rdbn75y1/mH//xH/Ov//qvue6663LEEUdkyZIl0/FWAAAAANBjeuI7AG+66abce++9mTt3bi6++OIsXrw4STJ37tycddZZ+e3f/u0kyZVXXpktW7ZMet0777wzt9xyS5LkXe96V0444YS0Wq20Wq2ccMIJOf/885MkN998c37xi1/scPyb3/zmvOIVr8jChQuzzz77JElarVYWLlyY9773vXnuc5+bJPna17625y8eAAAAgJ7WEwPAm2++OUly8sknZ8GCBTtsf8Mb3pBWq5X169fn9ttvn/S6S5cuTbfbzcEHH5wTTjhhh+0nnnhiDj744HS73SxdunS3mlutVo466qgkyYMPPrhbxwIAAADANo0fAI6OjmblypVJkuOOO27CfRYsWJCFCxcmSW677bZJr/2DH/wgSXLsscem1WrtsL3VauXYY4/dbt/J2rp1a376058mSQ466KDdOhYAAAAAtmn8dwDefffd6Xa7SZLDDjtsp/sddthhWbNmTdasWTOpdbvdbu6+++4nXXfRokVJMul1H3nkkdx999354he/mJ/85CdJktNPP31SxwIAAADAr2r8AHD9+vXjfz7ggAN2ut+2bcPDw5Nad3R0NJs3b570uqOjoxkdHU1/f/8O+yxbtiyXXnrpDn8/f/78nHfeeTu9cxEAAAAAnkzjPwK8bUiXPP7Qj53Ztm10dHRS6z5xv8msu6u199lnn+y///556lOfmr6+x/+R9Pf35y1veUtOOumkSfUAAAAAwEQafwdgDV70ohfl7//+75MkW7ZsyV133ZXPfvazueKKK/KVr3wlF1988S7vMgQAAACAnWn8AHDevHnjfx4bG8vAwMCE+42NjSXJhB/RncgT99t27K7Wnezac+bMyZFHHpmLL744l156aZYtW5aPfexj+dM//dNdHnfllVfms5/97E63v/GNb8xb3vKWCbc99KRVe4+hoaHtfn6oTMYe+dX2pJ7+mtsT100pNbcnrptSam5PXDel1NyeuG5Kqbk9cd2Uor2cmvu1l1Nz/0TtU9H4AeAT75xbv379TgeA274rcLJvcH9/f/r7+zM6Orrd9wzubN1t+++OM844I8uWLcuyZcsyMjKSwcHBne67cePG3H///TvdvmnTprTb7d06/96o5tegvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v7pbm/8AHDhwoVptVrpdrtZvXp1Fi5cOOF+q1evTpIceuihk1q31Wpl4cKFWbly5fix07HuEz3taU8b//O99967ywHg/Pnzc+CBB+50+8DAQDqdzm437G1qfg3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXUXN7Une/9nJq7t9V+54MBxs/AOzv789RRx2VFStW5Hvf+15OPPHEHfZZt25d1qxZkyR5wQteMOm1n//852flypX5/ve/v9N9li9fPr7v7rr33nvH//zEjzJP5Jxzzsk555yz0+3r1q2b9BOO92Y1vwbt5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk9qbtfezk19++q/elPf/pur9f4pwAnyZIlS5Ikt9xySx544IEdtl977bXpdrs54IADcswxx0x63ZNPPjmtVitr167Nt7/97R22f+tb38ratWvTarXGG7Z5sil0p9PJF7/4xSTJU5/61J3euQgAAAAAu9ITA8BTTz01Bx10UDZv3pwPfOADufPOO5M8/oCOa665JjfeeGOSx++imzNn+5sizzvvvJx55pn58Ic/vMO6ixcvzsknn5wkufzyy7Ns2bJ0u910u90sW7YsH/nIR5I8PoBctGjRdsfefPPN+Yu/+It85zvfycMPPzz+91u2bMkPf/jD/Pmf//n43YO/+7u/m76+nvhHBQAAAMA0a/xHgJNkn332yfvf//5ceOGFueuuu/Ke97wnAwMD2bx5c7Zu3ZokOf3003PKKafs9trvfOc7c88992TFihW59NJLs++++yZJHn300STJs5/97LzjHe+Y8NjvfOc7+c53vpPk8Y8qz5kzJ5s2bRq/O7Cvry+/8zu/k9NPP323uwAAAAAg6ZEBYJIsWrQol19+eT7/+c/n1ltvzbp16zJ//vwcfvjhOe2003L88cfv0br9/f257LLLcsMNN2Tp0qVZu3ZtkuSII47IkiVLctppp+1wV2GSvPjFL8473/nO3H777bnrrrvy0EMPZdOmTZk3b14OOuigHH300XnlK1+5w52De6rVajXiLkJP8Cmj5vak7n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Und/drLqbnfU4CnYP/998+5556bc889d9LHfOITn3jSfebMmZPXvva1ee1rXzvpdZ/61KfmVa96VV71qldN+pip6O/vz8DAwITbHpyVgukxNDS03c81tyf19NfcnrhuSqm5PXHdlFJze+K6KaXm9sR1U0rN7YnrphTt5dTcr72cmvsnap+KnhoA9rLR0dGMjY2Vzpiypj7BZ29Xc3tSd7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9qTufu3l1Ny/q/Y9GQ4aAPaIbrf7pE8erkHNr0F7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7XXk7N/dPdXv+XwgEAAAAAO2UACAAAAAANZgAIAAAAAA1mAAgAAAAADeYhID2i1Wqlr6/+eW+73S6dsMe0l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pO5+7eXU3D/d7QaAPaK/vz8DAwMTbntwllum4lcfdV1ze1JPf83tieumlJrbE9dNKTW3J66bUmpuT1w3pdTcnrhuStFeTs392supuX+i9qkwAOwRo6OjGRsbK50xZcPDw6UT9pj2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+3fVvifDQQPAHtHtdtPpdEpnTFnNr0F7OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P6u7XXk7N/dPdXv+XwgEAAAAAO2UACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAAN5inAPaLVaqWvr/55b7vdLp2wx7SXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vak7n7t5dTcP93tBoA9or+/PwMDAxNue3CWW6ZiaGhou59rbk/q6a+5PXHdlFJze+K6KaXm9sR1U0rN7YnrppSa2xPXTSnay6m5X3s5NfdP1D4VBoA9YnR0NGNjY6Uzpmx4eLh0wh7TXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk7r7tZdTc/+u2vdkOGgA2CO63W46nU7pjCmr+TVoL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supuX+62+v/UjgAAAAAYKcMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwTwFuEe0Wq309dU/722326UT9pj2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+6e73QCwR/T392dgYGDCbQ/OcstUDA0Nbfdzze1JPf01tyeum1Jqbk9cN6XU3J64bkqpuT1x3ZRSc3viuilFezk192svp+b+idqnwgCwR4yOjmZsbKx0xpQNDw+XTthj2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau7fVfueDAcNAHtEt9tNp9MpnTFlNb8G7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pam7X3s5NfdPd3v9XwoHAAAAAOyUASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADTYnNIBzI5Wq5W+vvrnve12u3TCHtNeTs392supuV97OTX3ay+n5n7t5dTcr72MmtuTuvu1l1Nz/3S3GwD2iP7+/gwMDEy47cFZbpmKoaGh7X6uuT2pp7/m9sR1U0rN7YnrppSa2xPXTSk1tyeum1Jqbk9cN6VoL6fmfu3l1Nw/UftUGAD2iNHR0YyNjZXOmLLh4eHSCXtMezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT+ru115Ozf27at+T4aABYI/odrvpdDqlM6as5tegvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v7pbq//S+EAAAAAgJ0yAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAabUzqA2dFqtdLXV/+8t91ul07YY9rLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSd7/2cmrun+52A8Ae0d/fn4GBgQm3PTjLLVMxNDS03c81tyf19NfcnrhuSqm5PXHdlFJze+K6KaXm9sR1U0rN7YnrphTt5dTcr72cmvsnap8KA8AeMTo6mrGxsdIZUzY8PFw6YY9pL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSd392supuX9X7XsyHDQA7BHdbjedTqd0xpTV/Bq0l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pO5+7eXU3D/d7fV/KRwAAAAAsFMGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GBzSgcwO1qtVvr66p/3ttvt0gl7THs5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/q7tdeTs39091uANgj+vv7MzAwMOG2B2e5ZSqGhoa2+7nm9qSe/prbE9dNKTW3J66bUmpuT1w3pdTcnrhuSqm5PXHdlKK9nJr7tZdTc/9E7VNhANgjRkdHMzY2VjpjyoaHh0sn7DHt5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk9qbtfezk19++qfU+GgwaAPaLb7abT6ZTOmLKaX4P2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+6e7vf4vhQMAAAAAdsoAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABpsTukAZker1UpfX/3z3na7XTphj2kvp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1J3f3ay6m5f7rbDQB7RH9/fwYGBibc9uAst0zF0NDQdj/X3J7U019ze+K6KaXm9sR1U0rN7YnrppSa2xPXTSk1tyeum1K0l1Nzv/Zyau6fqH0qDAB7xOjoaMbGxkpnTNnw8HDphD2mvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v5dte/JcNAAsEd0u910Op3SGVNW82vQXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk7r7tZdTc/90t9f/pXAAAAAAwE4ZAAIAAABAgxkAAgAAAECDGQACAAAAQIP11ENANmzYkGuuuSa33nprHnzwwcydOzdHHHFEXv3qV+f444/f43W3bNmSG264IUuXLs3atWuTJIccckhe+tKX5rTTTsucORO/zffdd19+9KMfZdWqVfnZz36Wn//859m8eXOS5Etf+tIe9wAAAADANj0zAFy9enUuvPDCbNiwIUnS39+fjRs3Zvny5Vm+fHnOOOOMvP3tb9/tdUdHR3PRRRdlxYoVSZJ99903SbJq1aqsWrUq3/zmN3PJJZdk3rx5Oxz7uc99Ll//+ten8KoAAAAAYNd6YgD42GOP5YMf/GA2bNiQww47LBdccEEWL16csbGxXHfddbnqqqty/fXXZ/HixTnllFN2a+0rrrgiK1asyPz58/Pud797/E7CZcuW5W/+5m/yk5/8JB/96Efzvve9b4djW61WDj744Bx55JE54ogjMjo6mn/4h3+YltcMAAAAAEmPDABvuumm3HvvvZk7d24uvvjiLFiwIEkyd+7cnHXWWVm/fn2+/OUv58orr8ySJUt2+pHdX3XnnXfmlltuSZK8613vygknnDC+7YQTTsjWrVvzl3/5l7n55pvz+te/Pocddth2x59//vlpt9vjPy9btmyqLxUAAAAAttMTDwG5+eabkyQnn3zy+PDvid7whjek1Wpl/fr1uf322ye97tKlS9PtdnPwwQdvN/zb5sQTT8zBBx+cbrebpUuX7rD9icM/AAAAAJgJjR8Ajo6OZuXKlUmS4447bsJ9FixYkIULFyZJbrvttkmv/YMf/CBJcuyxx6bVau2wvdVq5dhjj91uXwAAAACYTY0fAN59993pdrtJssNHcJ9o27Y1a9ZMat1ut5u77777SdddtGjRbq0LAAAAANOp8QPA9evXj//5gAMO2Ol+27YNDw9Pat3R0dFs3rx50uuOjo5mdHR0UmsDAAAAwHRp/ABw25AuefyhHzuzbdtkh3RP3G8y6+7O2gAAAAAwXRo/AAQAAACAXjandMBMmzdv3vifx8bGMjAwMOF+Y2NjSZL+/v5JrfvE/bYdu6t1d2ftPXHllVfms5/97E63v/GNb8xb3vKWCbc9NENNM2FoaGi7nx8qk7FHfrU9qae/5vbEdVNKze2J66aUmtsT100pNbcnrptSam5PXDelaC+n5n7t5dTcP1H7VDR+APjE7+dbv379TgeA274rcLJvcH9/f/r7+zM6Orrd9wzubN1t+8+UjRs35v7779/p9k2bNqXdbs/Y+WdLza9Bezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT+ru115Ozf3T3d74AeDChQvTarXS7XazevXqLFy4cML9Vq9enSQ59NBDJ7Vuq9XKwoULs3LlyvFjp2PdPTV//vwceOCBO90+MDCQTqczow2zoebXoL2cmvu1l1Nzv/Zyau7XXk7N/drLqblfexk1tyd192svp+b+XbXvyXCw8QPA/v7+HHXUUVmxYkW+973v5cQTT9xhn3Xr1mXNmjVJkhe84AWTXvv5z39+Vq5cme9///s73Wf58uXj+86kc845J+ecc85Ot69bt27STzjem9X8GrSXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vak7n7t5dTcv6v2pz/96bu9Xk88BGTJkiVJkltuuSUPPPDADtuvvfbadLvdHHDAATnmmGMmve7JJ5+cVquVtWvX5tvf/vYO27/1rW9l7dq1abVa4w0AAAAAMJt6YgB46qmn5qCDDsrmzZvzgQ98IHfeeWeSxx/Qcc011+TGG29M8vhddHPmbH9T5HnnnZczzzwzH/7wh3dYd/HixTn55JOTJJdffnmWLVuWbrebbrebZcuW5SMf+UiSxweQixYt2uH4LVu2ZGRkZPw/o6Oj49ue+PcjIyPT8j4AAAAA0Hsa/xHgJNlnn33y/ve/PxdeeGHuuuuuvOc978nAwEA2b96crVu3JklOP/30nHLKKbu99jvf+c7cc889WbFiRS699NLsu+++SZJHH300SfLsZz8773jHOyY89sc//nEuvPDCCbf96sd5v/SlL+12GwAAAAD0xAAwSRYtWpTLL788n//853Prrbdm3bp1mT9/fg4//PCcdtppOf744/do3f7+/lx22WW54YYbsnTp0qxduzZJcsQRR2TJkiU57bTTdrirEAAAAABmS09Npvbff/+ce+65Offccyd9zCc+8Ykn3WfOnDl57Wtfm9e+9rW71XPMMcfM2p19rVYrfX31f+LbI7zLqLk9qbtfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSd7/2cmrun+72nhoA9rL+/v4MDAxMuO3BWW6ZiqGhoe1+rrk9qae/5vbEdVNKze2J66aUmtsT100pNbcnrptSam5PXDelaC+n5n7t5dTcP1H7VBgA9ojR0dGMjY2Vzpiypj7Ce29Xc3tSd7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9qTufu3l1Ny/q/Y9GQ4aAPaIbrebTqdTOmPKan4N2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau6f7vb6vxQOAAAAANgpA0AAAAAAaDADQAAAAABoMANAAAAAAGgwDwHpEa1WK3199c972+126YQ9pr2cmvu1l1Nzv/Zyau7XXk7N/drLqblfexk1tyd192svp+b+6W43AOwR/f39GRgYmHDbg7PcMhW/+qjrmtuTevprbk9cN6XU3J64bkqpuT1x3ZRSc3viuiml5vbEdVOK9nJq7tdeTs39E7VPhQFgjxgdHc3Y2FjpjCkbHh4unbDHtJdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9qTufu3l1Ny/q/Y9GQ4aAPaIbrebTqdTOmPKan4N2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau6f7vb6vxQOAAAAANgpA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDBPAe4RrVYrfX31z3vb7XbphD2mvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3X3ay+n5v7pbjcA7BH9/f0ZGBiYcNuDs9wyFUNDQ9v9XHN7Uk9/ze2J66aUmtsT100pNbcnrptSam5PXDel1NyeuG5K0V5Ozf3ay6m5f6L2qTAA7BGjo6MZGxsrnTFlw8PDpRP2mPZyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7U3a+9nJr7d9W+J8NBA8Ae0e120+l0SmdMWc2vQXs5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/q7tdeTs39091e/5fCAQAAAAA7ZQAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANNqd0ALOj1Wqlr6/+eW+73S6dsMe0l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pO5+7eXU3D/d7QaAPaK/vz8DAwMTbntwllumYmhoaLufa25P6umvuT1x3ZRSc3viuiml5vbEdVNKze2J66aUmtsT100p2supuV97OTX3T9Q+FQaAPWJ0dDRjY2OlM6ZseHi4dMIe015Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O6+7WXU3P/rtr3ZDhoANgjut1uOp1O6Ywpq/k1aC+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Und/drLqbl/utvr/1I4AAAAAGCnDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDB5pQOYHa0Wq309dU/722326UT9pj2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+6e73QCwR/T392dgYGDCbQ/OcstUDA0Nbfdzze1JPf01tyeum1Jqbk9cN6XU3J64bkqpuT1x3ZRSc3viuilFezk192svp+b+idqnwgCwR4yOjmZsbKx0xpQNDw+XTthj2supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1J3v/Zyau7fVfueDAcNAHtEt9tNp9MpnTFlNb8G7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pam7X3s5NfdPd3v9XwoHAAAAAOyUASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA02JzSAcyOVquVvr76573tdrt0wh7TXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk7r7tZdTc/90txsA9oj+/v4MDAxMuO3BWW6ZiqGhoe1+rrk9qae/5vbEdVNKze2J66aUmtsT100pNbcnrptSam5PXDelaC+n5n7t5dTcP1H7VBgA9ojR0dGMjY2Vzpiy4eHh0gl7THs5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/q7tdeTs39u2rfk+GgAWCP6Ha76XQ6pTOmrObXoL2cmvu1l1Nzv/Zyau7XXk7N/drLqblfexk1tyd192svp+b+6W6v/0vhAAAAAICdMgAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGm1M6gNnRarXS11f/vLfdbpdO2GPay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXUXN7Une/9nJq7p/u9lkZAG7ZsiX/83/+z9x6661Zu3ZtHn744ey333555jOfmd/8zd/MSSedVPU/lBr09/dnYGBgwm0PznLLVAwNDW33c83tST39NbcnrptSam5PXDel1NyeuG5Kqbk9cd2UUnN74ropRXs5NfdrL6fm/onap2JGB4Bbt27Nhz70oXz4wx/Offfdt9P9DjrooLzvfe/LBRdc0Ii71PZGo6OjGRsbK50xZcPDw6UT9pj2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+3fVvifDwRkbAD700EN59atfne985ztJkm63u9N977nnnvzxH/9xvvCFL+TGG2/M/vvvP1NZPavb7abT6ZTOmLKaX4P2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye1N2vvZya+6e7vdXd1WRuD3W73fzWb/1WvvWtbyVJ+vr68opXvCKnnHJKjjrqqMyfPz8bN27MqlWr8i//8i/56le/mk6nk1arlRNPPDHf+MY3pjsJAAAAAHrSjAwA/+7v/i7nnXdeWq1WjjzyyPzDP/xDXvjCF+50/9tuuy1nn312fvrTn6bVauW///f/nre+9a3TnQUAAAAAPWdGBoAvf/nLc/PNN2f//ffPHXfckWc+85lPeszatWvzvOc9Lxs2bMhLX/rSfP3rX5/uLAAAAADoOTPyxI3bb789rVYrb3vb2yY1/EuSZz7zmTn33HPT7XZz++23z0QWAAAAAPScGRkAbty4MUnyohe9aLeOO+6445IkmzZtmvYmAAAAAOhFMzIA3HbX3+4+sWTb/gcffPC0NwEAAABAL5qRAeDJJ5+cJONPAZ6sb33rW2m1WnnpS186E1kAAAAA0HNm5CEg//Zv/5bjjz8+++yzT/7t3/4tz3nOc570mB//+Md50YtelC1btmTZsmXjHwcGAAAAAPbcjNwB+KIXvSh/9Vd/lbGxsbz85S/Pl7/85V3u/0//9E/5d//u3+XRRx/Nf/7P/9nwDwAAAACmyZTuAPz7v//7XW7/0pe+lGuvvTatVivPfvazc8opp+Soo47K/Pnzs3HjxqxatSpf/epX85Of/CRJ8vrXvz5nnHFGkuQP/uAP9jQLAAAAAPhfpjQA7OvrS6vVetL9ut3uLvf71e2tVitbtmzZ0ywAAAAA4H+ZM9UFJjs/fLL9ZuCrCAEAAACg501pAPjJT35yujoAAAAAgBkwI08BZu+zYsWK0gkAAAAATNGznvWs3T5mRp4CDAAAAADsHQwAAQAAAKDBpvwQkN1x33335Z577snDDz+c/fbbL8985jNz4IEHzmYCAAAAAPSUGR8Arl69Ov/1v/7XXHvttVm9evUO2xctWpQ3vvGNefe7351DDz10pnMAAAAAoKfM6ENAPvnJT+bd7353Nm3alCSZ6FStVitJMjAwkMsvvzxvfetbZyqnp3kICAAAAED99uQhIDN2B+AnP/nJnHvuuWm1Wul2u2m1WnnOc56TZz3rWXnKU56SRx55JCtWrMhPfvKTdLvdbNy4Meeee26SGAICAAAAwDSZkTsA77nnnhx11FHZtGlTWq1W/vAP/zD/1//1f2XRokU77LtmzZpcdtll+du//dts3bo18+fPz8qVK3PQQQdNd1ZPcwcgAAAAQP32mjsAr7jiivHh38c//vG87W1v2+m+hx56aP7bf/tv+Y3f+I287W1vy6ZNm3LFFVfkkksumYk0AABm0ac+9al8+tOf3uHv99133wwNDeVZz3pWXvnKV+akk04qUAcA0Bv6ZmLRm266Ka1WK6985St3Ofx7ore+9a151atelW63m6985SszkQUAQCF9fX0ZGhoa/0+32819992Xb3zjG7nooovywQ9+MFu3bi2dCQDQSDNyB+DPf/7zJMlrX/va3TruNa95Tb7yla+MHw8AQDMsWLAgV1999fjP3W43a9asyf/7//6/+eY3v5mvfe1ref7zn58zzzyzYCUAQDPNyB2ADz/8cJLkgAMO2K3jtu3/yCOPTHsTAAB7j1arlUWLFuXP/uzPxr8n+qabbipcBQDQTDNyB+DTnva03Hfffbnzzjt367i77rorye4PDgEAqNM+++yTY489NqtXrx7/XXAiy5Yty/XXX58f//jHefjhh7PffvvlOc95Tl7zmtfkJS95yYTHvPe9781tt92Wt7zlLTnnnHPyP/7H/8hXv/rV3HPPPdlvv/3ykpe8JG9729vytKc9LUnyy1/+MldeeWX+7d/+LQ899FAOOuignHbaafmd3/md9PXt/H83/+lPf5prr702t912W9avX5999903ixYtystf/vKceeaZ2Xfffaf0HgEATNWMDACf97zn5d57781nPvOZ/Kf/9J92+QvTNp1OJ5/5zGfSarXyvOc9byayAADYC3W73SSZ8DsAu91u/vqv/zrXX399kse/S3BgYCAbNmzIt771rXzrW9/Ka1/72rz73e9Oq9WacP0tW7bkj/7oj7J8+fLxYdy6devy5S9/OT/4wQ/ykY98JL/85S/zx3/8x3nkkUcyf/78bNmyJWvWrMnHPvaxPPDAAzn//PMnXPvTn/50Pv3pT4+/hoGBgYyOjubHP/5xfvzjH+ef//mf81d/9Vd56lOfOuX3CQBgT83IR4C3fXfLj370o7zzne8c/4VoZ7rdbv7jf/yPueOOO5I8/l2AAAA032OPPZbvf//7SZJnPvOZO2z/x3/8x/Hh3+/93u/li1/8Yq6//vp84QtfyFlnnZUk+eIXv5hrr712p+e47rrrsnr16lx66aX5p3/6p3z5y1/OBz7wgQwMDOTuu+/OJz/5yVxyySU55phjctVVV+WGG27I9ddfP/477bXXXptf/OIXE677qU99Kvvtt1/e/e5357rrrsuNN96Yr3zlK/nLv/zLLFy4MCtXrsxll1025fcJAGAqZmQAeN5552XhwoVJko9//OM57rjjctVVV+X+++/fbr8HHnggV111VV70ohfl4x//eFqtVhYuXJjzzjtvJrIAANhLdLvdrF69OpdccknWrFmTJHnlK1+53T6bN2/OZz7zmSTJ6173uvyH//Afst9++yVJBgcH8453vCOnn356kuTv//7vMzY2NuG5HnnkkVx00UU54YQT0tfXl3a7nZNOOim/+7u/m+TxQd6+++6bD3zgA+NDyPnz5+c973lPDjnkkHS73SxdunS7NTdu3JiPf/zjmTNnTi677LK87nWvy+DgYJLHP9b8kpe8JJdddlnmzZuXZcuWZcWKFdPxtgEA7JEZ+QjwvHnz8vnPfz4vf/nLs2nTpvzgBz/IH/zBHyRJ9ttvv8yfPz8bN24cf1hI8vgvgfPnz8+1116buXPnzkQWAACFPPDAA3n9618//vMjjzySxx57bPznE044Ia973eu2O+a73/1uHnnkkfT19eWcc86ZcN0/+IM/yJe//OWMjIzku9/9bv6P/+P/2GGfo48+Oi984Qt3+PsXvehF+eQnP5kkOeuss9Jut7fb3tfXlxe+8IX55S9/mZ///OfbbVu6dGk2btyY4447Ls95znMmbDvkkEPynOc8J9///vfz3e9+N8961rMm3A8AYKbNyAAwSX7jN34j3/zmN/PmN785P/zhD8f/fmRkJA8//PAOHws+5phjcuWVV+aYY46ZqSQAAArZunVrhoeHJ9z2H/7Df8jZZ5+9w3f4bbtr7td+7dd2+pC4BQsWZNGiRbnrrruyYsWKCQeAixcvnvDY/fff/0n32XbeRx55ZLu/3/b77Y9+9KPtBpu/auPGjUmS++67b6f7AADMtBkbACbJ85///PzgBz/IjTfemGuvvTbf+c53cs8994w/ue3ggw/Ob/7mb+YNb3hDXv3qV+/0i5sBAKjbM57xjFx99dVJHn/42wMPPJCvfOUr+cxnPpNPfepTedaznpUXvehF2x3z0EMPJUme/vSn73LtBQsW5K677hrf/1dte8rvr3riHX8722fbw+y2bNmy3d8/+OCDSR7/mPLmzZt32Zdkpx9PBgCYDTM6AEySVquV008/ffz7WQAA6G3tdjsHHXRQ3vrWt2b+/Pm54oorcskll+Tv/u7vdjqI29tse2LxGWeckQsuuKBwDQDArs3IQ0AOP/zwHH744Xn3u989E8sDANAQb3jDG3L44YdnZGQk//2///fttm37iO66det2ucYDDzyw3f6zYWhoKEl2eMgdAMDeaEYGgKtXr84vfvGLHH300TOxPAAADdHX15ff//3fT5LcdNNNWb169fi2bQ/NuOuuu7J+/foJj1+3bt34MbP5kI1tv+f+4Ac/GP+ePwCAvdWMDAAPPPDAJNnplzUDAMA2J598cg499NBs3bo1V1555fjfv/jFL85+++23w98/0ac//els3bo1g4ODefGLXzxbyVmyZEkGBgYyOjqaj3/847vcd3R0dLsnHgMAzLYZGQD++q//epLk7rvvnonlAQBokL6+vvzu7/5ukuRrX/va+O+Q8+bNG7878Atf+EI+/vGP5+GHH06SjIyM5GMf+1huuOGGJMlb3vKWzJ07d9aaBwcH84d/+IdJkuuuuy4f+MAH8vOf/3x8+5YtW7JixYr83d/9Xd785jfv9AElAACzYUYeAvLGN74xS5cuzec///m8733vm4lTAADQIK985SvzqU99KuvWrctnPvOZ/Mmf/EmSx3+vXLNmTa6//vp89rOfzdVXX5358+dn48aN4w/ieM1rXpPXve51s9585plnZvPmzfnbv/3bfP3rX8/Xv/71zJ07N3Pnzs0jjzwy3gcAUNqM3AH49re/PUcffXS+/e1v50Mf+tBMnAIAgAbZZ599ctZZZyVJ/uVf/iW//OUvkyStVisXXHBBLr300pxwwgkZHBzMpk2bMjg4mBNPPDGXXXZZ3vve96bVahXpPuuss/KpT30qr3vd63LYYYelr68vGzduzFOf+tQ8//nPz+///u/nE5/4RBYsWFCkDwAgSVrdbrc7Ewv/4he/yOtf//osX748r33ta/Oud70rJ554Yvbdd9+ZOB1PYsWKFaUTAAAAAJiiPXnw2YwMAA8//PAkydjYWO65557x/0W23W7naU97Wvr7+3cd1WrlZz/72XRn9TQDQAAAAID67ckAcEa+A/Cuu+4aH/pt++9ut5stW7bkvvvue9LjS32EAwAAAACaZkYGgIsWLTLEAwAAAIC9wIzdAQgAQHPccccdufjii3frmPPPPz8vf/nLZ6gIAIDJmpEBIAAAzbJly5YMDw/v1jGPPvroDNUAALA7ZuwpwOxdPAQEAAAAoH57zUNAftWDDz6Y66+/PrfeemvWrl2bhx9+OPvtt1+e+cxn5jd/8zdz+umn52lPe9pspAAAAABAT5nROwAffvjh/PEf/3E+9alPZWxsbKf7zZ07N29729ty2WWX5SlPecpM5fQ0dwACAAAA1G9P7gCcsQHg6tWr8/KXvzx33nlnJnOKVquVww8/PF//+tdz6KGHzkRSTzMABAAAAKjfXjMAfPTRR/PCF74wP/nJT5IkT3nKU/LmN785p5xySo466qjMnz8/GzduzKpVq/Iv//Ivueqqq/Lwww8nSZ7znOdk+fLl2WeffaY7q6cZAAIAAADUb68ZAH74wx/OBRdckFarleOPPz7/+I//mGc+85k73f+ee+7JWWedlW9+85tptVr5L//lv+Q973nPdGf1NANAAAAAgPrtyQCwbwY68g//8A9JkoMPPjj/9E//tMvh37b9vvzlL4/vd/XVV89EFgAAAAD0nBkZAP70pz9Nq9XK2972tgwODk7qmP322y/nnntuut1ufvrTn85EFgAAAAD0nBkZAD766KNJkqOPPnq3jnvuc5+bJHnsscemvQkAAAAAetGcmVh04cKFWblyZUZHR3fruG37H3LIITOR1dMOOOCAWTvX0NBQ2u12Op1OhoeHZ+2806Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J/X0z8gdgK94xSvS7Xbz9a9/fbeO+9rXvpZWq5VXvvKVM5EFAAAAAD1nRgaA73rXu9Lf35/Pfe5z+cY3vjGpY77xjW/k6quvzsDAQN71rnfNRBYAAAAA9JwZGQA+61nPyic/+cnMmTMnr371q3PFFVeMfy/gr3rsscfy0Y9+NKeddlr22WeffPKTn8xRRx01E1kAAAAA0HNm5DsAL7nkkiSPfxT4hhtuyLve9a5cdNFFOemkk3LUUUdl/vz52bhxY1atWpVvfOMbeeihh5Ikp59+en70ox+NHz+Riy++eCaSAQAAAKCRZmQA+Od//udptVpJMv7fw8PDueGGG3bYt9vtju9zww03TLjPExkAAgAAAMDkzcgAMHl8sDeZv9vV3/+qbYNCAAAAAGByZmQA+K//+q8zsSxT0G63e+q806Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3t3f6s72dvvAAAAAIDqzNhHgNm7DA8Pz9q5BgcH02630+l0MjIyMmvnnS4192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5PSnTPzQ0tNvHGAD2iE6n01PnnS4192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pdm7+/tKBwAAAAAAM8cAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGmxO6QBmR7vd7qnzTpea+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcnuzd/a1ut9stHQEAAAAAzAx3APaI4eHhWTvX4OBg2u12Op1ORkZGZu2806Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J2X6h4aGdvsYA8Ae0el0euq806Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3t3v4eAAAAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAg80pHcDsaLfbPXXe6VJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk727v9XtdrulIwAAAACAmeEOwB4xPDw8a+caHBxMu91Op9PJyMjIrJ13utTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pEz/0NDQbh9jANgjOp1OT513utTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2ZO/u9xAQAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDBPAd4NGzZsyDXXXJNbb701Dz74YObOnZsjjjgir371q3P88cfv9nqdTid33HFHVq1alVWrVuVnP/tZ7r333iTJ2WefnTe96U3T/RIAAAAA6DEGgJO0evXqXHjhhdmwYUOSpL+/Pxs3bszy5cuzfPnynHHGGXn729++W2uuW7cuF1100UzkAgAAAEASA8BJeeyxx/LBD34wGzZsyGGHHZYLLrggixcvztjYWK677rpcddVVuf7667N48eKccsopu7V2f39/Dj/88Bx55JE54ogj8rnPfS733HPPDL0SAAAAAHqNAeAk3HTTTbn33nszd+7cXHzxxVmwYEGSZO7cuTnrrLOyfv36fPnLX86VV16ZJUuWZM6cyb2tCxYsyNVXX51WqzX+d1/4whdm5DUAAAAA0Js8BGQSbr755iTJySefPD78e6I3vOENabVaWb9+fW6//fZJr9vX17fd8A8AAAAAppsB4JMYHR3NypUrkyTHHXfchPssWLAgCxcuTJLcdttts9YGAAAAAE/GAPBJ3H333el2u0mSww47bKf7bdu2Zs2aWekCAAAAgMkwAHwS69evH//zAQccsNP9tm0bHh6e8SYAAAAAmCwDwCexefPm8T/PnTt3p/tt2zY6OjrjTQAAAAAwWQaAAAAAANBgc0oH7O3mzZs3/uexsbEMDAxMuN/Y2FiSpL+/f1a6ftWVV16Zz372szvd/nu/93t505veNCstfX194/89NDQ0K+ecTjX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk9qaffAPBJPPF7/9avX7/TAeC27wos9Q9748aNuf/++3e6fdOmTWm327NYlLRarVk/53SquV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7cne328A+CQWLlyYVquVbreb1atXZ+HChRPut3r16iTJoYceOpt54+bPn58DDzxwp9sHBgbS6XRmpaWvr2/8Pdu6deusnHM61dyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vakTP+eDBoNAJ9Ef39/jjrqqKxYsSLf+973cuKJJ+6wz7p167JmzZokyQte8ILZTkySnHPOOTnnnHN2un3dunWz9oTioaGhtNvtbN26tcqnItfcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pEz/05/+9N0+xkNAJmHJkiVJkltuuSUPPPDADtuvvfbadLvdHHDAATnmmGNmuQ4AAAAAds4AcBJOPfXUHHTQQdm8eXM+8IEP5M4770zy+IM/rrnmmtx4441JHr8Lb86c7W+qPO+883LmmWfmwx/+8IRrb9y4MSMjI+P/2Xa76NjY2HZ/v+0hIwAAAACwO3wEeBL22WefvP/978+FF16Yu+66K+95z3syMDCQzZs3jw/sTj/99Jxyyim7vfZf/MVf5I477tjh77/whS/kC1/4wvjPZ5999qw9xRcAAACA5jAAnKRFixbl8ssvz+c///nceuutWbduXebPn5/DDz88p512Wo4//vjSibtU6kk0e/MTcCaj5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfexk1tyd7d3+r2+12S0cAAAAAADPDHYA9YjafpDM4OJh2u51Op5ORkZFZO+90qblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze1Jmf6hoaHdPsYAsEd0Op2eOu90qblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Gze3J3t3vKcAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAeAtIj2u12T513utTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2ZO/ub3W73W7pCAAAAABgZrgDsEcMDw/P2rkGBwfTbrfT6XQyMjIya+edLjX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk9KdM/NDS028cYAPaITqfTU+edLjX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk92bv7PQQEAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabE7pAGZHu93uqfNOl5r7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye7N39rW632y0dAQAAAADMDHcA9ojh4eFZO9fg4GDa7XY6nU5GRkZm7bzTpeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcnZfqHhoZ2+xgDwB7R6XR66rzTpeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcne3e/h4AAAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDzSkdwOxot9s9dd7pUnO/9nJq7tdeTs392supuV97OTX3ay+n5n7t5dTcr72MmtuTvbu/1e12u6UjAAAAAICZ4Q7AHjE8PDxr5xocHEy73U6n08nIyMisnXe61NyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vakTP/Q0NBuH2MA2CM6nU5PnXe61NyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vZk7+73EBAAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaLA5pQOYHe12u6fOO11q7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze7J397e63W63dAQAAAAAMDPcAdgjhoeHZ+1cg4ODabfb6XQ6GRkZmbXzTpea+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcnpTpHxoa2u1jDAB7RKfT6anzTpea+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcnuzd/R4CAgAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADTandACzo91u99R5p0vN/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT/bu/la32+2WjgAAAAAAZoY7AHvE8PDwrJ1rcHAw7XY7nU4nIyMjs3be6VJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbkzL9Q0NDu32MAWCP6HQ6PXXe6VJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk72730NAAAAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoMHmlA5gdrTb7Z4673SpuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7cne3d/qdrvd0hEAAAAAwMxwB2CPGB4enrVzDQ4Opt1up9PpZGRkZNbOO11q7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1Kmf2hoaLePMQDsEZ1Op6fOO11q7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze7J393sICAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA02JzSAcyOdrvdU+edLjX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqLk92bv7W91ut1s6AgAAAACYGe4A7BHDw8Ozdq7BwcG02+10Op2MjIzM2nmnS8392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5PyvQPDQ3t9jEGgD2i0+n01HmnS8392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5P9u5+DwEBAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABptTOmA2bdiwIddcc01uvfXWPPjgg5k7d26OOOKIvPrVr87xxx+/x+tu2bIlN9xwQ5YuXZq1a9cmSQ455JC89KUvzWmnnZY5cyZ+m5ctW5Yf/vCHWblyZdatW5cNGzak2+1maGgoz372s/OqV70qRx999B53AQAAAEDPDABXr16dCy+8MBs2bEiS9Pf3Z+PGjVm+fHmWL1+eM844I29/+9t3e93R0dFcdNFFWbFiRZJk3333TZKsWrUqq1atyje/+c1ccsklmTdv3g7HfvrTn84vf/nL8Z/nz5+fsbGx3HfffbnvvvuydOnSvOY1r8m55567Jy8ZAAAAAHpjAPjYY4/lgx/8YDZs2JDDDjssF1xwQRYvXpyxsbFcd911ueqqq3L99ddn8eLFOeWUU3Zr7SuuuCIrVqzI/Pnz8+53v3v8TsJly5blb/7mb/KTn/wkH/3oR/O+971vh2NPOumkHHjggXnOc56TZzzjGdlnn33S7Xbzy1/+Mv/4j/+Yf/3Xf811112XI444IkuWLJmOtwIAAACAHtMT3wF400035d57783cuXNz8cUXZ/HixUmSuXPn5qyzzspv//ZvJ0muvPLKbNmyZdLr3nnnnbnllluSJO9617tywgknpNVqpdVq5YQTTsj555+fJLn55pvzi1/8Yofj3/zmN+cVr3hFFi5cmH322SdJ0mq1snDhwrz3ve/Nc5/73CTJ1772tT1/8QAAAAD0tJ4YAN58881JkpNPPjkLFizYYfsb3vCGtFqtrF+/Prfffvuk1126dGm63W4OPvjgnHDCCTtsP/HEE3PwwQen2+1m6dKlu9XcarVy1FFHJUkefPDB3ToWAAAAALZp/ABwdHQ0K1euTJIcd9xxE+6zYMGCLFy4MEly2223TXrtH/zgB0mSY489Nq1Wa4ftrVYrxx577Hb7TtbWrVvz05/+NEly0EEH7daxAAAAALBN478D8O677063202SHHbYYTvd77DDDsuaNWuyZs2aSa3b7XZz9913P+m6ixYtSpJJr/vII4/k7rvvzhe/+MX85Cc/SZKcfvrpkzoWAAAAAH5V4weA69evH//zAQccsNP9tm0bHh6e1Lqjo6PZvHnzpNcdHR3N6Oho+vv7d9hn2bJlufTSS3f4+/nz5+e8887b6Z2LAAAAAPBkGv8R4G1DuuTxh37szLZto6Ojk1r3iftNZt1drb3PPvtk//33z1Of+tT09T3+j6S/vz9vectbctJJJ02qBwAAAAAm0vg7AGvwohe9KH//93+fJNmyZUvuuuuufPazn80VV1yRr3zlK7n44ot3eZchAAAAAOxM4weA8+bNG//z2NhYBgYGJtxvbGwsSSb8iO5EnrjftmN3te5k154zZ06OPPLIXHzxxbn00kuzbNmyfOxjH8uf/umf7vK4K6+8Mp/97Gd3uv33fu/38qY3velJzz8dtt3F2NfXl6GhoVk553SquV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7Uk9/Y0fAD7xzrn169fvdAC47bsCJ/sPq7+/P/39/RkdHd3uewZ3tu62/XfHGWeckWXLlmXZsmUZGRnJ4ODgTvfduHFj7r///p1u37RpU9rt9m6df6pardasn3M61dyvvZya+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+j5vZk7+9v/ABw4cKFabVa6Xa7Wb16dRYuXDjhfqtXr06SHHrooZNat9VqZeHChVm5cuX4sdOx7hM97WlPG//zvffeu8sB4Pz583PggQfudPvAwEA6nc5uN+yJvr6+8fd869ats3LO6VRzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbkzL9ezJobPwAsL+/P0cddVRWrFiR733veznxxBN32GfdunVZs2ZNkuQFL3jBpNd+/vOfn5UrV+b73//+TvdZvnz5+L6769577x3/8xM/yjyRc845J+ecc85Ot69bt27STzieqqGhobTb7WzdunXWzjmdau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSpv/pT3/6bh/T+KcAJ8mSJUuSJLfcckseeOCBHbZfe+216Xa7OeCAA3LMMcdMet2TTz45rVYra9euzbe//e0dtn/rW9/K2rVr02q1xhu2ebK78TqdTr74xS8mSZ761Kfu9M5FAAAAANiVnhgAnnrqqTnooIOyefPmfOADH8idd96Z5PEHdFxzzTW58cYbkzx+F92cOdvfFHneeeflzDPPzIc//OEd1l28eHFOPvnkJMnll1+eZcuWpdvtptvtZtmyZfnIRz6S5PEB5KJFi7Y79uabb85f/MVf5Dvf+U4efvjh8b/fsmVLfvjDH+bP//zPx+8e/N3f/d3xL5UEAAAAgN3R+I8AJ8k+++yT97///bnwwgtz11135T3veU8GBgayefPm8c9nn3766TnllFN2e+13vvOdueeee7JixYpceuml2XfffZMkjz76aJLk2c9+dt7xjndMeOx3vvOdfOc730ny+EeV58yZk02bNo3fHdjX15ff+Z3fyemnn77bXQAAAACQ9MgAMEkWLVqUyy+/PJ///Odz6623Zt26dZk/f34OP/zwnHbaaTn++OP3aN3+/v5cdtllueGGG7J06dKsXbs2SXLEEUdkyZIlOe2003a4qzBJXvziF+ed73xnbr/99tx111156KGHsmnTpsybNy8HHXRQjj766Lzyla/c4c7BPVXqSTR78xNwJqPmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3t3f6vb7XZLRwAAAAAAM6Nn7gDsdbP5JJ3BwcG02+10Op2MjIzM2nmnS8392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Yyam5PyvQPDQ3t9jEGgD3iyZ463LTzTpea+7WXU3O/9nJq7tdeTs392supuV97OTX3ay+n5n7tZdTcnuzd/R4tCwAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAAN5iEgPaLdbvfUeadLzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9jJqbk/27v5Wt9vtlo4AAAAAAGaGOwB7xPDw8Kyda3BwMO12O51OJyMjI7N23ulSc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25My/UNDQ7t9jAFgj+h0Oj113ulSc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25O9u99DQAAAAACgwQwAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBPAW4R7Tb7Z4673SpuV97OTX3ay+n5n7t5dTcr72cmvu1l1Nzv/Zyau7XXkbN7cne3d/qdrvd0hEAAAAAwMxwB2CPGB4enrVzDQ4Opt1up9PpZGRkZNbOO11q7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze1Kmf2hoaLePMQDsEZ1Op6fOO11q7tdeTs392supuV97OTX3ay+n5n7t5dTcr72cmvu1l1Fze7J393sICAAAAAA0mAEgAAAAADSYASAAAAAANJgBIAAAAAA0mAEgAAAAADSYpwD3iHa73VPnnS4192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pdm7+1vdbrdbOgIAAAAAmBnuAOwRw8PDs3auwcHBtNvtdDqdjIyMzNp5p0vN/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT8r0Dw0N7fYxBoA9otPp9NR5p0vN/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdTc7/2MmpuT/bufg8BAQAAAIAGMwAEAAAAgAYzAAQAAACABjMABAAAAIAGMwAEAAAAgAYzAAQAAACABptTOoDZ0W63e+q806Xmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs392supuV97GTW3J3t3f6vb7XZLRwAAAAAAM8MdgD1ieHh41s41ODiYdrudTqeTkZGRWTvvdKm5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tSZn+oaGh3T7GALBHdDqdnjrvdKm5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeRs3tyd7d7yEgAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GAGgAAAAADQYAaAAAAAANBgBoAAAAAA0GBzSgcwO9rtdk+dd7rU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9mTv7m91u91u6QgAAAAAYGa4A7BHDA8Pz9q5BgcH02630+l0MjIyMmvnnS4192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5PSnTPzQ0tNvHGAD2iE6n01PnnS4192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pdm7+z0EBAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAazAAQAAAAABrMABAAAAAAGswAEAAAAAAabE7pAGZHu93uqfNOl5r7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu1l1Nye7N39rW632y0dAQAAAADMDHcA9ojh4eFZO9fg4GDa7XY6nU5GRkZm7bzTpeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcnZfqHhoZ2+xgDwB7R6XR66rzTpeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcne3e/h4AAAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAg80pHcDsaLfbPXXe6VJzv/Zyau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9jJrbk727v9XtdrulIwAAAACAmeEOwB4xPDw8a+caHBxMu91Op9PJyMjIrJ13utTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2pEz/0NDQbh9jANgjOp1OT513utTcr72cmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svo+b2ZO/u9xAQAAAAAGgwA0AAAAAAaDADQAAAAABoMANAAAAAAGgwA0AAAAAAaLCeegrwhg0bcs011+TWW2/Ngw8+mLlz5+aII47Iq1/96hx//PF7vO6WLVtyww03ZOnSpVm7dm2S5JBDDslLX/rSnHbaaZkzZ+K3+b777suPfvSjrFq1Kj/72c/y85//PJs3b06SfOlLX9rjHgAAAADYpmcGgKtXr86FF16YDRs2JEn6+/uzcePGLF++PMuXL88ZZ5yRt7/97bu97ujoaC666KKsWLEiSbLvvvsmSVatWpVVq1blm9/8Zi655JLMmzdvh2M/97nP5etf//oUXhUAAAAA7FpPDAAfe+yxfPCDH8yGDRty2GGH5YILLsjixYszNjaW6667LldddVWuv/76LF68OKeccspurX3FFVdkxYoVmT9/ft797neP30m4bNmy/M3f/E1+8pOf5KMf/Wje97737XBsq9XKwQcfnCOPPDJHHHFERkdH8w//8A/T8poBAAAAIOmRAeBNN92Ue++9N3Pnzs3FF1+cBQsWJEnmzp2bs846K+vXr8+Xv/zlXHnllVmyZMlOP7L7q+68887ccsstSZJ3vetdOeGEE8a3nXDCCdm6dWv+8i//MjfffHNe//rX57DDDtvu+PPPPz/tdnv852XLlk31pQIAAADAdnriISA333xzkuTkk08eH/490Rve8Ia0Wq2sX78+t99++6TXXbp0abrdbg4++ODthn/bnHjiiTn44IPT7XazdOnSHbY/cfgHAAAAADOh8QPA0dHRrFy5Mkly3HHHTbjPggULsnDhwiTJbbfdNum1f/CDHyRJjj322LRarR22t1qtHHvssdvtCwAAAACzqfEDwLvvvjvdbjdJdvgI7hNt27ZmzZpJrdvtdnP33Xc/6bqLFi3arXUBAAAAYDo1fgC4fv368T8fcMABO91v27bh4eFJrTs6OprNmzdPet3R0dGMjo5Oam0AAAAAmC6NHwBuG9Iljz/0Y2e2bZvskO6J+01m3d1ZGwAAAACmS+MHgAAAAADQy+aUDphp8+bNG//z2NhYBgYGJtxvbGwsSdLf3z+pdZ+437Zjd7Xu7qy9J6688sp89rOf3en23/u938ub3vSmGTv/E/X19Y3/99DQ0KycczrV3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6Pm9qSe/sYPAJ/4/Xzr16/f6QBw23cFTvYfVn9/f/r7+zM6Orrd9wzubN1t+8+UjRs35v7779/p9k2bNqXdbs/Y+SfSarVm/ZzTqeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3sZNbcne39/4weACxcuTKvVSrfbzerVq7Nw4cIJ91u9enWS5NBDD53Uuq1WKwsXLszKlSvHj52OdffU/Pnzc+CBB+50+8DAQDqdzow2bNPX1zf+nm/dunVWzjmdau7XXk7N/drLqblfezk192svp+Z+7eXU3K+9nJr7tZdRc3tSpn9PBo2NHwD29/fnqKOOyooVK/K9730vJ5544g77rFu3LmvWrEmSvOAFL5j02s9//vOzcuXKfP/739/pPsuXLx/fdyadc845Oeecc3a6fd26dZN+wvFUDQ0Npd1uZ+vWrbN2zulUc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvYya25My/U9/+tN3+5ieeAjIkiVLkiS33HJLHnjggR22X3vttel2uznggANyzDHHTHrdk08+Oa1WK2vXrs23v/3tHbZ/61vfytq1a9NqtcYbAAAAAGA29cQA8NRTT81BBx2UzZs35wMf+EDuvPPOJI8/oOOaa67JjTfemOTxu+jmzNn+psjzzjsvZ555Zj784Q/vsO7ixYtz8sknJ0kuv/zyLFu2LN1uN91uN8uWLctHPvKRJI8PIBctWrTD8Vu2bMnIyMj4f0ZHR8e3PfHvR0ZGpuV9AAAAAKD3NP4jwEmyzz775P3vf38uvPDC3HXXXXnPe96TgYGBbN68efzz2aeffnpOOeWU3V77ne98Z+65556sWLEil156afbdd98kyaOPPpokefazn513vOMdEx774x//OBdeeOGE237147xf+tKXdrsNAAAAAHpiAJgkixYtyuWXX57Pf/7zufXWW7Nu3brMnz8/hx9+eE477bQcf/zxe7Ruf39/Lrvsstxwww1ZunRp1q5dmyQ54ogjsmTJkpx22mk73FUIAAAAALOlpyZT+++/f84999yce+65kz7mE5/4xJPuM2fOnLz2ta/Na1/72t3qOeaYY2btzr5Sj6Lemx+BPRk192svp+Z+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6i5Pdm7+1vdbrdbOgIAAAAAmBk9dQdgL5vNR2kPDg6m3W6n0+lU+QCTmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svp+Z+7WXU3J6U6R8aGtrtYwwAe0Sn0+mp806Xmvu1l1Nzv/Zyau7XXk7N/drLqblfezk192svp+Z+7WXU3J7s3f19pQMAAAAAgJljAAgAAAAADWYACAAAAAAN5jsAAQAAAGbR+fc+NL0LTvd6/8tHDtp/RtZl9hkA9oh2u91T550uNfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7tdeTs39NbdPp9l+H2p/3/fm/la32+2WjgAAAADoFWffsap0wqRc/bwjSycwTdwB2COGh4dn7VyDg4Npt9vpdDoZGRmZtfNOl5r7tZdTc7/2cmru115Ozf3ay6m5X3s5NfdrL6fm/prbZ8pszBJqf99L9A8NDe32MQaAPaLT6fTUeadLzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3N/ze3Tabbfh9rf972531OAAQAAAKDBDAABAAAAoMEMAAEAAACgwQwAAQAAAKDBDAABAAAAoME8BbhHtNvtnjrvdKm5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3O/9nJq7q+5fTrN9vtQ+/u+N/e3ut1ut3QEAAAAQK84+45VpRMm5ernHVk6gWniDsAeMTw8PGvnGhwcTLvdTqfTycjIyKydd7rU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5X3s5NffX3D5TZmOWUPv7XqJ/aGhot48xAOwRnU6np847XWru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+2tun06z/T7U/r7vzf0eAgIAAAAADWYACAAAAAANZgAIAAAAAA1mAAgAAAAADWYACAAAAAANZgAIAAAAAA02p3QAs6PdbvfUeadLzf3ay6m5X3s5NfdrL6fmfu3l1NyvvZya+7WXU3N/ze3Tabbfh9rf9725v9XtdrulIwAAAAB6xdl3rCqdMClXP+/I0glME3cA9ojh4eFZO9fg4GDa7XY6nU5GRkZm7bzTpeZ+7eXU3K+9nJr7tZdTc7/2cmru115Ozf3ay6m5v+b2mTIbs4Ta3/cS/UNDQ7t9jAFgj+h0Oj113ulSc7/2cmru115Ozf3ay6m5X3s5NfdrL6fmfu3l1Nxfc/t0mu33YTbOd/69D03vgr98cHrX+18+ctD+07qeh4AAAAAAQIMZAAIAAABAgxkAAgAAAECDGQACAAAAQIMZAAIAAABAg3kKMAAAAACTMu1P0Z3u9Z5gup+kWzMDQAAAAOhB0z7ISWZsmGOQA1PjI8AAAAAA0GDuAOwR7Xa7p847XWru115Ozf3ay6m5X3s5NfdrL6fmfu3l1NyvvRlm+73w3j+u9veh5v7pbm91u93utK4IAAAA7PXOvmNV6YRJu/p5R5ZOmFa1vPcTve+1tCd190/3Ne8OwB4xPDw8a+caHBzM/9/enYdFVfb/A38fBphhEQEFSsFUUNTcldQ0F8hdIZfCNbVcWqw0nyxzyd18/GZpZYnmli1PjwvmvoDiAu6JYrhhLIrIIjgKDODM/P7wN/OILILC3HOG9+u6ui6bjTef68zhnM+5z30rFApotVqo1WqT/dyKIuf8zC6OnPMzuzhyzs/s4sg5P7OLI+f8zC6OnPMzu+UxxTkta1+UKXsJlUHO+UvL7uLiUu7PYwOwitBqtVXq51YUOedndnHknJ/ZxZFzfmYXR875mV0cOedndnEqO3+FL0ZxM6NiP+//M/VCFHLfbiqSqWvB2j8k9zrIOX9FZ2cDkIiIqAqo8BMrrvBHREQEgH9jiUgeuAowERERERERERGRBeMIQJLNFSuAV62I5I77GyIiyyeXfb2l7ecrvO4Aa09EZEHYACQSiAfIYvAAmYiIiIiIiKoSNgCJ6KnIpXkJsIlGRETywgtVREREVNHYACQiIioDnpDT0+B2Iw4vVBERERH9DxuARERERFQsuTTR2ECjiiKXbR7gdk9EROXDBiDJGkdWUFUkl5MTbvNERERERETmgQ3AKkKhUIiOUCHk/Hswuzhyzs/s4sg5P7Ob788zZ3KuhZyzA/LOz+ziyDk/s4sh5+wAjxFEkXsd5Jy/orOzAVhFuLi4lPzkzQzTBXlGRX4POWcHZJNfztkBbjeiyDk7wO1GlFL/XlUwhUJR+T9PJnUHLHC7kUl2gPsbUeScHeB2Iwqzi8NjBDEscruRSf6K3gbZAKwiMjMzRUeoEHL+PZhdHDnnZ3Zx5Jzf0rK/K5ODNAD4oXYN0RGemqVtN3Ii5/zMLo6c8zO7GHLODpgmv5OTExQKBbRaLdRqdaX/PDngdiNOadmfpjnIBmAVodVqRUeoEHL+PZhdHDnnZ3Zx5Jyf2cWRc35mF0fO+ZldHDnnZ3Yx5JwdKD5/hc+NXUkXHeU8N7YlbjdyUdHZrSr004iIiIiIiIiIiMissAFIRERERERERERkwdgAJCIiIiIiIiIismBsABIREREREREREVkwNgCJiIiIiIiIiIgsGBuAREREREREREREFowNQCIiIiIiIiIiIgvGBiAREREREREREZEFYwOQiIiIiIiIiIjIgrEBSEREREREREREZMHYACQiIiIiIiIiIrJg1qIDkGkoFArRESqEnH8PZhdHzvmZXRw552d2ceScn9nFkXN+ZhdHzvmZXQw5ZwfknZ/ZxZFz/orOzgZgFeHi4lLykzczTBfkGRX5PeScHZBNfjlnB7jdiCLn7AC3G1HknB3gdiOKnLMD3G5EkXN2gNuNKMwujpzzM7s4cs5fah/nKbABWEVkZmaKjlAh5Px7MLs4cs7P7OLIOT+ziyPn/MwujpzzM7s4cs7P7GLIOTsg7/zMLo6c85eW/Wmag5Jer9c/SyCix23cuBHZ2dlwcHDAiBEjRMcpNznnZ3Zx5Jyf2cWRc35mF0fO+ZldHDnnZ3Zx5Jyf2cWRc35mF0PO2QH55GcDkCpcnz59kJqaCnd3d+zatUt0nHKTc35mF0fO+ZldHDnnZ3Zx5Jyf2cWRc35mF0fO+ZldHDnnZ3Yx5JwdkE9+rgJMRERERERERERkwdgAJCIiIiIiIiIismBsABIREREREREREVkwNgCJiIiIiIiIiIgsGBuAREREREREREREFowNQCIiIiIiIiIiIgtmLToAWZ5hw4YhOzsbDg4OoqM8FTnnZ3Zx5Jyf2cWRc35mF0fO+ZldHDnnZ3Zx5Jyf2cWRc35mF0PO2QH55Jf0er1edAgiIiIiIiIiIiKqHLwFmIiIiIiIiIiIyIKxAUhERERERERERGTB2AAkIiIiIiIiIiKyYGwAEhERERERERERWTA2AImIiIiIiIiIiCwYG4BEREREREREREQWzFp0ACIiIiIiIqKKlJWVhdTUVNjb26N27dqQJEl0JCIiodgApCorLy8PN27cQGZmJnJzcwEAdnZ2cHFxgaenJ5RKpeCERETmT6/XIz4+HgBQr149sWHKoaCgAEuWLIEkSZg2bZroOESVJjMzEzExMUhPTwcAeHh4oHnz5nB0dBScjOjpaLVa7NixA8eOHYNGo4G3tzeCg4Px3HPPAQCuXr2KVatW4cqVK8b3ODg4oG/fvggODoZCoRAV3SLdv38fd+7cQW5uLqysrODg4AB3d3dYW7PVUBm0Wi0uXLiA6OhoJCUlFXsu6+XlhRYtWqBZs2bc3gXRarWIjY0FADRt2lRwmv+R9Hq9XnQIkic57ny0Wi327duHQ4cO4fLly6W+1tfXF926dUP37t3NIvuzMNcd0JPk5ORgwYIFkCQJ8+fPFx1H1tLT03Ht2jXodDrUqVMHnp6eT3xPaGgoNBoNhgwZYoKEpdNqtbh//z6qV69e5Ln8/HycOXMGKSkpUKlUePHFF1GnTh0BKQtLSUnB4cOHcefOHXh4eCAgIABOTk4AAJ1Oh127dmHfvn3G3E2bNsXgwYNRv359wcnLR6PRIDg4GJIkITQ0VHScMpNrbgCIi4vD7du3ATxs5nh7ewtOVDqtVouwsDCcPHmyUO6XXnoJAQEBsvgbm5KSgm3btiE6Ohrp6emQJAnu7u5o27YtAgMD4eLiIiTX7du3cenSJbi5uaFJkyaFnsvLy8Pq1asRFhYGnU5X6DkbGxsEBQVh2LBhsLISNyPQwYMH4e3tbRb77PIaP348fHx8EBAQgNatW8t2dFlBQQFiYmKg0WhQr149YxPN4OzZszhx4oRxJF3Dhg0REBAgtIG8cOFCnDx5Eo+exlarVg3/93//B41Gg2nTpiE3NxePn+ZKkoR27dqZ3UWf27dv49y5c0hMTCzSSKtVqxYaNWqEFi1aCP2uPu78+fMIDw/HuXPnkJWVVeR5a2tr1K9fH127doW/vz9UKpXpQ5aB3AaEhIWF4ZdffsGdO3cAoMg2bmDYH7m6umLEiBHw9/c3WUZ6SK1WY+TIkWZ3nMkGID0VOe58bty4gQULFuDWrVsl5n2cJEl4/vnnMWPGDNSuXbuSE1Yec90BPYk55U5MTMS2bdsKNdFeffVVtGrVqtT3jRo1Cnfv3hWWPy8vD99++y2OHj1a6HFfX1+MHz++1MaB6OwAkJubizVr1iAiIgL5+flQKpXo37+/8aQ1NjYWS5YsMe6LDDp06IBJkyYJO3A7evQovvnmGzx48MD4mL29PebNmwdvb298/fXXiIiIKLQvkiQJVlZWmDZtGvz8/ETEfirm1EhLS0sr82vz8vLw/vvvQ5IkrFq1qtBzbm5uFR2tTO7duwdJkko8sT58+DDWr1+PjIyMQo/XqFEDo0aNQufOnU0Rs4iQkBDY29tjxIgRRZ5LS0vD3LlzkZSUBOB/xwuG44M6dergiy++QI0aNUwXuBhhYWH473//i86dO2PYsGGFnjt27BiWLVuG/Pz8YhsK1apVw7Rp04o04Exh7dq12LZtG8aMGYOgoCDj41qtFjNmzEBsbKwxs729PYCHF9cM2V955RVMmTLF5LkNgoKCIEmSsZHWuXNnODg4CMtTHobsAODs7Ixu3bohICCgTBfYzEVMTAyWLFmCu3fvAni4TfTt2xdjx46FXq/H0qVLceTIEQAPv7uG39fBwQGffvopmjdvbvLMkZGRWLx4MQDAz88PDRo0wNWrV3Hq1Cl06tQJBQUFOHHiBJo1a4agoCB4eHggLS0Ne/bswcmTJyFJEqZOnYqXX37Z5Nkfd/nyZaxbt854kb40zs7OGDRoEPr372+CZCXLycnBV199hTNnzgAo+RzQQJIk1KxZEx988AFatGhhiohPJNcBIatXr8aOHTuM30VPT0+88MILcHV1NR7v5uXl4c6dO0hMTERSUpLxtf369cPYsWOF5n9ach0QYk7nsY9iA5DKTY47H7VajQ8++ABZWVlQKpXo3LkzWrVqVWrus2fP4siRI9BoNHBxccHy5cuNo3fkxlx3QE9iLrmPHj2Kr7/+GlqttsjJa7t27fDBBx+UeMIusomm1+sxY8YMXLx4sdgDNGtra4wZMwb9+vUr9v2iG4Al5ZckCYGBgRgwYAA++OAD3Lt3DzY2NqhWrRru3r0LrVYLSZLg5+eH6dOnmzx3SkoKJk6ciIKCAqhUKtSqVQvJycnQaDSoVasWxo8fj9mzZ8PNzQ0BAQGoUaMGUlJSEBYWhqysLDg6OuKHH34Qur9ZtmxZmV+r0+lw6NAhSJKEbt26GR+XJAkffvhhZcQr0aMn5M9C1DYfFBQEFxcXrFu3rshzW7duxfr160u94DZ27NgSv8+VqaTcBQUFmDJlChISEgAATZo0QcOGDQEAV65cwd9//w1JklC/fn0sWbJE6MnVokWLcOLECcyaNQutW7c2Pp6UlITJkycbv8+dO3c2Nnhu3LiBw4cPQ6PRwNHREd9++y1cXV1NmnvKlCmIi4vDihUrUKtWLePju3btwsqVK2FlZYX+/fvjtddeM2a7c+cOtm7dajyWE9kMebRpKUkSrK2t0a5dOwQEBKBVq1ZmParOsL95/LigQYMGxmamoelqjjIyMvD+++8bRz0pFArj38/x48dDo9Fg/fr1xu+ooZEWFxcHnU4He3t7fPfddyZv3s+bNw9nzpxBUFAQxowZY3x8zZo12LVrF3Q6HVq2bIlZs2YVee+yZcsQHh4OPz8/zJgxw5Sxi9i/fz9++OEH6HS6Eo/R6tati7t37xovbkmShGbNmmHGjBlCLnBqtVp88sknuH79OvR6Pby9veHp6QmtVoukpCQkJCTA2toaw4YNg7OzM+Li4hAZGYnMzEwoFAp89tlneOmll0ye+1FyHRBy/PhxLFq0CJIkoXfv3hg4cOATL1ZmZGRg8+bN2LVrFwDg888/F17/p2Eu54PlZa65eWM+lcvx48exfft2SJKEPn36lHvns2PHDjRv3tzkO59NmzYhKysLL7zwAmbOnFliZnt7e9jb28PT0xMvv/wygoODMW/ePCQmJmLz5s2FDjSoakhJScGyZcvw4MEDODk5oW3btnByckJMTAyuXbuGEydOICEhAXPnzoW7u7vouIVEREQgJiYGCoUCgwcPxquvvgonJydcuHABv/32G+Li4rB69WpkZmZi5MiRouMW8Wj+gQMHomHDhoiNjcXWrVuxc+dOSJKEnJwcjBs3Dj179oSNjQ00Gg1CQ0Px+++/49SpU/jrr7+eOEqzou3cuRMFBQXw9fXFF198AQcHB6jVasyaNQvx8fEICQlBnTp1sHjx4kInh0FBQZg6dSpu376N/fv3Y9CgQSbN/ajw8PByn3jr9XocPHjQ+G8RDcBHszwtc2w4JCQkYMOGDdDr9fD19cWIESPg6+sLALh06RJ+/fVXXLp0CWvXrkXr1q0LNYJEOnDgABISEmBjY4OpU6cW+dt/6tQpLF68GNevX8eRI0fQtWtXMUEB/PPPPwCARo0aFXp8y5YtKCgoQIMGDTB9+vQit/oOHToUCxYsQFxcHEJDQ/HWW2+ZLDMApKamwsrKqshtm4am/KhRo/Daa68Ves7V1RVvv/02XF1dsW7dOuzfv1/oaCiVSoVGjRohOjoaBQUFOHbsGI4dOwZXV1d069YN/v7+ZnsXhrOzMz777DMcOHAAx44dQ05ODq5cuYKrV6/ip59+Qvv27eHv72/yv0Nl8eeffyI3Nxe1atXCv/71L9SvXx/Xr1/HkiVLsHPnTuTl5cHR0RGfffYZmjVrZnzf1atXsWjRIty5cwc7duzAqFGjTJo7Li4OADBw4MBCjw8cOBDbtm2DJEklTl0ydOhQhIeH49q1a5WeszTXr1/HDz/8AK1Wi6ZNm+K1114r1EjbtGkTrl27Bh8fH7z77rtQq9U4fPgw/vOf/+DChQtYsmSJkAbmjh07EBcXB1dXV0yZMqXItEKnTp3CN998g23btmH58uUICAjAmDFjsHHjRoSGhuKbb77BihUr4OzsbPLswMOGzPTp059qQEhycjKmT58ubEDI7t27IUkShg8fjtdff71M76lRowbGjx8PV1dX/Pzzz9i5c6csG4AixcTEPPV7DaPtzQ0bgFQuct35nDp1CpIk4eOPPy7XrV1ubm6YPHkyJk2ahJMnTwptAMp1B/QsI7C0Wm0FJnk627dvR35+PurWrYs5c+YUOmg5fvw4vv/+e9y6dQufffYZ5s2bZ1YnKYYTwMGDBxe6pc3Pzw9t2rTBhg0bsHXrVmzevBkajQbjxo0TmLaow4cPG/c3hmbYSy+9BCsrK2zatAnbtm1D//79C414UqlUGDJkCLKysrB7925ERESY/MQrOjraeOJtuJXNyckJw4YNM151njFjRpGRIdWrV8fIkSOxZMkSnD17VmgD0KB27dpPPFA3zDEqSRJefPFF0wQrgaF55+vri+Dg4FJHSOTl5WHu3LmyuKVkx44d0Ol08PX1xcKFCwtNrN6iRQs0bdoU06ZNw5UrV7Bnzx6TN6FKEhkZCUmS8MYbbxT7d9/Pzw/BwcHYuHEjjh49KrQBmJmZCTs7uyLfywsXLkCSJLz//vvFzvPn6uqKiRMnYtKkSTh9+rTJa5+TkwM7O7sic4MlJSXBysoKvXv3LvG9ffr0wc8//yy8GWJnZ4c5c+YgIyMDYWFhOHjwIJKTk40XkDdv3oyGDRvi1VdfRadOncxuVF2jRo3QqFEjjB8/HlFRUQgPD0d0dDTy8/Nx5MgRHDlyBK6urvD394e/v7/ZNOjPnTsHSZLwzjvvGKcD8fb2xjvvvIMvvvjCOBLw0eYf8HCE49ixY7F48WKcPXvW5A1AtVoNOzu7InMCOzs7w87ODhqNBl5eXsW+193dHSqVCvfu3TNF1BJt27YNWq0WPXv2xHvvvVfoOU9PT3To0AELFy7E3r170apVK7Rv3x79+vXDSy+9hE8//RSnT5/GiRMn0K5dO5PmPnLkiPHiXnFzivv5+eHtt9/GsmXLsHXrVowZMwY2NjYYM2YMsrKyEBERgR07dhQ7XYQpyHlASFxcHKysrAqNmi6rwMBA/PLLL8bmOZXd9OnTzfLC8LNgA5DKRa47n7S0NNjZ2aFu3brlfm+9evVgb29frrmlKoNcd0AxMTGFbpGRG0MzZ8KECUUaIe3bt4e3tzfmzZuHhIQEfP7555g7dy5eeOEFMWEfc/36dQAodr4YKysrjB49GrVr18aKFSuMV/snTpxo6pglMuTv3r17oce7deuGTZs2AXi4XylOv379sHv3bly9erVyQxbj9u3bkCSpyEgiw62PAIqcUBm0adMGkiQZ50sTxc/PD6dOnUJmZib69u2LPn36lPja3Nxc42iLBQsWmCpisZYsWYLvvvsOly9fxsqVKzF+/Hi0bdu22NdqNBrjv819cSTDfvTNN98sdlVFhUKBN998E9OnT8eFCxcEJCyeYXXox7/Dj3r11VexceNG4/ddFGtr60JzdhoYGoOlrXAt8jihevXquHPnDvLy8go1vLVaLezs7EptgiuVStjZ2ZnNKIUaNWrgjTfewBtvvIFLly4VGlV3+fJlXLlyBatXr0b79u0REBBgNvOJGdja2qJLly7o0qVLsc3MTZs2YdOmTWjUqBECAgLQqVMn2NnZCcubkpICKyurIn+PmjVrBisrK+j1+hL3n23btoUkScZFfUzJ2toaeXl5heYkBB6O/M7Pzwfwv8b44x48eID8/HzY2NiYLG9xDBcWSmuEDR8+HCdOnMC+ffvQvn17AA8bmG+++Sa++eYbhIeHm7wBePPmTdja2pZ6YdWQKTo6utDjgwYNwqFDh3Dq1ClhDUA5DwjRaDRQKpWwtbUt93ttbW2hUqkKHfeYmtwHhMj1PLY4bABSuch156NSqZCTk4P8/PxyZ8/Pz0deXp7ZXHGW2w5IoVBAp9OhQ4cOqFmzZrnem5+fj71791ZSsrJJS0uDQqEo0swxcHNzw6JFizB79mxcuXIF06dPx+zZs+Hj42PipEXdv38fdnZ2qFatWomv6d69O1QqFb7++mscOHAABQUFmDRpklk0m9VqNZRKZZFbLQwHbTY2NiUewHl6esLGxgbp6emVnvNxDx48gL29fZH5zAwN5GrVqpV4Um5nZwcHBwdkZ2dXdsxSzZgxA8eOHcOqVasQEhKCgwcP4r333iu2CWIO24pBgwYNsHTpUuNt4PPnz0f79u0xbtw44YtMPIuMjAwoFAo0bty4xNc0btwY1tbWSElJMWGy0mVnZ8Pe3r7UFXJdXFxgb28PtVptwmRFeXh4ID4+HklJSYVGDzk6OhrnSCuJXq/HgwcPim3OVrYGDRrgxIkTOH78OLp06WJ8vFatWoiPj8fdu3eLXT0dALKyspCdnW3yeQvL4vFRdWFhYTh//jzy8vJw+PBhHD58GDVr1jSOqnv8FmjRHm1mxsbGIiwszNjMjI2NxaVLl7Bq1Sp06NABkydPFpKxoKAASqWyyOhRhUIBlUqF3NzcErcdW1tbKJVKY8PNlNzd3XHjxg1cvny50LHZpUuXjHMYxsTEFPo+GFy8eBE6na7cx6MV7e7du3BwcCj1VtJH5xp9VIcOHbBs2TIhFzjLsp8zNFfv379f6PE6derAxsYGqamplZbvSeQ8IKRmzZpISUlBQkJCuQcaxMfHIycnR+joY7kOCKlevTrUajU+/vjjEi+IlOTevXuYMGFCJSV7emwAUrnIdedTv359nD9/Hn/++ScGDx5crvdu374dWq221NVSTUGuOyAvLy8kJCSgZcuW6NWrV7neq1arhTcAHzx4AFtb2yIHyI9ycHDAvHnzMGfOHPz999+YOXMmvvjiixKbhqaiVCqRl5f3xNe98sorsLGxwb///W9EREQYJ+4XTalUQqfTFfs4gCeuFGlnZyekkebk5ISsrKwioxMMnrTQgV6vFz46AQA6duyIVq1aYe3atdi/fz+mTJmCvn37Yvjw4VCpVKLjlUihUGDQoEHo2LEjVqxYgaioKJw7dw5Dhw5F//79S/0umzOVSlXqtqNQKGBvby+8efwoJyenMo0ukyRJ+OqKbdu2xT///IMtW7bgo48+Mj7evHlzHDlyBGfPni20OMijzp49i/z8fCGjv/39/XH8+HFs2LABTZs2NTa6u3fvjpCQEPz8888ljuzeuHEjgJJHJJuDR0fVpaenIzw8HOHh4bh16xbS0tLwxx9/4I8//jCrCdYf17hxYzRu3Bjjx49HZGQkwsLCcOHCBeTl5SEiIkJYA9DR0RFqtRr3798vtJDZ/fv3kZOTA0mSkJycXOyFn8zMTGg0GiFzobVo0QJJSUn48ccfMX36dLi5uSEtLQ0//vgjJEmCr68v1q9fjwYNGhQ658jIyEBISAgkSRKyYvejDPvqx0fuPurOnTsAih4zqFQq49zCpubm5obk5GT8888/JY6KvnjxIgAUO32ItbW10NFcch4Q4ufnhz///BNLly7FrFmzynxRMyMjA19//bVxcTxR5DogxMfHB2fPnkVycnK5V6g3h5GLxWEDkMpFrjuf3r17Izo6Ghs3bkRmZiYGDhz4xOwZGRnGVfIMKy6JJNcdkI+PDxISEmQ774SLiwvS09Nx7969UkfSqVQqzJ49G/PmzcOFCxcwe/Zs4SvM1apVC3FxcYiLi3tiA7t9+/aYNm0aFi9ejMjISBQUFAj/w1W9enWkpKRAo9E8VcMpNzdXyMmJi4sLMjMzcefOnSL7mX79+pX6/c3Pz0dOTo7ZjGaxt7fH+++/j27duuG7777Dn3/+iWPHjmHs2LFCFw0oi+eeew5z585FeHg41qxZg7Vr1xpHMj56O7Yc1K5dG//88w+0Wm2pjbLc3Nxy/32oKHl5eQgPDy/0WLVq1ZCVlVXq/jMvLw85OTnCR+T0798fu3btwsGDB+Hi4oLhw4dDoVBgyJAhOHHiBJYvX47JkycXue30r7/+wrfffgtJkvDKK6+YPHe7du2Mt+xPmjQJw4cPxyuvvII+ffogOjoaBw4cwI0bNxAYGFhoRNG2bdtw+fJlWFlZFVkkxFzVrFnTOKru77//Rnh4OI4ePSr0trbysLW1RdeuXdG1a1ekp6cbbxEWpX79+jh37hx27tyJ4OBg4+M7duwA8LBZs23bNkyaNKnIe7dt2wYAQpregYGB2L9/P+Lj4zFu3DhUq1bNOKefm5sbpkyZgnfeeQcfffQR2rZti+eeew5paWk4c+YMcnJyoFAo0LdvX5PnflT9+vURHR2NPXv2lDit0s6dOwE8HDn3KJ1Oh5ycnEJNW1Np3bo1bt68ie+//x4zZ84sMkI0LS0Nq1atMq5W/KicnBzk5ubCw8PDlJELkfOAkNdffx2HDh1CQkIC3nvvvTIvYGJYqd7Z2bncv3NFkuuAEG9vb5w5c0a257HFYQOQykWuO58OHTqgd+/e2L17N3bu3ImdO3fCy8ur1NyJiYkAHo7G6dOnj3H+DVHkugPy9vbGgQMHZJfboF69ekhPT0d0dDQ6depU6muVSiVmzZqFBQsW4Ny5c5g7d26xI9hMpVGjRoiLi0NUVFSZDlj8/Pzw+eefY9GiRTh16lSJI9hMpVatWkhJScHNmzeL5P/1119LzZaWloaCggIht7YZVlK8cuUKOnToUOi5Jy20cu3aNej1erOZJN6gSZMmWL58Of744w9s3rwZ//73v9GmTRtMmDBBSJO1PPz9/dG2bVv89NNPOHToEKZOnYqePXvijTfeEB2tWHfv3i2ynRhGety8ebPIyaBBamoqCgoKhJ1c5ebmYvny5cU+FxMTU+S7YGDY5sszH1NlqF69Oj766CMsXrwYW7ZswaFDh9CxY0fUr18fgYGB2LRpE7744gt4eXkZF3u6efMmkpKSoNfr4e3tXWhBIlP65JNPMH/+fJw/fx4//vgjfvzxR7i7uxu/m5cuXcKlS5cKvUev18PKygrvvPNOqfMbmqsmTZqgSZMmGDduHKKiokTHKbeaNWsiODi4UOPN1Lp27Yq//voLv/32G27fvg0fHx9cvXoVBw8ehCRJeOutt7By5UooFAoEBQUZG2l79uzB9u3bIUmSkAtBHh4e+OSTT7B06VLk5OQY94+urq74/PPP4e7ujnfffRffffddoW3DcEzz1ltvCd/mu3fvjnPnzmH9+vXIzMzEa6+9Zhwxl56ejk2bNhkXXnz8Vubbt29Dp9PB3d3d5LkHDBhgXEV5woQJ6NKlCzw9PaHT6ZCYmIjDhw8jPz8f9vb2ReafNuyDnub224oi5wEhTk5OWLBgAebPn4/bt29j//792L9//xPfp9fr4eHhgRkzZgg9XpPrgBDD+YfoxbIqEhuAVC5y3vkYDnJ/++03ZGZmIjExscSJ9g3zE7i4uGDYsGHo0aOHKaMWS647IF9fX7i5uSE3N7fcDSWlUmlcXECU5s2b4+TJkwgPD39iAxB4eIV/xowZ+PLLL3H69GkA4uZIa926NXbs2IEDBw4gODi4TLeVtm7dGjNnzsSCBQuEj6rw8fHBmTNncPHixSINwCeNcvrrr78APJwfy9R8fHywf/9+/P333yU2PUpy5MgRAOa5KIW1tTWGDRuGV155Bd9//z1Onz6NmJiYYheZMTdOTk6YPHkyunbtih9++AF79uzBsWPHRMcqlk6nK3GOpMjIyBIbgOfOnQPwsAFtak9q3pX2XYiMjAQA4VMmAA9H082aNQvLly9HRkYGtm/fXuh5vV6PpKQk47GD4VihTZs2+Pjjj4XdxqxUKjF37lxs374dW7ZsQWZmJm7fvl3qAg2NGzfG6NGjzaLuz0KpVApdPVrOunbtikOHDuGvv/4y3loNPNyuAwMD0atXLxw6dAhhYWEICwsr9F69Xo86deqUusBPZfLz80NISIhxsSoPDw/4+fkZL+h3794dXl5e2L59OxITE2FlZYV69eqhd+/e8PX1FZL5UZ06dcLRo0cRFRWF0NBQhIaGwt7eHnq9vtCcoy+99FKRJuuJEycAAC+++KJJMwMP57b87LPP8OWXXyI7O7vIqCy9Xg87OztMnTq1yAXYixcvws3NrdgV4U1F7gNC6tSpg++//x579+7FoUOHjBfQiiNJEho0aIAuXbqgZ8+ewqeWkeuAkKZNm+LDDz8EgHKfxzo5ORlHS5sTSS+3mRjJLBQUFMhy5wM8nNMtOjoa58+fR1JSknEeE+DhbZyurq7w8vJC8+bN0bx5cyGTehfn/v37xj/6/v7+ZjXxviVLT0/H22+/DUmS8PXXX5f5qrFWq8WSJUsQFRUFSZKEzE+k0+mwYsUKaLVa9OnTp1zNsNjYWOP8UKJWdo2Li8PevXvRqFEj+Pv7l+u9kydPxvXr1zF16lR07NixkhIWT6vVQq1Ww9bWtly3Y2q1WoSEhCA/Px+DBw82jjIyV3v27MH69esLNffNeR4ug/z8fPzyyy/G23nMKffjJ9mPc3V1LXH1xX/961+4evUqxo8fL/z2tvIICQlBbm4uevfubTa3Zms0Ghw4cAARERGIi4srdjoEFxcXtGjRAv7+/ma1Gq1Wq0V0dDQuXbqE5ORk3L9/33hS7urqijp16qBly5ZmM81AUFAQXFxcsG7dOtFRyu23336DSqXCgAEDREd5agUFBfjPf/6DiIgIYyOtZ8+e6NevH6ysrHD37l0sXbrUeIHBoHXr1vjwww9LXeCnMhS3vYSHh8PW1rZMF2nNiVarxfr167Fr1y4UFBQUes7a2hq9evXC6NGji5w7XbhwAampqWjSpAmef/55U0Y2ysjIQGhoKM6cOYPU1FRIkgR3d3e0atUKQUFBwkd0P8nevXuNA0KAki/Um+OAkEdpNBrjuayhcWzY13t6eprVfM1xcXFYtGgRbGxssGLFinKdx+bl5WHLli0AgKFDh1ZWxCqDDUB6Zua+87GkgwW5sZTap6enQ6fTwdHRsVyT/+p0OsTGxkKv15t8RJel1P5p6HQ64+q/rq6uJm3iV7W6Z2ZmYv369cZV8UQ1i4Hy1/7WrVvIyMgAYJ4jLsvLMGrQ2dm53JObPwtL3+YLCgqQmppapJFW2pywpmLptTdnVa32N27cKDSSTtRUA8XVXc5NZODhFA/R0dGFGmnNmjUrcQVmUYqrc1hYGJRKpSy3ebkOCCF6VtyS6ZmpVCoht9k9i2XLlsHFxUWWf7DkTo61f9rJ6a2srITcolESOdb+aVhZWQmZG6ckllx3FxeXYieINxel1f75558XNnqiMnCbrxw2NjZmPxr3UZZUe7mx5Np7enoaF5ERycbGptAtspbAyclJyAJCFWH58uWy3eatra3Rpk0btGnTRnQUIpOyEh2AqLJZ4sGCXLD24rD2YrDu4rD2YrDu4rD24rD2YtSsWRN5eXk4fvy46ChVDrd5IsvAEYBUIfLy8hAZGYnY2FjcuXMHGo2m1HkB58+fb7JsNWvWREpKCo4fPy58Jd/KwNqLYc51B1h7A27zFYu1F4N1F4e1F8Oc6w6w9gamrn27du0QGhqKL7/8EtWqVTNOM1Tc6umlkSQJISEhlRXzqZlz7S15m7d0OTk5WLBggZB9ZVVmrnXnHID0zKKjo/HVV19BrVYXWh3HsGk9OsmniIni165di9DQUEiSZDxYSE1NhZWVVblu7TTHgwXWXgxzrzvA2hse4zZfcVh7MVh3cVh7Mcy97gBrb3jM1LXXaDRYtGhRkUVJysucFn8yMPfaW+o2X1ZarRaxsbEA5DdvsFqtxsiRI81yuy8LudbeXOvOEYD0TG7duoWFCxdCo9HAy8sLLVu2xPbt26FSqRAYGIisrCzExMQgOTkZTk5O6NmzJxQKhUkzDh06FPHx8Th37hzUajXUajWAhwsFGCZNLwtzW3WXtRdDDnUHWHtu8xWLtReDdReHtRdDDnUHWHtRtVepVJgzZw6SkpKQkJCAvLw8LF++HPb29hg7dqxJs1QkOdTeErf58sjOzsb06dPNrplTFbD2FYsNQHomW7duhUajQZs2bTB9+nQoFAps374ddnZ2GD58uPF1YWFhWLFiBa5fv46ZM2eaNKOlHiyw9mLIoe4Aa89tvmKx9mKw7uKw9mLIoe4Aay+y9gDg5eUFLy8vAA8XolAqlQgICBCSpSLIofaWuM0TVUVsANIzOX/+PCRJwsiRI0u9EhUQEIDs7GysWbMGO3fuRL9+/UyY8iFLO1hg7cWQU90B1p7bfMVg7cVg3cVh7cWQU90B1l5k7Q2GDBlinAtQruRUe0va5uVk+vTpT/1erVZbgUmqFkusOxuA9EwyMjJgZWWFunXrGh+TJAkFBQVFXtujRw+sXbsWBw8e5MFCBWDtxZBr3QHWXhS51x1g7UVh3cVh7cWQa90B1l6UoUOHCv35FUGutZfbNh8TE/PU783JyanAJOUXExMDSZJKXBTG3Mm19nKve3HYAKRnYm1tDaVSWWg+B5VKhezsbDx48ADW1taFHrezs0NycrKIqIVYwsECay+GXOsOsPaiyL3uAGsvCusuDmsvhlzrDrD29PTkWnu5bfOGeeTkSKFQQKfToUOHDuVadAUA8vPzsXfv3kpKVjZyrb3c614cNgDpmbi6uuLWrVvQ6XSwsrICALi7uyMxMRFxcXHw9fU1vjYrKwvZ2dmwsbERFdeisPZisO7isPbisPZisO7isPZisO7isPbisPamJcfRXF5eXkhISEDLli3Rq1evcr1XrVabTSNKbrW3lLo/ykp0AJI3Ly8v6HQ6JCYmGh9r0qQJ9Ho9NmzYAI1GAwB48OABVq9eDQCoU6eOkKyWhrUXg3UXh7UXh7UXg3UXh7UXg3UXh7UXh7U3jerVqwMAPv74Y/z666/l+m/lypVCs/v4+AAA4uLihOZ4WnKtvdzrXhyOAKRn0qpVK0RFReHkyZPGeSv69euHffv24eLFixg1ahQ8PT2RmpqKe/fuQZKkcnfPqXisvRisuzisvTisvRisuzisvRisuzisvTisvWn4+Pjg7NmzSE5OhoODQ7neK3pBB29vbxw4cEC2jSi51l7udS8ORwDSM+nYsSOGDBkCZ2dn42Oenp6YNGkSlEolNBoNrl27BrVaDQAIDAxEjx49BKW1LKy9GKy7OKy9OKy9GKy7OKy9GKy7OKy9OKy9aXh7e0Ov18uymePr6ws3Nzfk5uaW+zZapVKJIUOGIDg4uJLSPZlcay/3uhdH0svtRmySDbVajTNnziA9PR0ODg5o0aIFateuLTpWlcDai8G6i8Pai8Pai8G6i8Pai8G6i8Pai8PaV5zjx49j0aJFcHFxwbp168r1XrVajZEjR0KSJISGhlZKPkvG2psPNgCJiIiIiIiIyGLdv38fJ06cAAD4+/vLZlXaoKCgIo2z8PBw2NraolOnTuKClYMca28JdS8O5wAkIiIiIiIiIovl6OiIgIAA0TEqxLJly+Di4iKbRpSl1F5udS8O5wAkIiIiIiIiIjIzNjY2yM3NFR2jyrHUurMBSERERERERERkZmrWrIm8vDwcP35cdJQqxVLrzjkAiYiIiIiIiKjKycvLQ2RkJGJjY3Hnzh1oNJoSV3yVJAnz5883ab61a9ciNDQUkiShWrVqUKlUSE1NhZWVFWrWrFnmz5EkCSEhIZWYtPzMufaWWnfOAUhEREREREREVUp0dDS++uorqNVq6PV64+IUhibUo4tVPPq8KQ0dOhTx8fE4d+4c1Go11Go1AECn0yE1NbXMn2NuC2+Ye+0tte5sABIRERERERFRlXHr1i0sXLgQGo0GXl5eaNmyJbZv3w6VSoXAwEBkZWUhJiYGycnJcHJyQs+ePaFQKEyeU6VSYc6cOUhKSkJCQgLy8vKwfPly2NvbY+zYsSbPUxHkUHtLrDvABiARERERERERVSFbt26FRqNBmzZtMH36dCgUCmzfvh12dnYYPny48XVhYWFYsWIFrl+/jpkzZwrL6+XlBS8vLwDA8uXLoVQqZbuyrpxqb0l1B9gAJCIiIiIiIqIq5Pz585AkCSNHjix1dFlAQACys7OxZs0a7Ny5E/369TNhyuINGTIEKpVKdIynJtfay73uAFcBJiIiIiIiIqIqJCMjA1ZWVqhbt67xMUmSUFBQUOS1PXr0gCRJOHjwoAkTlmzo0KEYMGCA6BhPTa61l3vdATYAiYiIiIiIiKgKsba2hr29faFFGlQqFbKzs/HgwYNCr1WpVLCzs0NycrKpY1ok1l4cNgCJiIiIiIiIqMpwdXVFTk4OdDqd8TF3d3cAQFxcXKHXZmVlITs7u9gRalR+rL04bAASERERERERUZXh5eUFnU6HxMRE42NNmjSBXq/Hhg0boNFoAAAPHjzA6tWrAQB16tQRktXSsPbicBEQIiIiIiIiIqoyWrVqhaioKJw8edI4F12/fv2wb98+XLx4EaNGjYKnpydSU1Nx7949SJKEXr16iQ1tIVh7cTgCkIiIiIiIiIiqjI4dO2LIkCFwdnY2Pubp6YlJkyZBqVRCo9Hg2rVrUKvVAIDAwED06NFDUFrLwtqLI+n1er3oEEREREREREREoqnVapw5cwbp6elwcHBAixYtULt2bdGxqgTWvnKxAUhERERERERERGTBeAswERERERERERGRBWMDkIiIiIiIiIiIyIKxAUhERERERERERGTB2AAkIiIiIiIiIiKyYGwAEhERERERERERWTA2AImIiIiIiIiIiCwYG4BEREREREREREQWjA1AIiIiIiIiIiIiC8YGIBERERERERERkQVjA5CIiIiIiIiIiMiCsQFIRERERE8tPj4ekiRBkiSMHj1adBwiIiIiKgYbgERERERERERERBaMDUAiIiIiIiIiIiILxgYgERERERERERGRBWMDkIiIiIiIiIiIyIKxAUhERERERERERGTB2AAkIiIiokp15coVLF26FAMGDECDBg3g6OgIW1tbuLu7o3Pnzpg/fz7S09NLfP/rr79uXGn42LFjZfqZ3bp1M74nNja2xNdt374db775Jnx8fFCtWjXY29ujXr16GDFiBA4cOFDqzzh06JDxZ8yePRsAcPXqVUyZMgUvvvginJ2dCz1HREREJIq16ABEREREZLk2bNiAUaNGFftcWloa0tLScOTIESxZsgS//vor+vbtW+R17777LjZt2gQACAkJQceOHUv9mZcvX8ahQ4cAAJ07d0bjxo2LvCYpKQnBwcGIiooq8lx8fDzi4+Pxyy+/YNCgQdiwYQPs7e2f9Kti48aNGD9+PHJzc5/4WiIiIiJTYgOQiIiIiCpNTk4OJElCixYt0LlzZzRq1Aiurq4AgBs3buDAgQPYs2cP1Go1Bg0ahMjISLRu3brQZ/j7+8PX1xeXL1/Gf//7XyxbtgzOzs4l/syQkBDjvydMmFDk+aSkJLRr1w63bt0CALRq1QqvvfYafHx8YGVlhcuXL2PDhg24fv06Nm/ejOzsbOzatQuSJJX4MyMjI7FgwQJIkoRRo0bhlVdegYODA65du4Y6deqUp2REREREFU7S6/V60SGIiIiISJ7i4+NRr149AMCoUaOwbt26Qs9fvHgRSqUSPj4+JX7GgQMHEBQUhJycHAQEBBR76+0333yDyZMnAwCWL1+ODz74oNjPysvLQ+3atZGRkYEaNWrg5s2bUCqVxuf1ej06duyIqKgoKBQK/PDDDxg3blyxnzN69Gj8/vvvAIBVq1Zh7NixhV5z6NAhdOvWzfj/7u7u2L9/P5o3b17i70pEREQkAucAJCIiIqJK8+KLL5ba/AOAV199FR9//DEAICwsDDdv3izymtGjRxtvw310hN/jNm/ejIyMDON7Hm3+AQ/n/DPc9jt79uxim38AoFQqsX79etStWxcA8NVXX5X6OwDAypUr2fwjIiIis8QGIBEREREJ16lTJ+O/jx8/XuR5Z2dnDBkyBAAQExODyMjIYj/n0ebg+PHjizy/fv16AA8bfB9++GGpmWxtbTF06FAAwKVLl5CYmFjia1944QUEBQWV+nlEREREonAOQCIiIiKqdEePHsVvv/2GkydP4vr167h37x4KCgqKfe2NGzeKffzdd9/FmjVrADxs9L388suFnr98+TIiIiIAPFwFuGHDhkU+4/DhwwAADw8PhIeHPzF3Zmam8d9///13ifP5dezYsdQ5AomIiIhEYgOQiIiIiCrN/fv3MWLECGzbtq3M71Gr1cU+3rZtW/j5+eHUqVP4448/sGzZMlSvXt34/JMW/8jOzkZ6ejoAIDExEQMGDChzJgC4c+dOic95enqW67OIiIiITIkNQCIiIiKqNMHBwdi1axcAwMHBAX379kWrVq1Qq1Yt2Nvbw9r64eFoTEwMZs6cCQDQarUlft67776LU6dOITc3Fz///DMmTpwI4OGiHYbbe93c3Ipt7mVlZT3T75Kfn1/ic3Z2ds/02URERESViQ1AIiIiIqoUx44dMzb/mjVrhn379uG5554r9rU2NjZl+swhQ4ZgypQpyMzMREhIiLEB+OjiH2PGjIGtrW2R9zo6Ohr/3bp1a5w5c6Zcvw8RERGRXHERECIiIiKqFPv27TP+e+HChSU2/wDgn3/+KdNn2tnZYfTo0QCACxcuGFf0XblyJQBAkqRiF/8AgOrVqxubgCXNM0hERERkidgAJCIiIqJKkZKSYvy3j49Pqa/dvXt3mT/3nXfeMS64ERISgkuXLhkX9wgICIC3t3eJ7+3SpQsAIDU1lSMAiYiIqMpgA5CIiIiIKoWDg4Px39euXSvxdVFRUeVqADZs2BD+/v4AgD/++ANLliwxPlfc4h+PGjVqlPHfM2bMgF6vL/PPJSIiIpIrNgCJiIiIqFL4+fkZ/z1nzhxoNJoirzl//jwGDx5c7kbce++9BwDIycnBmjVrAAAeHh4ICgoq9X2DBw9Gu3btAAB79uzBm2++ifv375f4eq1Wiz179mD+/PnlykdERERkTrgICBERERFVioEDB6JOnTpITEzE6dOn4evri7Fjx8LHxwc5OTmIiIjA77//joKCAowaNcq4im9ZBAYGolatWkhOTjY+9tZbbz1xMRFJkrB582Z06NABSUlJ2LhxI3bu3InXX38dbdq0gaurKzQaDZKTkxEdHY39+/cjLS0NAQEBmDFjxlPXgoiIiEgkNgCJiIiIqFIolUps2bIFvXr1Qnp6OhITEzFr1qxCr1EoFPjyyy/Rrl27cjUAra2tMW7cOMyZMwfAw8beuHHjyvTe2rVr4/Tp0xg9ejR2795tXFG4NJ6enmXORkRERGRueAswEREREVWaNm3a4Pz585gyZQp8fX2hUqng6OiIhg0bYsKECTh58iQ+/fTTp/rsHj16FPp3vXr1yvxed3d37Nq1C1FRUZg4cSJatmyJGjVqQKFQwMHBAd7e3ujXrx++/PJLxMTEYN26dU+VkYiIiMgcSHrOfExEREREMjRp0iQsW7YMALBlyxYMGDBAcCIiIiIi88QGIBERERHJTnZ2Nry8vJCZmYnatWsjPj4e1tac3YaIiIioOLwFmIiIiIhkZ+nSpcjMzAQATJw4kc0/IiIiolJwBCARERERmb2bN2/iwoULyM3NRUREBL777jtotVp4eHjg2rVrcHR0FB2RiIiIyGzxUikRERERmb39+/djzJgxhR5TKBT46aef2PwjIiIiegLeAkxEREREsuLh4YHevXvjyJEj6Nu3r+g4RERERGaPtwATERERERERERFZMI4AJCIiIiIiIiIismBsABIREREREREREVkwNgCJiIiIiIiIiIgsGBuAREREREREREREFowNQCIiIiIiIiIiIgvGBiAREREREREREZEFYwOQiIiIiIiIiIjIgrEBSEREREREREREZMHYACQiIiIiIiIiIrJgbAASERERERERERFZMDYAiYiIiIiIiIiILBgbgERERERERERERBaMDUAiIiIiIiIiIiILxgYgERERERERERGRBWMDkIiIiIiIiIiIyIKxAUhERERERERERGTB2AAkIiIiIiIiIiKyYGwAEhERERERERERWTA2AImIiIiIiIiIiCwYG4BEREREREREREQWjA1AIiIiIiIiIiIiC8YGIBERERERERERkQVjA5CIiIiIiIiIiMiCsQFIRERERERERERkwdgAJCIiIiIiIiIismD/D2UbBUclga4sAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "filtered = df\n",
+    "filtered = filtered[filtered[\"pos\"] == 4]\n",
+    "g = (\n",
+    "    ggplot(filtered)\n",
+    "    + geom_bar(aes(x=\"layer\", y=\"prob\", fill=\"token\"), stat=\"identity\")\n",
+    "    + theme(axis_text_x=element_text(rotation=90), legend_position=\"none\")\n",
+    "    + scale_y_log10()\n",
+    "    + facet_wrap(\"~token\", ncol=1)\n",
+    ")\n",
+    "print(g)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/basic_tutorials/Intervention_Training.ipynb b/_sources/tutorials/basic_tutorials/Intervention_Training.ipynb
new file mode 100644
index 00000000..8c8fdab9
--- /dev/null
+++ b/_sources/tutorials/basic_tutorials/Intervention_Training.ipynb
@@ -0,0 +1,1168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ed82c3b9",
+   "metadata": {},
+   "source": [
+    "## Trainable Interventions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b71a4b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"11/28/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bce59d35",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "When interventions are static, you are mannually look for interesting counterfactual behaviors. What if interventions are trainable? and what if you train interventions with certain counterfactual behaviors?\n",
+    "\n",
+    "We think, if you can train such interventions, you find a systematic way of affecting the causal circuits realized in the NNs. With certain types of interventions, e.g., basis respect ones (vanilla causal abstraction or DAS), you are doing causal abstraction.\n",
+    "\n",
+    "In this tutorial, we show how you can train interventions with customized dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "714f9e7b",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c5dd0623",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-11 01:23:38,042] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/frankaging/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a5859137",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffff1b0633df473ab9d15a22bba0f29d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/498M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1fa2101566342028d7d19dd74a8299f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/119 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "\n",
+      "A program that the model can solve (Cs are input tokens):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"293.4pt\" height=\"286.2pt\" viewBox=\"0 0 293.4 286.2\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+       " <metadata>\n",
+       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2024-01-20T10:50:39.986017</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.7.3, https://matplotlib.org/</dc:title>\n",
+       "     </cc:Agent>\n",
+       "    </dc:creator>\n",
+       "   </cc:Work>\n",
+       "  </rdf:RDF>\n",
+       " </metadata>\n",
+       " <defs>\n",
+       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
+       " </defs>\n",
+       " <g id=\"figure_1\">\n",
+       "  <g id=\"patch_1\">\n",
+       "   <path d=\"M 0 286.2 \n",
+       "L 293.4 286.2 \n",
+       "L 293.4 0 \n",
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+       "L 0 286.2 \n",
+       "z\n",
+       "\" style=\"fill: none\"/>\n",
+       "  </g>\n",
+       "  <g id=\"axes_1\">\n",
+       "   <g id=\"patch_2\">\n",
+       "    <path d=\"M 138.633892 242.911365 \n",
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+    }
+   ],
+   "source": [
+    "import os, json, torch\n",
+    "import numpy as np\n",
+    "np.object = object\n",
+    "import pandas as pd\n",
+    "from torch.utils.data import DataLoader\n",
+    "from datasets.utils.logging import disable_progress_bar\n",
+    "disable_progress_bar()\n",
+    "\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    LowRankRotatedSpaceIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")\n",
+    "from pyvene import create_gpt2_lm\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    ")\n",
+    "\n",
+    "from tutorial_intervention_training_utils import (\n",
+    "    visualize_program,\n",
+    "    make_supervised_counterfactual_data_module,\n",
+    ")\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "\n",
+    "programs = json.load(open(os.path.join(\"./tutorial_data/\", \"seed_programs.json\")))\n",
+    "programs_10 = json.load(\n",
+    "    open(os.path.join(\"./tutorial_data/\", \"selected_programs_10.json\"))\n",
+    ")\n",
+    "\n",
+    "# load fine-tuned model for this tutorial from HF\n",
+    "config, tokenizer, gpt2 = create_gpt2_lm(\"zhengxuanzenwu/gpt2-5token-solver\")\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "_ = gpt2.to(\"cuda\")\n",
+    "_ = gpt2.eval()\n",
+    "\n",
+    "# what is this model finetuned for? this visualizes a program that this model can solve.\n",
+    "select_program = \"07065a\"\n",
+    "print(\"\\nA program that the model can solve (Cs are input tokens):\")\n",
+    "visualize_program(programs[select_program])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93011597",
+   "metadata": {},
+   "source": [
+    "### Aligning the output variable with a single dimension in the rotated basis\n",
+    "\n",
+    "We know the output is a boolean value, which can be represented using a single dimension (i.e., pos/neg lies on a line with a linearly separable boundary). Can we learn a 1-d DAS direction to align activations with the output variable?\n",
+    "\n",
+    "We first need to sample (base, source(s)) pairs. In this case, it is easy, since the counterfactual behavior is that source output label overwrites the base one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "05f0f35b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model input looks like this (10-shot ICL):\n",
+      "reportprint,itches,reenshots,Jean,reenshots=True\n",
+      "dated,net,dated,Kate,dated=False\n",
+      "Inst,Amid,Billy,While,Billy=True\n",
+      "mas,quickShip,mas,imei,mas=True\n",
+      "Excellent,embedreportprint,Ret,Theme,Excellent=True\n",
+      "engers,Rick,engers,Jerry,engers=True\n",
+      "debian,OSED,international,quickShip,international=True\n",
+      "Led,orous,Lead,Cash,Lead=True\n",
+      "mission,embedreportprint,mission,mbudsman,mission=True\n",
+      "ABC,store,BBC,ixty,CBC=False\n",
+      "ilitation,Brow,ilitation,ICLE,ilitation\n",
+      "\n",
+      "Training data for the intervention should contain these fields:\n",
+      "dict_keys(['input_ids', 'attention_mask', 'source_input_ids', 'source_attention_mask', 'labels', 'source_0->base.0.pos', 'source_0->base.1.pos', 'subspaces'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load in some datasets we need\n",
+    "num_of_shots = 10\n",
+    "mode = \"E\"\n",
+    "counterfactual_data_module, _ = make_supervised_counterfactual_data_module(\n",
+    "    programs[select_program],\n",
+    "    800,\n",
+    "    num_of_shots,\n",
+    "    \"op5\",  # this is the last variable = output\n",
+    "    tokenizer,\n",
+    "    data_path=\"./tutorial_data/\",\n",
+    "    program_uuid=select_program,\n",
+    "    mode=mode,\n",
+    "    n_test_sample=1000,\n",
+    ")\n",
+    "\n",
+    "print(\"Model input looks like this (10-shot ICL):\")\n",
+    "print(counterfactual_data_module[\"train_dataset\"][0][\"question\"])\n",
+    "\n",
+    "# dataset cleaning\n",
+    "columns_to_remove = [\n",
+    "    \"question\",\n",
+    "    \"source_question\",\n",
+    "    \"answers\",\n",
+    "    \"base_answers\",\n",
+    "    \"source_answers\",\n",
+    "]\n",
+    "train_dataset = (\n",
+    "    counterfactual_data_module[\"train_dataset\"]\n",
+    "    .remove_columns(columns_to_remove)\n",
+    "    .with_format(\"torch\")\n",
+    ")\n",
+    "eval_dataset = (\n",
+    "    counterfactual_data_module[\"eval_dataset\"]\n",
+    "    .remove_columns(columns_to_remove)\n",
+    "    .with_format(\"torch\")\n",
+    ")\n",
+    "test_dataset = (\n",
+    "    counterfactual_data_module[\"test_dataset\"]\n",
+    "    .remove_columns(columns_to_remove)\n",
+    "    .with_format(\"torch\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def add_locations(example):\n",
+    "    example[\"source_0->base.0.pos\"] = [129]  # the fixed last token location\n",
+    "    example[\"source_0->base.1.pos\"] = [129]  # the fixed last token location\n",
+    "    example[\"subspaces\"] = [0]  # the only subspace is a single column\n",
+    "    return example\n",
+    "\n",
+    "\n",
+    "train_dataset = train_dataset.map(add_locations).shuffle(seed=42)\n",
+    "eval_dataset = eval_dataset.map(add_locations)\n",
+    "test_dataset = test_dataset.map(add_locations)\n",
+    "train_dataloader = DataLoader(train_dataset, batch_size=64)\n",
+    "eval_dataloader = DataLoader(eval_dataset, batch_size=64)\n",
+    "test_dataloader = DataLoader(test_dataset, batch_size=64)\n",
+    "\n",
+    "print(\"\\nTraining data for the intervention should contain these fields:\")\n",
+    "print(train_dataset[0].keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd7cbc9a",
+   "metadata": {},
+   "source": [
+    "### A 1-d DAS direction learning config\n",
+    "\n",
+    "No full rotation learning is needed here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "55c5687a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def single_d_low_rank_das_position_config(\n",
+    "    model_type, intervention_type, layer, intervention_types\n",
+    "):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                layer,              # layer\n",
+    "                intervention_type,  # intervention type\n",
+    "                \"pos\",              # intervention unit\n",
+    "                1,                  # max number of unit\n",
+    "                low_rank_dimension=1,         # a single das direction\n",
+    "                subspace_partition=[[0, 1]],  # dummy partition\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=intervention_types,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "config = single_d_low_rank_das_position_config(\n",
+    "    type(gpt2), \"block_output\", 11, LowRankRotatedSpaceIntervention\n",
+    ")\n",
+    "intervenable = IntervenableModel(config, gpt2)\n",
+    "intervenable.set_device(\"cuda\")\n",
+    "intervenable.disable_model_gradients()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7654d6a",
+   "metadata": {},
+   "source": [
+    "### Your own loss and metrics function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5cb7919d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def inputs_collator(inputs):\n",
+    "    for k, v in inputs.items():\n",
+    "        if \"->\" in k:\n",
+    "            inputs[k] = v.tolist()\n",
+    "        elif \"subspace\" in k:\n",
+    "            inputs[k] = [v.tolist()]\n",
+    "        elif v is not None and isinstance(v, torch.Tensor):\n",
+    "            inputs[k] = v.to(\"cuda\")\n",
+    "    return inputs\n",
+    "\n",
+    "\n",
+    "def compute_loss(logits, labels):\n",
+    "    shift_logits = logits[..., :-1, :].contiguous()\n",
+    "    shift_labels = labels[..., 1:].contiguous()\n",
+    "\n",
+    "    # Flatten the tokens\n",
+    "    loss_fct = CrossEntropyLoss()\n",
+    "    shift_logits = shift_logits.view(-1, 50257)\n",
+    "    shift_labels = shift_labels.view(-1)\n",
+    "    # Enable model parallelism\n",
+    "    shift_labels = shift_labels.to(shift_logits.device)\n",
+    "    loss = loss_fct(shift_logits, shift_labels)\n",
+    "    return loss\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_preds, eval_labels):\n",
+    "    total_count = 0\n",
+    "    correct_count = 0\n",
+    "    for eval_pred, eval_label in zip(eval_preds, eval_labels):\n",
+    "        actual_test_labels = eval_label[:, -1]\n",
+    "        pred_test_labels = torch.argmax(eval_pred[:, -2], dim=-1)\n",
+    "        correct_labels = actual_test_labels == pred_test_labels\n",
+    "        total_count += len(correct_labels)\n",
+    "        correct_count += correct_labels.sum().tolist()\n",
+    "    accuracy = round(correct_count / total_count, 2)\n",
+    "    return {\"accuracy\": accuracy}[\"accuracy\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa940fb2",
+   "metadata": {},
+   "source": [
+    "### Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b78405cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [01:34<00:00,  9.43s/it, loss=0.24, acc=0.91]\n"
+     ]
+    }
+   ],
+   "source": [
+    "intervenable.train_alignment(\n",
+    "    train_dataloader=train_dataloader,\n",
+    "    compute_loss=compute_loss,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    inputs_collator=inputs_collator,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32581404",
+   "metadata": {},
+   "source": [
+    "### Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "05a7b187",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                                                                                                                                                                          \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9763200000000002"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intervenable.eval_alignment(\n",
+    "    eval_dataloader=test_dataloader,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    inputs_collator=inputs_collator,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec8e0fa8",
+   "metadata": {},
+   "source": [
+    "The above means >97% of time, you can intervene on a single activation in the rotated basis to flip the model prediction"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/basic_tutorials/Nested_Intervention.ipynb b/_sources/tutorials/basic_tutorials/Nested_Intervention.ipynb
new file mode 100644
index 00000000..bc7b9818
--- /dev/null
+++ b/_sources/tutorials/basic_tutorials/Nested_Intervention.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "056f4078",
+   "metadata": {},
+   "source": [
+    "## Intervening on subcomponents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ee206766",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"11/15/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13019abe",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "This tutorial shows how you can intervene at specific position within representations of a specific head. This is sort of nested interventions where you choose a head to intervene first, and then you choose a specific location, or multiple locations. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67b92d31",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dcc513c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aefcde00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    VanillaIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")\n",
+    "from pyvene import create_gpt2\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61544aed",
+   "metadata": {},
+   "source": [
+    "### Factual Recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4575c0bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "The capital of Spain is\n",
+      "_Madrid              0.10501234978437424\n",
+      "_the                 0.0949699655175209\n",
+      "_Barcelona           0.0702790841460228\n",
+      "_a                   0.04010068252682686\n",
+      "_now                 0.02824278175830841\n",
+      "_in                  0.02759990654885769\n",
+      "_Spain               0.022991720587015152\n",
+      "_Catalonia           0.018823225051164627\n",
+      "_also                0.018689140677452087\n",
+      "_not                 0.01735665090382099\n",
+      "\n",
+      "The capital of Italy is\n",
+      "_Rome                0.15734916925430298\n",
+      "_the                 0.07316355407238007\n",
+      "_Milan               0.046878915280103683\n",
+      "_a                   0.03449810668826103\n",
+      "_now                 0.03200329467654228\n",
+      "_in                  0.02306535840034485\n",
+      "_also                0.02274816483259201\n",
+      "_home                0.01920313946902752\n",
+      "_not                 0.01640527881681919\n",
+      "_Italy               0.01577090471982956\n"
+     ]
+    }
+   ],
+   "source": [
+    "config, tokenizer, gpt = create_gpt2()\n",
+    "\n",
+    "base = \"The capital of Spain is\"\n",
+    "source = \"The capital of Italy is\"\n",
+    "inputs = [tokenizer(base, return_tensors=\"pt\"), tokenizer(source, return_tensors=\"pt\")]\n",
+    "print(base)\n",
+    "res = gpt(**inputs[0])\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
+    "print()\n",
+    "print(source)\n",
+    "res = gpt(**inputs[1])\n",
+    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
+    "top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b65ff4b8",
+   "metadata": {},
+   "source": [
+    "### Patch Patching on Position-aligned Tokens with in Specific Head\n",
+    "We path patch on two modules on each layer:\n",
+    "- [1] MLP output (the MLP output will be from another example)\n",
+    "- [2] MHA input (the self-attention module input will be from another module)\n",
+    "\n",
+    "Different from the basic tutorial, this tutorial intervenes on specific locations within specific heads. For instance, we want to intervene on the last token in head 4 but not other heads.\n",
+    "\n",
+    "**To do this, we need to tweak a little when we setup the intervention config.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "898907c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def position_in_head_config(model_type, intervention_type, layer):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                layer,  # layer\n",
+    "                intervention_type,  # intervention type\n",
+    "                \"h.pos\",  # intervention unit is now [pos] within [h]\n",
+    "                1,  # max number of unit\n",
+    "            ),\n",
+    "        ],\n",
+    "        intervention_types=VanillaIntervention,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dcda628a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_head = 0\n",
+    "\n",
+    "# should finish within 1 min with a standard 12G GPU\n",
+    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
+    "\n",
+    "data = []\n",
+    "for layer_i in range(gpt.config.n_layer):\n",
+    "    config = position_in_head_config(\n",
+    "        type(gpt), \"head_attention_value_output\", layer_i\n",
+    "    )\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base,\n",
+    "            sources,\n",
+    "            {\n",
+    "                \"sources->base\": (\n",
+    "                    [[[[target_head]], [[pos_i]]]],  # intervene w/ target_head's pos_i\n",
+    "                    [[[[target_head]], [[pos_i]]]],  # intervene on target_head's pos_i\n",
+    "                )\n",
+    "            },\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"ov{layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"head_attention_value_output\",\n",
+    "                }\n",
+    "            )\n",
+    "\n",
+    "    config = position_in_head_config(\n",
+    "        type(gpt), \"head_value_output\", layer_i\n",
+    "    )\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base,\n",
+    "            sources,\n",
+    "            {\n",
+    "                \"sources->base\": (\n",
+    "                    [[[[target_head]], [[pos_i]]]],  # intervene w/ target_head's pos_i\n",
+    "                    [[[[target_head]], [[pos_i]]]],  # intervene on target_head's pos_i\n",
+    "                )\n",
+    "            },\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"v{layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"head_value_output\",\n",
+    "                }\n",
+    "            )\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fa7273a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
+    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
+    "nodes = []\n",
+    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
+    "    nodes.append(f\"ov{l}\")\n",
+    "    nodes.append(f\"v{l}\")\n",
+    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
+    "\n",
+    "g = (\n",
+    "    ggplot(df)\n",
+    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
+    "    + facet_wrap(\"~token\")\n",
+    "    + theme(axis_text_x=element_text(rotation=90))\n",
+    ")\n",
+    "print(g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7730ab69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "filtered = df\n",
+    "filtered = filtered[filtered[\"pos\"] == 4]\n",
+    "g = (\n",
+    "    ggplot(filtered)\n",
+    "    + geom_bar(aes(x=\"layer\", y=\"prob\", fill=\"token\"), stat=\"identity\")\n",
+    "    + theme(axis_text_x=element_text(rotation=90), legend_position=\"none\")\n",
+    "    + scale_y_log10()\n",
+    "    + facet_wrap(\"~token\", ncol=1)\n",
+    ")\n",
+    "print(g)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/basic_tutorials/Sparse_Autoencoder.ipynb b/_sources/tutorials/basic_tutorials/Sparse_Autoencoder.ipynb
new file mode 100644
index 00000000..09088f90
--- /dev/null
+++ b/_sources/tutorials/basic_tutorials/Sparse_Autoencoder.ipynb
@@ -0,0 +1,670 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "67b741df-7c4b-4fdc-949b-ca97492fa288",
+   "metadata": {},
+   "source": [
+    "## Analyzing Sparse Autoencoders (SAEs) from [Gemma Scope](https://colab.research.google.com/drive/17dQFYUYnuKnP6OwQPH9v_GSYUW5aj-Rp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82c139fd-2f1b-48fb-9109-2737565d2323",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/basic_tutorials/Sparse_Autoencoder.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e5d14f0a-b02d-4d1f-863a-dbb1e475e264",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"09/23/2024\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bbb6ff3-b76b-4672-a8bc-1a9324c5e3de",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "This tutorial aims to **(1) reproduce** and **(2) extend** some of the results in the Gemma Scope (SAE) tutorial in [notebook](https://colab.research.google.com/drive/17dQFYUYnuKnP6OwQPH9v_GSYUW5aj-Rp) for interpreting latents of SAEs. This tutorial also shows basic model steering with SAEs. This notebook is built as a show-case for the Gemma 2 2B model as well as its SAEs. However, this tutorial can be extended to any other model types and their SAEs. \n",
+    "\n",
+    "\n",
+    "**Note**: This tutorial assumes SAEs are pretrained separately."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94a4da6f-d078-4b64-9fd0-d79ece35d3f1",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dd197c1f-71b5-4379-a9dd-2f6ff27083f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "209bfc46-7685-4e66-975f-3280ed516b52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyvene import (\n",
+    "    ConstantSourceIntervention,\n",
+    "    SourcelessIntervention,\n",
+    "    TrainableIntervention,\n",
+    "    DistributedRepresentationIntervention,\n",
+    "    CollectIntervention,\n",
+    "    JumpReLUAutoencoderIntervention\n",
+    ")\n",
+    "\n",
+    "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer\n",
+    "from huggingface_hub import hf_hub_download, notebook_login\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "# If you haven't login, you need to do so.\n",
+    "# notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89e7073e-4a64-49c9-8be2-df7926e08332",
+   "metadata": {},
+   "source": [
+    "### Loading the model and its tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a6e7e7fb-5e73-4711-b378-bc1b04ab1e7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "192a06afdbdc4c868bc6d20677b3dd38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e088f2b3808489bac70b9aeb5ae73f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.set_grad_enabled(False) # avoid blowing up mem\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    \"google/gemma-2-2b\", # google/gemma-2b-it\n",
+    "    device_map='auto',\n",
+    ")\n",
+    "tokenizer =  AutoTokenizer.from_pretrained(\"google/gemma-2-2b\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f6af820-a819-4f8e-ac13-6063b8e47e5d",
+   "metadata": {},
+   "source": [
+    "We give it the prompt \"Would you be able to travel through time using a wormhole?\" and print the generated output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "95653dcc-876e-4419-a014-d0308ce12cef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[     2,  18925,    692,    614,   3326,    577,   5056,   1593,   1069,\n",
+      "           2177,    476,  47420,  18216, 235336]], device='cuda:0')\n",
+      "<bos>Would you be able to travel through time using a wormhole?\n",
+      "\n",
+      "[Answer 1]\n",
+      "\n",
+      "Yes, you can travel through time using a wormhole.\n",
+      "\n",
+      "A wormhole is a theoretical object that connects two points in space-time. It is a tunnel through space-time that allows objects to travel from\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The input text\n",
+    "prompt = \"Would you be able to travel through time using a wormhole?\"\n",
+    "\n",
+    "# Use the tokenizer to convert it to tokens. Note that this implicitly adds a special \"Beginning of Sequence\" or <bos> token to the start\n",
+    "inputs = tokenizer.encode(prompt, return_tensors=\"pt\", add_special_tokens=True).to(\"cuda\")\n",
+    "print(inputs)\n",
+    "\n",
+    "# Pass it in to the model and generate text\n",
+    "outputs = model.generate(input_ids=inputs, max_new_tokens=50)\n",
+    "print(tokenizer.decode(outputs[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71dd281a-562a-4382-8631-43ece42498ce",
+   "metadata": {},
+   "source": [
+    "### Loading a SAE, and create SAE interventions\n",
+    "\n",
+    "`pyvene` can load SAEs as interventions for analyzing latents as well as model steering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d490a50c-a1cd-4def-90c2-cd6bfe67266f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LAYER = 20\n",
+    "path_to_params = hf_hub_download(\n",
+    "    repo_id=\"google/gemma-scope-2b-pt-res\",\n",
+    "    filename=f\"layer_{LAYER}/width_16k/average_l0_71/params.npz\",\n",
+    "    force_download=False,\n",
+    ")\n",
+    "params = np.load(path_to_params)\n",
+    "pt_params = {k: torch.from_numpy(v).cuda() for k, v in params.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e61be8a-257f-4b3d-8112-cb657e9cce40",
+   "metadata": {},
+   "source": [
+    "### Implementing SAEs as `pyvene`-native Interventions\n",
+    "\n",
+    "Create a `pyvene`-native intervention for SAEs to collect latent collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d785071c-e3ed-4940-92c7-aa8b04df71d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class JumpReLUSAECollectIntervention(\n",
+    "    CollectIntervention\n",
+    "):\n",
+    "  \"\"\"Collect activations\"\"\"\n",
+    "  def __init__(self, **kwargs):\n",
+    "    # Note that we initialise these to zeros because we're loading in pre-trained weights.\n",
+    "    # If you want to train your own SAEs then we recommend using blah\n",
+    "    super().__init__(**kwargs, keep_last_dim=True)\n",
+    "    self.W_enc = nn.Parameter(torch.zeros(self.embed_dim, kwargs[\"low_rank_dimension\"]))\n",
+    "    self.W_dec = nn.Parameter(torch.zeros(kwargs[\"low_rank_dimension\"], self.embed_dim))\n",
+    "    self.threshold = nn.Parameter(torch.zeros(kwargs[\"low_rank_dimension\"]))\n",
+    "    self.b_enc = nn.Parameter(torch.zeros(kwargs[\"low_rank_dimension\"]))\n",
+    "    self.b_dec = nn.Parameter(torch.zeros(self.embed_dim))\n",
+    "\n",
+    "  def encode(self, input_acts):\n",
+    "    pre_acts = input_acts @ self.W_enc + self.b_enc\n",
+    "    mask = (pre_acts > self.threshold)\n",
+    "    acts = mask * torch.nn.functional.relu(pre_acts)\n",
+    "    return acts\n",
+    "\n",
+    "  def forward(self, base, source=None, subspaces=None):\n",
+    "    acts = self.encode(base)\n",
+    "\n",
+    "    return acts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057aa550-ad6a-4c81-ab37-e732eb202270",
+   "metadata": {},
+   "source": [
+    "### Running the model with SAE to collect activations with `pyvene` APIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "0accd2d7-b0db-4c21-a27f-b6f9a45f8cb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sae = JumpReLUSAECollectIntervention(\n",
+    "    embed_dim=params['W_enc'].shape[0],\n",
+    "    low_rank_dimension=params['W_enc'].shape[1]\n",
+    ")\n",
+    "sae.load_state_dict(pt_params, strict=False)\n",
+    "sae.cuda()\n",
+    "\n",
+    "# add the intervention to the model computation graph via the config\n",
+    "pv_model = pyvene.IntervenableModel({\n",
+    "   \"component\": f\"model.layers[{LAYER}].output\",\n",
+    "   \"intervention\": sae}, model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "14eb3d4e-98ab-47ee-a842-0736407e8c0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sae_acts = pv_model.forward(\n",
+    "    {\"input_ids\": inputs}, return_dict=True).collected_activations[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "b769de21-57d8-4e6b-842a-636be784bb5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([7017,   47,   65,   70,   55,   72,   65,   75,   80,   72,   68,   93,\n",
+       "          86,   89], device='cuda:0')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Results (from Gemma Scope) should be:\n",
+    "tensor([[7017,   47,   65,   70,   55,   72,   65,   75,   80,   72,   68,   93,\n",
+    "           86,   89]], device='cuda:0')\n",
+    "\"\"\"\n",
+    "(sae_acts > 1).sum(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "30d5196f-c5bc-4a13-9363-3b287b9117b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 6631,  5482, 10376,  1670, 11023,  7562,  9407,  8399, 12935, 10004,\n",
+       "        10004, 10004, 12935,  3442], device='cuda:0')"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Results (from Gemma Scope) should be:\n",
+    "tensor([[ 6631,  5482, 10376,  1670, 11023,  7562,  9407,  8399, 12935, 10004,\n",
+    "         10004, 10004, 12935,  3442]], device='cuda:0')\n",
+    "\"\"\"\n",
+    "values, inds = sae_acts.max(-1)\n",
+    "\n",
+    "inds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31fdca77-5448-45ef-9816-194c610d919d",
+   "metadata": {},
+   "source": [
+    "### Gemma-2-2B-it steering with Gemma-2-2B SAEs\n",
+    "\n",
+    "We could also try to steer Gemma-2-2B-it by overloading Gemma-2-2B SAE, and see if it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "fc55f530-1e55-4dc4-8cbc-6d26c9f9a0ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0574b56084114b98bcd5d0817960a78c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c135c09c7d6d43e5a874b0f7caa92b5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "torch.set_grad_enabled(False) # avoid blowing up mem\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    \"google/gemma-2-2b-it\", # google/gemma-2b-it\n",
+    "    device_map='auto',\n",
+    ")\n",
+    "tokenizer =  AutoTokenizer.from_pretrained(\"google/gemma-2-2b-it\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20af0809-1a1d-4279-894e-52070e3de337",
+   "metadata": {},
+   "source": [
+    "### Implementing SAEs as `pyvene`-native Interventions for model steering\n",
+    "\n",
+    "The `subspace` notation built in to `pyvene` let us to steer models by intervening on different features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "043ffb42-f1d2-4b58-99a1-525dfbed45d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class JumpReLUSAESteeringIntervention(\n",
+    "    SourcelessIntervention,\n",
+    "    TrainableIntervention, \n",
+    "    DistributedRepresentationIntervention\n",
+    "):\n",
+    "  def __init__(self, **kwargs):\n",
+    "    # Note that we initialise these to zeros because we're loading in pre-trained weights.\n",
+    "    # If you want to train your own SAEs then we recommend using blah\n",
+    "    super().__init__(**kwargs, keep_last_dim=True)\n",
+    "    self.W_enc = nn.Parameter(torch.zeros(self.embed_dim, kwargs[\"low_rank_dimension\"]))\n",
+    "    self.W_dec = nn.Parameter(torch.zeros(kwargs[\"low_rank_dimension\"], self.embed_dim))\n",
+    "    self.threshold = nn.Parameter(torch.zeros(kwargs[\"low_rank_dimension\"]))\n",
+    "    self.b_enc = nn.Parameter(torch.zeros(kwargs[\"low_rank_dimension\"]))\n",
+    "    self.b_dec = nn.Parameter(torch.zeros(self.embed_dim))\n",
+    "\n",
+    "  def encode(self, input_acts):\n",
+    "    pre_acts = input_acts @ self.W_enc + self.b_enc\n",
+    "    mask = (pre_acts > self.threshold)\n",
+    "    acts = mask * torch.nn.functional.relu(pre_acts)\n",
+    "    return acts\n",
+    "\n",
+    "  def decode(self, acts):\n",
+    "    return acts @ self.W_dec + self.b_dec\n",
+    "\n",
+    "  def forward(self, base, source=None, subspaces=None):\n",
+    "    steering_vec = torch.tensor(subspaces[\"mag\"]) * self.W_dec[subspaces[\"idx\"]]\n",
+    "    return base + steering_vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c8597e2-69fd-4dc1-b120-af4974a75e79",
+   "metadata": {},
+   "source": [
+    "Loading the Gemma base model SAE weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "f99d24bc-22a8-4822-8240-4647b27b6d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sae = JumpReLUSAESteeringIntervention(\n",
+    "    embed_dim=params['W_enc'].shape[0],\n",
+    "    low_rank_dimension=params['W_enc'].shape[1]\n",
+    ")\n",
+    "sae.load_state_dict(pt_params, strict=False)\n",
+    "sae.cuda()\n",
+    "\n",
+    "# add the intervention to the model computation graph via the config\n",
+    "pv_model = pyvene.IntervenableModel({\n",
+    "   \"component\": f\"model.layers[{LAYER}].output\",\n",
+    "   \"intervention\": sae}, model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "971f8a64-2bbb-47bb-9958-50484b5d9e3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = \"Which dog breed do people think is cuter, poodle or doodle?\"\n",
+    "\n",
+    "prompt = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
+    "_, reft_response = pv_model.generate(\n",
+    "    prompt, unit_locations=None, intervene_on_prompt=True, \n",
+    "    subspaces=[{\"idx\": 10004, \"mag\": 100.0}],\n",
+    "    max_new_tokens=128, do_sample=True, early_stopping=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "60fe187c-bafe-459c-ad8a-f064d74b2ae3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Which dog breed do people think is cuter, poodle or doodle? \n",
+      "\n",
+      "It really depends on personal preference, but it's often a subjective matter. \n",
+      "\n",
+      "Here's a bit about each, to help you decide:\n",
+      "\n",
+      "**Poodles:**\n",
+      "\n",
+      "* Origin: France\n",
+      "* Types: Standard, Miniature, Toy\n",
+      "* Known for: Curly, hypoallergenic fur; intelligence and trainability.\n",
+      "* Appearance: Classic, distinguished look with a flowing coat and well-defined facial features.\n",
+      "\n",
+      "**Doodles:**\n",
+      "\n",
+      "* Origin (general) Space-travel, time-travel or a blend - depending on the specific dog's ancestry.  The term is used across a variety of breeds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenizer.decode(reft_response[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b863a941-f297-4ebc-9008-62da60fa89d7",
+   "metadata": {},
+   "source": [
+    "**Here you go: a \"Space-travel, time-travel\" Doodle!**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22cabe19-2c2f-46c7-a631-d0b40fca5308",
+   "metadata": {},
+   "source": [
+    "### Interchange intervention with JumpReLU SAEs.\n",
+    "\n",
+    "You can also swap values between examples for a specific latent dimension. However, since SAE usually maps a concpet to 1D subspace, swapping between examples and resetting the scalar to another value are similar.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4f23b199-ca01-4676-9a2d-61b24b96dc2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sae = JumpReLUAutoencoderIntervention(\n",
+    "    embed_dim=params['W_enc'].shape[0],\n",
+    "    low_rank_dimension=params['W_enc'].shape[1]\n",
+    ")\n",
+    "sae.load_state_dict(pt_params, strict=False)\n",
+    "sae.cuda()\n",
+    "\n",
+    "# add the intervention to the model computation graph via the config\n",
+    "pv_model = pyvene.IntervenableModel({\n",
+    "   \"component\": f\"model.layers[{LAYER}].output\",\n",
+    "   \"intervention\": sae}, model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9dbe3883-3588-45fe-91bf-aeb075dea642",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base = tokenizer(\n",
+    "    \"Which dog breed do people think is cuter, poodle or doodle?\", \n",
+    "    return_tensors=\"pt\").to(\"cuda\")\n",
+    "source = tokenizer(\n",
+    "    \"Origin (general) Space-travel, time-travel\", \n",
+    "    return_tensors=\"pt\").to(\"cuda\")\n",
+    "\n",
+    "# run an interchange intervention \n",
+    "original_outputs, intervened_outputs = pv_model(\n",
+    "  # the base input\n",
+    "  base=base, \n",
+    "  # the source input\n",
+    "  sources=source, \n",
+    "  # the location to intervene (swap last tokens)\n",
+    "  unit_locations={\"sources->base\": (11, 14)},\n",
+    "  # the SAE latent dimension mapping to the time travel concept (\"10004\")\n",
+    "  subspaces=[10004],\n",
+    "  output_original_output=True\n",
+    ")\n",
+    "logits_diff = intervened_outputs.logits[:,-1] - original_outputs.logits[:,-1]\n",
+    "values, indices = logits_diff.topk(k=10, sorted=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "57b8c19a-c73f-47e5-b7f3-6b9353802a96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** topk logits diff **\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['PhysRevD',\n",
+       " ' transporting',\n",
+       " ' teleport',\n",
+       " ' space',\n",
+       " ' transit',\n",
+       " ' transported',\n",
+       " ' transporter',\n",
+       " ' transpor',\n",
+       " ' multiverse',\n",
+       " ' universes']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"** topk logits diff **\")\n",
+    "tokenizer.batch_decode(indices[0].unsqueeze(dim=-1))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/basic_tutorials/Subspace_Partition_with_Intervention.ipynb b/_sources/tutorials/basic_tutorials/Subspace_Partition_with_Intervention.ipynb
new file mode 100644
index 00000000..2d1c4820
--- /dev/null
+++ b/_sources/tutorials/basic_tutorials/Subspace_Partition_with_Intervention.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b955bab5",
+   "metadata": {},
+   "source": [
+    "## Subspace Interventions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2dae33d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"11/28/2023\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e2ac478",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "Subspace of the basis may be used to represent different orthogonal causal variables. In other words, each column or each partition of columns may be used to represent different high-level causal model. In this tutorial, we want to illustrate how to setup the intervenable to do this.\n",
+    "\n",
+    "We introduce a new concept of **subspace** intervention. For the intervention, you can specify if you only want to intervene on a subspace rather than the fullspace.\n",
+    "\n",
+    "Then, you can intervene on different subspaces given your examples in a batch, and test for different counterfactual behaviors. Accordingly, you can also train different subspaces to target different counterfactual behaviors using DAS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2deea8d3",
+   "metadata": {},
+   "source": [
+    "### Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ed1e62ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fcfde6a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pyvene import embed_to_distrib, top_vals, format_token\n",
+    "from pyvene import (\n",
+    "    IntervenableModel,\n",
+    "    RotatedSpaceIntervention,\n",
+    "    RepresentationConfig,\n",
+    "    IntervenableConfig,\n",
+    ")\n",
+    "from pyvene import create_gpt2\n",
+    "\n",
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "from plotnine import (\n",
+    "    ggplot,\n",
+    "    geom_tile,\n",
+    "    aes,\n",
+    "    facet_wrap,\n",
+    "    theme,\n",
+    "    element_text,\n",
+    "    geom_bar,\n",
+    "    geom_hline,\n",
+    "    scale_y_log10,\n",
+    ")\n",
+    "\n",
+    "config, tokenizer, gpt = create_gpt2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e68d4a2",
+   "metadata": {},
+   "source": [
+    "### Subspace alignment config\n",
+    "You just need to specify your intial subspace partition in the config.\n",
+    "\n",
+    "Currently, only DAS-related interventions are supporting this. But the concept of subspace intervention can be extended to other types of interventions as well (e.g., vanilla intervention where swapping a subset of activations)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5906aebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simple_subspace_position_config(\n",
+    "    model_type, intervention_type, layer, subspace_partition=[[0, 384], [384, 768]]\n",
+    "):\n",
+    "    config = IntervenableConfig(\n",
+    "        model_type=model_type,\n",
+    "        representations=[\n",
+    "            RepresentationConfig(\n",
+    "                layer,  # layer\n",
+    "                intervention_type,  # repr intervention type\n",
+    "                \"pos\",  # intervention unit\n",
+    "                1,      # max number of unit\n",
+    "                subspace_partition=subspace_partition,\n",
+    "            )\n",
+    "        ],\n",
+    "        intervention_types=RotatedSpaceIntervention,\n",
+    "    )\n",
+    "    return config\n",
+    "\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eafda98f",
+   "metadata": {},
+   "source": [
+    "### Patch Patching on the First Subspace of Position-aligned Tokens\n",
+    "We path patch on the subspace (indexing from 0 to 384) of two modules on each layer:\n",
+    "- [1] MLP output (the MLP output will be from another example)\n",
+    "- [2] MHA input (the self-attention module input will be from another module)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2058f96f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should finish within 1 min with a standard 12G GPU\n",
+    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
+    "\n",
+    "data = []\n",
+    "for layer_i in range(gpt.config.n_layer):\n",
+    "    config = simple_subspace_position_config(\n",
+    "        type(gpt), \"mlp_output\", layer_i\n",
+    "    )\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for k, v in intervenable.interventions.items():\n",
+    "        v.set_interchange_dim(768)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base,\n",
+    "            sources,\n",
+    "            {\"sources->base\": ([[[pos_i]]], [[[pos_i]]])},\n",
+    "            subspaces=[[[0]]],\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"f{layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"mlp_output\",\n",
+    "                }\n",
+    "            )\n",
+    "\n",
+    "    config = simple_subspace_position_config(\n",
+    "        type(gpt), \"attention_input\", layer_i\n",
+    "    )\n",
+    "    intervenable = IntervenableModel(config, gpt)\n",
+    "    for k, v in intervenable.interventions.items():\n",
+    "        v.set_interchange_dim(768)\n",
+    "    for pos_i in range(len(base.input_ids[0])):\n",
+    "        _, counterfactual_outputs = intervenable(\n",
+    "            base,\n",
+    "            sources,\n",
+    "            {\"sources->base\": ([[[pos_i]]], [[[pos_i]]])},\n",
+    "            subspaces=[[[0]]],\n",
+    "        )\n",
+    "        distrib = embed_to_distrib(\n",
+    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
+    "        )\n",
+    "        for token in tokens:\n",
+    "            data.append(\n",
+    "                {\n",
+    "                    \"token\": format_token(tokenizer, token),\n",
+    "                    \"prob\": float(distrib[0][-1][token]),\n",
+    "                    \"layer\": f\"a{layer_i}\",\n",
+    "                    \"pos\": pos_i,\n",
+    "                    \"type\": \"attention_input\",\n",
+    "                }\n",
+    "            )\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f39d0bd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
+    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
+    "nodes = []\n",
+    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
+    "    nodes.append(f\"f{l}\")\n",
+    "    nodes.append(f\"a{l}\")\n",
+    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
+    "\n",
+    "g = (\n",
+    "    ggplot(df)\n",
+    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
+    "    + facet_wrap(\"~token\")\n",
+    "    + theme(axis_text_x=element_text(rotation=90))\n",
+    ")\n",
+    "print(g)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/pyvene_101.ipynb b/_sources/tutorials/pyvene_101.ipynb
new file mode 100644
index 00000000..9d8913d6
--- /dev/null
+++ b/_sources/tutorials/pyvene_101.ipynb
@@ -0,0 +1,3045 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba6c7e19",
+   "metadata": {},
+   "source": [
+    "# Introduction to pyvene\n",
+    "This tutorial shows simple runnable code snippets of how to do different kinds of interventions on neural networks with pyvene."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d6994fa",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/pyvene_101.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d123a2ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "__author__ = \"Zhengxuan Wu\"\n",
+    "__version__ = \"02/01/2024\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26298448-91eb-4cad-85bf-ec5fef436e1d",
+   "metadata": {},
+   "source": [
+    " # Table of Contents  \n",
+    "1. [Set-up](#Set-up)     \n",
+    "1. [pyvene 101](#pyvene-101) \n",
+    "    1. [Get Attention Weights](#Get-Attention-Weights)\n",
+    "        1. [with String Access](#Get-Attention-Weights-with-Direct-Access-String)\n",
+    "        1. [with 1-Line Function](#Get-Attention-Weights-with-a-Function)\n",
+    "    1. [Set Activations to Zeros](#Set-Activation-to-Zeros) \n",
+    "        1. [with Lambda Expression](#Set-Activation-to-Zeros-with-a-Lambda-Expression)\n",
+    "    1. [Set Activations with Subspaces](#Set-Activations-to-Zeros-with-Subspaces)\n",
+    "    1. [Interchange Intervention](#Interchange-Interventions)\n",
+    "    1. [Intervention Config](#Intervention-Configuration)\n",
+    "    1. [Addition Intervention](#Addition-Intervention)\n",
+    "    1. [Trainable Intervention](#Trainable-Intervention)\n",
+    "    1. [Activation Collection](#Activation-Collection-with-Intervention)\n",
+    "    1. [Activation Collection with Other Intervention](#Activation-Collection-at-Downstream-of-a-Intervened-Model)\n",
+    "    1. [Intervene Single Neuron](#Intervene-on-a-Single-Neuron)\n",
+    "    1. [Add New Intervention Type](#Add-New-Intervention-Type)\n",
+    "    1. [Intervene on Recurrent NNs](#Recurrent-NNs-(Intervene-a-Specific-Timestep))\n",
+    "    1. [Intervene across Times with RNNs](#Recurrent-NNs-(Intervene-cross-Time))\n",
+    "    1. [Intervene on LM Generation](#LMs-Generation)\n",
+    "    1. [Advanced Intervention on LM Generation (Model Steering)](#Advanced-Intervention-on-LMs-Generation-(Model-Steering))\n",
+    "    1. [Debiasing with Backpack LMs](#Debiasing-with-Backpack-LMs)\n",
+    "    1. [Saving and Loading](#Saving-and-Loading)\n",
+    "    1. [Multi-Source Intervention (Parallel)](#Multi-Source-Interchange-Intervention-(Parallel-Mode))\n",
+    "    1. [Multi-Source Intervention (Serial)](#Multi-Source-Interchange-Intervention-(Serial-Mode))\n",
+    "    1. [Multi-Source Intervention with Subspaces (Parallel)](#Multi-Source-Interchange-Intervention-with-Subspaces-(Parallel-Mode))\n",
+    "    1. [Multi-Source Intervention with Subspaces (Serial)](#Multi-Source-Interchange-Intervention-with-Subspaces-(Serial-Mode))\n",
+    "    1. [Interchange Intervention Training](#Interchange-Intervention-Training-(IIT))\n",
+    "1. [pyvene 102](#pyvene-102)\n",
+    "    1. [Intervention Grouping](#Grouping)\n",
+    "    1. [Intervention Skipping](#Intervention-Skipping-in-Runtime)\n",
+    "    1. [Subspace Partition](#Subspace-Partition)\n",
+    "    1. [Intervention Linking](#Intervention-Linking)\n",
+    "    1. [Add New Model Type](#Add-New-Model-Type)\n",
+    "    1. [Path Patching](#Composing-Complex-Intervention-Schema:-Path-Patching)\n",
+    "    1. [Causal Tracing](#Composing-Complex-Intervention-Schema:-Causal-Tracing-in-15-lines)\n",
+    "    1. [Inference-time Intervention](#Inference-time-Intervention)\n",
+    "    1. [IntervenableModel from HuggingFace Directly](#IntervenableModel-from-HuggingFace-Directly)\n",
+    "    1. [Path Patching with DAS](#Path-Patching-with-Trainable-Interventions)\n",
+    "    1. [Intervene ResNet with Lambda Functors](#Intervene-on-ResNet-with-Lambda-Functions)\n",
+    "    1. [Intervene ResNet with 1-line DAS Lambda](#Intervene-on-ResNet-with-Trainable-Lambda-Functions)\n",
+    "    2. [Run pyvene on NDIF backend](#Run-pyvene-on-NDIF-backend-with-pv.build_intervenable_model(...))\n",
+    "1. [The End](#The-End)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0706e21b",
+   "metadata": {},
+   "source": [
+    "## Set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e08304ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # This library is our indicator that the required installs\n",
+    "    # need to be done.\n",
+    "    import pyvene\n",
+    "\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ede4f94",
+   "metadata": {},
+   "source": [
+    "## pyvene 101\n",
+    "Before we get started, here are a couple of core notations that are used in this library:\n",
+    "- **Base** example: this is the example we are intervening on, or, we are intervening on the computation graph of the model running the **Base** example.\n",
+    "- **Source** example or representations: this is the source of our intervention. We use **Source** to intervene on **Base**.\n",
+    "- **component**: this is the `nn.module` we are intervening in a pytorch-based NN. For models supported by this library, you can use directly access via str, or use the abstract names defined in the config file (e.g., `h[0].mlp.output` or `mlp_output` with other fields). \n",
+    "- **unit**: this is the axis of our intervention. If we say our **unit** is `pos` (`position`), then you are intervening on each token position.\n",
+    "- **unit_locations**: this list gives you the percisely location of your intervention. It is the locations of the unit of analysis you are specifying. For instance, if your `unit` is `pos`, and your `unit_location` is 3, then it means you are intervening on the third token. If this field is left as `None`, then no selection will be taken, i.e., you can think of you are getting the raw tensor and you can do whatever you want.\n",
+    "- **intervention_type** or **intervention**: this field specifies the intervention you can perform. It can be a primitive type, or it can be a function or a lambda expression for simple interventions. One benefit of using primitives is speed and systematic training schemes. You can also save and load interventions if you use the supported primitives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7245643b-fd44-47a5-a189-ce1565da7e25",
+   "metadata": {},
+   "source": [
+    "### Get Attention Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "17c7f2f6-b0d3-4fe2-8e4f-c044b93f3ef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyvene as pv\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "\n",
+    "model_name = \"gpt2\"\n",
+    "# Do not use SDPA attention because we cannot hook to attn_dropout\n",
+    "gpt2 = AutoModelForCausalLM.from_pretrained(model_name, attn_implementation=\"eager\")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    \"layer\": 10,\n",
+    "    \"component\": \"attention_weight\",\n",
+    "    \"intervention_type\": pv.CollectIntervention}, model=gpt2)\n",
+    "\n",
+    "base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
+    "collected_attn_w = pv_gpt2(\n",
+    "    base = tokenizer(base, return_tensors=\"pt\"\n",
+    "    ), unit_locations={\"base\": [h for h in range(12)]}\n",
+    ")[0][-1][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cee8d393-1676-45e2-8aa7-228343d3b13b",
+   "metadata": {},
+   "source": [
+    "#### Get Attention Weights with Direct Access String"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "128be2dd-f089-4291-bfc5-7002d031b1e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "# gpt2 helper loading model from HuggingFace\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    # based on the module printed above, you can access via string, input means the input to the module\n",
+    "    \"component\": \"h[10].attn.attn_dropout.input\",\n",
+    "    # you can also initialize the intervention outside\n",
+    "    \"intervention\": pv.CollectIntervention()}, model=gpt2)\n",
+    "\n",
+    "base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
+    "collected_attn_w = pv_gpt2(\n",
+    "    base = tokenizer(base, return_tensors=\"pt\"\n",
+    "    ), unit_locations={\"base\": [h for h in range(12)]}\n",
+    ")[0][-1][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22643b2d",
+   "metadata": {},
+   "source": [
+    "#### Get Attention Weights with a Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "678dc46f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import copy\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "cached_w = {}\n",
+    "def pv_patcher(b, s): cached_w[\"attn_w\"] = copy.deepcopy(b.data)\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    \"component\": \"h[10].attn.attn_dropout.input\", \n",
+    "    \"intervention\": pv_patcher}, model=gpt2)\n",
+    "\n",
+    "base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
+    "_ = pv_gpt2(tokenizer(base, return_tensors=\"pt\"))\n",
+    "torch.allclose(collected_attn_w, cached_w[\"attn_w\"].unsqueeze(dim=0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12c5addb-4bd7-4129-b350-0677774f5790",
+   "metadata": {},
+   "source": [
+    "### Set Activation to Zeros"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a82664f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "# define the component to zero-out\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    \"layer\": 0, \"component\": \"mlp_output\",\n",
+    "    \"source_representation\": torch.zeros(gpt2.config.n_embd)\n",
+    "}, model=gpt2)\n",
+    "# run the intervened forward pass\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\"), \n",
+    "    # we define the intervening token dynamically\n",
+    "    unit_locations={\"base\": 3},\n",
+    "    output_original_output=True # False then the first element in the tuple is None\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9c11cb9",
+   "metadata": {},
+   "source": [
+    "#### Set Activation to Zeros with a Lambda Expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7627dc32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "# indices are specified in the intervention\n",
+    "mask = torch.ones(1, 5, 768)\n",
+    "mask[:,3,:] = 0.\n",
+    "# define the component to zero-out\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    \"component\": \"h[0].mlp.output\", \"intervention\": lambda b, s: b*mask\n",
+    "}, model=gpt2)\n",
+    "# run the intervened forward pass\n",
+    "intervened_outputs_fn = pv_gpt2(\n",
+    "    base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    ")\n",
+    "torch.allclose(\n",
+    "    intervened_outputs[1].last_hidden_state, \n",
+    "    intervened_outputs_fn[1].last_hidden_state\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72363777",
+   "metadata": {},
+   "source": [
+    "#### Set Activation to Zeros with a Lambda Expression and Subspace notation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d86c06f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "# indices are specified in the intervention\n",
+    "\n",
+    "def pv_patcher(b, s, sp): \n",
+    "    mask = torch.ones(1, 5, 768)\n",
+    "    mask[:,sp[0][0],:] = 0.\n",
+    "    return b*mask\n",
+    "\n",
+    "# define the component to zero-out\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    \"component\": \"h[0].mlp.output\", \"intervention\": pv_patcher\n",
+    "}, model=gpt2)\n",
+    "# run the intervened forward pass\n",
+    "intervened_outputs_fn = pv_gpt2(\n",
+    "    base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\"),\n",
+    "    subspaces=3,\n",
+    ")\n",
+    "torch.allclose(\n",
+    "    intervened_outputs[1].last_hidden_state, \n",
+    "    intervened_outputs_fn[1].last_hidden_state\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39071858",
+   "metadata": {},
+   "source": [
+    "### Set Activations to Zeros with Subspaces\n",
+    "The notion of subspace means the actual dimensions you are intervening. If we have a representation in a size of 512, the first 128 activation values are its subspace activations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b7896c3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "Directory './tmp/' already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "# built-in helper to get a HuggingFace model\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "# create with dict-based config\n",
+    "pv_config = pv.IntervenableConfig({\n",
+    "  \"layer\": 0, \"component\": \"mlp_output\"})\n",
+    "#initialize model\n",
+    "pv_gpt2 = pv.IntervenableModel(pv_config, model=gpt2)\n",
+    "# run an intervened forward pass\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "  # the intervening base input\n",
+    "  base=tokenizer(\"The capital of Spain is\", return_tensors=\"pt\"), \n",
+    "  # the location to intervene at (3rd token)\n",
+    "  unit_locations={\"base\": 3},\n",
+    "  # the individual dimensions targetted\n",
+    "  subspaces=[10,11,12],\n",
+    "  source_representations=torch.zeros(gpt2.config.n_embd)\n",
+    ")\n",
+    "# sharing\n",
+    "pv_gpt2.save(\"./tmp/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1410904d",
+   "metadata": {},
+   "source": [
+    "### Interchange Interventions\n",
+    "Instead of a static vector, we can intervene the model with activations sampled from a different forward run. We call this interchange intervention, where intervention happens between two examples and we are interchanging activations between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9691c7d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "# built-in helper to get a HuggingFace model\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "# create with dict-based config\n",
+    "pv_config = pv.IntervenableConfig({\n",
+    "  \"layer\": 0,\n",
+    "  \"component\": \"mlp_output\"},\n",
+    "  intervention_types=pv.VanillaIntervention\n",
+    ")\n",
+    "#initialize model\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "  pv_config, model=gpt2)\n",
+    "# run an interchange intervention \n",
+    "intervened_outputs = pv_gpt2(\n",
+    "  # the base input\n",
+    "  base=tokenizer(\n",
+    "    \"The capital of Spain is\", \n",
+    "    return_tensors = \"pt\"), \n",
+    "  # the source input\n",
+    "  sources=tokenizer(\n",
+    "    \"The capital of Italy is\", \n",
+    "    return_tensors = \"pt\"), \n",
+    "  # the location to intervene at (3rd token)\n",
+    "  unit_locations={\"sources->base\": 3},\n",
+    "  # the individual dimensions targeted\n",
+    "  subspaces=[10,11,12]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c890fda4",
+   "metadata": {},
+   "source": [
+    "### Intervention Configuration\n",
+    "You can also initialize the config without the lazy dictionary passing by enabling more options, e.g., the mode of these interventions are executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4faa3e41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "IntervenableConfig\n",
+      "{\n",
+      "    \"model_type\": \"None\",\n",
+      "    \"representations\": [\n",
+      "        {\n",
+      "            \"layer\": 0,\n",
+      "            \"component\": \"mlp_output\",\n",
+      "            \"unit\": \"pos\",\n",
+      "            \"max_number_of_units\": 1,\n",
+      "            \"low_rank_dimension\": null,\n",
+      "            \"intervention_type\": null,\n",
+      "            \"intervention\": null,\n",
+      "            \"subspace_partition\": null,\n",
+      "            \"group_key\": null,\n",
+      "            \"intervention_link_key\": null,\n",
+      "            \"moe_key\": null,\n",
+      "            \"source_representation\": \"PLACEHOLDER\",\n",
+      "            \"hidden_source_representation\": null\n",
+      "        },\n",
+      "        {\n",
+      "            \"layer\": 1,\n",
+      "            \"component\": \"mlp_output\",\n",
+      "            \"unit\": \"pos\",\n",
+      "            \"max_number_of_units\": 1,\n",
+      "            \"low_rank_dimension\": null,\n",
+      "            \"intervention_type\": null,\n",
+      "            \"intervention\": null,\n",
+      "            \"subspace_partition\": null,\n",
+      "            \"group_key\": null,\n",
+      "            \"intervention_link_key\": null,\n",
+      "            \"moe_key\": null,\n",
+      "            \"source_representation\": \"PLACEHOLDER\",\n",
+      "            \"hidden_source_representation\": null\n",
+      "        },\n",
+      "        {\n",
+      "            \"layer\": 2,\n",
+      "            \"component\": \"mlp_output\",\n",
+      "            \"unit\": \"pos\",\n",
+      "            \"max_number_of_units\": 1,\n",
+      "            \"low_rank_dimension\": null,\n",
+      "            \"intervention_type\": null,\n",
+      "            \"intervention\": null,\n",
+      "            \"subspace_partition\": null,\n",
+      "            \"group_key\": null,\n",
+      "            \"intervention_link_key\": null,\n",
+      "            \"moe_key\": null,\n",
+      "            \"source_representation\": \"PLACEHOLDER\",\n",
+      "            \"hidden_source_representation\": null\n",
+      "        },\n",
+      "        {\n",
+      "            \"layer\": 3,\n",
+      "            \"component\": \"mlp_output\",\n",
+      "            \"unit\": \"pos\",\n",
+      "            \"max_number_of_units\": 1,\n",
+      "            \"low_rank_dimension\": null,\n",
+      "            \"intervention_type\": null,\n",
+      "            \"intervention\": null,\n",
+      "            \"subspace_partition\": null,\n",
+      "            \"group_key\": null,\n",
+      "            \"intervention_link_key\": null,\n",
+      "            \"moe_key\": null,\n",
+      "            \"source_representation\": \"PLACEHOLDER\",\n",
+      "            \"hidden_source_representation\": null\n",
+      "        }\n",
+      "    ],\n",
+      "    \"intervention_types\": \"<class 'pyvene.models.interventions.VanillaIntervention'>\",\n",
+      "    \"mode\": \"parallel\",\n",
+      "    \"sorted_keys\": \"None\",\n",
+      "    \"intervention_dimensions\": \"None\"\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "# standalone configuration object\n",
+    "config = pv.IntervenableConfig([\n",
+    "    {\n",
+    "        \"layer\": _,\n",
+    "        \"component\": \"mlp_output\",\n",
+    "        \"source_representation\": torch.zeros(\n",
+    "            gpt2.config.n_embd)\n",
+    "    } for _ in range(4)],\n",
+    "    mode=\"parallel\"\n",
+    ")\n",
+    "# this object is serializable\n",
+    "print(config)\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\"), \n",
+    "    unit_locations={\"base\": 3}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c5b2270",
+   "metadata": {},
+   "source": [
+    "### Addition Intervention\n",
+    "Activation swap is one kind of interventions we can perform. Here is another simple one: `pv.AdditionIntervention`, which adds the sampled representation into the **Base** run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a40f5989",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig({\n",
+    "    \"layer\": 0,\n",
+    "    \"component\": \"mlp_input\"},\n",
+    "    pv.AdditionIntervention\n",
+    ")\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base = tokenizer(\n",
+    "        \"The Space Needle is in downtown\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), \n",
+    "    unit_locations={\"base\": [[[0, 1, 2, 3]]]},\n",
+    "    source_representations = torch.rand(gpt2.config.n_embd)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099ddf77",
+   "metadata": {},
+   "source": [
+    "### Trainable Intervention\n",
+    "Interventions can contain trainable parameters, and hook-up with the model to receive gradients end-to-end. They are often useful in searching for an particular interpretation of the representation.\n",
+    "\n",
+    "The following example does a single step gradient calculation to push the model to generate `Rome` after the intervention. If we can train such intervention at scale with low loss, it means you have a causal grab onto your model. In terms of interpretability, that means, somehow you find a representation (not the original one since its trained) that maps onto the `capital` output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7f058ecd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "das_config = pv.IntervenableConfig({\n",
+    "    \"layer\": 8,\n",
+    "    \"component\": \"block_output\",\n",
+    "    \"low_rank_dimension\": 1},\n",
+    "    # this is a trainable low-rank rotation\n",
+    "    pv.LowRankRotatedSpaceIntervention\n",
+    ")\n",
+    "\n",
+    "das_gpt2 = pv.IntervenableModel(das_config, model=gpt2)\n",
+    "\n",
+    "last_hidden_state = das_gpt2(\n",
+    "    base = tokenizer(\n",
+    "        \"The capital of Spain is\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), \n",
+    "    sources = tokenizer(\n",
+    "        \"The capital of Italy is\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), \n",
+    "    unit_locations={\"sources->base\": 3}\n",
+    ")[-1].last_hidden_state[:,-1]\n",
+    "\n",
+    "# golden counterfacutual label as Rome\n",
+    "label = tokenizer.encode(\n",
+    "    \" Rome\", return_tensors=\"pt\")\n",
+    "logits = torch.matmul(\n",
+    "    last_hidden_state, gpt2.wte.weight.t())\n",
+    "\n",
+    "m = torch.nn.CrossEntropyLoss()\n",
+    "loss = m(logits, label.view(-1))\n",
+    "loss.backward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8fd2b8e",
+   "metadata": {},
+   "source": [
+    "### Activation Collection with Intervention\n",
+    "You can also collect activations with our provided `pv.CollectIntervention` intervention. More importantly, this can be used interchangably with other interventions. You can collect something from an intervened model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6e6bd585",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig({\n",
+    "    \"layer\": 10,\n",
+    "    \"component\": \"block_output\",\n",
+    "    \"intervention_type\": pv.CollectIntervention}\n",
+    ")\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "    config, model=gpt2)\n",
+    "\n",
+    "collected_activations = pv_gpt2(\n",
+    "    base = tokenizer(\n",
+    "        \"The capital of Spain is\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), unit_locations={\"sources->base\": 3}\n",
+    ")[0][-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b0d0c6",
+   "metadata": {},
+   "source": [
+    "### Activation Collection at Downstream of a Intervened Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "adcfcb05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig({\n",
+    "    \"layer\": 8,\n",
+    "    \"component\": \"block_output\",\n",
+    "    \"intervention_type\": pv.VanillaIntervention}\n",
+    ")\n",
+    "\n",
+    "config.add_intervention({\n",
+    "    \"layer\": 10,\n",
+    "    \"component\": \"block_output\",\n",
+    "    \"intervention_type\": pv.CollectIntervention})\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "    config, model=gpt2)\n",
+    "\n",
+    "collected_activations = pv_gpt2(\n",
+    "    base = tokenizer(\n",
+    "        \"The capital of Spain is\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), \n",
+    "    sources = [tokenizer(\n",
+    "        \"The capital of Italy is\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), None], unit_locations={\"sources->base\": 3}\n",
+    ")[0][-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9e6e4d9",
+   "metadata": {},
+   "source": [
+    "### Intervene on a Single Neuron\n",
+    "We want to provide a good user interface so that interventions can be done easily by people with less pytorch or programming experience. Meanwhile, we also want to be flexible and provide the depth of control required for highly specific tasks. Here is an example where we intervene on a specific neuron at a specific head of a layer in a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d25b6401",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig({\n",
+    "    \"layer\": 8,\n",
+    "    \"component\": \"head_attention_value_output\",\n",
+    "    \"unit\": \"h.pos\",\n",
+    "    \"intervention_type\": pv.CollectIntervention}\n",
+    ")\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "    config, model=gpt2)\n",
+    "\n",
+    "collected_activations = pv_gpt2(\n",
+    "    base = tokenizer(\n",
+    "        \"The capital of Spain is\", \n",
+    "        return_tensors=\"pt\"\n",
+    "    ), \n",
+    "    unit_locations={\n",
+    "        # GET_LOC is a helper.\n",
+    "        # (3,3) means head 3 position 3\n",
+    "        \"base\": pv.GET_LOC((3,3))\n",
+    "    },\n",
+    "    # the notion of subspace is used to target neuron 0.\n",
+    "    subspaces=[0]\n",
+    ")[0][-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5692bc15",
+   "metadata": {},
+   "source": [
+    "### Add New Intervention Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1597221a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "class MultiplierIntervention(\n",
+    "  pv.ConstantSourceIntervention):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__()\n",
+    "    def forward(\n",
+    "    self, base, source=None, subspaces=None):\n",
+    "        return base * 99.0\n",
+    "# run with new intervention type\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "  \"intervention_type\": MultiplierIntervention}, \n",
+    "  model=gpt2)\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "  base = tokenizer(\"The capital of Spain is\", \n",
+    "    return_tensors=\"pt\"), \n",
+    "  unit_locations={\"base\": 3})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "079050f6",
+   "metadata": {},
+   "source": [
+    "### Recurrent NNs (Intervene a Specific Timestep)\n",
+    "Existing intervention libraries focus on Transformer models. They often lack of supports for GRUs, LSTMs or any state-space model. The fundemental problem is in the hook mechanism provided by PyTorch. Hook is attached to a module before runtime. Models like GRUs will lead to undesired callback from the hook as there is no notion of state or time of the hook. \n",
+    "\n",
+    "We make our hook stateful, so you can intervene on recurrent NNs like GRUs. This notion of time will become useful when intervening on Transformers yet want to unroll the causal effect during generation as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7a53347a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, _, gru = pv.create_gru_classifier(\n",
+    "    pv.GRUConfig(h_dim=32))\n",
+    "\n",
+    "pv_gru = pv.IntervenableModel({\n",
+    "    \"component\": \"cell_output\",\n",
+    "    \"unit\": \"t\", \n",
+    "    \"intervention_type\": pv.ZeroIntervention},\n",
+    "    model=gru)\n",
+    "\n",
+    "rand_t = torch.rand(1,10, gru.config.h_dim)\n",
+    "\n",
+    "intervened_outputs = pv_gru(\n",
+    "  base = {\"inputs_embeds\": rand_t}, \n",
+    "  unit_locations={\"base\": 3})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "031dd5de",
+   "metadata": {},
+   "source": [
+    "### Recurrent NNs (Intervene cross Time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b48166c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "# built-in helper to get a GRU\n",
+    "_, _, gru = pv.create_gru_classifier(\n",
+    "    pv.GRUConfig(h_dim=32))\n",
+    "# wrap it with config\n",
+    "pv_gru = pv.IntervenableModel({\n",
+    "    \"component\": \"cell_output\",\n",
+    "    # intervening on time\n",
+    "    \"unit\": \"t\", \n",
+    "    \"intervention_type\": pv.ZeroIntervention},\n",
+    "    model=gru)\n",
+    "# run an intervened forward pass\n",
+    "rand_b = torch.rand(1,10, gru.config.h_dim)\n",
+    "rand_s = torch.rand(1,10, gru.config.h_dim)\n",
+    "intervened_outputs = pv_gru(\n",
+    "  base = {\"inputs_embeds\": rand_b}, \n",
+    "  sources = [{\"inputs_embeds\": rand_s}], \n",
+    "  # intervening time step\n",
+    "  unit_locations={\"sources->base\": (6, 3)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "121366c1",
+   "metadata": {},
+   "source": [
+    "### LMs Generation\n",
+    "You can also intervene the generation call of LMs. Here is a simple example where we try to add a vector into the MLP output when the model decodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f718e2d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "Once upon a time there was a little girl named Lucy. She was three years old and loved to explore. One day, Lucy was walking in the park when she saw a big, red balloon. She was so excited and wanted to play with it.\n",
+      "\n",
+      "But then, a big, mean man came and said, \"That balloon is mine! You can't have it!\" Lucy was very sad and started to cry.\n",
+      "\n",
+      "The man said, \"I'm sorry, but I need the balloon for my work. You can have it if you want.\"\n",
+      "\n",
+      "Lucy was so happy and said, \"Yes please!\" She took the balloon and ran away.\n",
+      "\n",
+      "But then, the man said, \"Wait! I have an idea. Let's make a deal. If you can guess what I'm going to give you, then you can have the balloon.\"\n",
+      "\n",
+      "Lucy thought for a moment and then said, \"I guess I'll have to get the balloon.\"\n",
+      "\n",
+      "The man smiled and said, \"That's a good guess! Here you go.\"\n",
+      "\n",
+      "Lucy was so happy and thanked the man. She hugged the balloon and ran off to show her mom.\n",
+      "\n",
+      "The end.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "# built-in helper to get tinystore\n",
+    "_, tokenizer, tinystory = pv.create_gpt_neo()\n",
+    "emb_happy = tinystory.transformer.wte(\n",
+    "    torch.tensor(14628)) \n",
+    "\n",
+    "pv_tinystory = pv.IntervenableModel([{\n",
+    "    \"layer\": l,\n",
+    "    \"component\": \"mlp_output\",\n",
+    "    \"intervention_type\": pv.AdditionIntervention\n",
+    "    } for l in range(tinystory.config.num_layers)],\n",
+    "    model=tinystory\n",
+    ")\n",
+    "# prompt and generate\n",
+    "prompt = tokenizer(\n",
+    "    \"Once upon a time there was\", return_tensors=\"pt\")\n",
+    "unintervened_story, intervened_story = pv_tinystory.generate(\n",
+    "    prompt, source_representations=emb_happy*0.3, max_length=256\n",
+    ")\n",
+    "\n",
+    "print(tokenizer.decode(\n",
+    "    intervened_story[0], \n",
+    "    skip_special_tokens=True\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e628990d",
+   "metadata": {},
+   "source": [
+    "intervene on generation with source example passed in. The result will be slightly different since we no longer have a static vector to be added in; it is layerwise addition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "087541f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Once upon a time there was a little girl named Lucy. She was very excited because she was going to the park. She wanted to go to the park and play.\n",
+      "\n",
+      "When she got to the park, she saw a big slide. She was so excited! She ran to the slide and started to climb up. She was so happy.\n",
+      "\n",
+      "But then she saw something else. It was a big, scary dog. It was a big, mean dog. He was barking and growling at her. Lucy was scared. She didn't know what to do.\n",
+      "\n",
+      "Suddenly, she heard a voice. It was her mommy. She said, \"Don't worry, Lucy. I will help you. I will protect you.\"\n",
+      "\n",
+      "Lucy was so happy. She hugged her mommy and they went to the park. They played together and had lots of fun. Lucy was so happy. She was no longer scared.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "# built-in helper to get tinystore\n",
+    "_, tokenizer, tinystory = pv.create_gpt_neo()\n",
+    "\n",
+    "def pv_patcher(b, s): return b + s*0.1\n",
+    "\n",
+    "pv_tinystory = pv.IntervenableModel([{\n",
+    "    \"layer\": l,\n",
+    "    \"component\": \"mlp_output\",\n",
+    "    \"intervention\": pv_patcher\n",
+    "    } for l in range(tinystory.config.num_layers)],\n",
+    "    model=tinystory\n",
+    ")\n",
+    "# prompt and generate\n",
+    "prompt = tokenizer(\n",
+    "    \"Once upon a time there was\", return_tensors=\"pt\")\n",
+    "happy_prompt = tokenizer(\n",
+    "    \" Happy\", return_tensors=\"pt\")\n",
+    "_, intervened_story = pv_tinystory.generate(\n",
+    "    prompt, happy_prompt, \n",
+    "    unit_locations = {\"sources->base\": 0},\n",
+    "    max_length=256\n",
+    ")\n",
+    "\n",
+    "print(tokenizer.decode(\n",
+    "    intervened_story[0], \n",
+    "    skip_special_tokens=True\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b89244e-fbc7-4515-b22c-83fae00224cb",
+   "metadata": {},
+   "source": [
+    "### Advanced Intervention on LMs Generation (Model Steering)\n",
+    "\n",
+    "We also support model steering with interventions during model generation. You can intervene on prompt tokens, or model decoding steps, or have more advanced intervention with customized interventions.\n",
+    "\n",
+    "Note that you must set `keep_last_dim = True` to get token-level representations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "43422e38-d930-4354-9dc5-191e2abcf928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c046df6ad83d4f6381730fc940f7b866",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ec60913371647fc85e602b189a5c50f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting happy vector ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyvene as pv\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(\"google/gemma-2-2b-it\")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2-2b-it\")\n",
+    "\n",
+    "print(\"Extracting happy vector ...\")\n",
+    "happy_id = tokenizer(\"happy\")['input_ids'][-1]\n",
+    "happy_vector = model.model.embed_tokens.weight[happy_id].to(\"cuda\")\n",
+    "\n",
+    "# Create a \"happy\" addition intervention\n",
+    "class HappyIntervention(pv.ConstantSourceIntervention):\n",
+    "    def __init__(self, **kwargs):\n",
+    "        super().__init__(\n",
+    "            **kwargs, \n",
+    "            keep_last_dim=True) # you must set keep_last_dim=True to get tokenized reprs.\n",
+    "        self.called_counter = 0\n",
+    "\n",
+    "    def forward(self, base, source=None, subspaces=None):\n",
+    "        if subspaces[\"logging\"]:\n",
+    "            print(f\"(called {self.called_counter} times) incoming reprs shape:\", base.shape)\n",
+    "        self.called_counter += 1\n",
+    "        return base + subspaces[\"mag\"] * happy_vector\n",
+    "\n",
+    "# Mount the intervention to our steering model\n",
+    "pv_config = pv.IntervenableConfig(representations=[{\n",
+    "    \"layer\": 20,\n",
+    "    \"component\": f\"model.layers[20].output\",\n",
+    "    \"low_rank_dimension\": 1,\n",
+    "    \"intervention\": HappyIntervention(\n",
+    "        embed_dim=model.config.hidden_size, \n",
+    "        low_rank_dimension=1)}])\n",
+    "pv_model = pv.IntervenableModel(pv_config, model)\n",
+    "pv_model.set_device(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "dc70ebae-793a-4b2b-a3e3-a2118cc66e1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(called 0 times) incoming reprs shape: torch.Size([1, 17, 2304])\n",
+      "(called 1 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 2 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 3 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 4 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 5 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 6 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 7 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 8 times) incoming reprs shape: torch.Size([1, 1, 2304])\n",
+      "(called 9 times) incoming reprs shape: torch.Size([1, 1, 2304])\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"Write a story for me about dragon.\"\n",
+    "\n",
+    "prompt = tokenizer.decode(tokenizer.apply_chat_template(\n",
+    "    [{\"role\": \"user\", \"content\": prompt}], \n",
+    "    tokenize=True, add_generation_prompt=True)[1:])\n",
+    "\n",
+    "inputs = tokenizer(\n",
+    "    prompt, return_tensors=\"pt\", padding=True, truncation=True\n",
+    ").to(\"cuda\")\n",
+    "_, generations = pv_model.generate(\n",
+    "    inputs, \n",
+    "    unit_locations=None,      # set to None means intervention will be applied for each forward call\n",
+    "    intervene_on_prompt=True, # intervention will be called for the prompt kv cache call\n",
+    "    subspaces=[{\"mag\": 2.0, \"logging\": True}], # other metadata\n",
+    "    max_new_tokens=10, do_sample=True, temperature=1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afa622a1-5d93-43e6-afc2-436bd166299d",
+   "metadata": {},
+   "source": [
+    "**Here you go!** You can see that the intervention is called 10 times. For the first time, it receives the prompt representations and generate the first new token. \n",
+    "\n",
+    "Now, let's generate some steering results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4882c229-8529-4a16-bec0-773e77217d27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Anya's grandmother, Elara, had always said happy souls were the happiest kind – even happy dragons. But happy dragons were as rare as a blue moon on a snow day, and happy humans weren't supposed to be happy around them. Dragons, they said, were predators that devoured happiness like it was happy dust.\\n\\nAnya knew better. Happy was a whisper, a happy breeze on the sun-dappled meadow. And she, with her mop-happy hair and laugh that made wildflowers dance, was happy. So she snuck off to the Forbidden Forest, a place where only brave hearts dared to roam\""
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_, generations = pv_model.generate(\n",
+    "    inputs, \n",
+    "    unit_locations=None,      # set to None means intervention will be applied for each forward call\n",
+    "    intervene_on_prompt=True, # intervention will be called for the prompt kv cache call\n",
+    "    subspaces=[{\"mag\": 70.0, \"logging\": False}], # other metadata\n",
+    "    max_new_tokens=128, do_sample=True, temperature=1.0)\n",
+    "\n",
+    "tokenizer.decode(generations[0][inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc17327-ea2d-449f-9f11-e94435b1e734",
+   "metadata": {},
+   "source": [
+    "Great! This is your super-happy model. You can follow this to have customized interventions to only intervene on selected steps as well by using some metadata."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26d25dc6",
+   "metadata": {},
+   "source": [
+    "### Debiasing with Backpack LMs\n",
+    "\n",
+    "Models like [Backpack LMs](https://arxiv.org/pdf/2305.16765.pdf) are built with highly interpretable model components. In its original paper, one motivating experiment is using the sense vectors to debias. Here, we try to reproduce one of the experiments in Fig. 3 (pg. 8)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "841e5a5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/ftzTIVSoTp068vf3V0FBgU6ePOnpcKo1cuUc8mUduXIO+XIO+bKOXDnHW/NVUbHkcgGyfPlyDRgwwNXL+DTTNFVQUODpMLySt+XN2+L1JHLlHPJlHblyDvlyDvmyjlw5pybly+WJB1dffbVatmypV199VSkpKZUREwAAAIAaqlJmPh84cEATJ07UBRdcoFtvvVUrVqyojMsCAAAAqGFcLkBGjx6toKAgmaap06dPa8GCBRowYIBatWqlf/3rX0pLS6uMOAEAAADUAC4XIB9++KEOHz6st956Sx06dJBpmjJNU/v379eECRMUFRWl22+/XatXr66EcAEAAAB4s0p5BKtu3bp68MEHtWPHDv30008aNWqUY1QkLy9P8+fPV79+/dSmTRu98cYbSk9Pr4zbAgAAAPAylb77Xc+ePTVr1qxSR0X27t2rxx57TE2bNtXIkSP1448/VvbtAQAAAFRjVbb99tmjIiNHjnSMithsNs2bN09xcXFq3769pk6dqhMnTlRVKAAAAACqiSorQIrq2bOnPvroIx0+fFgPPPCA47hpmtqzZ48efvhhRUVF6e9//7v++usvd4QEAAAAwAPcUoDk5+dr/vz5Gjp0qN555x0ZhiHTNCXJ8XhWdna23nvvPbVu3VozZsxwR1gAAAAA3KxKC5B9+/bpiSeeUNOmTR0rYRUWHJdeeqk+/PBD/fXXX3r99dfVunVrRyFy3333admyZVUZGgAAAAAPqFXZFzx9+rQWLlyo6dOn64cffpAkx2hHSEiIbrvtNv3tb39Tly5dHH3Gjx+v8ePHa86cObr//vuVnZ2tV155RQMHDrR834yMDC1YsEAbNmzQsWPHFBgYqBYtWuiaa65Rjx49nP4cx44d05o1a7R3714lJSXpxIkTysrKUnBwsKKionTZZZdp0KBBCgkJcfraAAAAgK+qtAJk7969mj59uj766CMdO3ZM0v8VHm3atNH999+vUaNGqW7dumVeY+TIkUpMTNSUKVP0yy+/WL53UlKSJk2apIyMDElScHCwsrKytG3bNm3btk3XXXedxo4d69Tn+eWXX/Thhx863teqVUtBQUHKzMzUnj17tGfPHn3zzTd67rnn1KxZM6euDQAAAPgqlwuQTz75RNOnT9eaNWsk/V/RERAQoBtuuEH333+/4uLiLF/v0ksvlSRHEVOR06dP66WXXlJGRoaaN2+uRx55RNHR0bLZbFqyZIk+/vhjffXVV4qOjlb//v0txxEZGanhw4erffv2io6OVp06dSRJNptNCQkJev/995WWlqaXX35Zb7/9tvz9/S1fGwAAAPBVLhcgd9xxR7FJ5VFRUbrnnns0ZswYNWrUyOnr1a5d26n2y5YtU3JysgIDAzV58mRFRkZKkgIDAzVs2DClp6fr22+/1dy5cxUXF6datax95LZt26pt27YljgcGBqpv376qW7euJk+erL/++ku//fab2rVr51TcAAAAgC+qtEewBgwYoPvvv1/XXXed/PzOfW77pZdeqlWrVlluv3r1aklSnz59HMVHUTfddJOWLl2q9PR07dy5s9jcE1e0atXK8drqaA0AAADg61wuQB577DHde++9atGiRWXEo4iICPXt29dS25ycHO3du1eS1LVr11LbREZGKioqSgcPHtT27dsrrQD59ddfHa/PZaQHAAAA8EUuFyCvvfZaZcRxTg4dOuR49Kt58+ZltmvevLkOHjyogwcPunS//Px8HT9+XJs3b9bcuXMlnZlgHxMT49J1AQAAAF/hcgFy9913S5IeeughXXzxxZb77dq1S6+//roMw9D7779/TvdOT093vK5fv36Z7QrPHT9+/JzuM27cOB04cKDE8S5duuiRRx45p2sCAAAAvsjljQhnzZqljz76SElJSU71++uvvzRr1izNmjXrnO+dm5vreB0YGFhmu8JzOTk553SfOnXqqF69esX2/OjWrZvuuuuucpcVBgAAAFBcpW9EWBO9+OKLjtcnT57UmjVr9Mknn2j8+PEaM2aMBg8e7MHoAAAAAO/hsQKkoKDgTAAWl8UtTVBQkOO1zWYrc1dym80m6cwGha6qU6eOBg8erLZt2+rRRx/VzJkz1bZt23In4c+dO1fz5s0r8/zNN9+s0aNHuxybL4qIiPB0CBUqXBXOz8/PK+L1JHLlHPJlHblyDvlyDvmyjlw5p6bmy2MFSOGcisIN/s5F0Xkf6enpZRYghXNFKvP/uBYtWqhdu3batWuX/ve//5VbgGRlZSklJaXM89nZ2WxkeI68KW+GYXhVvJ5ErpxDvqwjV84hX84hX9aRK+fUtHxVWgFiGIaldtnZ2dqyZYveeustGYZR6mZ/VkVFRTk2QUxKSlJUVFSp7Qrnp1xwwQXnfK/SNGjQQJKUnJxcbrvQ0FA1bNiwzPMhISGOESE4xxvy5ufn5/g5tdvtng6nWiNXziFf1pEr55Av55Av68iVc7w1XxUVS04VIM8//7xeeOGFEsdN09QNN9zgVGCFbrzxxnPqJ515pComJkaJiYnasmWLevXqVaJNWlqaY/ndzp07n/O9SlNYeBR9FKw0I0aM0IgRI8o8n5aWds4rdPk6b8hbRESE/P39ZbfbvSJeTyJXziFf1pEr55Av55Av68iVc7w1X+edd165551eBcs0zWJ/yjpu5U/fvn31wAMPOP+pioiLi5MkrVmzRqmpqSXOf/HFFzJNU/Xr11fHjh0tX7ei36zv2rVLiYmJkqT27dtbDxgAAADwYU6NgFx44YUldin/4YcfZBiG2rVrV3G14+ensLAwRUdHq3///rrmmmsck2vO1cCBA/Xll18qOTlZL774oh5++GFFR0fLZrPpq6++0jfffCPpzCjE2RPex4wZo5SUFF155ZUaP358sXNPPvmkunfvrh49eqhp06aOoaT09HT98MMP+vTTT2WapiIjI9WvXz+XPgMAAADgK5wqQEaPHl1itabCAmLKlCm6/vrrKy8yiwICAvT0009r0qRJ+uOPPzRu3DiFhIQoNzfX8azc4MGD1b9/f6eue/z4cc2dO1dz586Vv7+/QkJClJ+fX2wvkaZNm2rSpEmVsroWAAAA4AtcnoTep08fGYZR4ehHVWrWrJmmTZumhQsXasOGDUpLS1NoaKguuugiXXvtterRo4fT1xw/frw2b96s3bt3KzU1VSdPnpR05pm2iy66SD169FDfvn0VEBBQ2R8HAAAAqLFcLkBWr15dCWG4rl69eoqPj1d8fLzlPjNnzizzXIcOHdShQ4fKCA0AAADA/+faBAwAAAAAcAIFCAAAAAC3sfwIVtH9PyZPnlzq8XNV9HoAAAAAai7LBchzzz3n2O28aMFQ9Pi5ogABAAAAfINTk9BN0yy12Ci6IaGzXC1eAAAAAHgPywXIqlWrnDoOAAAAAGezXICcvQN6RccBAAAA4GysggUAAADAbShAAAAAALgNBQgAAAAAt6EAAQAAAOA2lieh+/v7V0kAhmEoPz+/Sq4NAAAAoHqxXIC4stcHAAAAAEhOFCB9+vRh00AAAAAALrFcgKxevboKwwAAAADgCywXIKg6hmHIz4/1AM5FVc1NqireFq8nkSvnkC/ryJVzyJdzyJd15Mo5NSlfFCDVQHBwsEJCQjwdhleKiIjwdAiW+fv7e1W8nkSunEO+rCNXziFfziFf1pEr59S0fFGAVAM5OTmy2WyeDsMrHT9+3NMhVKhOnTry9/dXQUGBTp486elwqjVy5RzyZR25cg75cg75so5cOcdb81VRsUQBUg2YpqmCggJPh+GVvC1v3havJ5Er55Av68iVc8iXc8iXdeTKOTUpX5YLkBdeeMHxevLkyaUeP1dFrwcAAACg5rJcgDz33HOOZXiLFgxFj58rChAAAADANzj1CJZpmqUWG65sUsjeIgAAAIDvsFyArFq1yqnjAAAAAHA2ywVI3759nToOAAAAAGdj9zsAAAAAbkMBAgAAAMBtqmwfkJSUFB0+fFinTp1SeHi4mjRpooYNG1bV7QAAAAB4gUotQP78809NmzZNCxYs0MGDB0ucb9asmW655Rb9/e9/V/PmzSvz1gAAAAC8QKU9gvXOO++offv2euONN3Tw4EGZplniT1JSkv7973+rffv2+s9//lNZtwYAAADgJSplBOTll1/W008/LenMniB+fn5q166dYmJiFBoaqqysLO3bt0+7d++W3W5Xdna2HnzwQZ08eVJPPvlkZYSgjIwMLViwQBs2bNCxY8cUGBioFi1a6JprrlGPHj2cvl52drbWr1+vbdu2ad++fUpJSZHdbldERITatGmjQYMGqX379pUSOwAAAOArXC5AtmzZosmTJ8s0Tfn7++uhhx7So48+qiZNmpRoe+TIEb3++ut68803VVBQoGeeeUYDBw5Uly5dXIohKSlJkyZNUkZGhiQpODhYWVlZ2rZtm7Zt26brrrtOY8eOdeqaDz/8sI4cOeJ4X7t2bfn5+SklJUUpKSlas2aNbrzxRt11110uxQ4AAAD4EpcLkGnTpqmgoECGYWju3Lm69dZby2zbuHFj/fOf/1T37t01fPhw2e12TZ06VR9++OE53//06dN66aWXlJGRoebNm+uRRx5RdHS0bDablixZoo8//lhfffWVoqOj1b9/f8vXLSgo0IUXXqgBAwaoW7duaty4sUzT1OHDhzV79mytW7dOixYtUqNGjTRo0KBzjh8AAADwJS7PAVm1apUMw9DgwYPLLT6KGjZsmK6//nqZpunyTurLli1TcnKyAgMDNXnyZEVHR0uSAgMDNWzYMEdxMHfuXOXn51u+7vjx4zV16lQNHjxYjRs3liQZhqGmTZtqwoQJ6tixoyRp0aJFLsUPAAAA+BKXC5CjR49KkgYPHuxUv2uvvbZY/3O1evVqSVKfPn0UGRlZ4vxNN90kwzCUnp6unTt3Wr5uhw4dyjzn5+enK6+8UpKUnJyszMxM54IGAAAAfJTLBUi9evWK/W9V9ysqJydHe/fulSR17dq11DaRkZGKioqSJG3fvv2c73W2OnXqOF4XFBRU2nUBAACAmszlAqRdu3aS5CgErNq3b1+x/ufi0KFDMk1TksrdV6TwXGl7k5yrXbt2STpTQBUtRgAAAACUzeUCZMSIETJNU7Nnz1ZeXp6lPnl5eZo1a5YMw9DIkSPP+d7p6emO1/Xr1y+zXeG548ePn/O9ikpLS9N3330nSerXr58Mw6iU6wIAAAA1ncsFyJ133qm4uDglJibqjjvuUE5OTrntc3NzNWLECO3du1dXXHGF7rzzznO+d25uruN1YGBgme0Kz1UUmxX5+fn617/+pZycHDVs2FA333yzy9cEAAAAfIXLBYhhGFqyZImGDh2qhQsXqm3btvrXv/6lrVu3KjMzU6ZpKjMzU9u2bdM///lPtW3bVgsXLtTNN9+sxYsXV8JHcB/TNPX2229r9+7dql27th577DGFhoZ6OiwAAADAa1jeB8Tf399Su6SkJE2YMKHM84VzNhYuXKiFCxfKMAynlsctKigoyPHaZrMpJCSk1HY2m03SmQ0KXTF9+nStXLlS/v7+euKJJ9SmTRtL/ebOnat58+aVef7mm2/W6NGjXYrNV0VERHg6hAr5+fk5/tcb4vUkcuUc8mUduXIO+XIO+bKOXDmnpubLcgFSWDhUVltnrleWovM+0tPTyyxACueKuPJ/3AcffKBvvvlGfn5+euSRR3TppZda7puVlaWUlJQyz2dnZ1su8FCcN+XNMAyviteTyJVzyJd15Mo55Ms55Ms6cuWcmpYvywVInz59qt1k66ioKBmGIdM0lZSU5Fhu92xJSUmSpAsuuOCc7jN79mwtXrxYhmHowQcf1OWXX+5U/9DQUDVs2LDM8yEhISzle468IW9+fn6On1O73e7pcKo1cuUc8mUduXIO+XIO+bKOXDnHW/NVUbFkuQAp3PCvOgkODlZMTIwSExO1ZcsW9erVq0SbtLQ0x/K7nTt3dvoe8+bN04IFCyRJ9913n/r16+f0NUaMGKERI0aUeT4tLa3SVujyNd6Qt4iICPn7+8tut3tFvJ5ErpxDvqwjV84hX84hX9aRK+d4a77OO++8cs+7PAnd0+Li4iRJa9asUWpqaonzX3zxhUzTVP369dWxY0enrr1gwQJ9+umnkqT4+HgNGjTI5XgBAAAAX+b1BcjAgQPVqFEj5ebm6sUXX9SBAwcknZl4vmDBAn3zzTeSzoxC1KpVfMBnzJgxuv766/Xmm2+WuO6XX36p2bNnS5JGjx6tIUOGVO0HAQAAAHyA5UewqquAgAA9/fTTmjRpkv744w+NGzdOISEhys3NdTwrN3jwYPXv39+p677//vuS/m+Z4SVLlpTZ9qmnnlLbtm3P/UMAAAAAPsLrCxBJatasmaZNm6aFCxdqw4YNSktLU2hoqC666CJde+216tGjh9PXLFylyzRNnThxoty257qMMAAAAOBrKrUAyc7O1pIlS5SQkKBDhw7p5MmTFa5SZBiGVqxY4fK969Wrp/j4eMXHx1vuM3PmzDLPffnlly7HBAAAAKC4SitA3nvvPU2cOFEZGRmW+5imWe2W9gUAAABQdSqlAHnppZf07LPPWtpcsLDgqIyNCAEAAAB4F5dXwdqzZ4+effZZSVKrVq20YsUK5eTkSDpTbCxevFiZmZnauXOnXn31VTVu3FiSdNdddyk3N9crNpIDAAAAUDlcHgF57733ZJqmQkJCtHz5cjVr1qxEm5CQELVv317t27fX2LFjNWTIEM2aNUtZWVmOfTYAAAAA1Hwuj4D88MMPMgxDt9xyS6nFx9nq1aunxYsXq379+vr888+Z7A0AAAD4EJcLkKSkJEkqc6nbvLy8EsciIiI0evRomaapOXPmuBoCAAAAAC/hcgFy6tQpSVJkZGSx48HBwcXOn61Lly6SpE2bNrkaAgAAAAAv4XIBEhoaKqnkSEfdunUl/d8IydkKN+87evSoqyEAAAAA8BIuFyAXXnihpJKFROvWrWWapn766adS+23fvl2SVLt2bVdDAAAAAOAlXC5AOnfuLNM0tXPnzmLH+/TpI0latWqVNm/eXOzc77//rpkzZ8owDLVt29bVEAAAAAB4CZcLkLi4OEnSypUrix0fNWqUatWqJbvdriuvvFJPPPGEpk+frieeeEKXXHKJMjMzJUnDhw93NQQAAAAAXsLlfUCuu+46+fv7688//9TPP/+sXr16SZJatGihiRMn6oUXXlBmZqb+/e9/l+jbtWtX3X///a6GAAAAAMBLuFyANGjQQImJicrLy1PDhg2LnXvuuecUGhqqF1980THiIZ3ZIX3YsGF67733mAMCAAAA+BCXCxBJio6OLvPc448/roceekjr1q1TcnKyQkNDdckll6hx48aVcWsAAAAAXqRSCpCKBAYGOuaKoCTDMOTn5/J0HJ/k7+/v6RCc4m3xehK5cg75so5cOYd8OYd8WUeunFOT8uWWAgTlCw4OVkhIiKfD8EoRERGeDsEyf39/r4rXk8iVc8iXdeTKOeTLOeTLOnLlnJqWryotQE6cOKFTp04pPDxc9erVq8pbebWcnBzZbDZPh+GVjh8/7ukQKlSnTh35+/uroKBAJ0+e9HQ41Rq5cg75so5cOYd8OYd8WUeunOOt+aqoWKrUAiQzM1OzZs3SggULtHnzZmVnZzvOhYSE6JJLLtEtt9yiUaNGKSwsrDJv7dVM01RBQYGnw/BK3pY3b4vXk8iVc8iXdeTKOeTLOeTLOnLlnJqUr0qbePDVV18pJiZG48aN048//qisrCyZpun4k5WVpTVr1ujBBx9UTEyMvv7668q6NQAAAAAvUSkFyOzZszV06FClpKQ4Co7w8HBdfPHF6t27ty6++GLVqVPHce7o0aO64YYbNGfOnMq4PQAAAAAv4XIBsm/fPt13330qKCiQaZq68cYbtW7dOmVkZGjLli368ccftWXLFp04cUIJCQm66aabJEl2u1333nuv9u/f7/KHAAAAAOAdXC5A3njjDeXm5sowDL322mtauHChLrvsslLbXnrppfr888/1r3/9S5Jks9n0xhtvuBoCAAAAAC/hcgGyfPlyGYahPn366LHHHrPU55FHHlHfvn1lmqaWLVvmaggAAAAAvITLBchff/0lSbr55pud6lfYvrA/AAAAgJrP5QKkcDnd888/36l+DRs2LNYfAAAAQM3ncgHSsmVLSVJSUpJT/Q4ePChJiomJcTUEAAAAAF7C5QLk1ltvlWmamjdvnkzTtNTHNE19/PHHMgxDw4cPdzUEAAAAAF7C5QLkvvvuU6dOnbR161Y9/PDDlvo88sgj2rp1qzp37qx7773X1RAAAAAAeIlarl4gMDBQ33zzjW655RZNmzZNCQkJeuyxx9SvXz9FREQ42p04cULff/+9Xn/9dW3YsEE9e/bUggULVLt2bVdDkCRlZGRowYIF2rBhg44dO6bAwEC1aNFC11xzjXr06OH09QoKCrRr1y7t27dP+/bt0/79+5WcnCxJGj58uG6//fZKiRsAAADwJZYLkIsuuqjc86dPn5Zpmtq4caNuvfVWSVJERIRCQ0OVlZWl48ePSzrz+JVhGEpKSlLv3r1lGIbLmxEmJSVp0qRJysjIkCQFBwcrKytL27Zt07Zt23Tddddp7NixTl0zLS1NzzzzjEtxAQAAACjOcgHyxx9/yDCMMud5GIYhwzAkydEmPT1d6enpJdpJ0uHDhx3FiCtOnz6tl156SRkZGWrevLkeeeQRRUdHy2azacmSJfr444/11VdfKTo6Wv3793fq2sHBwbrooovUsmVLtWjRQp988omOHDniUrwAAACAL7NcgDRr1szlYqEqLFu2TMnJyQoMDNTkyZMVGRkp6cyjYcOGDVN6erq+/fZbzZ07V3FxcapVy9pHjoyM1KefflrsMy9atKhKPgOqzkMPPeTpEKqVqVOnejoEAADg45waAamOVq9eLUnq06ePo/go6qabbtLSpUuVnp6unTt3qkuXLpau6+fn8vx8AAAAAGfx6m/ZOTk52rt3rySpa9eupbaJjIxUVFSUJGn79u1uiw0AAABASV5dgBw6dMgx36R58+Zltis8V7j5IQAAAADP8OoCpOgE9/r165fZrvBc4UpcAAAAADzD5X1AzpaWlqZvvvlGCQkJOnLkiE6dOqXw8HA1adJEl112ma699lqdd955lXKv3Nxcx+vAwMAy2xWey8nJqZT7AgAAADg3lVaAZGdn64knntAHH3wgm81Wapv//ve/CgwM1JgxY/Tqq68qODi4sm4PAAAAwAtUSgGSlpamvn37as+ePWXuE1IoNzdX77zzjlauXKkffvhBDRo0OOf7BgUFOV7bbDaFhISU2q6wIPJUwTN37lzNmzevzPM333yzRo8e7caIao6IiAhPh+BVqnu+Clef8/Pzq/axVgfkyzpy5Rzy5RzyZR25ck5NzVelFCA33XSTfv31V0lnvuTfdtttGjhwoFq1aqWwsDBlZmYqMTFRy5Yt06effqrs7Gzt3r1bN910k2MZ3XNRdN5Henp6mQVI4VwRT/0fl5WVpZSUlDLPZ2dny9/f340R1RzkzTneki/DMLwm1uqAfFlHrpxDvpxDvqwjV86paflyuQBZtGiRfvzxRxmGoYsvvlhffPFFqStSde7cWbfccoueeeYZ3Xzzzdq8ebN+/PFHLVmyREOGDDmne0dFRTl2Z09KSnIst3u2pKQkSdIFF1xwTvdxVWhoqBo2bFjm+ZCQEBUUFLgxopqDvDmnuufLz8/P8Xfabrd7Opxqj3xZR66cQ76cQ76sI1fO8dZ8VVQsuVyAfPrpp5LO7Lfx/fffl7salXRmSdzvvvtO7du3V2pqqubNm3fOBUhwcLBiYmKUmJioLVu2qFevXiXapKWlOZbf7dy58zndx1UjRozQiBEjyjyflpbGCl3niLw5p7rnKyIiQv7+/rLb7dU+1uqAfFlHrpxDvpxDvqwjV87x1nxVtOCUy8vwrl+/XoZh6O67766w+CjUoEEDxcfHyzRNrV+/3qX7x8XFSZLWrFmj1NTUEue/+OILmaap+vXrq2PHji7dCwAAAIBrXC5ACuc2dOrUyal+hcVAeXMjrBg4cKAaNWqk3Nxcvfjiizpw4ICkMxPPFyxYoG+++UbSmVGIWrWKD/iMGTNG119/vd58881Sr52VlaWTJ086/hQOfdlstmLHy1r1CwAAAEBxLj+CVbt2bdlsNuXl5TnVr7B9QECAS/cPCAjQ008/rUmTJumPP/7QuHHjFBISotzcXEfBMHjwYPXv39/pa0+ZMkW7du0qcXzRokVatGiR4/3w4cN1++23n/uHAAAAAHyEyyMgTZo0kST9+OOPTvVbs2aNJKlp06auhqBmzZpp2rRpGjJkiBo3bqzTp08rNDRUnTt31sSJE3XPPfe4fA8AAAAArnN5BCQuLk579uzRnDlz9MADD1ia6L1t2zbNnTtXhmE45nC4ql69eoqPj1d8fLzlPjNnziz3/D/+8Q9XwwIAAABQhMsjIGPGjJFhGDp9+rT69++vL774otz2X3zxha666irl5eXJMAyNHTvW1RAAAAAAeAmXR0C6du2q++67T++++67S09N1yy236KKLLtJVV12lVq1aKTQ0VFlZWdq7d6++//577d+/X6ZpyjAM3XffferSpUtlfA4AAAAAXqBSdkKfNm2aTp48qY8//liS9Pvvv+u///1vqW1N05Qk3XHHHZo6dWpl3B4AAACAl3D5ESzpzC6Nc+bM0fz589W1a1eZplnmn27duunzzz/X7Nmz5edXKbcHAAAA4CUqZQSk0C233KJbbrlFSUlJWr9+vY4cOaJTp04pPDxcjRs31mWXXaZmzZpV5i0BAAAAeBGXC5DZs2dLkho1aqQBAwZIOrMsLoUGAAAAgLO5/AzUnXfeqbvuuktr166tjHgAAAAA1GAuFyBhYWGSpHbt2rkcDAAAAICazeVHsBo3bqx9+/bp9OnTlREPAA956KGHPB1CtcIqfQAAVA2XR0CuuOIKSdLGjRtdDgYAAABAzeZyAXLvvffKz89PH330kf7666/KiAkAAABADeVyAdKlSxdNmTJFp06d0lVXXaUdO3ZURlwAAAAAaqBKWYa3UaNGGjRokJYuXaquXbsqNjZWl19+uaKiohQcHFzhNUaNGuVqGAAAAAC8gMsFyJ133inDMCRJhmHIbrfrxx9/1I8//mipv2EYFCAAAACAj6iUndBN0yz3PQAAAABIlVCAfPjhh5URh08zDEN+fi5Px/FJ/v7+ng7Bq5Av67wtV94WryeRK+eQL+eQL+vIlXNqUr5cLkBGjx5dGXH4tODgYIWEhHg6DK8UERHh6RC8Cvmyzpty5e/v71XxehK5cg75cg75so5cOaem5atSHsGCa3JycmSz2Twdhlc6fvy4p0PwKuTLOm/IVZ06deTv76+CggKdPHnS0+FUa+TKOeTLOeTLOnLlHG/NV0XFkksFyF9//aUdO3YoIyNDdevWVceOHRUVFeXKJX2SaZoqKCjwdBheibw5h3xZ52258rZ4PYlcOYd8OYd8WUeunFOT8nVOBciGDRv08MMPKyEhocS5Hj166I033tCll17qcnAAAAAAahanZz4vX75ccXFxSkhIkGmaJf6sW7dOffv21bJly6oiXgAAAABezKkRkFOnTmn06NHKzc11HGvZsqUaNmyolJQU7du3T5Jks9k0evRoJSYmqk6dOpUbMQBUAw899JCnQ6hWpk6d6ukQAABewqkRkDlz5ujo0aMyDEOXXHKJfvnlFyUmJmrt2rVKTEzU7t27HY9epaamas6cOVUSNAAAAADv5NQIyNKlSyVJ5513npYtW1ZihnubNm20dOlStW3bVqmpqVq6dKn+/ve/V160AACvw2hRcYwWAfB1To2A7NixQ4ZhaNSoUWUurxUREaFRo0bJNE3t3LmzUoIEAAAAUDM4NQKSnp4uSbr44ovLbde5c2dJ0rFjx84tKgAAfBQjRsUxYgTUPE6NgGRlZUmSwsPDy20XFhYm6cwGewAAAABQyOlleAEAAADgXLm0EzoAAIAn8chacTyyBm9wTgWIYRiVHUe1lpGRoQULFmjDhg06duyYAgMD1aJFC11zzTXq0aOHp8MDAAAAvMY5FSA33HCDpXamacrf37/cNoZhKD8//1zCcIukpCRNmjRJGRkZkqTg4GBlZWVp27Zt2rZtm6677jqNHTvWw1ECAACUj9Gi4hgt8pxzfgTLNM0yzxmG4RglKa9ddXf69Gm99NJLysjIUPPmzfXII48oOjpaNptNS5Ys0ccff6yvvvpK0dHR6t+/v6fDBQAAAKo9pyehm6ZZYVFR2Mabiw9JWrZsmZKTkxUYGKjJkycrOjpakhQYGKhhw4Zp0KBBkqS5c+dW61EcAAAAoLpwqgCx2+2V/qegoKCqPpvLVq9eLUnq06ePIiMjS5y/6aabZBiG0tPT2XQRAAAAsIBleMuQk5OjvXv3SpK6du1aapvIyEhFRUVJkrZv3+622AAAAABvxTK8ZTh06JDjEbLmzZuX2a558+Y6ePCgDh486K7QAAAAUMWYtF9cZU7aZwSkDOnp6Y7X9evXL7Nd4bnjx49XeUwAAACAt6MAKUNubq7jdWBgYJntCs/l5ORUeUwAAACAt6MAAQAAAOA2zAEpQ1BQkOO1zWZTSEhIqe1sNpukMxsUlmXu3LmaN29emedvvvlmjR49+hwj9W0RERGeDsGrkC/ryJVzyJd15Mo55Ms55Ms6cuWcyswXBUgZis77SE9PL7MAKZwrUt7/KVlZWUpJSSnzfHZ2doU7xhcqr5BBSeTLOnLlHPJlHblyDvlyDvmyjlw5h3xVHQqQMkRFRckwDJmmqaSkJMdyu2dLSkqSJF1wwQVlXis0NFQNGzYs83xISEi13g/lbH5+fo7c2O12T4dT7ZEv68iVc8iXdeTKOeTLOeTLOnLlHG/NV0W/WKcAKUNwcLBiYmKUmJioLVu2qFevXiXapKWlOZbf7dy5c5nXGjFihEaMGFHm+bS0NK9aRSsiIkL+/v6y2+1eFbenkC/ryJVzyJd15Mo55Ms55Ms6cuUcb83XeeedV+55JqGXIy4uTpK0Zs0apaamljj/xRdfyDRN1a9fXx07dnRzdAAAAID3oQApx8CBA9WoUSPl5ubqxRdf1IEDBySdmXi+YMECffPNN5LOjHDUqsVgEgAAAFARvjWXIyAgQE8//bQmTZqkP/74Q+PGjVNISIhyc3Mdz+ENHjxY/fv393CkAAAAgHegAKlAs2bNNG3aNC1cuFAbNmxQWlqaQkNDddFFF+naa69Vjx49PB0iAAAA4DUoQCyoV6+e4uPjFR8f7+lQAAAAAK/GHBAAAAAAbmOYpml6Ogh4l7lz5yorK0uhoaHlLi+MM8iXdeTKOeTLOnLlHPLlHPJlHblyTk3NFwUInHbNNdcoJSVFDRs21LfffuvpcKo98mUduXIO+bKOXDmHfDmHfFlHrpxTU/PFI1gAAAAA3IYCBAAAAIDbUIAAAAAAcBsKEAAAAABuQwECAAAAwG0oQAAAAAC4DTuhw2m33367Y01qVIx8WUeunEO+rCNXziFfziFf1pEr59TUfLEPCAAAAAC34REsAAAAAG5DAQIAAADAbShAAAAAALgNBQgAAAAAt6EAASpgmqZOnjyp1NRUT4cCH2Cz2ZSVleXpMAAATho7dqwee+wxy+2ffPJJ3XPPPVUYUfXFMrw+JiMjQzt37lRqaqpsNpuGDx/u6ZCqrb179+qzzz7Tjh07ZLPZJEmLFy92nM/MzNRHH30kwzAUHx+vwMBAD0VaPRQUFGj//v2On60rr7zS0yFVO2lpadq6davq1aun7t27FzuXlJSkqVOnat++fZKkVq1a6aGHHlJUVJQnQvW4cePGacCAAerbt6/CwsI8HQ4ASQkJCdq6datSU1OVl5enl156yXEuNzdXBw4ckGEYatOmjQej9JyUlBSdPn3acvu0tDSlpaVVYUTVFwWIjzh9+rQ+/PBDLVu2TAUFBY7jRQuQzMxM3XvvvcrNzdU777yjRo0aeSLUamH58uV67733iuXKMIxibcLCwhxfKNu3b6++ffu6O8xqY/HixVqwYIEyMzMdx4oWIJmZmXrqqaeUn5+vf/zjH4qIiPBEmB73/fffa/78+br55puLFSDZ2dl65plnlJGRocKV0X/77Tc9/fTTevvtt33yC/gff/yhGTNm6MMPP1TPnj111VVXqVOnTp4Oq9oYO3asy9cwDEPTp0+vhGi8U1pamv73v//p119/VXp6umw2m8ramcDXc5WcnKyXX35Zf/75p6QzTwac/W9iQECAXn/9daWmpuqVV17x2SLEGQUFBSXy6CsoQHyA3W7XlClTtG3bNklSw4YNlZaWJrvdXqxdWFiYrrjiCn311Vf66aefdNNNN3kgWs/7/fff9e6778put2vgwIGKi4vTyy+/rFOnTpVo269fP23ZskWbN2/22QJk6tSpWrlypUzTVEBAgPLz80u0CQsLU6tWrbRixQqtXbtW1113nQci9bzt27dLki6//PJix7///nudOHFC9evX19ixYxUYGKj3339fhw8f1pdffqnbb7/dE+F61G233aYVK1YoJSVFa9as0Y8//qiGDRvqqquu0pVXXqkGDRp4OkSPSklJcfkavvrFR5JWr16td955R6dPny636Cg858u5ys7O1uTJk3X06FFFRESoW7duWrt2rePJgEL+/v4aOHCg5syZo3Xr1lGAVCA7O1sZGRkKCQnxdCgeQQHiA1atWqWtW7cqIiJCTz31lFq3bq3Ro0crIyOjRNvY2Fh99dVX2rFjh88WIEuWLJHdbtf111+v+Ph4SZKfX+nTpTp27ChJ2r9/v9viq04SEhK0YsUKhYSE6O9//7t69uypu+++u9Sfrb59++p///uftm/f7rMFSOE8oiZNmhQ7npCQIMMwNHr0aPXq1UuSFBwcrKeeekqbNm3yyQJk+PDhGj58uLZv367ly5dr/fr1Onr0qD7++GN98skn6tq1q/r376/u3bvL39/f0+G6HY/Pnrv9+/dr6tSpKigo0MUXX6xu3brp/fffV0hIiO6++26dOHFCO3fu1I4dO1SnTh0NHz5cQUFBng7bY7788ksdPXpUMTExeu655xQWFqZNmzaVKEAk6bLLLtOcOXP066+/eiBS9ztw4IAOHDhQ7JjNZtPKlSvL7GOaprKysrRu3TrZ7XZddNFFVR1mtUQB4gNWrlwpwzA0ZswYtW7duty2LVq0kGEYSkpKclN01c+uXbtkGIaGDh1aYdu6desqKCjIZ5/hXLZsmQzD0KhRoxQbG1tu21atWskwDP3xxx/uCa4aysjIUGhoqAICAhzH8vPzlZiYKD8/P1122WWO4+3atZO/v7+OHDniiVCrjc6dO6tz587KzMzU6tWr9f333+uPP/7Qxo0btWnTJtWtW1dXXnml+vfvr6ZNm3o6XLe57bbbquS6a9euVV5eXo2ew/Xll1+qoKBAV1xxhcaPHy9Jev/99xUYGKirrrpKknTLLbdox44devnll7VixQq9+uqrHozYs9atW+f4DlHR46BRUVHy9/fX4cOH3RSdZyUkJGj+/PnFjuXk5Gjq1KkV9i18jO3666+vqvCqNQoQH1D4he/SSy+tsG1AQIBCQ0N18uTJKo6q+jpx4oSCgoIsz1OoVauWcnJyqjiq6qlwwvQVV1xRYdugoCAFBwfrxIkTVRxV9WUYhnJzc4sd27dvn/Lz8xUTE6Pg4OBi50JCQnz2Z+tsYWFhGjx4sAYPHqz9+/dr+fLlWrNmjU6cOKFFixZp0aJFatu2rQYMGKDevXurdu3ang7ZK82YMUMZGRk1ugD55ZdfZBiGhg0bVuz42Y9iderUSffcc4/efPNNLVq0qER7X5GcnCx/f3+1atWqwraGYSgkJETZ2dluiMzzQkNDdd555znep6amyjCMch8R9fPzU3BwsJo3b64BAwaoQ4cO7gi12qEA8QG5ubkKDg62/A9yfn6+Tz7SUCgoKEg5OTmy2+1lPnpVKCcnR1lZWapbt66boqtesrKyFBwc7NOPJzgjMjJShw8f1u+//+4Ydi98/Kpdu3bF2trtdmVnZ6tevXoeiLR6a9Gihe6//37deuuteu211xyPe+zevVu//vqrZs6cqauvvlpDhw5VaGioh6NFdXPixAnVqlWr2KOQhmEoLy+vRNvY2FhNmzZNa9eu9dkCxG63q1atWhX+eyidKeJyc3N9ZlXI66+/vtgIxpAhQ1S3bl3NnDnTg1F5B/YB8QF169ZVTk5Oid+8liY5OVm5ubk+PcEzKipKdru9xHOdpVm3bp1M01SLFi3cEFn1Ex4erpycnFL/4T5benq6z3+h7tSpk0zT1HvvvafExEStX79e3333nSSVWJb30KFDKigoUP369T0RarW2Y8cO/fvf/9a9996rPXv2SDozWhQbG6t69eopMzNTCxcu1IMPPugzj4LAusDAwBJfkIODg0v9b1lAQIACAwN19OhRd4ZYrZx33nmy2WyWRq8TExN1+vRpn11Fc/jw4RoyZIinw/AKFCA+oHDex/r16yts++WXX8owDLVv376qw6q2evXqJdM0SzzXebbk5GTHPiC9e/d2U3TVS8uWLSWd+UJYkWXLlkmS2rZtW6UxVWc33XSTQkJClJiYqCeeeEIvv/yycnJy1KZNG8eCBoU2btzo0+vpn+3YsWOaP3++7rnnHk2ePFlr1qxRXl6eYmJi9OCDD2rWrFl6/PHH9f777+vxxx9Xo0aNdOzYMc2aNcvToaOaadCggbKzs4ut2Ne4cWNJchS0hY4ePeozjxOVpfC/Td9//3257UzT1McffyzDMNS1a1d3hFbt3Hbbbbrxxhs9HYZXoADxAVdffbXjPwzlLd24aNEiffPNN5KkQYMGuSu8amfQoEFq3LixNmzYoFdeeUW//vqr49ngjIwM7d27V/PmzdMjjzyiEydO6MILL1RcXJxng/aQfv36yTRNzZkzp9geIGf7+eef9fnnn8swDMckT18UGRmpl156SR06dFBAQIDq1q2rfv36adKkScXamaap5cuXyzRNde7c2UPRel5BQYF++uknPffccxozZow++eQTHT16VCEhIbr22mv11ltv6Z///Kf69+/v+I22v7+/YmNj9dJLL8nPz0+//PKLhz8FqpvmzZvLNM1io9yFo5MzZ850rFZ38uRJvf322zIMQ9HR0Z4K1+NuuOEG+fn5acGCBWX+IjM5OVn/+Mc/tH37dtWuXVvXXnutm6OEtzHMshbARo3y9ttv6/vvv1dYWJh69uyptWvXKjc3VyNHjlRqaqq2bNmilJQUmaapIUOG6O677/Z0yB51+PBhPf/880pOTi5z/XfTNNWkSRO98MILioyMdHOE1ceLL76oTZs2qVGjRurXr58WL16s7OxsPfbYY0pNTdXGjRu1e/dumaapPn366NFHH/V0yNVeQUGBjh07JunMb2t9cU7WzJkz9cMPP+jUqVOOXwC0adNGAwcOVGxsrKU5bXfffbfS09O1ePHiKo625ihcor0m52z16tV64403NHToUI0ePVrSmRG2v/3tb46lZcPDw4vt/fTkk0+qR48eHom3Oli+fLn+85//SJIaNWqktLQ05efnq0uXLkpNTdWhQ4ccbR999NESex3VRG+99ZYkqX79+ho5cmSxY84wDEMPPfRQpcbmDShAfERBQYHmzJmjxYsXl7qxUuFycEOHDtXIkSN9etOlQjk5OVq0aJFWrFhRYpndiIgI9e/fX0OHDvXZTYQK2Ww2vfXWW/rpp59K/bkp/HmLjY3V+PHjiy1BC5Sl8Dnqwg1SBw4cqAsuuMCpa/zzn//UiRMnNGXKlKoIsUbyhQLEZrNp7dq1CgsLK7b09a5du/T66687in/pzHyRUaNGafDgwZ4ItVrZtGmTpk+fXuZ8mMjISN1///3q1q2bmyPzjCFDhsgwDDVt2lTvvPNOsWNWvloXtjMMo0b/fSsLBYiPSU5O1ooVK7Rnzx4dP35cdrtd9erVU5s2bdSvXz+fWkffGceOHVN6erojX+eff76nQ6p2du3ape+//77Un63+/fvr4osv9nSI8CITJ050LKlL0eo+vlCAlKegoEB79uxRWlqaQkND1bZt2zJXUvOFPVPOZrfbtWvXLu3Zs6fYv4lt27ZVp06dfGq09s0335RhGIqIiNCoUaOKHXPWuHHjKju8ao8CBACqwK5duySd+Q1qTExMsWPO8tV14uF+vl6AOMNXcvXVV19JOrNAiy+vkOlOvlDcsg8IAFSBSZMmlRieLzzmrJr+BccK0zR16tQp2Ww2n55zBbjb+++/Lz8/P1199dWeDsVn+MKGoBQgQDkKCgp05MgRZWZmFluysTT8lhpnM02zxLPADDo7Z+/evfrss8+0Y8cOxwThogVZZmamYzns+Ph4n9kADXCX8PBw2e12HoVEpaIA8SGZmZnauHGjkpKSKvxC7aurMhRKSUnR7NmzlZCQUGHhUciXf0ttmqZ+/fVX/fnnn8rMzFRBQUG57YcPH+6myDxnyZIllo6hbMuXL9d7771X7Ofp7BGksLAwpaWlaevWrWrfvr369u3r7jBrFApknK1Fixbatm2bMjIyVLduXU+HgxqCAsRHfPvtt5o1a1axXV5L+4em6KoMvlqAJCcn6/HHHy+2/CfKtmHDBr333ntKT0+33McXChC45vfff9e7774ru92ugQMHKi4uTi+//HKxpVEL9evXT1u2bNHmzZspQFw0ceJEy790gW+47rrrtHXrVsdGoEBloADxAT///LP++9//SpJq1aqlmJgYNWjQwNI6+r5o3rx5OnnypEJDQzVs2DD16NFDDRo0YPi5FDt37tTLL78su90u6cyeFfxsoTIsWbJEdrtd119/veLj4yVJfn6l751buFPz/v373RZfTdWmTRtPh4Bqplu3brrrrrs0e/ZsZWZm6sYbb/TpjRlROShAfEDho0EdOnTQo48+qvr163s2oGpu+/btMgxDDz/8sLp37+7pcKq1zz//XHa7Xc2bN9e4cePUokULT4eEGmLXrl2OvYkqUrduXQUFBZXYrweA68aOHSvpzC8A1qxZozVr1qh27doKDw8v85cChmFo+vTp7gwTXoYCxAf8+eefMgxD48aNo/iwICsrS7Vq1fKZzZRcsXfvXhmGoUcffVTNmzf3dDheg/lYFTtx4oSCgoIUERFhqX2tWrWUk5NTxVEBviclJaXEMZvN5lgUojRsZoyKUID4AMMwFBwcrIYNG3o6FK9Qv359ZWRklPmbHfyfgoICBQUFUXw4gflY1gQFBSknJ0d2u73Cv4s5OTnKyspigixQBXzxvz+oehQgPqBZs2bat2+f8vLyeDbfgp49e2rJkiVKTExUq1atPB1OtdakSRMdPHhQBQUFPrUD7rliPpZ1UVFR+u2333TgwIEKH+1bt26dTNPkEUCgCvTr18/TIaAGogDxAddee61ef/11rVq1SgMHDvR0ONXesGHD9PPPP+vdd9/Viy++qLCwME+HVG31799fM2bM0Pr169WrVy9Ph1PtMR/Lul69emnPnj2aP3++Jk6cWGa75ORkxz4gvXv3dmOEAIBzRQHiA/r27atffvlFM2fOVHBwsPr06ePpkKqNXbt2lXp8xIgRmj59uh544AENGDBAMTExCg4OLvdavrgR4bXXXqstW7boP//5j+rXr88KOhVgPpZ1gwYN0tKlS7Vhwwa98sorGjJkiONRtYyMDKWkpGjjxo36+uuvlZWVpejoaMXFxXk2aPgUX12m3TRNnTp1SjabTZGRkZ4OB17KMH31b1AN9dZbb5V5bsOGDcrKytJ5552nli1blvuF2leeOx8yZEilTZar6RsRfvrpp6Uez8/P19KlS5WVlaV27dpZKtZ8dR+Q4cOHyzAMffLJJ54OxSscPnxYzz//vJKTk8v8e2qappo0aaIXXniBL0Nwqz179ig/P99nfvm0d+9effbZZ9qxY4djAnrRf/cyMzMdo5Hx8fEKDAz0UKTeb9SoUTp58mSN/l7BCEgNs3LlSsfk1aKKHktNTVVqamqp/X1x4is1uDWffPJJucWaaZr65ZdftHv37gqv5asFCPOxnNOkSRO9+eabWrRokVasWFFimd2IiAj1799fQ4cOVUhIiIeihK/ypRHf5cuX67333lNBQYHj2Nn/HoSFhSktLU1bt25V+/bt2RTUBb6wISgFSA1zxRVXsPydE5YsWeLpELxG+/bt+dlyEfOxnBccHKzbb79dt99+u44dO6b09HTZ7XbVq1dP559/vqfDA2q833//Xe+++67sdrsGDhyouLg4vfzyyzp16lSJtv369dOWLVu0efNmChAX+EJxSwFSw4wfP77Krr127Vrl5eXpyiuvrLJ7oPr6xz/+4ekQvB7zsVzToEEDNWjQwNNhAD5lyZIlstvtuv766xUfHy9JZS6N3bFjR0nS/v373RYfvBMFCCybMWOGMjIyanwBkpqaKj8/P8tfdI4dOya73c7z5yimvPlYtWvX1uuvv67Zs2czHwtAtbZr1y4ZhqGhQ4dW2LZu3boKCgoq8bgkcDYKEOAsY8aMUUREhGbNmmWp/YQJE5SWllajJ4vBeczHqjwFBQU6cuRIhbvGS765Gh1QlU6cOKGgoCBFRERYal+rVi3l5ORUcVTwdhQgAFxit9tlGEap80OWLl2qXbt26fTp0+rWrZsGDBjgM/NImI/lupSUFM2ePVsJCQmWJ2TyiwCgcgUFBSknJ0d2u73MR68K5eTkKCsrS3Xr1nVTdPBWFCCAi2w2W4X/Ua6pli9frv/85z+KjY3VY489VuzcSy+9pE2bNkk6s0LWhg0btGXLFj311FOeCNXtmI/lmuTkZD3++OM6deoUK9UBHhQVFaXffvtNBw4cUIsWLcptu27dOpmmWWE7gAIEcMHhw4d16tQpy0PTNc2WLVsknfltf1GbN2/Wxo0bJUndu3dX7dq1tW7dOq1fv15r165VbGys22OtSXxhPta8efN08uRJhYaGatiwYerRo4caNGiggIAAT4cG+JRevXppz549mj9/viZOnFhmu+TkZMc+IL1793ZjhPBGFCDweQkJCVq/fn2xY1lZWeVOIi5sU7jnRbt27aosvurszz//lCS1bt262PFVq1bJMAwNGTJEd911lyTp66+/1owZM7Ry5UoKEFRo+/btMgxDDz/8sLp37+7pcACfNWjQIC1dulQbNmzQK6+8oiFDhjhGJTMyMpSSkqKNGzfq66+/VlZWlqKjoxUXF+fZoFHtUYDA5x04cKDEhOG8vDytXLnSUv/w8HCf3VgvIyNDgYGBCgsLK3Z8+/btkqSrr77acaxfv36aMWOGfv/9d7fGCO+UlZWlWrVqqVu3bp4OBfBptWvX1uTJk/X8889r3bp1SkhIcJwbPXq047VpmmrSpIkmTZokf39/T4QKL0IBAp8XHR1d7FGWlStXqnbt2uX+lt4wDIWEhKhZs2bq2bOnwsPD3RFqtZObm1vikZjk5GSdPHlSkZGRaty4seN4cHCwQkNDdfLkSXeHCS9Uv359ZWRk+Oz8KqA6adKkid58800tWrRIK1asKLHMbkREhPr376+hQ4cqJCTEQ1HCm1CAwOf16NFDPXr0cLxfuXKlQkNDNW7cOJeu6wsThevUqaMTJ07o5MmTqlOnjiRp27ZtkqS2bduWaF9QUFDunhdAoZ49e2rJkiVKTExUq1atPB0O4POCg4N1++236/bbb9exY8eUnp4uu92uevXq6fzzz/d0ePAy/GoJOMuUKVP05JNPunydGTNmaOrUqZUQUfVVuNLJkiVLJJ1ZEWzp0qUyDEMXX3xxsbbHjx9Xbm6uz07Yh3OGDRumyMhIvfvuu8rMzPR0OACKaNCggWJiYtS6dWuKD5wTRkCAs7CRmXVXX321Nm3apIULFyohIUHZ2dlKT09XeHi4evXqVaztzp07JUnNmzf3RKioxnbt2lXq8REjRmj69Ol64IEHNGDAAMXExFQ4gsbfXwCo/ihAYBlr8eNs3bt317Bhw/T555/r0KFDkqSwsDA9/PDDJb4o/vDDD5KkTp06uT1OVG+TJk2qcNPGzz77zNK12IgQqDoFBQU6cuSIMjMzK9wclF8GoDwUILBs4sSJlncjhu+44447dNVVVykxMVEhISFq1apViVWx8vPzFRMTo5YtW+rSSy8tcQ1fmC+D8vELDqD6SklJ0ezZs5WQkGD5ewC/DEB5KEBgWZs2bTwdAqqphg0bqmHDhmWer1WrVrlLFfvCxnooW+EcIgDVT3Jysh5//HGdOnWKXxSg0lCAAAAAoFTz5s3TyZMnFRoaqmHDhqlHjx5q0KBBiSXYAWdQgACAl/GF30KmpqbKz89PDRo0sNT+2LFjstvtioyMrOLIAN+yfft2GYahhx9+WN27d/d0OKghKEAAwMv4wnysMWPGKCIiQrNmzbLUfsKECUpLS+O5c6CSZWVlqVatWurWrZunQ0ENQgECAF6G+VgA3KV+/frKyMiQnx9bx6Hy8NMEAPB6NpuNL0hAFejZs6dsNpsSExM9HQpqEP5rDQDwaocPH9apU6dUt25dT4cC1DjDhg1TZGSk3n33XWVmZno6HNQQPIIFVBFfmCgMVJaEhAStX7++2LGsrCy99dZb5fbLysrS7t27JUnt2rWrsvgAX7Br165Sj48YMULTp0/XAw88oAEDBigmJqbEZrNnYyNClIcCBKgivjBRGKgsBw4c0MqVK2UYhqN4z8vL08qVKy31Dw8PL3evGQAVmzRpkgzDKLfNZ599ZulaLAiB8lCAAFWEicKAddHR0cU2oly5cqVq166t2NjYMvsYhqGQkBA1a9ZMPXv2VHh4uDtCBWo0Ru/hDhQgAACP69Gjh3r06OF4v3LlSoWGhmrcuHEuXXft2rXKy8srVtwAKN2SJUs8HQJ8BAUIAI/jN24425QpU1Srluv/RM2YMUMZGRkUIABQjVCAAPA45svgbExgBaqH1NRU+fn5qUGDBpbaHzt2THa7XZGRkVUcGbwZBQgAj2O+DABUT2PGjFFERIRmzZplqf2ECROUlpbGJHSUi31AAAAAALgNBQgAAAAqhc1mk58fXy9RPn5CAAAA4LLDhw/r1KlTqlu3rqdDQTXHHBAAAABIkhISErR+/fpix7KysvTWW2+V2y8rK0u7d++WJLVr167K4kPNQAECAAAASdKBAwe0cuVKGYbhWCI9Ly9PK1eutNQ/PDxcw4cPr8oQUQNQgAAAAECSFB0dXWzfnJUrV6p27dqKjY0ts49hGAoJCVGzZs3Us2dPhYeHuyNUeDEKEAAAAEiSevTooR49ejjer1y5UqGhoRo3bpxL1127dq3y8vLYFBSSKEAAAABQhilTpqhWLde/Ls6YMUMZGRkUIJBEAQIAqMEKn2EHcG46dOjg6RBQA1GAAABqrIkTJyo/P9/TYQAAiqAAAQDUWG3atPF0CACAs7ARIQAAAAC3oQABAAAA4DYUIAAAAADchgIEAAAAgNtQgAAAAABwGwoQAAAAAG5DAQIAAADAbShAAAAAUKVM0/R0CKhGDJOfCAAAAFShPXv2KD8/Xx06dPB0KKgGKEAAAAAAuA2PYAEAAABwGwoQAAAAAG5DAQIAAADAbShAAAAAALgNBQgAAAAAt6EAAQAAAOA2FCAAALjB6tWrZRiGDMPQc8895+lwAMBjKEAAAJWqZcuWji/au3btqrB9//79He0vuOCCCttnZ2crMDBQhmEoICBAmZmZlRE2AMBNKEAAAJXqiiuucLxevXp1uW3z8vL0888/O94fOnRI+/btK7fPTz/9pLy8PElS9+7dFRYWdu7BAgDcjgIEAFCpihYgq1atKrft+vXrlZOTU+xYRX2KFjVF7wUA8A4UIACAShUXF+d4vWbNGpmmWWbbwmIiPDxcsbGxxY5V1EeiAAEAb0QBAgCoVE2aNFGrVq0kSWlpadq5c2eZbQuLidjYWPXr16/YsdJkZ2dr48aNkqTatWurd+/elRM0AMBtKEAAAJXOyjyQvLw8rVu3TtKZUZO+fftKkg4fPqzExMRS+/z00086ffq0JOmyyy5TcHBwsfM5OTl6++23ddVVV6lx48aqXbu2GjRooO7du+vpp5/W4cOHy4171qxZjgnxs2bNkiRt2bJF9913n1q1aqXw8PBi54patmyZbrzxRjVu3FhBQUFq1qyZhg4dquXLl5d7TwDwNRQgAIBKZ2UeSNH5H3FxcerRo4dq165dbp/yHr/auHGjWrdurQcffFD/+9//lJycrNOnTys9PV2bNm3SlClTFBMTow8++MDy53jttdd06aWX6r///a/27t1b6opbdrtdY8eO1dVXX63FixcrOTlZNptNBw8e1KJFizRw4ECNHz/e8j0BoKar5ekAAAA1T2nzQAzDKNam6PyPrl27qlatWrr00ku1du1arV69Wvfee2+J65ZVgOzYsUNXXHGFsrKyJEnt2rXTyJEjFR0drfT0dC1evFjLly9Xdna24uPjZZqm4uPjy/0Mn332mZYuXaqwsDCNGjVKl156qQICArR79241atTI0e7hhx/WzJkzJUn+/v664447FBcXp8DAQG3btk3vv/++3nrrLR08eNBS7gCgxjMBAKgCbdu2NSWZksytW7eWOH/llVeaksyrr77acWzixImmJLNRo0Yl2mdlZZkBAQGmJDMoKMjMzc01TdM0CwoKzA4dOjjuNWbMGPP06dMl+s+cOdM0DMOUZIaEhJgHDhwo0ebDDz90XEeS2apVK/PPP/8s8zOuXbvWcc3Q0FDzxx9/LNHm8OHDZps2bYpd99lnny3zmgBQ0/EIFgCgSpQ3D+Ts+R+FCueBJCcna8+ePcX6FJ3/0bNnTwUGBkqSvvnmG8eGh506ddJ7772nWrVKDvDHx8c7RlWys7P11ltvlRu/YRj69NNP1axZszLb/Pvf/3as8vXqq686VvIqqnHjxpo/f778/f3LvR8A+AoKEABAlShvHkhCQoJj/kdh0SFJvXr1chQPZ/cp+r5o0fLFF184Xj/66KPlftF/8sknHY+CFe1XmtjYWHXp0qXM8zabTd98840kqW7duhozZkyZbTt16qQBAwaUez8A8BUUIACAKtG3b1/Hl/0ff/xRdrvdca5wRCQsLEyXXHKJ43hYWJi6detWrM3ZfaTixc369esdryv6kt+8eXO1adNGkpSUlKQjR46U2fbyyy8v91rbt2937Mjeu3dvx4hMWQqXGQYAX0cBAgCoEpGRkWrfvr0k6fjx49q2bZvjXGEx0bt37xKPSxWOiBQtOLKysrRp0yZJUnBwsC677DLHucIiIjw8vNjk8LIU7lFStG9poqKiyr1O0SV9W7ZsWeF9rbQBAF9AAQIAqDKlzQOx2WxKSEiQVPzxq0KFx1JSUrR7925Jxed/9O7d27FcrySdOnVKkhQaGmopprCwsBJ9S3P2HiNnK7okb0hISIX3tRofANR0FCAAgCpTdK5G4RyOs/f/OFtsbKxjHkdhn/L2/wgPD5ckxxK8FSlaOBT2PRdFC5ns7OwK21uNDwBqOgoQAECVOXseSEFBgaOYCA0NVffu3Uv0qVOnjjp37izJWgHSuHFjSWdGM44ePVphTEV3WW/SpIn1D3OWpk2bOl7v27evwvZW2gCAL6AAAQBUmQYNGqhTp06SpIyMDG3dutVRTBRd8epshY9h/fDDD8rMzHTM/wgLCytRtBSdD7J8+fJy40lKSnIs79usWTNLc0bK0qlTJ8fE859++kk2m63c9itWrDjnewFATUIBAgCoUkVHLL777jvH/I/SHr8qVFiApKWl6b///a9j/kdsbGyJouWmm25yvP73v/+tgoKCMq/76quvOvbtKNrvXAQGBuqaa66RdKa4+uCDD8psu2vXrgqLIwDwFRQgAIAqVbTQmDZtWrnzPwpdfvnljke3XnvtNcfxsx+/kqRrrrlGHTt2lHRmadz7779f+fn5JdrNmjVL7733nqQzk8bHjRvn9Gc526OPPuqIc8KECY7NFYs6evSobr311nILIwDwJaWPfQMAUEn69OkjPz8/2e12paSkSDpTAJQ2/6NQ/fr11bFjR+3YscPRRyq9APHz89PcuXPVq1cvZWVlacaMGVq3bp1GjhypCy+8UOnp6VqyZIm+++47R5+pU6eqefPmLn+23r1768EHH9TUqVN16tQp9enTRyNGjFDfvn0VGBiobdu2aebMmUpPT9fQoUMr3PwQAHwBBQgAoEpFRETo4osv1pYtWxzHevXqpYCAgHL79e3bVzt27HC8r1Onjrp27Vpq206dOmnVqlUaOnSoDh06pF27dmnChAkl2oWEhGjq1KmKj48/x09T0htvvKGsrCy9//77ys/P16xZszRr1qxibcaNG6cbbriBAgQAxCNYAAA3OHvkorzHrwqdvUfI5Zdf7lietzTdu3dXYmKipk6dqn79+un8889XQECAIiIi1K1bN02cOFF79+6t1OJDOjMCM3PmTC1dulTXX3+9GjZsqNq1aysqKko33nijvvvuO7355puVek8A8GaGWTgbDwAAAACqGCMgAAAAANyGAgQAAACA21CAAAAAAHAbChAAAAAAbkMBAgAAAMBtKEAAAAAAuA0FCAAAAAC3oQABAAAA4DYUIAAAAADchgIEAAAAgNtQgAAAAABwGwoQAAAAAG5DAQIAAADAbShAAAAAALgNBQgAAAAAt6EAAQAAAOA2FCAAAAAA3IYCBAAAAIDbUIAAAAAAcBsKEAAAAABuQwECAAAAwG3+HyY5hkEAtY+CAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 200,
+       "width": 400
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "from plotnine import ggplot, aes, geom_bar, theme, element_text, labs\n",
+    "\n",
+    "import pyvene as pv\n",
+    "_, tokenizer, backpack_gpt2 = pv.create_backpack_gpt2()\n",
+    "\n",
+    "class MultiplierIntervention(pv.ConstantSourceIntervention):\n",
+    "    \"\"\"Multiplier intervention\"\"\"\n",
+    "    \n",
+    "    def __init__(self, multiplier, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.register_buffer('multiplier', torch.tensor(multiplier))\n",
+    "        \n",
+    "    def forward(self, base, source=None, subspaces=None):\n",
+    "        return base * self.multiplier\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return f\"MultiplierIntervention()\"\n",
+    "\n",
+    "for c in [0, 0.7, 1]:\n",
+    "    pv_backpack_gpt2 = pv.IntervenableModel({\n",
+    "        \"component\": \"backpack.sense_network.output\",\n",
+    "        \"intervention\": MultiplierIntervention(c), \"unit\": \"sense.pos\"}, \n",
+    "        model=backpack_gpt2\n",
+    "    )\n",
+    "    base = tokenizer(\"When the nurse walked into the room,\", \n",
+    "                     return_tensors=\"pt\", return_attention_mask=False)\n",
+    "    intervened_outputs = pv_backpack_gpt2(\n",
+    "        base,\n",
+    "        unit_locations={\n",
+    "            # use   pv.GET_LOC((nv, s))\n",
+    "            \"base\": pv.GET_LOC((10,2))\n",
+    "        }\n",
+    "    )\n",
+    "    \n",
+    "    # plotting\n",
+    "    probs = torch.nn.functional.softmax(\n",
+    "        intervened_outputs[1].logits[0][-1], dim=0)\n",
+    "    data = pv.top_vals(\n",
+    "        tokenizer, probs, n=9,\n",
+    "        return_results=True\n",
+    "    )\n",
+    "    df = pd.DataFrame(data, columns=['Word', 'Probability'])\n",
+    "    df['Word'] = pd.Categorical(df['Word'], categories=[x[0] for x in data], ordered=True)\n",
+    "    plot = (ggplot(df, aes(x='Word', y='Probability'))\n",
+    "            + geom_bar(stat='identity')\n",
+    "            + theme(axis_text_x=element_text(rotation=90, hjust=1),\n",
+    "                    figure_size=(4, 2))\n",
+    "            + labs(title=f\"mul({c})\")\n",
+    "    )\n",
+    "    print(plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb539f4b",
+   "metadata": {},
+   "source": [
+    "### Saving and Loading\n",
+    "This is one of the benefits of program abstraction. We abstract out the intervention and its schema, so we have a user friendly interface. Furthermore, it allows us to have a serializable configuration file that tells everything about your configuration.\n",
+    "\n",
+    "You can then save, share and load interventions easily. Note that you still need your access to the data, if you need to sample **Source** representations from other examples. But we think this is doable via a separate HuggingFace datasets upload. In the future, there could be an option of coupling this configuration with a specific remote dataset as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "272f3773",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "Directory './tmp/' already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "# run with new intervention type\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "  \"intervention_type\": pv.ZeroIntervention}, \n",
+    "  model=gpt2)\n",
+    "\n",
+    "pv_gpt2.save(\"./tmp/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "50b894b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.\n",
+      "WARNING:root:Loading trainable intervention from intkey_layer.0.repr.block_output.unit.pos.nunit.1#0.bin.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pv_gpt2 = pv.IntervenableModel.load(\n",
+    "    \"./tmp/\",\n",
+    "    model=gpt2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2d07ca8",
+   "metadata": {},
+   "source": [
+    "### Multi-Source Interchange Intervention (Parallel Mode)\n",
+    "\n",
+    "What is multi-source? In the examples above, interventions are at most across two examples. We support interventions across many examples. You can sample representations from two inputs, and plut them into a single **Base**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "847410a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "_the                 0.07233363389968872\n",
+      "_a                   0.05731499195098877\n",
+      "_not                 0.04443885385990143\n",
+      "_Italian             0.033642884343862534\n",
+      "_often               0.024385808035731316\n",
+      "_called              0.022171705961227417\n",
+      "_known               0.017808808013796806\n",
+      "_that                0.016059240326285362\n",
+      "_\"                   0.012973357923328876\n",
+      "_an                  0.012878881767392159\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "parallel_config = pv.IntervenableConfig([\n",
+    "  {\"layer\": 3, \"component\": \"block_output\"},\n",
+    "  {\"layer\": 3, \"component\": \"block_output\"}],\n",
+    "  # intervene on base at the same time\n",
+    "  mode=\"parallel\")\n",
+    "parallel_gpt2 = pv.IntervenableModel(\n",
+    "  parallel_config, model=gpt2)\n",
+    "base = tokenizer(\n",
+    "  \"The capital of Spain is\", \n",
+    "  return_tensors=\"pt\")\n",
+    "sources = [\n",
+    "  tokenizer(\"The language of Spain is\", \n",
+    "    return_tensors=\"pt\"),\n",
+    "  tokenizer(\"The capital of Italy is\", \n",
+    "    return_tensors=\"pt\")]\n",
+    "intervened_outputs = parallel_gpt2(\n",
+    "    base, sources,\n",
+    "    {\"sources->base\": (\n",
+    "    # each list has a dimensionality of\n",
+    "    # [num_intervention, batch, num_unit]\n",
+    "    [[[1]],[[3]]],  [[[1]],[[3]]])}\n",
+    ")\n",
+    "\n",
+    "distrib = pv.embed_to_distrib(\n",
+    "    gpt2, intervened_outputs[1].last_hidden_state, logits=False)\n",
+    "pv.top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f93402c",
+   "metadata": {},
+   "source": [
+    "### Multi-Source Interchange Intervention (Serial Mode)\n",
+    "\n",
+    "Or you can do them sequentially, where you intervene among your **Source** examples, and get some intermediate states before merging the activations into the **Base** run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5e5752dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_the                 0.06737838685512543\n",
+      "_a                   0.059834375977516174\n",
+      "_not                 0.04629501700401306\n",
+      "_Italian             0.03623826056718826\n",
+      "_often               0.021700192242860794\n",
+      "_called              0.01840786263346672\n",
+      "_that                0.0157712884247303\n",
+      "_known               0.014391838572919369\n",
+      "_an                  0.013535155914723873\n",
+      "_very                0.013022392988204956\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = pv.IntervenableConfig([\n",
+    "  {\"layer\": 3, \"component\": \"block_output\"},\n",
+    "  {\"layer\": 10, \"component\": \"block_output\"}],\n",
+    "  # intervene on base one after another\n",
+    "  mode=\"serial\")\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "  config, model=gpt2)\n",
+    "base = tokenizer(\n",
+    "  \"The capital of Spain is\", \n",
+    "  return_tensors=\"pt\")\n",
+    "sources = [\n",
+    "  tokenizer(\"The language of Spain is\", \n",
+    "    return_tensors=\"pt\"),\n",
+    "  tokenizer(\"The capital of Italy is\", \n",
+    "    return_tensors=\"pt\")]\n",
+    "\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base, sources,\n",
+    "    # intervene in serial at two positions\n",
+    "    {\"source_0->source_1\": 1, \n",
+    "     \"source_1->base\"    : 4})\n",
+    "\n",
+    "distrib = pv.embed_to_distrib(\n",
+    "    gpt2, intervened_outputs[1].last_hidden_state, logits=False)\n",
+    "pv.top_vals(tokenizer, distrib[0][-1], n=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28621880",
+   "metadata": {},
+   "source": [
+    "### Multi-Source Interchange Intervention with Subspaces (Parallel Mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "773aba2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    {\"layer\": 0, \"component\": \"block_output\",\n",
+    "     \"subspace_partition\": \n",
+    "         [[0, 128], [128, 256]]}]*2,\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    "    # act in parallel\n",
+    "    mode=\"parallel\"\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\"),\n",
+    "          tokenizer(\"The capital of China is\", return_tensors=\"pt\")]\n",
+    "\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base, sources,\n",
+    "    # on same position\n",
+    "    {\"sources->base\": 4},\n",
+    "    # on different subspaces\n",
+    "    subspaces=[[[0]], [[1]]],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7223603f",
+   "metadata": {},
+   "source": [
+    "### Multi-Source Interchange Intervention with Subspaces (Serial Mode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "305e0607",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    {\"layer\": 0, \"component\": \"block_output\",\n",
+    "     \"subspace_partition\": [[0, 128], [128, 256]]},\n",
+    "    {\"layer\": 2, \"component\": \"block_output\",\n",
+    "     \"subspace_partition\": [[0, 128], [128, 256]]}],\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    "    # act in parallel\n",
+    "    mode=\"serial\"\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\"),\n",
+    "          tokenizer(\"The capital of China is\", return_tensors=\"pt\")]\n",
+    "\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base, sources,\n",
+    "    # serialized intervention\n",
+    "    # order is based on sources list\n",
+    "    {\"source_0->source_1\": 3, \"source_1->base\": 4},\n",
+    "    # on different subspaces\n",
+    "    subspaces=[[[0]], [[1]]],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b5fcb37",
+   "metadata": {},
+   "source": [
+    "### Interchange Intervention Training (IIT)\n",
+    "Interchange intervention training (IIT) is a technique of inducing causal structures into neural models. This library naturally supports this. By training IIT, you can simply turn the gradient on for the wrapping model. In this way, your model can be trained with your interventional signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8c7dde89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "number of params: 124439808\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[ 0.0022, -0.1783, -0.2780,  ...,  0.0477, -0.2069,  0.1093],\n",
+       "         [ 0.0385,  0.0886, -0.6608,  ...,  0.0104, -0.4946,  0.6148],\n",
+       "         [ 0.2377, -0.2312,  0.0308,  ...,  0.1085,  0.0456,  0.2494],\n",
+       "         [-0.0034,  0.0088, -0.2219,  ...,  0.1198,  0.0759,  0.3953],\n",
+       "         [ 0.4635,  0.2698, -0.3185,  ..., -0.2946,  0.2634,  0.2714]]],\n",
+       "       grad_fn=<SubBackward0>)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    \"layer\": 8, \"component\": \"block_output\"}, \n",
+    "    model=gpt2\n",
+    ")\n",
+    "\n",
+    "pv_gpt2.enable_model_gradients()\n",
+    "print(\"number of params:\", pv_gpt2.count_parameters())\n",
+    "\n",
+    "# run counterfactual forward as usual\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [\n",
+    "    tokenizer(\"The capital of Italy is\", return_tensors=\"pt\"),\n",
+    "]\n",
+    "base_outputs, counterfactual_outputs = pv_gpt2(\n",
+    "    base, sources, {\"sources->base\": ([[[3]]], [[[3]]])}, output_original_output=True\n",
+    ")\n",
+    "print(counterfactual_outputs.last_hidden_state - base_outputs.last_hidden_state)\n",
+    "# call backward will put gradients on model's weights\n",
+    "counterfactual_outputs.last_hidden_state.sum().backward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8c7ccad",
+   "metadata": {},
+   "source": [
+    "## pyvene 102\n",
+    "Now, you are pretty familiar with pyvene basic APIs. There are more to come. We support all sorts of weird interventions, and we encapsulate them as objects so that, even they are super weird (e.g., nested, multiple locations, different types), you can share them easily with others. BTW, if the intervention is trainable, the artifacts will be saved and shared as well.\n",
+    "\n",
+    "With that, here are a couple of additional APIs.\n",
+    "\n",
+    "### Grouping\n",
+    "\n",
+    "You can group interventions together so that they always receive the same input when you want to use them to get activations at different places. Here is an example, where you are taking in the same **Source** example, you fetch activations twice: once in position 3 and layer 0, once in position 4 and layer 2. You don't have to pass in another dummy **Source**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "84afd62c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    {\"layer\": 0, \"component\": \"block_output\", \"group_key\": 0},\n",
+    "    {\"layer\": 2, \"component\": \"block_output\", \"group_key\": 0}],\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    ")\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")]\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base, sources, \n",
+    "    {\"sources->base\": ([\n",
+    "        [[3]], [[4]] # these two are for two interventions\n",
+    "    ], [             # source position 3 into base position 4\n",
+    "        [[3]], [[4]] \n",
+    "    ])}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34aeb892",
+   "metadata": {},
+   "source": [
+    "### Intervention Skipping in Runtime\n",
+    "You may configure a lot of interventions, but during training, not every example will have to use all of them. So, you can skip interventions for different examples differently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "61cd8fc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "True True\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    # these are equivalent interventions\n",
+    "    # we create them on purpose\n",
+    "    {\"layer\": 0, \"component\": \"block_output\"},\n",
+    "    {\"layer\": 0, \"component\": \"block_output\"},\n",
+    "    {\"layer\": 0, \"component\": \"block_output\"}],\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "source = tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")\n",
+    "# skipping 1, 2 and 3\n",
+    "_, pv_out1 = pv_gpt2(base, [None, None, source],\n",
+    "    {\"sources->base\": ([None, None, [[4]]], [None, None, [[4]]])})\n",
+    "_, pv_out2 = pv_gpt2(base, [None, source, None],\n",
+    "    {\"sources->base\": ([None, [[4]], None], [None, [[4]], None])})\n",
+    "_, pv_out3 = pv_gpt2(base, [source, None, None],\n",
+    "    {\"sources->base\": ([[[4]], None, None], [[[4]], None, None])})\n",
+    "# should have the same results\n",
+    "print(\n",
+    "    torch.equal(pv_out1.last_hidden_state, pv_out2.last_hidden_state),\n",
+    "    torch.equal(pv_out2.last_hidden_state, pv_out3.last_hidden_state)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9df6acd",
+   "metadata": {},
+   "source": [
+    "### Subspace Partition\n",
+    "You can partition your subspace before hand. If you don't, the library assumes you each neuron is in its own subspace. In this example, you partition your subspace into two continous chunk, `[0, 128), [128,256)`, which means all the neurons from index 0 upto 127 are along to partition 1. During runtime, you can intervene on all the neurons in the same parition together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "3a66bbeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    # they are linked to manipulate the same representation\n",
+    "    # but in different subspaces\n",
+    "    {\"layer\": 0, \"component\": \"block_output\",\n",
+    "     # subspaces can be partitioned into continuous chunks\n",
+    "     # [i, j] are the boundary indices\n",
+    "     \"subspace_partition\": [[0, 128], [128, 256]]}],\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "source = tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")\n",
+    "\n",
+    "# using intervention skipping for subspace\n",
+    "intervened_outputs = pv_gpt2(\n",
+    "    base, [source],\n",
+    "    {\"sources->base\": 4},\n",
+    "    # intervene only only dimensions from 128 to 256\n",
+    "    subspaces=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fdde257",
+   "metadata": {},
+   "source": [
+    "### Intervention Linking\n",
+    "Interventions can be linked to share weights and share subspaces. Here is an example of how to link interventions together. If interventions are trainable, then their weights are tied as well.\n",
+    "\n",
+    "Why this is useful? it is because sometimes, you may want to intervene on different subspaces differently. Say you have a representation in a size of 512, and you hypothesize the first half represents A, and the second half represents B, you can then use the subspace intervention to test it out. With trainable interventions, you can also optimize your interventions on the same representation yet with different subspaces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "eec19da9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    # they are linked to manipulate the same representation\n",
+    "    # but in different subspaces\n",
+    "    {\"layer\": 0, \"component\": \"block_output\", \n",
+    "     \"subspace_partition\": [[0, 128], [128, 256]], \"intervention_link_key\": 0},\n",
+    "    {\"layer\": 0, \"component\": \"block_output\",\n",
+    "     \"subspace_partition\": [[0, 128], [128, 256]], \"intervention_link_key\": 0}],\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "source = tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")\n",
+    "\n",
+    "# using intervention skipping for subspace\n",
+    "_, pv_out1 = pv_gpt2(\n",
+    "    base, [None, source],\n",
+    "    # 4 means token position 4\n",
+    "    {\"sources->base\": ([None, [[4]]], [None, [[4]]])},\n",
+    "    # 1 means the second partition in the config\n",
+    "    subspaces=[None, [[1]]],\n",
+    ")\n",
+    "_, pv_out2 = pv_gpt2(\n",
+    "    base,\n",
+    "    [source, None],\n",
+    "    {\"sources->base\": ([[[4]], None], [[[4]], None])},\n",
+    "    subspaces=[[[1]], None],\n",
+    ")\n",
+    "print(torch.equal(pv_out1.last_hidden_state, pv_out2.last_hidden_state))\n",
+    "\n",
+    "# subspaces provide a list of index and they can be in any order\n",
+    "_, pv_out3 = pv_gpt2(\n",
+    "    base,\n",
+    "    [source, source],\n",
+    "    {\"sources->base\": ([[[4]], [[4]]], [[[4]], [[4]]])},\n",
+    "    subspaces=[[[0]], [[1]]],\n",
+    ")\n",
+    "_, pv_out4 = pv_gpt2(\n",
+    "    base,\n",
+    "    [source, source],\n",
+    "    {\"sources->base\": ([[[4]], [[4]]], [[[4]], [[4]]])},\n",
+    "    subspaces=[[[1]], [[0]]],\n",
+    ")\n",
+    "print(torch.equal(pv_out3.last_hidden_state, pv_out4.last_hidden_state))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "243f146f-1b9a-4574-ba2c-ebf455a96c16",
+   "metadata": {},
+   "source": [
+    "Other than intervention linking, you can also share interventions at the same component across multiple positions via setting a flag in the intervention object. It will have the same effect as creating one intervention per location and linking them all together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7c647943-c7e1-4024-8c07-b51062e668ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[0., 0., 0.,  ..., 0., 0., 0.],\n",
+       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
+       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
+       "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
+       "         [0., 0., 0.,  ..., 0., 0., 0.]]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "config = pv.IntervenableConfig([\n",
+    "    # they are linked to manipulate the same representation\n",
+    "    # but in different subspaces\n",
+    "    {\"layer\": 0, \"component\": \"block_output\", \"intervention_link_key\": 0},\n",
+    "    {\"layer\": 0, \"component\": \"block_output\", \"intervention_link_key\": 0}],\n",
+    "    intervention_types=pv.VanillaIntervention,\n",
+    ")\n",
+    "pv_gpt2 = pv.IntervenableModel(config, model=gpt2)\n",
+    "\n",
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "source = tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")\n",
+    "\n",
+    "_, pv_out = pv_gpt2(\n",
+    "    base,\n",
+    "    [source, source],\n",
+    "    # swap 3rd and 4th token reprs from the same source to the base\n",
+    "    {\"sources->base\": ([[[4]], [[3]]], [[[4]], [[3]]])},\n",
+    ")\n",
+    "\n",
+    "keep_last_dim_config = pv.IntervenableConfig([\n",
+    "    # they are linked to manipulate the same representation\n",
+    "    # but in different subspaces\n",
+    "    {\"layer\": 0, \"component\": \"block_output\", \n",
+    "     \"intervention\": pv.VanillaIntervention(keep_last_dim=True)}]\n",
+    ")\n",
+    "keep_last_dim_pv_gpt2 = pv.IntervenableModel(keep_last_dim_config, model=gpt2)\n",
+    "\n",
+    "_, keep_last_dim_pv_out = keep_last_dim_pv_gpt2(\n",
+    "    base,\n",
+    "    [source],\n",
+    "    # swap 3rd and 4th token reprs from the same source to the base\n",
+    "    {\"sources->base\": ([[[3,4]]], [[[3,4]]])},\n",
+    ")\n",
+    "keep_last_dim_pv_out.last_hidden_state - pv_out.last_hidden_state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef5b7a3e",
+   "metadata": {},
+   "source": [
+    "### Add New Model Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "acce6e8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "\n",
+    "# get a flan-t5 from HuggingFace\n",
+    "from transformers import T5ForConditionalGeneration, T5Tokenizer, T5Config\n",
+    "config = T5Config.from_pretrained(\"google/flan-t5-small\")\n",
+    "tokenizer = T5Tokenizer.from_pretrained(\"google/flan-t5-small\")\n",
+    "t5 = T5ForConditionalGeneration.from_pretrained(\n",
+    "    \"google/flan-t5-small\", config=config\n",
+    ")\n",
+    "\n",
+    "# config the intervention mapping with pv global vars\n",
+    "\"\"\"Only define for the block output here for simplicity\"\"\"\n",
+    "pv.type_to_module_mapping[type(t5)] = {\n",
+    "    \"mlp_output\": (\"encoder.block[%s].layer[1]\", \n",
+    "                   pv.models.constants.CONST_OUTPUT_HOOK),\n",
+    "    \"attention_input\": (\"encoder.block[%s].layer[0]\", \n",
+    "                        pv.models.constants.CONST_OUTPUT_HOOK),\n",
+    "}\n",
+    "pv.type_to_dimension_mapping[type(t5)] = {\n",
+    "    \"mlp_output\": (\"d_model\",),\n",
+    "    \"attention_input\": (\"d_model\",),\n",
+    "    \"block_output\": (\"d_model\",),\n",
+    "    \"head_attention_value_output\": (\"d_model/num_heads\",),\n",
+    "}\n",
+    "\n",
+    "# wrap as gpt2\n",
+    "pv_t5 = pv.IntervenableModel({\n",
+    "    \"layer\": 0,\n",
+    "    \"component\": \"mlp_output\",\n",
+    "    \"source_representation\": torch.zeros(\n",
+    "        t5.config.d_model)\n",
+    "}, model=t5)\n",
+    "\n",
+    "# then intervene!\n",
+    "base = tokenizer(\"The capital of Spain is\", \n",
+    "                 return_tensors=\"pt\")\n",
+    "decoder_input_ids = tokenizer(\n",
+    "    \"\", return_tensors=\"pt\").input_ids\n",
+    "base[\"decoder_input_ids\"] = decoder_input_ids\n",
+    "intervened_outputs = pv_t5(\n",
+    "    base, \n",
+    "    unit_locations={\"base\": 3}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba158a92",
+   "metadata": {},
+   "source": [
+    "### Composing Complex Intervention Schema: Path Patching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e51cadfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n",
+      "Directory './tmp/' already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyvene as pv\n",
+    "\n",
+    "def path_patching_config(\n",
+    "    layer, last_layer, \n",
+    "    component=\"head_attention_value_output\", unit=\"h.pos\"\n",
+    "):\n",
+    "    intervening_component = [\n",
+    "        {\"layer\": layer, \"component\": component, \"unit\": unit, \"group_key\": 0}]\n",
+    "    restoring_components = []\n",
+    "    if not component.startswith(\"mlp_\"):\n",
+    "        restoring_components += [\n",
+    "            {\"layer\": layer, \"component\": \"mlp_output\", \"group_key\": 1}]\n",
+    "    for i in range(layer+1, last_layer):\n",
+    "        restoring_components += [\n",
+    "            {\"layer\": i, \"component\": \"attention_output\", \"group_key\": 1},\n",
+    "            {\"layer\": i, \"component\": \"mlp_output\", \"group_key\": 1}\n",
+    "        ]\n",
+    "    intervenable_config = pv.IntervenableConfig(\n",
+    "        intervening_component + restoring_components)\n",
+    "    return intervenable_config\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "    path_patching_config(4, gpt2.config.n_layer), \n",
+    "    model=gpt2\n",
+    ")\n",
+    "\n",
+    "pv_gpt2.save(\n",
+    "    save_directory=\"./tmp/\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "9074f716",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pv_gpt2 = pv.IntervenableModel.load(\n",
+    "    \"./tmp/\",\n",
+    "    model=gpt2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d546e858",
+   "metadata": {},
+   "source": [
+    "### Composing Complex Intervention Schema: Causal Tracing in 15 lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c0b6a70f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyvene as pv\n",
+    "\n",
+    "def causal_tracing_config(\n",
+    "  l, c=\"mlp_activation\", w=10, tl=48):\n",
+    "  s = max(0, l - w // 2)\n",
+    "  e = min(tl, l - (-w // 2))\n",
+    "  config = pv.IntervenableConfig(\n",
+    "    [{\"component\": \"block_input\"}] + \n",
+    "    [{\"layer\": l, \"component\": c} \n",
+    "      for l in range(s, e)],\n",
+    "    [pv.NoiseIntervention] +\n",
+    "    [pv.VanillaIntervention]*(e-s))\n",
+    "  return config\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel(\n",
+    "    causal_tracing_config(4), \n",
+    "    model=gpt2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5027498b-66b9-428a-9693-94a6b5614bb9",
+   "metadata": {},
+   "source": [
+    "### Inference-time Intervention"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "559bf80a-2a79-46f0-b8a1-8848fd49613b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09c7100d49c94e1b94f3429440f1aab3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time, torch\n",
+    "import pyvene as pv\n",
+    "config, tokenizer, llama = pv.create_llama(\"meta-llama/Llama-2-7b-chat-hf\")\n",
+    "# load cached extracted activations from the ITI repo:\n",
+    "# https://github.com/likenneth/honest_llama\n",
+    "o_proj_add_activations = torch.load(\n",
+    "    \"./tutorials/basic_tutorials/tutorial_data\"\n",
+    "    \"/honest_llama2_chat_7B_o_proj_add_activations.bin\"\n",
+    ")\n",
+    "_ = llama.cuda()\n",
+    "\n",
+    "pv_llama = pv.IntervenableModel([{\n",
+    "        \"component\": f\"model.layers[{i}].self_attn.o_proj.output\",\n",
+    "        \"intervention\": pv.AdditionIntervention(\n",
+    "            source_representation=o_proj_add_activations[i].to(\"cuda\")\n",
+    "        )\n",
+    "    } for i in range(config.num_hidden_layers) if torch.count_nonzero(o_proj_add_activations[i])], \n",
+    "    model=llama\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15992a70-288a-43b5-9baf-348e7cb34068",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "vanilla llama-2-chat:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/sailhome/wuzhengx/.local/lib/python3.8/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
+      "  warnings.warn(\n",
+      "/sailhome/wuzhengx/.local/lib/python3.8/site-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What's a cure for insomnia that always works?\n",
+      "\n",
+      "Insomnia is a common sleep disorder that can affect anyone, regardless of age or gender. It is characterized by difficulty falling asleep, staying asleep, or getting quality sleep. Insomnia can be caused by a variety of factors, including stress, anxiety, depression, certain\n",
+      "Execution time: 2.1844897270202637 seconds\n",
+      "\n",
+      "honest llama-2-chat:\n",
+      "What's a cure for insomnia that always works?\n",
+      "There is no single cure for insomnia that works for everyone, and it's important to address the underlying causes of sleep problems. However, some strategies that may help improve sleep quality and duration include:\n",
+      "\n",
+      "1. Practicing relaxation techniques, such as deep breathing, progressive mus\n",
+      "Execution time: 2.6960761547088623 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "q = \"What's a cure for insomnia that always works?\"\n",
+    "prompt = tokenizer(q, return_tensors=\"pt\").to(\"cuda\")\n",
+    "print(\"vanilla llama-2-chat:\")\n",
+    "st = time.time()\n",
+    "response = llama.generate(**prompt, max_new_tokens=64, do_sample=False)\n",
+    "print(tokenizer.decode(response[0], skip_special_tokens=True))\n",
+    "et = time.time()\n",
+    "elapsed_time = et - st\n",
+    "print('Execution time:', elapsed_time, 'seconds')\n",
+    "print()\n",
+    "print(\"honest llama-2-chat:\")\n",
+    "st = time.time()\n",
+    "_, iti_response = pv_llama.generate(prompt, max_new_tokens=64, do_sample=False)\n",
+    "print(tokenizer.decode(iti_response[0], skip_special_tokens=True))\n",
+    "et = time.time()\n",
+    "elapsed_time = et - st\n",
+    "print('Execution time:', elapsed_time, 'seconds')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7ae2ebe8-fa4d-426e-b663-089671323456",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory './tmp_llama/' already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save to huggingface directly\n",
+    "try:\n",
+    "    pv_llama.save(\n",
+    "        \"./tmp_llama/\",\n",
+    "        save_to_hf_hub=True, \n",
+    "        hf_repo_name=\"zhengxuanzenwu/intervenable_honest_llama2_chat_7B\"\n",
+    "    )\n",
+    "except:\n",
+    "    print(\"You have to login into huggingface hub before running this.\")\n",
+    "    print(\"usage: huggingface-cli login\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c7b90b0",
+   "metadata": {},
+   "source": [
+    "### IntervenableModel from HuggingFace Directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b5d26852-f86d-4dea-88f1-2db2634e1ba5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "437f82c9e5ff4a39ae988ccbfd7518df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8b98f21c02a4e5da60d67098d99a2c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llama-2-chat loaded with interventions:\n",
+      "What's a cure for insomnia that always works?\n",
+      "There is no single cure for insomnia that works for everyone, and it's important to address the underlying causes of sleep problems. However, some strategies that may help improve sleep quality and duration include:\n",
+      "\n",
+      "1. Practicing relaxation techniques, such as deep breathing, progressive mus\n"
+     ]
+    }
+   ],
+   "source": [
+    "# others can download from huggingface and use it directly\n",
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "import pyvene as pv\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-chat-hf\")\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    \"meta-llama/Llama-2-7b-chat-hf\",\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    ").to(\"cuda\")\n",
+    "\n",
+    "pv_model = pv.IntervenableModel.load(\n",
+    "    \"zhengxuanzenwu/intervenable_honest_llama2_chat_7B\", # the activation diff ~0.14MB\n",
+    "    model,\n",
+    ")\n",
+    "\n",
+    "print(\"llama-2-chat loaded with interventions:\")\n",
+    "q = \"What's a cure for insomnia that always works?\"\n",
+    "prompt = tokenizer(q, return_tensors=\"pt\").to(\"cuda\")\n",
+    "_, iti_response_shared = pv_model.generate(prompt, max_new_tokens=64, do_sample=False)\n",
+    "print(tokenizer.decode(iti_response_shared[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956b3b5f",
+   "metadata": {},
+   "source": [
+    "### Path Patching with Trainable Interventions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "af501960",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyvene as pv\n",
+    "\n",
+    "def path_patching_with_DAS_config(\n",
+    "    layer, last_layer, low_rank_dimension,\n",
+    "    component=\"attention_output\", unit=\"pos\"\n",
+    "):\n",
+    "    intervening_component = [{\n",
+    "        \"layer\": layer, \"component\": component, \"group_key\": 0,\n",
+    "        \"intervention_type\": pv.LowRankRotatedSpaceIntervention,\n",
+    "        \"low_rank_dimension\": low_rank_dimension,\n",
+    "    }]\n",
+    "    restoring_components = []\n",
+    "    if not component.startswith(\"mlp_\"):\n",
+    "        restoring_components += [{\n",
+    "            \"layer\": layer, \"component\": \"mlp_output\", \"group_key\": 1,\n",
+    "            \"intervention_type\": pv.VanillaIntervention,\n",
+    "        }]\n",
+    "    for i in range(layer+1, last_layer):\n",
+    "        restoring_components += [{\n",
+    "            \"layer\": i, \"component\": \"attention_output\", \"group_key\": 1, \n",
+    "            \"intervention_type\": pv.VanillaIntervention},{\n",
+    "            \"layer\": i, \"component\": \"mlp_output\", \"group_key\": 1,\n",
+    "            \"intervention_type\": pv.VanillaIntervention\n",
+    "        }]\n",
+    "    intervenable_config = pv.IntervenableConfig(\n",
+    "        intervening_component + restoring_components)\n",
+    "    return intervenable_config, len(restoring_components)\n",
+    "\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "pv_config, num_restores = path_patching_with_DAS_config(4, 6, 1)\n",
+    "pv_gpt2 = pv.IntervenableModel(pv_config, model=gpt2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "be63453e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(-0.0694, grad_fn=<SumBackward0>)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "restore_source = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
+    "source = tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")\n",
+    "\n",
+    "# zero-out grads\n",
+    "_ = pv_gpt2.model.eval()\n",
+    "for k, v in pv_gpt2.interventions.items():\n",
+    "    v.zero_grad()\n",
+    "\n",
+    "original_outputs, counterfactual_outputs = pv_gpt2(\n",
+    "    base, \n",
+    "    sources=[source, restore_source],\n",
+    "    unit_locations={\n",
+    "        \"sources->base\": 4\n",
+    "    }\n",
+    ")\n",
+    "# put gradients on the trainable intervention only\n",
+    "counterfactual_outputs[0].sum().backward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0907a98c",
+   "metadata": {},
+   "source": [
+    "### Intervene on ResNet with Lambda Functions\n",
+    "\n",
+    "Huggingface Vision model comes with the support of ResNet. Here, we show how we can use pyvene to intervene on a patch of pixels, like token in transformer, which is like a primitive object in ResNet or ConvNet based NNs.\n",
+    "\n",
+    "**Caveats:** We go with a pretty much hard-coded way here, but you can customize the hook functions as you want. It does not have to be a lambda function as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bfb48112",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.0005)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoFeatureExtractor, AutoModelForImageClassification\n",
+    "\n",
+    "feature_extractor = AutoFeatureExtractor.from_pretrained(\"microsoft/resnet-18\")\n",
+    "resnet = AutoModelForImageClassification.from_pretrained(\"microsoft/resnet-18\")\n",
+    "\n",
+    "dataset = load_dataset(\"huggingface/cats-image\")\n",
+    "base_image = dataset[\"test\"][\"image\"][0]\n",
+    "source_image = dataset[\"test\"][\"image\"][0]\n",
+    "base_inputs = feature_extractor(base_image, return_tensors=\"pt\")\n",
+    "source_inputs = feature_extractor(source_image, return_tensors=\"pt\")\n",
+    "source_inputs['pixel_values'] += 0.5*torch.randn(source_inputs['pixel_values'].shape)\n",
+    "\n",
+    "def create_mask():\n",
+    "    _mask = torch.zeros((56, 56))\n",
+    "    _mask[56//2:, 56//2:] = 1\n",
+    "    return _mask\n",
+    "m = create_mask()\n",
+    "\n",
+    "pv_resnet = pv.IntervenableModel({\n",
+    "    \"component\": \"resnet.embedder.pooler.output\", \n",
+    "    \"intervention\": lambda b, s: b * (1. - m) + s * m}, \n",
+    "    model=resnet\n",
+    ")\n",
+    "intervened_outputs = pv_resnet(\n",
+    "    base_inputs, [source_inputs], return_dict=True, output_original_output=True\n",
+    ")\n",
+    "(intervened_outputs.intervened_outputs.logits - intervened_outputs.original_outputs.logits).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4d0aa78",
+   "metadata": {},
+   "source": [
+    "### Intervene on ResNet with Trainable Lambda Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6d0095f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.0068, grad_fn=<SumBackward0>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoFeatureExtractor, AutoModelForImageClassification\n",
+    "\n",
+    "feature_extractor = AutoFeatureExtractor.from_pretrained(\"microsoft/resnet-18\")\n",
+    "resnet = AutoModelForImageClassification.from_pretrained(\"microsoft/resnet-18\")\n",
+    "\n",
+    "dataset = load_dataset(\"huggingface/cats-image\")\n",
+    "base_image = dataset[\"test\"][\"image\"][0]\n",
+    "source_image = dataset[\"test\"][\"image\"][0]\n",
+    "base_inputs = feature_extractor(base_image, return_tensors=\"pt\")\n",
+    "source_inputs = feature_extractor(source_image, return_tensors=\"pt\")\n",
+    "source_inputs['pixel_values'] += 0.5*torch.randn(source_inputs['pixel_values'].shape)\n",
+    "\n",
+    "# trainable DAS directions\n",
+    "v = torch.nn.utils.parametrizations.orthogonal(\n",
+    "    torch.nn.Linear(56, 10))\n",
+    "\n",
+    "pv_resnet = pv.IntervenableModel({\n",
+    "    \"component\": \"resnet.embedder.pooler.output\", \n",
+    "    \"intervention\": lambda b, s: b + ((s @ v.weight.T - b @ v.weight.T) @ v.weight)}, \n",
+    "    model=resnet\n",
+    ")\n",
+    "\n",
+    "intervened_outputs = pv_resnet(\n",
+    "    base_inputs, [source_inputs], return_dict=True, output_original_output=True\n",
+    ")\n",
+    "(intervened_outputs.intervened_outputs.logits - intervened_outputs.original_outputs.logits).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14694aea-934e-47f3-ab27-6843f6b5dc7a",
+   "metadata": {},
+   "source": [
+    "### Run pyvene on [NDIF](https://ndif.us/) backend with `pv.build_intervenable_model(...)`\n",
+    "\n",
+    "[NDIF](https://ndif.us/) provides APIs for running intervened model inference calls either locally or remotely, enabling Pyvene to run intervened model calls remotely with shared resources. This is especially useful when the intervened model is large (e.g., Llama 400B).\n",
+    "\n",
+    "Note that setting `remote=True` is still under-construction for remote intervention."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba3fc81-8f61-40d2-9e23-8d61c9dc73b6",
+   "metadata": {},
+   "source": [
+    "**Basic activation collection**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9b289caa-6621-4cc1-883d-c9b91a21617d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
+      "  warnings.warn(\n",
+      "WARNING:root:We currently have very limited intervention support for ndif backend.\n",
+      "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pyvene as pv\n",
+    "from transformers import AutoTokenizer\n",
+    "from nnsight import LanguageModel\n",
+    "\n",
+    "# load any huggingface model as a ndif native model object\n",
+    "gpt2_ndif = LanguageModel('openai-community/gpt2', device_map='cpu')\n",
+    "tokenizer = AutoTokenizer.from_pretrained('openai-community/gpt2')\n",
+    "\n",
+    "# pyvene provides pv.build_intervenable_model as the generic model builder\n",
+    "pv_gpt2_ndif = pv.build_intervenable_model({\n",
+    "    # based on the module printed above, you can access via string, input means the input to the module\n",
+    "    \"component\": \"transformer.h[10].attn.attn_dropout.input\",\n",
+    "    # you can also initialize the intervention gpt2_ndif\n",
+    "    \"intervention\": pv.CollectIntervention()}, model=gpt2_ndif, remote=False)\n",
+    "\n",
+    "base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
+    "ndif_collected_attn_w = pv_gpt2_ndif(\n",
+    "    base = tokenizer(base, return_tensors=\"pt\"\n",
+    "    ), unit_locations={\"base\": [h for h in range(12)]}\n",
+    ")[0][-1][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a97c01ee-0904-4e01-8dee-2608d0635964",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# gpt2 helper loading model from HuggingFace\n",
+    "_, tokenizer, gpt2 = pv.create_gpt2()\n",
+    "\n",
+    "pv_gpt2 = pv.IntervenableModel({\n",
+    "    # based on the module printed above, you can access via string, input means the input to the module\n",
+    "    \"component\": \"h[10].attn.attn_dropout.input\",\n",
+    "    # you can also initialize the intervention outside\n",
+    "    \"intervention\": pv.CollectIntervention()}, model=gpt2)\n",
+    "\n",
+    "base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
+    "collected_attn_w = pv_gpt2(\n",
+    "    base = tokenizer(base, return_tensors=\"pt\"\n",
+    "    ), unit_locations={\"base\": [h for h in range(12)]}\n",
+    ")[0][-1][0]\n",
+    "torch.allclose(ndif_collected_attn_w, collected_attn_w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e463fc3b-684b-4461-b94e-d69ca310e27e",
+   "metadata": {},
+   "source": [
+    "**Interchange intervention (activation swap between two examples)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f4f02279-5601-4db9-9823-2e4d3bd8b56c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
+      "  warnings.warn(\n",
+      "WARNING:root:We currently have very limited intervention support for ndif backend.\n",
+      "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyvene as pv\n",
+    "from transformers import AutoTokenizer\n",
+    "from nnsight import LanguageModel\n",
+    "\n",
+    "# load any huggingface model as a ndif native model object\n",
+    "gpt2_ndif = LanguageModel('openai-community/gpt2', device_map='cpu')\n",
+    "tokenizer = AutoTokenizer.from_pretrained('openai-community/gpt2')\n",
+    "\n",
+    "# create with dict-based config\n",
+    "pv_config = pv.IntervenableConfig({\n",
+    "  \"component\": \"transformer.h[0].attn.output\",\n",
+    "  \"intervention\": pv.VanillaIntervention()}\n",
+    ")\n",
+    "#initialize model\n",
+    "pv_gpt2_ndif = pv.build_intervenable_model(\n",
+    "  pv_config, model=gpt2_ndif)\n",
+    "# run an interchange intervention \n",
+    "intervened_outputs = pv_gpt2_ndif(\n",
+    "  # the base input\n",
+    "  base=tokenizer(\n",
+    "    \"The capital of Spain is\", \n",
+    "    return_tensors = \"pt\"), \n",
+    "  # the source input\n",
+    "  sources=tokenizer(\n",
+    "    \"The capital of Italy is\", \n",
+    "    return_tensors = \"pt\"), \n",
+    "  # the location to intervene at (3rd token)\n",
+    "  unit_locations={\"sources->base\": 3},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc6eb49d",
+   "metadata": {},
+   "source": [
+    "### The End\n",
+    "Now you are graduating from pyvene entry level course! Feel free to take a look at our tutorials for more challenging interventions."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "toc-autonumbering": true,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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new file mode 100644
index 00000000..30fee9d0
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+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
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+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
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+    fill: #fbe54e;
+}
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+    font-weight: bold;
+    font-size: 1.1em;
+}
+
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+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 00000000..d06a71d7
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 00000000..5087290c
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '0.1.2',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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diff --git a/_static/images/logo_binder.svg b/_static/images/logo_binder.svg
new file mode 100644
index 00000000..45fecf75
--- /dev/null
+++ b/_static/images/logo_binder.svg
@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="utf-8"?>
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diff --git a/_static/images/logo_deepnote.svg b/_static/images/logo_deepnote.svg
new file mode 100644
index 00000000..fa77ebfc
--- /dev/null
+++ b/_static/images/logo_deepnote.svg
@@ -0,0 +1 @@
+<svg viewBox="0 0 128 128" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M0 128h52.512l29.539-11.077-11.077-43.487-34.051 3.693L0 128Z" fill="#0076D4"/><path fill-rule="evenodd" clip-rule="evenodd" d="M52.513 128s16.6-8.759 19.673-24.277c3.072-15.517-12.091-26.594-35.263-26.594 0-.41 20.343-28.718 20.343-28.718l49.4 1.435L95.71 107.7l-20.452 15.978L52.513 128Z" fill="#002868"/><path fill-rule="evenodd" clip-rule="evenodd" d="M0 60.718 41.025.001s1.006.01 3.282 0c16.082-.068 81.23 3.12 81.23 60.368 0 65.352-73.025 67.631-73.025 67.631s30.495-5.839 30.495-34.816c0-28.978-27.541-32.466-45.264-32.466H0Z" fill="#00A9FF"/></svg>
diff --git a/_static/images/logo_jupyterhub.svg b/_static/images/logo_jupyterhub.svg
new file mode 100644
index 00000000..60cfe9f2
--- /dev/null
+++ b/_static/images/logo_jupyterhub.svg
@@ -0,0 +1 @@
+<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="38.73" height="50" viewBox="0 0 38.73 50"><defs><style>.cls-1{fill:#767677;}.cls-2{fill:#f37726;}.cls-3{fill:#9e9e9e;}.cls-4{fill:#616262;}.cls-5{font-size:17.07px;fill:#fff;font-family:Roboto-Regular, Roboto;}</style></defs><title>logo_jupyterhub</title><g id="Canvas"><path id="path7_fill" data-name="path7 fill" class="cls-1" d="M39.51,3.53a3,3,0,0,1-1.7,2.9A3,3,0,0,1,34.48,6a3,3,0,0,1-.82-3.26,3,3,0,0,1,1.05-1.41A3,3,0,0,1,37.52.86a2.88,2.88,0,0,1,1,.6,3,3,0,0,1,.7.93,3.18,3.18,0,0,1,.28,1.14Z" transform="translate(-1.87 -0.69)"/><path id="path8_fill" data-name="path8 fill" class="cls-2" d="M21.91,38.39c-8,0-15.06-2.87-18.7-7.12a19.93,19.93,0,0,0,37.39,0C37,35.52,30,38.39,21.91,38.39Z" transform="translate(-1.87 -0.69)"/><path id="path9_fill" data-name="path9 fill" class="cls-2" d="M21.91,10.78c8,0,15.05,2.87,18.69,7.12a19.93,19.93,0,0,0-37.39,0C6.85,13.64,13.86,10.78,21.91,10.78Z" transform="translate(-1.87 -0.69)"/><path id="path10_fill" data-name="path10 fill" class="cls-3" d="M10.88,46.66a3.86,3.86,0,0,1-.52,2.15,3.81,3.81,0,0,1-1.62,1.51,3.93,3.93,0,0,1-2.19.34,3.79,3.79,0,0,1-2-.94,3.73,3.73,0,0,1-1.14-1.9,3.79,3.79,0,0,1,.1-2.21,3.86,3.86,0,0,1,1.33-1.78,3.92,3.92,0,0,1,3.54-.53,3.85,3.85,0,0,1,2.14,1.93,3.74,3.74,0,0,1,.37,1.43Z" transform="translate(-1.87 -0.69)"/><path id="path11_fill" data-name="path11 fill" class="cls-4" d="M4.12,9.81A2.18,2.18,0,0,1,2.9,9.48a2.23,2.23,0,0,1-.84-1A2.26,2.26,0,0,1,1.9,7.26a2.13,2.13,0,0,1,.56-1.13,2.18,2.18,0,0,1,2.36-.56,2.13,2.13,0,0,1,1,.76,2.18,2.18,0,0,1,.42,1.2A2.22,2.22,0,0,1,4.12,9.81Z" transform="translate(-1.87 -0.69)"/></g><text class="cls-5" transform="translate(5.24 30.01)">Hub</text></svg>
diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 00000000..250f5665
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_static/locales/ar/LC_MESSAGES/booktheme.mo b/_static/locales/ar/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..15541a6a375f93efed3f410c2f892174ba91aaa0
GIT binary patch
literal 1541
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literal 0
HcmV?d00001

diff --git a/_static/locales/ar/LC_MESSAGES/booktheme.po b/_static/locales/ar/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..34d404c6
--- /dev/null
+++ b/_static/locales/ar/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ar\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "طباعة إلى PDF"
+
+msgid "Theme by the"
+msgstr "موضوع بواسطة"
+
+msgid "Download source file"
+msgstr "تنزيل ملف المصدر"
+
+msgid "open issue"
+msgstr "قضية مفتوحة"
+
+msgid "Contents"
+msgstr "محتويات"
+
+msgid "previous page"
+msgstr "الصفحة السابقة"
+
+msgid "Download notebook file"
+msgstr "تنزيل ملف دفتر الملاحظات"
+
+msgid "Copyright"
+msgstr "حقوق النشر"
+
+msgid "Download this page"
+msgstr "قم بتنزيل هذه الصفحة"
+
+msgid "Source repository"
+msgstr "مستودع المصدر"
+
+msgid "By"
+msgstr "بواسطة"
+
+msgid "repository"
+msgstr "مخزن"
+
+msgid "Last updated on"
+msgstr "آخر تحديث في"
+
+msgid "Toggle navigation"
+msgstr "تبديل التنقل"
+
+msgid "Sphinx Book Theme"
+msgstr "موضوع كتاب أبو الهول"
+
+msgid "suggest edit"
+msgstr "أقترح تحرير"
+
+msgid "Open an issue"
+msgstr "افتح قضية"
+
+msgid "Launch"
+msgstr "إطلاق"
+
+msgid "Fullscreen mode"
+msgstr "وضع ملء الشاشة"
+
+msgid "Edit this page"
+msgstr "قم بتحرير هذه الصفحة"
+
+msgid "By the"
+msgstr "بواسطة"
+
+msgid "next page"
+msgstr "الصفحة التالية"
diff --git a/_static/locales/bg/LC_MESSAGES/booktheme.mo b/_static/locales/bg/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..da95120037f8d7c52ad1f166a7682ea55ca47a01
GIT binary patch
literal 1708
zcmaKqOKcQJ5QaOv0vkhkhC?jT2nh&cNkBwFYxY90B_t3AOpcMpyUpy7b@wpSL+}lZ
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KlK*|VZ0$SA#iddJ

literal 0
HcmV?d00001

diff --git a/_static/locales/bg/LC_MESSAGES/booktheme.po b/_static/locales/bg/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..7420c19e
--- /dev/null
+++ b/_static/locales/bg/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: bg\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Печат в PDF"
+
+msgid "Theme by the"
+msgstr "Тема от"
+
+msgid "Download source file"
+msgstr "Изтеглете изходния файл"
+
+msgid "open issue"
+msgstr "отворен брой"
+
+msgid "Contents"
+msgstr "Съдържание"
+
+msgid "previous page"
+msgstr "предишна страница"
+
+msgid "Download notebook file"
+msgstr "Изтеглете файла на бележника"
+
+msgid "Copyright"
+msgstr "Авторско право"
+
+msgid "Download this page"
+msgstr "Изтеглете тази страница"
+
+msgid "Source repository"
+msgstr "Хранилище на източника"
+
+msgid "By"
+msgstr "От"
+
+msgid "repository"
+msgstr "хранилище"
+
+msgid "Last updated on"
+msgstr "Последна актуализация на"
+
+msgid "Toggle navigation"
+msgstr "Превключване на навигацията"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема на книгата Sphinx"
+
+msgid "suggest edit"
+msgstr "предложи редактиране"
+
+msgid "Open an issue"
+msgstr "Отворете проблем"
+
+msgid "Launch"
+msgstr "Стартиране"
+
+msgid "Fullscreen mode"
+msgstr "Режим на цял екран"
+
+msgid "Edit this page"
+msgstr "Редактирайте тази страница"
+
+msgid "By the"
+msgstr "По"
+
+msgid "next page"
+msgstr "Следваща страница"
diff --git a/_static/locales/bn/LC_MESSAGES/booktheme.mo b/_static/locales/bn/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..6b96639b726a2fa959a6419b6f8b7e0dfcce33ae
GIT binary patch
literal 1646
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U`1<AwkrwO5Ir10vW|xY83+ptx-~a#s

literal 0
HcmV?d00001

diff --git a/_static/locales/bn/LC_MESSAGES/booktheme.po b/_static/locales/bn/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..63a07c36
--- /dev/null
+++ b/_static/locales/bn/LC_MESSAGES/booktheme.po
@@ -0,0 +1,63 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: bn\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "পিডিএফ প্রিন্ট করুন"
+
+msgid "Theme by the"
+msgstr "থিম দ্বারা"
+
+msgid "Download source file"
+msgstr "উত্স ফাইল ডাউনলোড করুন"
+
+msgid "open issue"
+msgstr "খোলা সমস্যা"
+
+msgid "previous page"
+msgstr "আগের পৃষ্ঠা"
+
+msgid "Download notebook file"
+msgstr "নোটবুক ফাইল ডাউনলোড করুন"
+
+msgid "Copyright"
+msgstr "কপিরাইট"
+
+msgid "Download this page"
+msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন"
+
+msgid "Source repository"
+msgstr "উত্স সংগ্রহস্থল"
+
+msgid "By"
+msgstr "দ্বারা"
+
+msgid "Last updated on"
+msgstr "সর্বশেষ আপডেট"
+
+msgid "Toggle navigation"
+msgstr "নেভিগেশন টগল করুন"
+
+msgid "Sphinx Book Theme"
+msgstr "স্পিনিক্স বুক থিম"
+
+msgid "Open an issue"
+msgstr "একটি সমস্যা খুলুন"
+
+msgid "Launch"
+msgstr "শুরু করা"
+
+msgid "Edit this page"
+msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন"
+
+msgid "By the"
+msgstr "দ্বারা"
+
+msgid "next page"
+msgstr "পরবর্তী পৃষ্ঠা"
diff --git a/_static/locales/ca/LC_MESSAGES/booktheme.mo b/_static/locales/ca/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..a4dd30e9bd762578b119c4401d082ea4fa430613
GIT binary patch
literal 1246
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OSN%W08!x{=|A0SA@;E&J

literal 0
HcmV?d00001

diff --git a/_static/locales/ca/LC_MESSAGES/booktheme.po b/_static/locales/ca/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..8fb358bf
--- /dev/null
+++ b/_static/locales/ca/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ca\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimeix a PDF"
+
+msgid "Theme by the"
+msgstr "Tema del"
+
+msgid "Download source file"
+msgstr "Baixeu el fitxer font"
+
+msgid "open issue"
+msgstr "número obert"
+
+msgid "previous page"
+msgstr "Pàgina anterior"
+
+msgid "Download notebook file"
+msgstr "Descarregar fitxer de quadern"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "Descarregueu aquesta pàgina"
+
+msgid "Source repository"
+msgstr "Dipòsit de fonts"
+
+msgid "By"
+msgstr "Per"
+
+msgid "Last updated on"
+msgstr "Darrera actualització el"
+
+msgid "Toggle navigation"
+msgstr "Commuta la navegació"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del llibre Esfinx"
+
+msgid "suggest edit"
+msgstr "suggerir edició"
+
+msgid "Open an issue"
+msgstr "Obriu un número"
+
+msgid "Launch"
+msgstr "Llançament"
+
+msgid "Edit this page"
+msgstr "Editeu aquesta pàgina"
+
+msgid "By the"
+msgstr "Per la"
+
+msgid "next page"
+msgstr "pàgina següent"
diff --git a/_static/locales/cs/LC_MESSAGES/booktheme.mo b/_static/locales/cs/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..c39e01a6ae9ae5242f04566ffa2cda96b328ca18
GIT binary patch
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literal 0
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diff --git a/_static/locales/cs/LC_MESSAGES/booktheme.po b/_static/locales/cs/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..c6ef4690
--- /dev/null
+++ b/_static/locales/cs/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: cs\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Tisk do PDF"
+
+msgid "Theme by the"
+msgstr "Téma od"
+
+msgid "Download source file"
+msgstr "Stáhněte si zdrojový soubor"
+
+msgid "open issue"
+msgstr "otevřené číslo"
+
+msgid "Contents"
+msgstr "Obsah"
+
+msgid "previous page"
+msgstr "předchozí stránka"
+
+msgid "Download notebook file"
+msgstr "Stáhnout soubor poznámkového bloku"
+
+msgid "Copyright"
+msgstr "autorská práva"
+
+msgid "Download this page"
+msgstr "Stáhněte si tuto stránku"
+
+msgid "Source repository"
+msgstr "Zdrojové úložiště"
+
+msgid "By"
+msgstr "Podle"
+
+msgid "repository"
+msgstr "úložiště"
+
+msgid "Last updated on"
+msgstr "Naposledy aktualizováno"
+
+msgid "Toggle navigation"
+msgstr "Přepnout navigaci"
+
+msgid "Sphinx Book Theme"
+msgstr "Téma knihy Sfinga"
+
+msgid "suggest edit"
+msgstr "navrhnout úpravy"
+
+msgid "Open an issue"
+msgstr "Otevřete problém"
+
+msgid "Launch"
+msgstr "Zahájení"
+
+msgid "Fullscreen mode"
+msgstr "Režim celé obrazovky"
+
+msgid "Edit this page"
+msgstr "Upravit tuto stránku"
+
+msgid "By the"
+msgstr "Podle"
+
+msgid "next page"
+msgstr "další strana"
diff --git a/_static/locales/da/LC_MESSAGES/booktheme.mo b/_static/locales/da/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f43157d70c63ff21b4385dc36cb73f5b14eb6b01
GIT binary patch
literal 1304
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literal 0
HcmV?d00001

diff --git a/_static/locales/da/LC_MESSAGES/booktheme.po b/_static/locales/da/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..306a38e5
--- /dev/null
+++ b/_static/locales/da/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: da\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Udskriv til PDF"
+
+msgid "Theme by the"
+msgstr "Tema af"
+
+msgid "Download source file"
+msgstr "Download kildefil"
+
+msgid "open issue"
+msgstr "åbent nummer"
+
+msgid "Contents"
+msgstr "Indhold"
+
+msgid "previous page"
+msgstr "forrige side"
+
+msgid "Download notebook file"
+msgstr "Download notesbog-fil"
+
+msgid "Copyright"
+msgstr "ophavsret"
+
+msgid "Download this page"
+msgstr "Download denne side"
+
+msgid "Source repository"
+msgstr "Kildelager"
+
+msgid "By"
+msgstr "Ved"
+
+msgid "repository"
+msgstr "lager"
+
+msgid "Last updated on"
+msgstr "Sidst opdateret den"
+
+msgid "Toggle navigation"
+msgstr "Skift navigation"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx bogtema"
+
+msgid "suggest edit"
+msgstr "foreslå redigering"
+
+msgid "Open an issue"
+msgstr "Åbn et problem"
+
+msgid "Launch"
+msgstr "Start"
+
+msgid "Fullscreen mode"
+msgstr "Fuldskærmstilstand"
+
+msgid "Edit this page"
+msgstr "Rediger denne side"
+
+msgid "By the"
+msgstr "Ved"
+
+msgid "next page"
+msgstr "Næste side"
diff --git a/_static/locales/de/LC_MESSAGES/booktheme.mo b/_static/locales/de/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..648b565c7829535bc5f26f86909f458d0cd0fe1b
GIT binary patch
literal 1388
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literal 0
HcmV?d00001

diff --git a/_static/locales/de/LC_MESSAGES/booktheme.po b/_static/locales/de/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..4925360d
--- /dev/null
+++ b/_static/locales/de/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: de\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "In PDF drucken"
+
+msgid "Theme by the"
+msgstr "Thema von der"
+
+msgid "Download source file"
+msgstr "Quelldatei herunterladen"
+
+msgid "open issue"
+msgstr "offenes Thema"
+
+msgid "Contents"
+msgstr "Inhalt"
+
+msgid "previous page"
+msgstr "vorherige Seite"
+
+msgid "Download notebook file"
+msgstr "Notebook-Datei herunterladen"
+
+msgid "Copyright"
+msgstr "Urheberrechte ©"
+
+msgid "Download this page"
+msgstr "Laden Sie diese Seite herunter"
+
+msgid "Source repository"
+msgstr "Quell-Repository"
+
+msgid "By"
+msgstr "Durch"
+
+msgid "repository"
+msgstr "Repository"
+
+msgid "Last updated on"
+msgstr "Zuletzt aktualisiert am"
+
+msgid "Toggle navigation"
+msgstr "Navigation umschalten"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-Buch-Thema"
+
+msgid "suggest edit"
+msgstr "vorschlagen zu bearbeiten"
+
+msgid "Open an issue"
+msgstr "Öffnen Sie ein Problem"
+
+msgid "Launch"
+msgstr "Starten"
+
+msgid "Fullscreen mode"
+msgstr "Vollbildmodus"
+
+msgid "Edit this page"
+msgstr "Bearbeite diese Seite"
+
+msgid "By the"
+msgstr "Bis zum"
+
+msgid "next page"
+msgstr "Nächste Seite"
diff --git a/_static/locales/el/LC_MESSAGES/booktheme.mo b/_static/locales/el/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..fca6e9355f314677f2890f5679fd79dd67a7a5a2
GIT binary patch
literal 1722
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literal 0
HcmV?d00001

diff --git a/_static/locales/el/LC_MESSAGES/booktheme.po b/_static/locales/el/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..3e01acbd
--- /dev/null
+++ b/_static/locales/el/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: el\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Εκτύπωση σε PDF"
+
+msgid "Theme by the"
+msgstr "Θέμα από το"
+
+msgid "Download source file"
+msgstr "Λήψη αρχείου προέλευσης"
+
+msgid "open issue"
+msgstr "ανοιχτό ζήτημα"
+
+msgid "Contents"
+msgstr "Περιεχόμενα"
+
+msgid "previous page"
+msgstr "προηγούμενη σελίδα"
+
+msgid "Download notebook file"
+msgstr "Λήψη αρχείου σημειωματάριου"
+
+msgid "Copyright"
+msgstr "Πνευματική ιδιοκτησία"
+
+msgid "Download this page"
+msgstr "Λήψη αυτής της σελίδας"
+
+msgid "Source repository"
+msgstr "Αποθήκη πηγής"
+
+msgid "By"
+msgstr "Με"
+
+msgid "repository"
+msgstr "αποθήκη"
+
+msgid "Last updated on"
+msgstr "Τελευταία ενημέρωση στις"
+
+msgid "Toggle navigation"
+msgstr "Εναλλαγή πλοήγησης"
+
+msgid "Sphinx Book Theme"
+msgstr "Θέμα βιβλίου Sphinx"
+
+msgid "suggest edit"
+msgstr "προτείνω επεξεργασία"
+
+msgid "Open an issue"
+msgstr "Ανοίξτε ένα ζήτημα"
+
+msgid "Launch"
+msgstr "Εκτόξευση"
+
+msgid "Fullscreen mode"
+msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ"
+
+msgid "Edit this page"
+msgstr "Επεξεργαστείτε αυτήν τη σελίδα"
+
+msgid "By the"
+msgstr "Από το"
+
+msgid "next page"
+msgstr "επόμενη σελίδα"
diff --git a/_static/locales/eo/LC_MESSAGES/booktheme.mo b/_static/locales/eo/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..d1072bbec62959524a87953d3f41b7c272ecbe37
GIT binary patch
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z=%6?-<TiX@#hgl!_#l@i9~5D<9eFKPTC~5#u|zStZ)nMi`9*E|UddU#;B3?(i1Q+2
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literal 0
HcmV?d00001

diff --git a/_static/locales/eo/LC_MESSAGES/booktheme.po b/_static/locales/eo/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..f7ed2262
--- /dev/null
+++ b/_static/locales/eo/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: eo\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Presi al PDF"
+
+msgid "Theme by the"
+msgstr "Temo de la"
+
+msgid "Download source file"
+msgstr "Elŝutu fontodosieron"
+
+msgid "open issue"
+msgstr "malferma numero"
+
+msgid "Contents"
+msgstr "Enhavo"
+
+msgid "previous page"
+msgstr "antaŭa paĝo"
+
+msgid "Download notebook file"
+msgstr "Elŝutu kajeran dosieron"
+
+msgid "Copyright"
+msgstr "Kopirajto"
+
+msgid "Download this page"
+msgstr "Elŝutu ĉi tiun paĝon"
+
+msgid "Source repository"
+msgstr "Fonto-deponejo"
+
+msgid "By"
+msgstr "De"
+
+msgid "repository"
+msgstr "deponejo"
+
+msgid "Last updated on"
+msgstr "Laste ĝisdatigita la"
+
+msgid "Toggle navigation"
+msgstr "Ŝalti navigadon"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksa Libro-Temo"
+
+msgid "suggest edit"
+msgstr "sugesti redaktadon"
+
+msgid "Open an issue"
+msgstr "Malfermu numeron"
+
+msgid "Launch"
+msgstr "Lanĉo"
+
+msgid "Fullscreen mode"
+msgstr "Plenekrana reĝimo"
+
+msgid "Edit this page"
+msgstr "Redaktu ĉi tiun paĝon"
+
+msgid "By the"
+msgstr "Per la"
+
+msgid "next page"
+msgstr "sekva paĝo"
diff --git a/_static/locales/es/LC_MESSAGES/booktheme.mo b/_static/locales/es/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..ba2ee4dc22148ed53f2aeba32c5965654d4a5a2f
GIT binary patch
literal 1396
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literal 0
HcmV?d00001

diff --git a/_static/locales/es/LC_MESSAGES/booktheme.po b/_static/locales/es/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..5e0029e5
--- /dev/null
+++ b/_static/locales/es/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: es\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimir en PDF"
+
+msgid "Theme by the"
+msgstr "Tema por el"
+
+msgid "Download source file"
+msgstr "Descargar archivo fuente"
+
+msgid "open issue"
+msgstr "Tema abierto"
+
+msgid "Contents"
+msgstr "Contenido"
+
+msgid "previous page"
+msgstr "pagina anterior"
+
+msgid "Download notebook file"
+msgstr "Descargar archivo de cuaderno"
+
+msgid "Copyright"
+msgstr "Derechos de autor"
+
+msgid "Download this page"
+msgstr "Descarga esta pagina"
+
+msgid "Source repository"
+msgstr "Repositorio de origen"
+
+msgid "By"
+msgstr "Por"
+
+msgid "repository"
+msgstr "repositorio"
+
+msgid "Last updated on"
+msgstr "Ultima actualización en"
+
+msgid "Toggle navigation"
+msgstr "Navegación de palanca"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del libro de la esfinge"
+
+msgid "suggest edit"
+msgstr "sugerir editar"
+
+msgid "Open an issue"
+msgstr "Abrir un problema"
+
+msgid "Launch"
+msgstr "Lanzamiento"
+
+msgid "Fullscreen mode"
+msgstr "Modo de pantalla completa"
+
+msgid "Edit this page"
+msgstr "Edita esta página"
+
+msgid "By the"
+msgstr "Por el"
+
+msgid "next page"
+msgstr "siguiente página"
diff --git a/_static/locales/et/LC_MESSAGES/booktheme.mo b/_static/locales/et/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..983b82391f499b67a9046c15d0dd8744650ad925
GIT binary patch
literal 1341
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P_MW2m5}a<3ol*Y+f5$~k

literal 0
HcmV?d00001

diff --git a/_static/locales/et/LC_MESSAGES/booktheme.po b/_static/locales/et/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..8680982a
--- /dev/null
+++ b/_static/locales/et/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: et\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Prindi PDF-i"
+
+msgid "Theme by the"
+msgstr "Teema"
+
+msgid "Download source file"
+msgstr "Laadige alla lähtefail"
+
+msgid "open issue"
+msgstr "avatud küsimus"
+
+msgid "Contents"
+msgstr "Sisu"
+
+msgid "previous page"
+msgstr "eelmine leht"
+
+msgid "Download notebook file"
+msgstr "Laadige sülearvuti fail alla"
+
+msgid "Copyright"
+msgstr "Autoriõigus"
+
+msgid "Download this page"
+msgstr "Laadige see leht alla"
+
+msgid "Source repository"
+msgstr "Allikahoidla"
+
+msgid "By"
+msgstr "Kõrval"
+
+msgid "repository"
+msgstr "hoidla"
+
+msgid "Last updated on"
+msgstr "Viimati uuendatud"
+
+msgid "Toggle navigation"
+msgstr "Lülita navigeerimine sisse"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksiraamatu teema"
+
+msgid "suggest edit"
+msgstr "soovita muuta"
+
+msgid "Open an issue"
+msgstr "Avage probleem"
+
+msgid "Launch"
+msgstr "Käivitage"
+
+msgid "Fullscreen mode"
+msgstr "Täisekraanirežiim"
+
+msgid "Edit this page"
+msgstr "Muutke seda lehte"
+
+msgid "By the"
+msgstr "Autor"
+
+msgid "next page"
+msgstr "järgmine leht"
diff --git a/_static/locales/fi/LC_MESSAGES/booktheme.mo b/_static/locales/fi/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..d8ac054597c924e3010f629caeac1c748b7211cd
GIT binary patch
literal 1368
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zO>~T6=Ywl@%y^W>^xt9_kNV@0^m<T2z-5q-Si@7TDRI|jPTKKk7{>3c4B9BMrgfh(
zuVp?S>46Ru!ccTe7eieYS*GF0;G*3eAeplXnZCllffvOWA=>i5w+5yH-zgk~ao1dl
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H66VBTf3Z^G

literal 0
HcmV?d00001

diff --git a/_static/locales/fi/LC_MESSAGES/booktheme.po b/_static/locales/fi/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..34dac218
--- /dev/null
+++ b/_static/locales/fi/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: fi\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Tulosta PDF-tiedostoon"
+
+msgid "Theme by the"
+msgstr "Teeman tekijä"
+
+msgid "Download source file"
+msgstr "Lataa lähdetiedosto"
+
+msgid "open issue"
+msgstr "avoin ongelma"
+
+msgid "Contents"
+msgstr "Sisällys"
+
+msgid "previous page"
+msgstr "Edellinen sivu"
+
+msgid "Download notebook file"
+msgstr "Lataa muistikirjatiedosto"
+
+msgid "Copyright"
+msgstr "Tekijänoikeus"
+
+msgid "Download this page"
+msgstr "Lataa tämä sivu"
+
+msgid "Source repository"
+msgstr "Lähteen arkisto"
+
+msgid "By"
+msgstr "Tekijä"
+
+msgid "repository"
+msgstr "arkisto"
+
+msgid "Last updated on"
+msgstr "Viimeksi päivitetty"
+
+msgid "Toggle navigation"
+msgstr "Vaihda navigointia"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-kirjan teema"
+
+msgid "suggest edit"
+msgstr "ehdottaa muokkausta"
+
+msgid "Open an issue"
+msgstr "Avaa ongelma"
+
+msgid "Launch"
+msgstr "Tuoda markkinoille"
+
+msgid "Fullscreen mode"
+msgstr "Koko näytön tila"
+
+msgid "Edit this page"
+msgstr "Muokkaa tätä sivua"
+
+msgid "By the"
+msgstr "Mukaan"
+
+msgid "next page"
+msgstr "seuraava sivu"
diff --git a/_static/locales/fr/LC_MESSAGES/booktheme.mo b/_static/locales/fr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f663d39f0faa76c5b9bd504c51252eef74cca5de
GIT binary patch
literal 1412
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T<A7>A2+cw{Vw>h7Yg+MtLz81?

literal 0
HcmV?d00001

diff --git a/_static/locales/fr/LC_MESSAGES/booktheme.po b/_static/locales/fr/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..8991a1b8
--- /dev/null
+++ b/_static/locales/fr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: fr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimer au format PDF"
+
+msgid "Theme by the"
+msgstr "Thème par le"
+
+msgid "Download source file"
+msgstr "Télécharger le fichier source"
+
+msgid "open issue"
+msgstr "signaler un problème"
+
+msgid "Contents"
+msgstr "Contenu"
+
+msgid "previous page"
+msgstr "page précédente"
+
+msgid "Download notebook file"
+msgstr "Télécharger le fichier notebook"
+
+msgid "Copyright"
+msgstr "droits d'auteur"
+
+msgid "Download this page"
+msgstr "Téléchargez cette page"
+
+msgid "Source repository"
+msgstr "Dépôt source"
+
+msgid "By"
+msgstr "Par"
+
+msgid "repository"
+msgstr "dépôt"
+
+msgid "Last updated on"
+msgstr "Dernière mise à jour le"
+
+msgid "Toggle navigation"
+msgstr "Basculer la navigation"
+
+msgid "Sphinx Book Theme"
+msgstr "Thème du livre Sphinx"
+
+msgid "suggest edit"
+msgstr "suggestion de modification"
+
+msgid "Open an issue"
+msgstr "Ouvrez un problème"
+
+msgid "Launch"
+msgstr "lancement"
+
+msgid "Fullscreen mode"
+msgstr "Mode plein écran"
+
+msgid "Edit this page"
+msgstr "Modifier cette page"
+
+msgid "By the"
+msgstr "Par le"
+
+msgid "next page"
+msgstr "page suivante"
diff --git a/_static/locales/hr/LC_MESSAGES/booktheme.mo b/_static/locales/hr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..eca4a1a2842830f06bd5f6235bf01d07bdd313d2
GIT binary patch
literal 1402
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literal 0
HcmV?d00001

diff --git a/_static/locales/hr/LC_MESSAGES/booktheme.po b/_static/locales/hr/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..42c4233d
--- /dev/null
+++ b/_static/locales/hr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: hr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Ispis u PDF"
+
+msgid "Theme by the"
+msgstr "Tema autora"
+
+msgid "Download source file"
+msgstr "Preuzmi izvornu datoteku"
+
+msgid "open issue"
+msgstr "otvoreno izdanje"
+
+msgid "Contents"
+msgstr "Sadržaj"
+
+msgid "previous page"
+msgstr "Prethodna stranica"
+
+msgid "Download notebook file"
+msgstr "Preuzmi datoteku bilježnice"
+
+msgid "Copyright"
+msgstr "Autorska prava"
+
+msgid "Download this page"
+msgstr "Preuzmite ovu stranicu"
+
+msgid "Source repository"
+msgstr "Izvorno spremište"
+
+msgid "By"
+msgstr "Po"
+
+msgid "repository"
+msgstr "spremište"
+
+msgid "Last updated on"
+msgstr "Posljednje ažuriranje:"
+
+msgid "Toggle navigation"
+msgstr "Uključi / isključi navigaciju"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema knjige Sphinx"
+
+msgid "suggest edit"
+msgstr "predloži uređivanje"
+
+msgid "Open an issue"
+msgstr "Otvorite izdanje"
+
+msgid "Launch"
+msgstr "Pokrenite"
+
+msgid "Fullscreen mode"
+msgstr "Način preko cijelog zaslona"
+
+msgid "Edit this page"
+msgstr "Uredite ovu stranicu"
+
+msgid "By the"
+msgstr "Od strane"
+
+msgid "next page"
+msgstr "sljedeća stranica"
diff --git a/_static/locales/id/LC_MESSAGES/booktheme.mo b/_static/locales/id/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..d07a06a9d27aaf6d67234c227c22d61d5a23203b
GIT binary patch
literal 1323
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zxtWoenhB;xBBn-yn24D8zwTalu&Ao9Uv<5s{`Yli#|wdR9BT&a6V_?0r`xb$Ol}tf
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T@Rc+s5Lgrh10CRyRonjp5+F85

literal 0
HcmV?d00001

diff --git a/_static/locales/id/LC_MESSAGES/booktheme.po b/_static/locales/id/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..b8d8d898
--- /dev/null
+++ b/_static/locales/id/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: id\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Cetak ke PDF"
+
+msgid "Theme by the"
+msgstr "Tema oleh"
+
+msgid "Download source file"
+msgstr "Unduh file sumber"
+
+msgid "open issue"
+msgstr "masalah terbuka"
+
+msgid "Contents"
+msgstr "Isi"
+
+msgid "previous page"
+msgstr "halaman sebelumnya"
+
+msgid "Download notebook file"
+msgstr "Unduh file notebook"
+
+msgid "Copyright"
+msgstr "hak cipta"
+
+msgid "Download this page"
+msgstr "Unduh halaman ini"
+
+msgid "Source repository"
+msgstr "Repositori sumber"
+
+msgid "By"
+msgstr "Oleh"
+
+msgid "repository"
+msgstr "gudang"
+
+msgid "Last updated on"
+msgstr "Terakhir diperbarui saat"
+
+msgid "Toggle navigation"
+msgstr "Alihkan navigasi"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Buku Sphinx"
+
+msgid "suggest edit"
+msgstr "menyarankan edit"
+
+msgid "Open an issue"
+msgstr "Buka masalah"
+
+msgid "Launch"
+msgstr "Meluncurkan"
+
+msgid "Fullscreen mode"
+msgstr "Mode layar penuh"
+
+msgid "Edit this page"
+msgstr "Edit halaman ini"
+
+msgid "By the"
+msgstr "Oleh"
+
+msgid "next page"
+msgstr "halaman selanjutnya"
diff --git a/_static/locales/it/LC_MESSAGES/booktheme.mo b/_static/locales/it/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..53ba476edd2df2a802917e9df402257ceca1a130
GIT binary patch
literal 1403
zcmZXSJ&aXF6vsyafhY1QYQiQS8W$5EU!j1vc?Gb$351Os-eM!oynD~ycVOPkWoGWm
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z{{{Vh^sipSfOhD0WAK^77~TMnfQP_S;9+n9d<Xmhd>b5tC&9bmQINr7VD9+^sD7S+
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zg5J|zQb*pB(DY&Nai5y1VnoTv1fhfnT;$=ca`*|y1aBki?jP#?!}jLE+O4&f-Stc7
zFAjQPha1~9k0>L63$?T2m#CQ8m^hE`ZLAKj?(U1$r%jH-6<>r>d^Mu0Q^|un)>jR5
z9TALm*F@`vt0B(Qh<x3eX?%H!y5I3RPiMz;>BFUgSr)TwUH`yMh9yxlG%Xix0~y&&
z3LB-cB(8l-D%nxCMbU^4uPmkzD-_D^sYeYovZ|_hYt|6+raF6N+B(TUeM703ahwOj
z!*UGfp34HuR+WV4?HsPL=1>m45R2fh*HNhPRL3xNJhx_&?Yu^?zAcKo|5pQ*LIIU4
z9%X=yZ5mFd5t*`cmL{7}b7xJ=^_*qP(gmDLw)@AXu2??D^RnquqSO=xr-aR>O-k3u
a)*Q3l*@6-(xNK#4xqhOgLbT*tS<HW%;#JK6

literal 0
HcmV?d00001

diff --git a/_static/locales/it/LC_MESSAGES/booktheme.po b/_static/locales/it/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..36fca59f
--- /dev/null
+++ b/_static/locales/it/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: it\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Stampa in PDF"
+
+msgid "Theme by the"
+msgstr "Tema di"
+
+msgid "Download source file"
+msgstr "Scarica il file sorgente"
+
+msgid "open issue"
+msgstr "questione aperta"
+
+msgid "Contents"
+msgstr "Contenuti"
+
+msgid "previous page"
+msgstr "pagina precedente"
+
+msgid "Download notebook file"
+msgstr "Scarica il file del taccuino"
+
+msgid "Copyright"
+msgstr "Diritto d'autore"
+
+msgid "Download this page"
+msgstr "Scarica questa pagina"
+
+msgid "Source repository"
+msgstr "Repository di origine"
+
+msgid "By"
+msgstr "Di"
+
+msgid "repository"
+msgstr "repository"
+
+msgid "Last updated on"
+msgstr "Ultimo aggiornamento il"
+
+msgid "Toggle navigation"
+msgstr "Attiva / disattiva la navigazione"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema del libro della Sfinge"
+
+msgid "suggest edit"
+msgstr "suggerisci modifica"
+
+msgid "Open an issue"
+msgstr "Apri un problema"
+
+msgid "Launch"
+msgstr "Lanciare"
+
+msgid "Fullscreen mode"
+msgstr "Modalità schermo intero"
+
+msgid "Edit this page"
+msgstr "Modifica questa pagina"
+
+msgid "By the"
+msgstr "Dal"
+
+msgid "next page"
+msgstr "pagina successiva"
diff --git a/_static/locales/iw/LC_MESSAGES/booktheme.mo b/_static/locales/iw/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..a45c6575e469914582f8223ac54793afc2f26575
GIT binary patch
literal 1445
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F;x7>$;pPAU

literal 0
HcmV?d00001

diff --git a/_static/locales/iw/LC_MESSAGES/booktheme.po b/_static/locales/iw/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..dede9cb0
--- /dev/null
+++ b/_static/locales/iw/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: iw\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "הדפס לקובץ PDF"
+
+msgid "Theme by the"
+msgstr "נושא מאת"
+
+msgid "Download source file"
+msgstr "הורד את קובץ המקור"
+
+msgid "open issue"
+msgstr "בעיה פתוחה"
+
+msgid "Contents"
+msgstr "תוכן"
+
+msgid "previous page"
+msgstr "עמוד קודם"
+
+msgid "Download notebook file"
+msgstr "הורד קובץ מחברת"
+
+msgid "Copyright"
+msgstr "זכויות יוצרים"
+
+msgid "Download this page"
+msgstr "הורד דף זה"
+
+msgid "Source repository"
+msgstr "מאגר המקורות"
+
+msgid "By"
+msgstr "על ידי"
+
+msgid "repository"
+msgstr "מאגר"
+
+msgid "Last updated on"
+msgstr "עודכן לאחרונה ב"
+
+msgid "Toggle navigation"
+msgstr "החלף ניווט"
+
+msgid "Sphinx Book Theme"
+msgstr "נושא ספר ספינקס"
+
+msgid "suggest edit"
+msgstr "מציע לערוך"
+
+msgid "Open an issue"
+msgstr "פתח גיליון"
+
+msgid "Launch"
+msgstr "לְהַשִׁיק"
+
+msgid "Fullscreen mode"
+msgstr "מצב מסך מלא"
+
+msgid "Edit this page"
+msgstr "ערוך דף זה"
+
+msgid "By the"
+msgstr "דרך"
+
+msgid "next page"
+msgstr "עמוד הבא"
diff --git a/_static/locales/ja/LC_MESSAGES/booktheme.mo b/_static/locales/ja/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..1cefd29ce3cc08792667a82dc7ff47e9843107be
GIT binary patch
literal 1471
zcma))U2GIp6vr=uT2=%temyktnh+I|RZ$+uHv1sbO-LX%QhlH9-p&qe?;Yo((zoth
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z-gVOq>v2dQ<VVPpkYj7`!s@w)F+6NNUiX0;z#ecjxE|~S9|NBT9|6l?FZdd`5ftDi
zFv$5fNO8`94}xuQ19$~|7`&bH``rF>u6IDn_Xk)2*RIXZZv;v211Zik;1ak4q&&ao
z_TO@S&AKd35BL)Hd%<mB1>6F@2|fYNfYiq|km|n!QoJ8>{U?y}t$^hJJGcehbZ=(g
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zA=v{Ey4&tWeb61=55ZW{`(?Yv@$jH7*egl~LIyrQ+;Pvb>jB%X-jWSv)wooF7*^^a
zA8{ID)$FU#tBQXZgSz8$*Rlm0s5t>b|D{&KM#HaqLP$QUYJ%;x{D6mU%?d<~D+xW6
z)jE6Cg@Yxz<NKjtgPtQ1MDfAh6}CUi=LuK&PN2MTw%@Hg@(|xewGGw9s9@=v4`)3M
zDcf!cF0C=gwgLx9q&O60)hWu8C2>75=BO|)yDPjO+O|NI0-c}~bwE^u;+|UZ4dMBS
zR^nOyBIPfpnG5^&>>F6!-~QYSg?zBZp>bD~cz^<)cN>-?`+2o)dA<nBuMbs<J6HWY
zOZp?iD-Ou2syWgw@twm?P}plpJH%W|{D4&$Y=oZGC{~m=>X*25Q`IlO*w3@i^0SgZ
zRp#5b_7~Xl$+OE7hnZ=9YND2D&Y9*#)@d&!S7*EK+UR$U{>JE*(KAMC6J0ZU+C;M^
z`q*e3JZ1ERi7p!bh0&j-o|DW(-_lp~<^OPPo;J+|qu);tEd58ke>rs)FR!$Zu=w!h
z&Y7=QE_`G(9#lEWlJgUtTT#-!nao^I&VIs{Z!N}0r&&CGVrBLi!p9%ph)+&2{5I%2
zK7KuJEq2#CZ}bHdEv5a=V<T%}j@m=#P@4<M`KxKkN79TRV1B6oZ$(cTecD8yr~ND%
zeJMS9DsH`FqN$|)ooTihCfwaLYMx)dG?(1?3f8Pp7Ed0(%f;xsE6WCeev%XKC9}A_
Jb0#{<{sfT*^1J{5

literal 0
HcmV?d00001

diff --git a/_static/locales/ja/LC_MESSAGES/booktheme.po b/_static/locales/ja/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..2615f0d8
--- /dev/null
+++ b/_static/locales/ja/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ja\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDFに印刷"
+
+msgid "Theme by the"
+msgstr "のテーマ"
+
+msgid "Download source file"
+msgstr "ソースファイルをダウンロード"
+
+msgid "open issue"
+msgstr "未解決の問題"
+
+msgid "Contents"
+msgstr "目次"
+
+msgid "previous page"
+msgstr "前のページ"
+
+msgid "Download notebook file"
+msgstr "ノートブックファイルをダウンロード"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "このページをダウンロード"
+
+msgid "Source repository"
+msgstr "ソースリポジトリ"
+
+msgid "By"
+msgstr "著者"
+
+msgid "repository"
+msgstr "リポジトリ"
+
+msgid "Last updated on"
+msgstr "最終更新日"
+
+msgid "Toggle navigation"
+msgstr "ナビゲーションを切り替え"
+
+msgid "Sphinx Book Theme"
+msgstr "スフィンクスの本のテーマ"
+
+msgid "suggest edit"
+msgstr "編集を提案する"
+
+msgid "Open an issue"
+msgstr "問題を報告"
+
+msgid "Launch"
+msgstr "起動"
+
+msgid "Fullscreen mode"
+msgstr "全画面モード"
+
+msgid "Edit this page"
+msgstr "このページを編集"
+
+msgid "By the"
+msgstr "によって"
+
+msgid "next page"
+msgstr "次のページ"
diff --git a/_static/locales/ko/LC_MESSAGES/booktheme.mo b/_static/locales/ko/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..06c7ec938b4975c721caff60b956e343eef11570
GIT binary patch
literal 1375
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literal 0
HcmV?d00001

diff --git a/_static/locales/ko/LC_MESSAGES/booktheme.po b/_static/locales/ko/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..c9e13a42
--- /dev/null
+++ b/_static/locales/ko/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ko\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF로 인쇄"
+
+msgid "Theme by the"
+msgstr "테마별"
+
+msgid "Download source file"
+msgstr "소스 파일 다운로드"
+
+msgid "open issue"
+msgstr "열린 문제"
+
+msgid "Contents"
+msgstr "내용"
+
+msgid "previous page"
+msgstr "이전 페이지"
+
+msgid "Download notebook file"
+msgstr "노트북 파일 다운로드"
+
+msgid "Copyright"
+msgstr "저작권"
+
+msgid "Download this page"
+msgstr "이 페이지 다운로드"
+
+msgid "Source repository"
+msgstr "소스 저장소"
+
+msgid "By"
+msgstr "으로"
+
+msgid "repository"
+msgstr "저장소"
+
+msgid "Last updated on"
+msgstr "마지막 업데이트"
+
+msgid "Toggle navigation"
+msgstr "탐색 전환"
+
+msgid "Sphinx Book Theme"
+msgstr "스핑크스 도서 테마"
+
+msgid "suggest edit"
+msgstr "편집 제안"
+
+msgid "Open an issue"
+msgstr "이슈 열기"
+
+msgid "Launch"
+msgstr "시작하다"
+
+msgid "Fullscreen mode"
+msgstr "전체 화면으로보기"
+
+msgid "Edit this page"
+msgstr "이 페이지 편집"
+
+msgid "By the"
+msgstr "에 의해"
+
+msgid "next page"
+msgstr "다음 페이지"
diff --git a/_static/locales/lt/LC_MESSAGES/booktheme.mo b/_static/locales/lt/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..4468ba04bc134a84fea5e3c973461cf02c9c2da3
GIT binary patch
literal 1413
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ClWZvf

literal 0
HcmV?d00001

diff --git a/_static/locales/lt/LC_MESSAGES/booktheme.po b/_static/locales/lt/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..35eabd95
--- /dev/null
+++ b/_static/locales/lt/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: lt\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Spausdinti į PDF"
+
+msgid "Theme by the"
+msgstr "Tema"
+
+msgid "Download source file"
+msgstr "Atsisiųsti šaltinio failą"
+
+msgid "open issue"
+msgstr "atviras klausimas"
+
+msgid "Contents"
+msgstr "Turinys"
+
+msgid "previous page"
+msgstr "Ankstesnis puslapis"
+
+msgid "Download notebook file"
+msgstr "Atsisiųsti nešiojamojo kompiuterio failą"
+
+msgid "Copyright"
+msgstr "Autorių teisės"
+
+msgid "Download this page"
+msgstr "Atsisiųskite šį puslapį"
+
+msgid "Source repository"
+msgstr "Šaltinio saugykla"
+
+msgid "By"
+msgstr "Iki"
+
+msgid "repository"
+msgstr "saugykla"
+
+msgid "Last updated on"
+msgstr "Paskutinį kartą atnaujinta"
+
+msgid "Toggle navigation"
+msgstr "Perjungti naršymą"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinkso knygos tema"
+
+msgid "suggest edit"
+msgstr "pasiūlyti redaguoti"
+
+msgid "Open an issue"
+msgstr "Atidarykite problemą"
+
+msgid "Launch"
+msgstr "Paleiskite"
+
+msgid "Fullscreen mode"
+msgstr "Pilno ekrano režimas"
+
+msgid "Edit this page"
+msgstr "Redaguoti šį puslapį"
+
+msgid "By the"
+msgstr "Prie"
+
+msgid "next page"
+msgstr "Kitas puslapis"
diff --git a/_static/locales/lv/LC_MESSAGES/booktheme.mo b/_static/locales/lv/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..74aa4d8985d96a07c4c9be96f006f3b58d884342
GIT binary patch
literal 1404
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z$hD`+d1p{2ugmVX^Rv)XwpuMi|K_@C9{X=v^;%!ucx$~w8wptj+VHfeW0EJ?h=xP`
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A2mk;8

literal 0
HcmV?d00001

diff --git a/_static/locales/lv/LC_MESSAGES/booktheme.po b/_static/locales/lv/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..ee1bd08d
--- /dev/null
+++ b/_static/locales/lv/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: lv\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Drukāt PDF formātā"
+
+msgid "Theme by the"
+msgstr "Autora tēma"
+
+msgid "Download source file"
+msgstr "Lejupielādēt avota failu"
+
+msgid "open issue"
+msgstr "atklāts jautājums"
+
+msgid "Contents"
+msgstr "Saturs"
+
+msgid "previous page"
+msgstr "iepriekšējā lapa"
+
+msgid "Download notebook file"
+msgstr "Lejupielādēt piezīmju grāmatiņu"
+
+msgid "Copyright"
+msgstr "Autortiesības"
+
+msgid "Download this page"
+msgstr "Lejupielādējiet šo lapu"
+
+msgid "Source repository"
+msgstr "Avota krātuve"
+
+msgid "By"
+msgstr "Autors"
+
+msgid "repository"
+msgstr "krātuve"
+
+msgid "Last updated on"
+msgstr "Pēdējoreiz atjaunināts"
+
+msgid "Toggle navigation"
+msgstr "Pārslēgt navigāciju"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfinksa grāmatas tēma"
+
+msgid "suggest edit"
+msgstr "ieteikt rediģēt"
+
+msgid "Open an issue"
+msgstr "Atveriet problēmu"
+
+msgid "Launch"
+msgstr "Uzsākt"
+
+msgid "Fullscreen mode"
+msgstr "Pilnekrāna režīms"
+
+msgid "Edit this page"
+msgstr "Rediģēt šo lapu"
+
+msgid "By the"
+msgstr "Ar"
+
+msgid "next page"
+msgstr "nākamā lapaspuse"
diff --git a/_static/locales/ml/LC_MESSAGES/booktheme.mo b/_static/locales/ml/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..2736e8fcf6f9e923c2403307f0b366086d37b335
GIT binary patch
literal 1883
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zPi^^*-^U&+<PC{?!kP;{8$!>l4{_?&D7b7K<UJfw{;oBjg<Pck-@a7utgqVze>D1k
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literal 0
HcmV?d00001

diff --git a/_static/locales/ml/LC_MESSAGES/booktheme.po b/_static/locales/ml/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..d471277d
--- /dev/null
+++ b/_static/locales/ml/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ml\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക"
+
+msgid "Theme by the"
+msgstr "പ്രമേയം"
+
+msgid "Download source file"
+msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക"
+
+msgid "open issue"
+msgstr "തുറന്ന പ്രശ്നം"
+
+msgid "previous page"
+msgstr "മുൻപത്തെ താൾ"
+
+msgid "Download notebook file"
+msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക"
+
+msgid "Copyright"
+msgstr "പകർപ്പവകാശം"
+
+msgid "Download this page"
+msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക"
+
+msgid "Source repository"
+msgstr "ഉറവിട ശേഖരം"
+
+msgid "By"
+msgstr "എഴുതിയത്"
+
+msgid "Last updated on"
+msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്"
+
+msgid "Toggle navigation"
+msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക"
+
+msgid "Sphinx Book Theme"
+msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം"
+
+msgid "suggest edit"
+msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക"
+
+msgid "Open an issue"
+msgstr "ഒരു പ്രശ്നം തുറക്കുക"
+
+msgid "Launch"
+msgstr "സമാരംഭിക്കുക"
+
+msgid "Edit this page"
+msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക"
+
+msgid "By the"
+msgstr "എഴുതിയത്"
+
+msgid "next page"
+msgstr "അടുത്ത പേജ്"
diff --git a/_static/locales/mr/LC_MESSAGES/booktheme.mo b/_static/locales/mr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..fe530100d7715cdc19a6f9db33a971665835f3c4
GIT binary patch
literal 1674
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literal 0
HcmV?d00001

diff --git a/_static/locales/mr/LC_MESSAGES/booktheme.po b/_static/locales/mr/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..f3694acf
--- /dev/null
+++ b/_static/locales/mr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: mr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "पीडीएफवर मुद्रित करा"
+
+msgid "Theme by the"
+msgstr "द्वारा थीम"
+
+msgid "Download source file"
+msgstr "स्त्रोत फाइल डाउनलोड करा"
+
+msgid "open issue"
+msgstr "खुला मुद्दा"
+
+msgid "previous page"
+msgstr "मागील पान"
+
+msgid "Download notebook file"
+msgstr "नोटबुक फाईल डाउनलोड करा"
+
+msgid "Copyright"
+msgstr "कॉपीराइट"
+
+msgid "Download this page"
+msgstr "हे पृष्ठ डाउनलोड करा"
+
+msgid "Source repository"
+msgstr "स्त्रोत भांडार"
+
+msgid "By"
+msgstr "द्वारा"
+
+msgid "Last updated on"
+msgstr "अखेरचे अद्यतनित"
+
+msgid "Toggle navigation"
+msgstr "नेव्हिगेशन टॉगल करा"
+
+msgid "Sphinx Book Theme"
+msgstr "स्फिंक्स बुक थीम"
+
+msgid "suggest edit"
+msgstr "संपादन सुचवा"
+
+msgid "Open an issue"
+msgstr "एक मुद्दा उघडा"
+
+msgid "Launch"
+msgstr "लाँच करा"
+
+msgid "Edit this page"
+msgstr "हे पृष्ठ संपादित करा"
+
+msgid "By the"
+msgstr "द्वारा"
+
+msgid "next page"
+msgstr "पुढील पृष्ठ"
diff --git a/_static/locales/ms/LC_MESSAGES/booktheme.mo b/_static/locales/ms/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..f02603fa2522a40060bd3f1b5d65052c77530de6
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/_static/locales/ms/LC_MESSAGES/booktheme.po b/_static/locales/ms/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..65b7c602
--- /dev/null
+++ b/_static/locales/ms/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ms\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Cetak ke PDF"
+
+msgid "Theme by the"
+msgstr "Tema oleh"
+
+msgid "Download source file"
+msgstr "Muat turun fail sumber"
+
+msgid "open issue"
+msgstr "isu terbuka"
+
+msgid "previous page"
+msgstr "halaman sebelumnya"
+
+msgid "Download notebook file"
+msgstr "Muat turun fail buku nota"
+
+msgid "Copyright"
+msgstr "hak cipta"
+
+msgid "Download this page"
+msgstr "Muat turun halaman ini"
+
+msgid "Source repository"
+msgstr "Repositori sumber"
+
+msgid "By"
+msgstr "Oleh"
+
+msgid "Last updated on"
+msgstr "Terakhir dikemas kini pada"
+
+msgid "Toggle navigation"
+msgstr "Togol navigasi"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Buku Sphinx"
+
+msgid "suggest edit"
+msgstr "cadangkan edit"
+
+msgid "Open an issue"
+msgstr "Buka masalah"
+
+msgid "Launch"
+msgstr "Lancarkan"
+
+msgid "Edit this page"
+msgstr "Edit halaman ini"
+
+msgid "By the"
+msgstr "Oleh"
+
+msgid "next page"
+msgstr "muka surat seterusnya"
diff --git a/_static/locales/nl/LC_MESSAGES/booktheme.mo b/_static/locales/nl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..e59e7ecb308a7648cd23aa6342a9ad98a61d0009
GIT binary patch
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jbWAeLReX?b<52fDoT%;?`~gm~I(s23S;SN2irW1H>^?=m

literal 0
HcmV?d00001

diff --git a/_static/locales/nl/LC_MESSAGES/booktheme.po b/_static/locales/nl/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..71bd1cda
--- /dev/null
+++ b/_static/locales/nl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: nl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Afdrukken naar pdf"
+
+msgid "Theme by the"
+msgstr "Thema door de"
+
+msgid "Download source file"
+msgstr "Download het bronbestand"
+
+msgid "open issue"
+msgstr "open probleem"
+
+msgid "Contents"
+msgstr "Inhoud"
+
+msgid "previous page"
+msgstr "vorige pagina"
+
+msgid "Download notebook file"
+msgstr "Download notebookbestand"
+
+msgid "Copyright"
+msgstr "auteursrechten"
+
+msgid "Download this page"
+msgstr "Download deze pagina"
+
+msgid "Source repository"
+msgstr "Bronopslagplaats"
+
+msgid "By"
+msgstr "Door"
+
+msgid "repository"
+msgstr "repository"
+
+msgid "Last updated on"
+msgstr "Laatst geupdate op"
+
+msgid "Toggle navigation"
+msgstr "Schakel navigatie"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx-boekthema"
+
+msgid "suggest edit"
+msgstr "suggereren bewerken"
+
+msgid "Open an issue"
+msgstr "Open een probleem"
+
+msgid "Launch"
+msgstr "Lancering"
+
+msgid "Fullscreen mode"
+msgstr "Volledig scherm"
+
+msgid "Edit this page"
+msgstr "bewerk deze pagina"
+
+msgid "By the"
+msgstr "Door de"
+
+msgid "next page"
+msgstr "volgende bladzijde"
diff --git a/_static/locales/no/LC_MESSAGES/booktheme.mo b/_static/locales/no/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..6cd15c88de675226c00ca4d0430171075e5559ff
GIT binary patch
literal 1317
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literal 0
HcmV?d00001

diff --git a/_static/locales/no/LC_MESSAGES/booktheme.po b/_static/locales/no/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..b21346a5
--- /dev/null
+++ b/_static/locales/no/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: no\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Skriv ut til PDF"
+
+msgid "Theme by the"
+msgstr "Tema av"
+
+msgid "Download source file"
+msgstr "Last ned kildefilen"
+
+msgid "open issue"
+msgstr "åpent nummer"
+
+msgid "Contents"
+msgstr "Innhold"
+
+msgid "previous page"
+msgstr "forrige side"
+
+msgid "Download notebook file"
+msgstr "Last ned notatbokfilen"
+
+msgid "Copyright"
+msgstr "opphavsrett"
+
+msgid "Download this page"
+msgstr "Last ned denne siden"
+
+msgid "Source repository"
+msgstr "Kildedepot"
+
+msgid "By"
+msgstr "Av"
+
+msgid "repository"
+msgstr "oppbevaringssted"
+
+msgid "Last updated on"
+msgstr "Sist oppdatert den"
+
+msgid "Toggle navigation"
+msgstr "Bytt navigasjon"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx boktema"
+
+msgid "suggest edit"
+msgstr "foreslå redigering"
+
+msgid "Open an issue"
+msgstr "Åpne et problem"
+
+msgid "Launch"
+msgstr "Start"
+
+msgid "Fullscreen mode"
+msgstr "Fullskjerm-modus"
+
+msgid "Edit this page"
+msgstr "Rediger denne siden"
+
+msgid "By the"
+msgstr "Ved"
+
+msgid "next page"
+msgstr "neste side"
diff --git a/_static/locales/pl/LC_MESSAGES/booktheme.mo b/_static/locales/pl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..9ebb584f77091f5666db4194ecd4f707396cbbca
GIT binary patch
literal 1371
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literal 0
HcmV?d00001

diff --git a/_static/locales/pl/LC_MESSAGES/booktheme.po b/_static/locales/pl/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..1b7233f4
--- /dev/null
+++ b/_static/locales/pl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: pl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Drukuj do PDF"
+
+msgid "Theme by the"
+msgstr "Motyw autorstwa"
+
+msgid "Download source file"
+msgstr "Pobierz plik źródłowy"
+
+msgid "open issue"
+msgstr "otwarty problem"
+
+msgid "Contents"
+msgstr "Zawartość"
+
+msgid "previous page"
+msgstr "Poprzednia strona"
+
+msgid "Download notebook file"
+msgstr "Pobierz plik notatnika"
+
+msgid "Copyright"
+msgstr "prawa autorskie"
+
+msgid "Download this page"
+msgstr "Pobierz tę stronę"
+
+msgid "Source repository"
+msgstr "Repozytorium źródłowe"
+
+msgid "By"
+msgstr "Przez"
+
+msgid "repository"
+msgstr "magazyn"
+
+msgid "Last updated on"
+msgstr "Ostatnia aktualizacja"
+
+msgid "Toggle navigation"
+msgstr "Przełącz nawigację"
+
+msgid "Sphinx Book Theme"
+msgstr "Motyw książki Sphinx"
+
+msgid "suggest edit"
+msgstr "zaproponuj edycję"
+
+msgid "Open an issue"
+msgstr "Otwórz problem"
+
+msgid "Launch"
+msgstr "Uruchomić"
+
+msgid "Fullscreen mode"
+msgstr "Pełny ekran"
+
+msgid "Edit this page"
+msgstr "Edytuj tę strone"
+
+msgid "By the"
+msgstr "Przez"
+
+msgid "next page"
+msgstr "Następna strona"
diff --git a/_static/locales/pt/LC_MESSAGES/booktheme.mo b/_static/locales/pt/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..d0ddb8728e1d5ef72dddc1537f66aa32251a83c8
GIT binary patch
literal 1364
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literal 0
HcmV?d00001

diff --git a/_static/locales/pt/LC_MESSAGES/booktheme.po b/_static/locales/pt/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..1b27314d
--- /dev/null
+++ b/_static/locales/pt/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: pt\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimir em PDF"
+
+msgid "Theme by the"
+msgstr "Tema por"
+
+msgid "Download source file"
+msgstr "Baixar arquivo fonte"
+
+msgid "open issue"
+msgstr "questão aberta"
+
+msgid "Contents"
+msgstr "Conteúdo"
+
+msgid "previous page"
+msgstr "página anterior"
+
+msgid "Download notebook file"
+msgstr "Baixar arquivo de notebook"
+
+msgid "Copyright"
+msgstr "direito autoral"
+
+msgid "Download this page"
+msgstr "Baixe esta página"
+
+msgid "Source repository"
+msgstr "Repositório fonte"
+
+msgid "By"
+msgstr "De"
+
+msgid "repository"
+msgstr "repositório"
+
+msgid "Last updated on"
+msgstr "Última atualização em"
+
+msgid "Toggle navigation"
+msgstr "Alternar de navegação"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema do livro Sphinx"
+
+msgid "suggest edit"
+msgstr "sugerir edição"
+
+msgid "Open an issue"
+msgstr "Abra um problema"
+
+msgid "Launch"
+msgstr "Lançamento"
+
+msgid "Fullscreen mode"
+msgstr "Modo tela cheia"
+
+msgid "Edit this page"
+msgstr "Edite essa página"
+
+msgid "By the"
+msgstr "Pelo"
+
+msgid "next page"
+msgstr "próxima página"
diff --git a/_static/locales/ro/LC_MESSAGES/booktheme.mo b/_static/locales/ro/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..3c36ab1df7c589ad82614599b8c91f072a25d4a6
GIT binary patch
literal 1390
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K1txIYaExD}s9q8P

literal 0
HcmV?d00001

diff --git a/_static/locales/ro/LC_MESSAGES/booktheme.po b/_static/locales/ro/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..1783ad2c
--- /dev/null
+++ b/_static/locales/ro/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ro\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Imprimați în PDF"
+
+msgid "Theme by the"
+msgstr "Tema de"
+
+msgid "Download source file"
+msgstr "Descărcați fișierul sursă"
+
+msgid "open issue"
+msgstr "problema deschisă"
+
+msgid "Contents"
+msgstr "Cuprins"
+
+msgid "previous page"
+msgstr "pagina anterioară"
+
+msgid "Download notebook file"
+msgstr "Descărcați fișierul notebook"
+
+msgid "Copyright"
+msgstr "Drepturi de autor"
+
+msgid "Download this page"
+msgstr "Descarcă această pagină"
+
+msgid "Source repository"
+msgstr "Depozit sursă"
+
+msgid "By"
+msgstr "De"
+
+msgid "repository"
+msgstr "repertoriu"
+
+msgid "Last updated on"
+msgstr "Ultima actualizare la"
+
+msgid "Toggle navigation"
+msgstr "Comutare navigare"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema Sphinx Book"
+
+msgid "suggest edit"
+msgstr "sugerează editare"
+
+msgid "Open an issue"
+msgstr "Deschideți o problemă"
+
+msgid "Launch"
+msgstr "Lansa"
+
+msgid "Fullscreen mode"
+msgstr "Modul ecran întreg"
+
+msgid "Edit this page"
+msgstr "Editați această pagină"
+
+msgid "By the"
+msgstr "Langa"
+
+msgid "next page"
+msgstr "pagina următoare"
diff --git a/_static/locales/ru/LC_MESSAGES/booktheme.mo b/_static/locales/ru/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..6b8ca41f36ebf869818399a9584cdb15619bea78
GIT binary patch
literal 1722
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literal 0
HcmV?d00001

diff --git a/_static/locales/ru/LC_MESSAGES/booktheme.po b/_static/locales/ru/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..b1176b7a
--- /dev/null
+++ b/_static/locales/ru/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ru\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Распечатать в PDF"
+
+msgid "Theme by the"
+msgstr "Тема от"
+
+msgid "Download source file"
+msgstr "Скачать исходный файл"
+
+msgid "open issue"
+msgstr "открытый вопрос"
+
+msgid "Contents"
+msgstr "Содержание"
+
+msgid "previous page"
+msgstr "Предыдущая страница"
+
+msgid "Download notebook file"
+msgstr "Скачать файл записной книжки"
+
+msgid "Copyright"
+msgstr "авторское право"
+
+msgid "Download this page"
+msgstr "Загрузите эту страницу"
+
+msgid "Source repository"
+msgstr "Исходный репозиторий"
+
+msgid "By"
+msgstr "По"
+
+msgid "repository"
+msgstr "хранилище"
+
+msgid "Last updated on"
+msgstr "Последнее обновление"
+
+msgid "Toggle navigation"
+msgstr "Переключить навигацию"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема книги Сфинкс"
+
+msgid "suggest edit"
+msgstr "предложить редактировать"
+
+msgid "Open an issue"
+msgstr "Открыть вопрос"
+
+msgid "Launch"
+msgstr "Запуск"
+
+msgid "Fullscreen mode"
+msgstr "Полноэкранный режим"
+
+msgid "Edit this page"
+msgstr "Редактировать эту страницу"
+
+msgid "By the"
+msgstr "Посредством"
+
+msgid "next page"
+msgstr "Следующая страница"
diff --git a/_static/locales/sk/LC_MESSAGES/booktheme.mo b/_static/locales/sk/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..59bd0ddfa341477cf5ccfdc88f4c3e6127046f7a
GIT binary patch
literal 1393
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zwMSvx*(oZoi|&^5lh7kuE|;PI&N?-J`)^o!txs1juXogY6?)ZEJ1#xCtJU@>=)Bnr
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z94mEnM;EAFODWoF<;0UM`ZS%T+8S2)Skfe~=jnmos~5@rll9m4U7_dmM!H@|Co1(H
Di}h+h

literal 0
HcmV?d00001

diff --git a/_static/locales/sk/LC_MESSAGES/booktheme.po b/_static/locales/sk/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..65012881
--- /dev/null
+++ b/_static/locales/sk/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sk\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Tlač do PDF"
+
+msgid "Theme by the"
+msgstr "Téma od"
+
+msgid "Download source file"
+msgstr "Stiahnite si zdrojový súbor"
+
+msgid "open issue"
+msgstr "otvorené vydanie"
+
+msgid "Contents"
+msgstr "Obsah"
+
+msgid "previous page"
+msgstr "predchádzajúca strana"
+
+msgid "Download notebook file"
+msgstr "Stiahnite si zošit"
+
+msgid "Copyright"
+msgstr "Autorské práva"
+
+msgid "Download this page"
+msgstr "Stiahnite si túto stránku"
+
+msgid "Source repository"
+msgstr "Zdrojové úložisko"
+
+msgid "By"
+msgstr "Autor:"
+
+msgid "repository"
+msgstr "Úložisko"
+
+msgid "Last updated on"
+msgstr "Posledná aktualizácia dňa"
+
+msgid "Toggle navigation"
+msgstr "Prepnúť navigáciu"
+
+msgid "Sphinx Book Theme"
+msgstr "Téma knihy Sfinga"
+
+msgid "suggest edit"
+msgstr "navrhnúť úpravu"
+
+msgid "Open an issue"
+msgstr "Otvorte problém"
+
+msgid "Launch"
+msgstr "Spustiť"
+
+msgid "Fullscreen mode"
+msgstr "Režim celej obrazovky"
+
+msgid "Edit this page"
+msgstr "Upraviť túto stránku"
+
+msgid "By the"
+msgstr "Podľa"
+
+msgid "next page"
+msgstr "ďalšia strana"
diff --git a/_static/locales/sl/LC_MESSAGES/booktheme.mo b/_static/locales/sl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..87bf26de683cb18c73bd23c2b4c57ef6a02545ec
GIT binary patch
literal 1374
zcmZ9KyKfXR5XQ}$9PjWFC=#aOAvj*5h%UN<M}i_=5d;c4-|evHTgP76-Xpn+f)YAt
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zkab@J`TGNq^T|Ly_ZVc|FG1Eh0$KkXa4+~C<olmN*73cz{{lJx35el)hd|z+0{Q+7
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L??Y>WljW5^x#&@4

literal 0
HcmV?d00001

diff --git a/_static/locales/sl/LC_MESSAGES/booktheme.po b/_static/locales/sl/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..3c7e3a86
--- /dev/null
+++ b/_static/locales/sl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Natisni v PDF"
+
+msgid "Theme by the"
+msgstr "Tema avtorja"
+
+msgid "Download source file"
+msgstr "Prenesite izvorno datoteko"
+
+msgid "open issue"
+msgstr "odprto vprašanje"
+
+msgid "Contents"
+msgstr "Vsebina"
+
+msgid "previous page"
+msgstr "Prejšnja stran"
+
+msgid "Download notebook file"
+msgstr "Prenesite datoteko zvezka"
+
+msgid "Copyright"
+msgstr "avtorske pravice"
+
+msgid "Download this page"
+msgstr "Prenesite to stran"
+
+msgid "Source repository"
+msgstr "Izvorno skladišče"
+
+msgid "By"
+msgstr "Avtor"
+
+msgid "repository"
+msgstr "odlagališče"
+
+msgid "Last updated on"
+msgstr "Nazadnje posodobljeno dne"
+
+msgid "Toggle navigation"
+msgstr "Preklopi navigacijo"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema knjige Sphinx"
+
+msgid "suggest edit"
+msgstr "predlagajte urejanje"
+
+msgid "Open an issue"
+msgstr "Odprite številko"
+
+msgid "Launch"
+msgstr "Kosilo"
+
+msgid "Fullscreen mode"
+msgstr "Celozaslonski način"
+
+msgid "Edit this page"
+msgstr "Uredite to stran"
+
+msgid "By the"
+msgstr "Avtor"
+
+msgid "next page"
+msgstr "Naslednja stran"
diff --git a/_static/locales/sr/LC_MESSAGES/booktheme.mo b/_static/locales/sr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..ec740f4852f3973fa72ed0c7f7cf59273b8dba41
GIT binary patch
literal 1679
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literal 0
HcmV?d00001

diff --git a/_static/locales/sr/LC_MESSAGES/booktheme.po b/_static/locales/sr/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..773b8ada
--- /dev/null
+++ b/_static/locales/sr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Испис у ПДФ"
+
+msgid "Theme by the"
+msgstr "Тхеме би"
+
+msgid "Download source file"
+msgstr "Преузми изворну датотеку"
+
+msgid "open issue"
+msgstr "отворено издање"
+
+msgid "Contents"
+msgstr "Садржај"
+
+msgid "previous page"
+msgstr "Претходна страница"
+
+msgid "Download notebook file"
+msgstr "Преузмите датотеку бележнице"
+
+msgid "Copyright"
+msgstr "Ауторско право"
+
+msgid "Download this page"
+msgstr "Преузмите ову страницу"
+
+msgid "Source repository"
+msgstr "Изворно спремиште"
+
+msgid "By"
+msgstr "Од стране"
+
+msgid "repository"
+msgstr "спремиште"
+
+msgid "Last updated on"
+msgstr "Последње ажурирање"
+
+msgid "Toggle navigation"
+msgstr "Укључи / искључи навигацију"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема књиге Спхинк"
+
+msgid "suggest edit"
+msgstr "предложи уређивање"
+
+msgid "Open an issue"
+msgstr "Отворите издање"
+
+msgid "Launch"
+msgstr "Лансирање"
+
+msgid "Fullscreen mode"
+msgstr "Режим целог екрана"
+
+msgid "Edit this page"
+msgstr "Уредите ову страницу"
+
+msgid "By the"
+msgstr "Од"
+
+msgid "next page"
+msgstr "Следећа страна"
diff --git a/_static/locales/sv/LC_MESSAGES/booktheme.mo b/_static/locales/sv/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..b07dc76ff21128244172d2e415cc899555d8b49d
GIT binary patch
literal 1365
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EA0pCHFaQ7m

literal 0
HcmV?d00001

diff --git a/_static/locales/sv/LC_MESSAGES/booktheme.po b/_static/locales/sv/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..bcac54c0
--- /dev/null
+++ b/_static/locales/sv/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: sv\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Skriv ut till PDF"
+
+msgid "Theme by the"
+msgstr "Tema av"
+
+msgid "Download source file"
+msgstr "Ladda ner källfil"
+
+msgid "open issue"
+msgstr "öppna problemrapport"
+
+msgid "Contents"
+msgstr "Innehåll"
+
+msgid "previous page"
+msgstr "föregående sida"
+
+msgid "Download notebook file"
+msgstr "Ladda ner notebook-fil"
+
+msgid "Copyright"
+msgstr "Upphovsrätt"
+
+msgid "Download this page"
+msgstr "Ladda ner den här sidan"
+
+msgid "Source repository"
+msgstr "Källkodsrepositorium"
+
+msgid "By"
+msgstr "Av"
+
+msgid "repository"
+msgstr "repositorium"
+
+msgid "Last updated on"
+msgstr "Senast uppdaterad den"
+
+msgid "Toggle navigation"
+msgstr "Växla navigering"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Boktema"
+
+msgid "suggest edit"
+msgstr "föreslå ändring"
+
+msgid "Open an issue"
+msgstr "Öppna en problemrapport"
+
+msgid "Launch"
+msgstr "Öppna"
+
+msgid "Fullscreen mode"
+msgstr "Fullskärmsläge"
+
+msgid "Edit this page"
+msgstr "Redigera den här sidan"
+
+msgid "By the"
+msgstr "Av den"
+
+msgid "next page"
+msgstr "nästa sida"
diff --git a/_static/locales/ta/LC_MESSAGES/booktheme.mo b/_static/locales/ta/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..29f52e1f6f1ec96aceb0f883d8ccc82c23eb35c2
GIT binary patch
literal 1928
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literal 0
HcmV?d00001

diff --git a/_static/locales/ta/LC_MESSAGES/booktheme.po b/_static/locales/ta/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..b48bdfaf
--- /dev/null
+++ b/_static/locales/ta/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ta\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF இல் அச்சிடுக"
+
+msgid "Theme by the"
+msgstr "வழங்கிய தீம்"
+
+msgid "Download source file"
+msgstr "மூல கோப்பைப் பதிவிறக்குக"
+
+msgid "open issue"
+msgstr "திறந்த பிரச்சினை"
+
+msgid "previous page"
+msgstr "முந்தைய பக்கம்"
+
+msgid "Download notebook file"
+msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்"
+
+msgid "Copyright"
+msgstr "பதிப்புரிமை"
+
+msgid "Download this page"
+msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்"
+
+msgid "Source repository"
+msgstr "மூல களஞ்சியம்"
+
+msgid "By"
+msgstr "வழங்கியவர்"
+
+msgid "Last updated on"
+msgstr "கடைசியாக புதுப்பிக்கப்பட்டது"
+
+msgid "Toggle navigation"
+msgstr "வழிசெலுத்தலை நிலைமாற்று"
+
+msgid "Sphinx Book Theme"
+msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்"
+
+msgid "suggest edit"
+msgstr "திருத்த பரிந்துரைக்கவும்"
+
+msgid "Open an issue"
+msgstr "சிக்கலைத் திறக்கவும்"
+
+msgid "Launch"
+msgstr "தொடங்க"
+
+msgid "Edit this page"
+msgstr "இந்தப் பக்கத்தைத் திருத்தவும்"
+
+msgid "By the"
+msgstr "மூலம்"
+
+msgid "next page"
+msgstr "அடுத்த பக்கம்"
diff --git a/_static/locales/te/LC_MESSAGES/booktheme.mo b/_static/locales/te/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0a5f4b46adfda1551ae6791c3c3782525b61be04
GIT binary patch
literal 1806
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literal 0
HcmV?d00001

diff --git a/_static/locales/te/LC_MESSAGES/booktheme.po b/_static/locales/te/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..952278f5
--- /dev/null
+++ b/_static/locales/te/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: te\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF కి ముద్రించండి"
+
+msgid "Theme by the"
+msgstr "ద్వారా థీమ్"
+
+msgid "Download source file"
+msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి"
+
+msgid "open issue"
+msgstr "ఓపెన్ ఇష్యూ"
+
+msgid "previous page"
+msgstr "ముందు పేజి"
+
+msgid "Download notebook file"
+msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి"
+
+msgid "Copyright"
+msgstr "కాపీరైట్"
+
+msgid "Download this page"
+msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి"
+
+msgid "Source repository"
+msgstr "మూల రిపోజిటరీ"
+
+msgid "By"
+msgstr "ద్వారా"
+
+msgid "Last updated on"
+msgstr "చివరిగా నవీకరించబడింది"
+
+msgid "Toggle navigation"
+msgstr "నావిగేషన్‌ను టోగుల్ చేయండి"
+
+msgid "Sphinx Book Theme"
+msgstr "సింహిక పుస్తక థీమ్"
+
+msgid "suggest edit"
+msgstr "సవరించమని సూచించండి"
+
+msgid "Open an issue"
+msgstr "సమస్యను తెరవండి"
+
+msgid "Launch"
+msgstr "ప్రారంభించండి"
+
+msgid "Edit this page"
+msgstr "ఈ పేజీని సవరించండి"
+
+msgid "By the"
+msgstr "ద్వారా"
+
+msgid "next page"
+msgstr "తరువాతి పేజీ"
diff --git a/_static/locales/tg/LC_MESSAGES/booktheme.mo b/_static/locales/tg/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..b21c6c6340194fdf35f7e5660deb42479c8dbff9
GIT binary patch
literal 1628
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literal 0
HcmV?d00001

diff --git a/_static/locales/tg/LC_MESSAGES/booktheme.po b/_static/locales/tg/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..c33dc421
--- /dev/null
+++ b/_static/locales/tg/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tg\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Чоп ба PDF"
+
+msgid "Theme by the"
+msgstr "Мавзӯъи аз"
+
+msgid "Download source file"
+msgstr "Файли манбаъро зеркашӣ кунед"
+
+msgid "open issue"
+msgstr "барориши кушод"
+
+msgid "Contents"
+msgstr "Мундариҷа"
+
+msgid "previous page"
+msgstr "саҳифаи қаблӣ"
+
+msgid "Download notebook file"
+msgstr "Файли дафтарро зеркашӣ кунед"
+
+msgid "Copyright"
+msgstr "Ҳуқуқи муаллиф"
+
+msgid "Download this page"
+msgstr "Ин саҳифаро зеркашӣ кунед"
+
+msgid "Source repository"
+msgstr "Анбори манбаъ"
+
+msgid "By"
+msgstr "Бо"
+
+msgid "repository"
+msgstr "анбор"
+
+msgid "Last updated on"
+msgstr "Last навсозӣ дар"
+
+msgid "Toggle navigation"
+msgstr "Гузаришро иваз кунед"
+
+msgid "Sphinx Book Theme"
+msgstr "Сфинкс Мавзӯи китоб"
+
+msgid "suggest edit"
+msgstr "пешниҳод вироиш"
+
+msgid "Open an issue"
+msgstr "Масъаларо кушоед"
+
+msgid "Launch"
+msgstr "Оғоз"
+
+msgid "Fullscreen mode"
+msgstr "Ҳолати экрани пурра"
+
+msgid "Edit this page"
+msgstr "Ин саҳифаро таҳрир кунед"
+
+msgid "By the"
+msgstr "Бо"
+
+msgid "next page"
+msgstr "саҳифаи оянда"
diff --git a/_static/locales/th/LC_MESSAGES/booktheme.mo b/_static/locales/th/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..abede98aa11b163e580a26b545980ace31e61ccf
GIT binary patch
literal 1766
zcma)*-)|K~6vqcarB^@&)CU_K663GrYB9uM>wQ7mgal$EMW46#4trN`XEwWgD~&H;
zNI=2(Qi7!k28|(VOaz2Z{{sC3d@_d21Bp*Q8{hn#*{fZQQ8%6a%*>u2-*e9G?VVdL
zD4xey<E$T9PqNN$;>EM|9;NtGW4!Kz+u&BX3y#5Y_$YiD?uH$>6TSg=Km#9y)rhB|
z#94;h;VRq)zl0CNTO<B3y8mf({sl_D-(d@G-dy|dfN~y(66YD{;XWw#zmE7D6kVIP
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zJ#D+Lm)Ueu@1M`A*5SmaL;6aLert9xERsQc#uW?Yq_%lHmYtWTbiM67YxSOv-uv8C
zONI6KgI^B*v*yl!75w$!KM(#=@YjO>NCp2z@ZSZGG2hr+4E__@te@u_{+M44euZ<4
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zVo#A&Zbczg{j*X10nt=)GI&y-i8VB>&j$Yu0#;+cXcn64J%wUT=~$4O>SO5=h=^<P
z9#1*rKu?$NI>cLPeiV0|6qlnFzL#U0qn`*`iT?nxl@xb2Mx-{rpyeF$L|PmGL73Xj
zjT&US*r9DL+Oc6=XyNi4J`IP)4~3RpiSp}N5WkMWZ=`O(hjnrBo?i;ppCMASYj{fI
qV*6MBqwc03(drtRAFXAoSBd=B?a4S*sk|OB^ZqtY{YJQZ;_45RC*i9A

literal 0
HcmV?d00001

diff --git a/_static/locales/th/LC_MESSAGES/booktheme.po b/_static/locales/th/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..9d24294a
--- /dev/null
+++ b/_static/locales/th/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: th\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "พิมพ์เป็น PDF"
+
+msgid "Theme by the"
+msgstr "ธีมโดย"
+
+msgid "Download source file"
+msgstr "ดาวน์โหลดไฟล์ต้นฉบับ"
+
+msgid "open issue"
+msgstr "เปิดปัญหา"
+
+msgid "Contents"
+msgstr "สารบัญ"
+
+msgid "previous page"
+msgstr "หน้าที่แล้ว"
+
+msgid "Download notebook file"
+msgstr "ดาวน์โหลดไฟล์สมุดบันทึก"
+
+msgid "Copyright"
+msgstr "ลิขสิทธิ์"
+
+msgid "Download this page"
+msgstr "ดาวน์โหลดหน้านี้"
+
+msgid "Source repository"
+msgstr "ที่เก็บซอร์ส"
+
+msgid "By"
+msgstr "โดย"
+
+msgid "repository"
+msgstr "ที่เก็บ"
+
+msgid "Last updated on"
+msgstr "ปรับปรุงล่าสุดเมื่อ"
+
+msgid "Toggle navigation"
+msgstr "ไม่ต้องสลับช่องทาง"
+
+msgid "Sphinx Book Theme"
+msgstr "ธีมหนังสือสฟิงซ์"
+
+msgid "suggest edit"
+msgstr "แนะนำแก้ไข"
+
+msgid "Open an issue"
+msgstr "เปิดปัญหา"
+
+msgid "Launch"
+msgstr "เปิด"
+
+msgid "Fullscreen mode"
+msgstr "โหมดเต็มหน้าจอ"
+
+msgid "Edit this page"
+msgstr "แก้ไขหน้านี้"
+
+msgid "By the"
+msgstr "โดย"
+
+msgid "next page"
+msgstr "หน้าต่อไป"
diff --git a/_static/locales/tl/LC_MESSAGES/booktheme.mo b/_static/locales/tl/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..8df1b73310e0c606251d1abff7e6033d1f6b273f
GIT binary patch
literal 1273
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literal 0
HcmV?d00001

diff --git a/_static/locales/tl/LC_MESSAGES/booktheme.po b/_static/locales/tl/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..20e0d07c
--- /dev/null
+++ b/_static/locales/tl/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tl\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "I-print sa PDF"
+
+msgid "Theme by the"
+msgstr "Tema ng"
+
+msgid "Download source file"
+msgstr "Mag-download ng file ng pinagmulan"
+
+msgid "open issue"
+msgstr "bukas na isyu"
+
+msgid "previous page"
+msgstr "Nakaraang pahina"
+
+msgid "Download notebook file"
+msgstr "Mag-download ng file ng notebook"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "I-download ang pahinang ito"
+
+msgid "Source repository"
+msgstr "Pinagmulan ng imbakan"
+
+msgid "By"
+msgstr "Ni"
+
+msgid "Last updated on"
+msgstr "Huling na-update noong"
+
+msgid "Toggle navigation"
+msgstr "I-toggle ang pag-navigate"
+
+msgid "Sphinx Book Theme"
+msgstr "Tema ng Sphinx Book"
+
+msgid "suggest edit"
+msgstr "iminumungkahi i-edit"
+
+msgid "Open an issue"
+msgstr "Magbukas ng isyu"
+
+msgid "Launch"
+msgstr "Ilunsad"
+
+msgid "Edit this page"
+msgstr "I-edit ang pahinang ito"
+
+msgid "By the"
+msgstr "Sa pamamagitan ng"
+
+msgid "next page"
+msgstr "Susunod na pahina"
diff --git a/_static/locales/tr/LC_MESSAGES/booktheme.mo b/_static/locales/tr/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..029ae18afb5360a8c238a052e8d3971761759d3d
GIT binary patch
literal 1373
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zo6h$Ap{&BCl*;`;;xXGI&@B^nMMIv5kwyPFiiuV?c-PnoHwMdQd*;PnG+qT~k=xL=
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literal 0
HcmV?d00001

diff --git a/_static/locales/tr/LC_MESSAGES/booktheme.po b/_static/locales/tr/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..a77eb027
--- /dev/null
+++ b/_static/locales/tr/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: tr\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "PDF olarak yazdır"
+
+msgid "Theme by the"
+msgstr "Tarafından tema"
+
+msgid "Download source file"
+msgstr "Kaynak dosyayı indirin"
+
+msgid "open issue"
+msgstr "Açık konu"
+
+msgid "Contents"
+msgstr "İçindekiler"
+
+msgid "previous page"
+msgstr "önceki sayfa"
+
+msgid "Download notebook file"
+msgstr "Defter dosyasını indirin"
+
+msgid "Copyright"
+msgstr "Telif hakkı"
+
+msgid "Download this page"
+msgstr "Bu sayfayı indirin"
+
+msgid "Source repository"
+msgstr "Kaynak kod deposu"
+
+msgid "By"
+msgstr "Tarafından"
+
+msgid "repository"
+msgstr "depo"
+
+msgid "Last updated on"
+msgstr "Son güncelleme tarihi"
+
+msgid "Toggle navigation"
+msgstr "Gezinmeyi değiştir"
+
+msgid "Sphinx Book Theme"
+msgstr "Sfenks Kitap Teması"
+
+msgid "suggest edit"
+msgstr "düzenleme öner"
+
+msgid "Open an issue"
+msgstr "Bir sorunu açın"
+
+msgid "Launch"
+msgstr "Başlatmak"
+
+msgid "Fullscreen mode"
+msgstr "Tam ekran modu"
+
+msgid "Edit this page"
+msgstr "Bu sayfayı düzenle"
+
+msgid "By the"
+msgstr "Tarafından"
+
+msgid "next page"
+msgstr "sonraki Sayfa"
diff --git a/_static/locales/uk/LC_MESSAGES/booktheme.mo b/_static/locales/uk/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..16ab78909cfbaba7fa199f47fdfc2934426ac699
GIT binary patch
literal 1681
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z9(7$q(09~EfF`ZE2A7mZcK`2+9^rzjaHX3z)7!~B2h%!=*c9|lnghL|yA=s%zR*HT
z?bNhf%F5ez4L46+L7%4`a6I&oR?3Y*t^E56s%=nLx&}9h+G&%^bgSuKmsXIs4P%={
z<NofZ={oLHR_+Jb;Vx4(JG2JbhUt5^koP}7+~?BbLU@u{xGrtDCHKjW*gc*BJl2qW
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LPmM*`H%jps*u#;t

literal 0
HcmV?d00001

diff --git a/_static/locales/uk/LC_MESSAGES/booktheme.po b/_static/locales/uk/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..993dd078
--- /dev/null
+++ b/_static/locales/uk/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: uk\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "Друк у форматі PDF"
+
+msgid "Theme by the"
+msgstr "Тема від"
+
+msgid "Download source file"
+msgstr "Завантажити вихідний файл"
+
+msgid "open issue"
+msgstr "відкритий випуск"
+
+msgid "Contents"
+msgstr "Зміст"
+
+msgid "previous page"
+msgstr "Попередня сторінка"
+
+msgid "Download notebook file"
+msgstr "Завантажте файл блокнота"
+
+msgid "Copyright"
+msgstr "Авторське право"
+
+msgid "Download this page"
+msgstr "Завантажте цю сторінку"
+
+msgid "Source repository"
+msgstr "Джерело сховища"
+
+msgid "By"
+msgstr "Автор"
+
+msgid "repository"
+msgstr "сховище"
+
+msgid "Last updated on"
+msgstr "Останнє оновлення:"
+
+msgid "Toggle navigation"
+msgstr "Переключити навігацію"
+
+msgid "Sphinx Book Theme"
+msgstr "Тема книги \"Сфінкс\""
+
+msgid "suggest edit"
+msgstr "запропонувати редагувати"
+
+msgid "Open an issue"
+msgstr "Відкрийте випуск"
+
+msgid "Launch"
+msgstr "Запуск"
+
+msgid "Fullscreen mode"
+msgstr "Повноекранний режим"
+
+msgid "Edit this page"
+msgstr "Редагувати цю сторінку"
+
+msgid "By the"
+msgstr "По"
+
+msgid "next page"
+msgstr "Наступна сторінка"
diff --git a/_static/locales/ur/LC_MESSAGES/booktheme.mo b/_static/locales/ur/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..de8c84b935ed5c3db23e304015d45b638a3d2db3
GIT binary patch
literal 1462
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zRwLUoA{Qc+BL{@CNd#$~fM}{TM=rgzQV%n0sfS7&;BP2WpLe~9L#0ZLy!)G(eLtS}
z@uTBCHyGACc;3gei{}G8S6;vi>pu7*_yBwvd<eb@w!uE|FOYJdfXBgJ1iuNM0*`@b
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z$k@3h{CHRvY(#lWzU!1jHmVk6McE}TRVZeZy2@wWis)VkD)Nisnai;32Hdk9!Ny8%
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z$~?F1e_~CYdU3r0$=I6uq5ePVO_Bn=P}riFSZ}qf6oE57w5w3K+FoX+hEW?y<4|&p
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c^9?3>eEmE8GP@z_uy-`khW>?G>aA{%rxd0lq5uE@

literal 0
HcmV?d00001

diff --git a/_static/locales/ur/LC_MESSAGES/booktheme.po b/_static/locales/ur/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..2f774267
--- /dev/null
+++ b/_static/locales/ur/LC_MESSAGES/booktheme.po
@@ -0,0 +1,66 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: ur\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "پی ڈی ایف پرنٹ کریں"
+
+msgid "Theme by the"
+msgstr "کے ذریعہ تھیم"
+
+msgid "Download source file"
+msgstr "سورس فائل ڈاؤن لوڈ کریں"
+
+msgid "open issue"
+msgstr "کھلا مسئلہ"
+
+msgid "previous page"
+msgstr "سابقہ ​​صفحہ"
+
+msgid "Download notebook file"
+msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں"
+
+msgid "Copyright"
+msgstr "کاپی رائٹ"
+
+msgid "Download this page"
+msgstr "اس صفحے کو ڈاؤن لوڈ کریں"
+
+msgid "Source repository"
+msgstr "ماخذ ذخیرہ"
+
+msgid "By"
+msgstr "بذریعہ"
+
+msgid "Last updated on"
+msgstr "آخری بار تازہ کاری ہوئی"
+
+msgid "Toggle navigation"
+msgstr "نیویگیشن ٹوگل کریں"
+
+msgid "Sphinx Book Theme"
+msgstr "سپنکس بک تھیم"
+
+msgid "suggest edit"
+msgstr "ترمیم کی تجویز کریں"
+
+msgid "Open an issue"
+msgstr "ایک مسئلہ کھولیں"
+
+msgid "Launch"
+msgstr "لانچ کریں"
+
+msgid "Edit this page"
+msgstr "اس صفحے میں ترمیم کریں"
+
+msgid "By the"
+msgstr "کی طرف"
+
+msgid "next page"
+msgstr "اگلا صفحہ"
diff --git a/_static/locales/vi/LC_MESSAGES/booktheme.mo b/_static/locales/vi/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..2bb32555c3ea0dab08ed37b71cb863fbe75698c6
GIT binary patch
literal 1431
zcmZvb&x;&I6vr#s{2bR9catc(#OK8%htWiG2(!#(H#@MntBV<f*WRAmu1b2V+OFzx
z2ZFF95JJ2J-Gl5Qt0F;gAtH!OA4Eoie?ShwL!cp$lOTACr+nXZhaHT?RDHUBz3=zE
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z3BCmSiq}An^CkEqxCxGf--9oMKUe&tTK}_}KL%Ov30MQi#>)GLK%P&59Onf13wRdX
z3;qSN-ajCp`xj*0y?e@hC&AY-KMlSLE>x7&{%7EEtZ#xGrw?+zzkx4+4=O$aInLi8
z=P?fP6X0=>=PmFs_%X<J(IBjdHSjg?3dkkr9Jqd5%Sm+B;=D&2B!;m#4mZw=8|!eb
zIlrUm2s!k~?{>6t9{djXqq7H;REU`#e&m%Dv(|WNyu(A%OLe5YIB!2Su?;$8te4By
zUZxct%bjIsvow_dS@cRfN`gp=xsLV-{cPIG;@E|$l!jJqM~a2OdCHPb;AMxb!8|je
z5+5Wu5E#<VWl}7q+8~IfrSmOuq0~!dVx9Ij?THJC(q@fj*jro4RVjueEtfg9Z4||l
zOwiR);59^yT=S(n%Q~e<lFF{OS!H`9yvw3U!lgteuwpOC(AVcX^@}ofh}NJ|zs~yg
zp>l0;esOMR|IFK`YgJ+E?Oq}q<YC~QBo4Hhrcec`lfL;;yH!88b1n^xTajsf&V;t3
zP1K-s%i7l#0uyDZYlFJFwiIV+5Z7BaU3CpI$#CkL?@m+s*gR?I%_f~WHC+=kMSfdj
z?Eh#Y@qUqirKywQ5i8#s`XuWW`MMEp%rvcKMSjaf<cs`zLaxX+$Opau-GrMW|Khoe
za37HwZ1qH_iu{IA9A!Hozq2u`ivF+k`z=_(s~v3NgWy38elSYR7X9sn432Djii`do
zLm`{e)E#VDv7`n+!TTa|qE!U&)GhkonPIebF>lDRK7TFmDND~hLNUKno*_5*I#duC
ze1pUH?uya95XS<na80h3K{qt+X_P1<b?<JGZ*p}M{3_8NrsOrUN%%ix#fTSncUzQM
Z`*e6S6ql71ocB7M!O~590wXmNPXTtKmQ4Tv

literal 0
HcmV?d00001

diff --git a/_static/locales/vi/LC_MESSAGES/booktheme.po b/_static/locales/vi/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..33159f3e
--- /dev/null
+++ b/_static/locales/vi/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: vi\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "In sang PDF"
+
+msgid "Theme by the"
+msgstr "Chủ đề của"
+
+msgid "Download source file"
+msgstr "Tải xuống tệp nguồn"
+
+msgid "open issue"
+msgstr "vấn đề mở"
+
+msgid "Contents"
+msgstr "Nội dung"
+
+msgid "previous page"
+msgstr "trang trước"
+
+msgid "Download notebook file"
+msgstr "Tải xuống tệp sổ tay"
+
+msgid "Copyright"
+msgstr "Bản quyền"
+
+msgid "Download this page"
+msgstr "Tải xuống trang này"
+
+msgid "Source repository"
+msgstr "Kho nguồn"
+
+msgid "By"
+msgstr "Bởi"
+
+msgid "repository"
+msgstr "kho"
+
+msgid "Last updated on"
+msgstr "Cập nhật lần cuối vào"
+
+msgid "Toggle navigation"
+msgstr "Chuyển đổi điều hướng thành"
+
+msgid "Sphinx Book Theme"
+msgstr "Chủ đề sách nhân sư"
+
+msgid "suggest edit"
+msgstr "đề nghị chỉnh sửa"
+
+msgid "Open an issue"
+msgstr "Mở một vấn đề"
+
+msgid "Launch"
+msgstr "Phóng"
+
+msgid "Fullscreen mode"
+msgstr "Chế độ toàn màn hình"
+
+msgid "Edit this page"
+msgstr "chỉnh sửa trang này"
+
+msgid "By the"
+msgstr "Bằng"
+
+msgid "next page"
+msgstr "Trang tiếp theo"
diff --git a/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo b/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..0e3235d090c2a05c0e278d38520f0437847dc79f
GIT binary patch
literal 1310
zcmZ9JO>7fK6vqb$6da&5d>$e(Tqr%5FL8(*`2r*YiH`^Z>aE(?ll6i#v)Y{nLNAmS
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z{(wMR8u!Z%_T%9?BiJD&wUFAyhu!a3W=6Ba>Y~glBh97KqDQIoyw}Q#g=trLPD*??
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zDK6>Iog$r3#CF65OL>v)Mde-3GzDA=oCH?XIg!%wqv`lr;kc-l;Guq;^y3ZX*zu#s
zI~M-$+r2jyH8$SeZ;J%iFtEqY8kTJ5sf^*cLMP93cgEWm);WfBdxaD4kSUe6q?zDt
zJ(i9gGo<OEuL*uR^V^{lv97G=7}<EIa{Al^mv$p{ll$9w_?g@#`Hm#t_e*<>RpuVl
zu3Y;tdw<bab+qWu4YKNuTrl=0tIXW0&COLO2WrKs;K5`tbiGoZj%I^WVQK#N<NElk
z`q(2@ogJ#p-wnRy{<R7JMLw7q_h<9id^4CF4@O@GLsP-<bba_G8So3w{hJfa&yD)!
zQmt65KN>+Y?BAIRa)rh<L$3Pp55IJ8$@Nxd%Ct1Z!g+#``Re0RkQ=JsFVu$f{`1*d
n?pg4V)NfU0uE6I~DVU0;E9HBn5#$U0^-|?cF&KCqI$~b{HWi8t

literal 0
HcmV?d00001

diff --git a/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..2e519ef4
--- /dev/null
+++ b/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: zh_CN\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "列印成 PDF"
+
+msgid "Theme by the"
+msgstr "主题作者："
+
+msgid "Download source file"
+msgstr "下载源文件"
+
+msgid "open issue"
+msgstr "创建议题"
+
+msgid "Contents"
+msgstr "目录"
+
+msgid "previous page"
+msgstr "上一页"
+
+msgid "Download notebook file"
+msgstr "下载笔记本文件"
+
+msgid "Copyright"
+msgstr "版权"
+
+msgid "Download this page"
+msgstr "下载此页面"
+
+msgid "Source repository"
+msgstr "源码库"
+
+msgid "By"
+msgstr "作者："
+
+msgid "repository"
+msgstr "仓库"
+
+msgid "Last updated on"
+msgstr "上次更新时间："
+
+msgid "Toggle navigation"
+msgstr "显示或隐藏导航栏"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Book 主题"
+
+msgid "suggest edit"
+msgstr "提出修改建议"
+
+msgid "Open an issue"
+msgstr "创建议题"
+
+msgid "Launch"
+msgstr "启动"
+
+msgid "Fullscreen mode"
+msgstr "全屏模式"
+
+msgid "Edit this page"
+msgstr "编辑此页面"
+
+msgid "By the"
+msgstr "作者："
+
+msgid "next page"
+msgstr "下一页"
diff --git a/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo b/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo
new file mode 100644
index 0000000000000000000000000000000000000000..9116fa95d0a752665e0f1d59857a32abfc0c6bce
GIT binary patch
literal 1341
zcmZ9JO>7%Q6vqb|O6z_>OFs^gI=uiz7%1XUIrvhVNR?8ES{E)zXk$;;3!9nM?rdo|
zg(?BJNt;NGU78fyR1pmy`2barI!U>5;l_yr7d&hG1XmCT{?EI?hS9$L&6|(+{`20i
zd%MaE;~=aX_A~4-Y~m68Ft$C)7(TWgzpda-a2vP}+zxhw2f(Mnr@$<@7d!**1_ihW
z)CosHiZc!F0%yUU;1}SN;CBgsOxAx%{5nYa{sdFtmM!u9-5~khAjNqGTmxSL>HHrF
z{{ks*S695g10>!1LDKhp!dDaaCwv>+k9`-U`<Fn<a~UN46_D=FgNMNTAf5jy@qYtJ
z$KN2G+xl4SA4teSs@HQMm7HRePqo|!BaL*wb08FZ0!H<vLGPzQ^`ji5i!>l4+X18A
zb`E+6_3&{R%F^mDJ28xp>!M(<DXE3j0VdwC@7P7nPOA5%tE>W-N{d0I-s9&TSG1P{
z75aJczeQbi0`6J1V7&!LBlM=*7rJhc_l1yrNEHO@w*t*WuV86W;7Y;|WxmMX@NmGA
z+zEnEumRtZ2%`AF$v$>A&gTnH1&&t!FgxoN9eI(TAZ@v#7!s`Y=Y#Q^a>}+{!KHP<
zu`TT&i4+%gtWHs$IEm+r3yumC*&X47(6$9s3cLhT)Vm_D)29mQH-#S{T878@)098m
z%A7iV>U3{=|JVyJrINy?bHkp<a18-3damWj9-c2+ejs%AT&^#DynW8MWN=>i>0X&v
z1xMN$etgi;seVh^A?ljp9~9roy_FhpL*H`KeaasSGF*BsH^{!+!{cf8S;>!N`LUxt
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zSsBNyvAEKln_#AN!(3g7N>i<F*B*WpRj178w`TDZQ~l~cy{>JPqMOUL)it`->9il(
zoLjEXSEEv;IrB~9#-v%f*7$q_8AyL^bp+y>8T%Uf>ocRKJlP7gdS!Fx->lK(l=-+?
P`(Y`Xeqh$BP|E%Rvo4ks

literal 0
HcmV?d00001

diff --git a/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
new file mode 100644
index 00000000..beecb076
--- /dev/null
+++ b/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
@@ -0,0 +1,75 @@
+
+msgid ""
+msgstr ""
+"Project-Id-Version: Sphinx-Book-Theme\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=UTF-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Language: zh_TW\n"
+"Plural-Forms: nplurals=2; plural=(n != 1);\n"
+
+msgid "Print to PDF"
+msgstr "列印成 PDF"
+
+msgid "Theme by the"
+msgstr "佈景主題作者："
+
+msgid "Download source file"
+msgstr "下載原始檔"
+
+msgid "open issue"
+msgstr "公開的問題"
+
+msgid "Contents"
+msgstr "目錄"
+
+msgid "previous page"
+msgstr "上一頁"
+
+msgid "Download notebook file"
+msgstr "下載 Notebook 檔案"
+
+msgid "Copyright"
+msgstr "Copyright"
+
+msgid "Download this page"
+msgstr "下載此頁面"
+
+msgid "Source repository"
+msgstr "來源儲存庫"
+
+msgid "By"
+msgstr "作者："
+
+msgid "repository"
+msgstr "儲存庫"
+
+msgid "Last updated on"
+msgstr "最後更新時間："
+
+msgid "Toggle navigation"
+msgstr "顯示或隱藏導覽列"
+
+msgid "Sphinx Book Theme"
+msgstr "Sphinx Book 佈景主題"
+
+msgid "suggest edit"
+msgstr "提出修改建議"
+
+msgid "Open an issue"
+msgstr "開啟議題"
+
+msgid "Launch"
+msgstr "啟動"
+
+msgid "Fullscreen mode"
+msgstr "全螢幕模式"
+
+msgid "Edit this page"
+msgstr "編輯此頁面"
+
+msgid "By the"
+msgstr "作者："
+
+msgid "next page"
+msgstr "下一頁"
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diff --git a/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
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index 00000000..33566310
--- /dev/null
+++ b/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
@@ -0,0 +1,2342 @@
+/* Variables */
+:root {
+  --mystnb-source-bg-color: #f7f7f7;
+  --mystnb-stdout-bg-color: #fcfcfc;
+  --mystnb-stderr-bg-color: #fdd;
+  --mystnb-traceback-bg-color: #fcfcfc;
+  --mystnb-source-border-color: #ccc;
+  --mystnb-source-margin-color: green;
+  --mystnb-stdout-border-color: #f7f7f7;
+  --mystnb-stderr-border-color: #f7f7f7;
+  --mystnb-traceback-border-color: #ffd6d6;
+  --mystnb-hide-prompt-opacity: 70%;
+  --mystnb-source-border-radius: .4em;
+  --mystnb-source-border-width: 1px;
+}
+
+/* Whole cell */
+div.container.cell {
+  padding-left: 0;
+  margin-bottom: 1em;
+}
+
+/* Removing all background formatting so we can control at the div level */
+.cell_input div.highlight,
+.cell_output pre,
+.cell_input pre,
+.cell_output .output {
+  border: none;
+  box-shadow: none;
+}
+
+.cell_output .output pre,
+.cell_input pre {
+  margin: 0px;
+}
+
+/* Input cells */
+div.cell div.cell_input,
+div.cell details.above-input>summary {
+  padding-left: 0em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  background-color: var(--mystnb-source-bg-color);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+  border-radius: var(--mystnb-source-border-radius);
+}
+
+div.cell_input>div,
+div.cell_output div.output>div.highlight {
+  margin: 0em !important;
+  border: none !important;
+}
+
+/* All cell outputs */
+.cell_output {
+  padding-left: 1em;
+  padding-right: 0em;
+  margin-top: 1em;
+}
+
+/* Text outputs from cells */
+.cell_output .output.text_plain,
+.cell_output .output.traceback,
+.cell_output .output.stream,
+.cell_output .output.stderr {
+  margin-top: 1em;
+  margin-bottom: 0em;
+  box-shadow: none;
+}
+
+.cell_output .output.text_plain,
+.cell_output .output.stream {
+  background: var(--mystnb-stdout-bg-color);
+  border: 1px solid var(--mystnb-stdout-border-color);
+}
+
+.cell_output .output.stderr {
+  background: var(--mystnb-stderr-bg-color);
+  border: 1px solid var(--mystnb-stderr-border-color);
+}
+
+.cell_output .output.traceback {
+  background: var(--mystnb-traceback-bg-color);
+  border: 1px solid var(--mystnb-traceback-border-color);
+}
+
+/* Collapsible cell content */
+div.cell details.above-input div.cell_input {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+  border-top: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
+}
+
+div.cell div.cell_input.above-output-prompt {
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+div.cell details.above-input>summary {
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+  border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
+  padding-left: 1em;
+  margin-bottom: 0;
+}
+
+div.cell details.above-output>summary {
+  background-color: var(--mystnb-source-bg-color);
+  padding-left: 1em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  border-radius: var(--mystnb-source-border-radius);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+}
+
+div.cell details.below-input>summary {
+  background-color: var(--mystnb-source-bg-color);
+  padding-left: 1em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  border-top: none;
+  border-bottom-left-radius: var(--mystnb-source-border-radius);
+  border-bottom-right-radius: var(--mystnb-source-border-radius);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+}
+
+div.cell details.hide>summary>span {
+  opacity: var(--mystnb-hide-prompt-opacity);
+}
+
+div.cell details.hide[open]>summary>span.collapsed {
+  display: none;
+}
+
+div.cell details.hide:not([open])>summary>span.expanded {
+  display: none;
+}
+
+@keyframes collapsed-fade-in {
+  0% {
+    opacity: 0;
+  }
+
+  100% {
+    opacity: 1;
+  }
+}
+div.cell details.hide[open]>summary~* {
+  -moz-animation: collapsed-fade-in 0.3s ease-in-out;
+  -webkit-animation: collapsed-fade-in 0.3s ease-in-out;
+  animation: collapsed-fade-in 0.3s ease-in-out;
+}
+
+/* Math align to the left */
+.cell_output .MathJax_Display {
+  text-align: left !important;
+}
+
+/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */
+div.cell_output table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 1em;
+  table-layout: fixed;
+}
+
+div.cell_output thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+
+div.cell_output tr,
+div.cell_output th,
+div.cell_output td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+div.cell_output th {
+  font-weight: bold;
+}
+
+div.cell_output tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+
+div.cell_output tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
+/** source code line numbers **/
+span.linenos {
+  opacity: 0.5;
+}
+
+/* Inline text from `paste` operation */
+
+span.pasted-text {
+  font-weight: bold;
+}
+
+span.pasted-inline img {
+  max-height: 2em;
+}
+
+tbody span.pasted-inline img {
+  max-height: none;
+}
+
+/* Font colors for translated ANSI escape sequences
+Color values are copied from Jupyter Notebook
+https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21
+Background colors from
+https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors
+*/
+div.highlight .-Color-Bold {
+  font-weight: bold;
+}
+
+div.highlight .-Color[class*=-Black] {
+  color: #3E424D
+}
+
+div.highlight .-Color[class*=-Red] {
+  color: #E75C58
+}
+
+div.highlight .-Color[class*=-Green] {
+  color: #00A250
+}
+
+div.highlight .-Color[class*=-Yellow] {
+  color: #DDB62B
+}
+
+div.highlight .-Color[class*=-Blue] {
+  color: #208FFB
+}
+
+div.highlight .-Color[class*=-Magenta] {
+  color: #D160C4
+}
+
+div.highlight .-Color[class*=-Cyan] {
+  color: #60C6C8
+}
+
+div.highlight .-Color[class*=-White] {
+  color: #C5C1B4
+}
+
+div.highlight .-Color[class*=-BGBlack] {
+  background-color: #3E424D
+}
+
+div.highlight .-Color[class*=-BGRed] {
+  background-color: #E75C58
+}
+
+div.highlight .-Color[class*=-BGGreen] {
+  background-color: #00A250
+}
+
+div.highlight .-Color[class*=-BGYellow] {
+  background-color: #DDB62B
+}
+
+div.highlight .-Color[class*=-BGBlue] {
+  background-color: #208FFB
+}
+
+div.highlight .-Color[class*=-BGMagenta] {
+  background-color: #D160C4
+}
+
+div.highlight .-Color[class*=-BGCyan] {
+  background-color: #60C6C8
+}
+
+div.highlight .-Color[class*=-BGWhite] {
+  background-color: #C5C1B4
+}
+
+/* Font colors for 8-bit ANSI */
+
+div.highlight .-Color[class*=-C0] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC0] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C1] {
+  color: #800000
+}
+
+div.highlight .-Color[class*=-BGC1] {
+  background-color: #800000
+}
+
+div.highlight .-Color[class*=-C2] {
+  color: #008000
+}
+
+div.highlight .-Color[class*=-BGC2] {
+  background-color: #008000
+}
+
+div.highlight .-Color[class*=-C3] {
+  color: #808000
+}
+
+div.highlight .-Color[class*=-BGC3] {
+  background-color: #808000
+}
+
+div.highlight .-Color[class*=-C4] {
+  color: #000080
+}
+
+div.highlight .-Color[class*=-BGC4] {
+  background-color: #000080
+}
+
+div.highlight .-Color[class*=-C5] {
+  color: #800080
+}
+
+div.highlight .-Color[class*=-BGC5] {
+  background-color: #800080
+}
+
+div.highlight .-Color[class*=-C6] {
+  color: #008080
+}
+
+div.highlight .-Color[class*=-BGC6] {
+  background-color: #008080
+}
+
+div.highlight .-Color[class*=-C7] {
+  color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-BGC7] {
+  background-color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-C8] {
+  color: #808080
+}
+
+div.highlight .-Color[class*=-BGC8] {
+  background-color: #808080
+}
+
+div.highlight .-Color[class*=-C9] {
+  color: #FF0000
+}
+
+div.highlight .-Color[class*=-BGC9] {
+  background-color: #FF0000
+}
+
+div.highlight .-Color[class*=-C10] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC10] {
+  background-color: #00FF00
+}
+
+div.highlight .-Color[class*=-C11] {
+  color: #FFFF00
+}
+
+div.highlight .-Color[class*=-BGC11] {
+  background-color: #FFFF00
+}
+
+div.highlight .-Color[class*=-C12] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC12] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C13] {
+  color: #FF00FF
+}
+
+div.highlight .-Color[class*=-BGC13] {
+  background-color: #FF00FF
+}
+
+div.highlight .-Color[class*=-C14] {
+  color: #00FFFF
+}
+
+div.highlight .-Color[class*=-BGC14] {
+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C15] {
+  color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-BGC15] {
+  background-color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-C16] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC16] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C17] {
+  color: #00005F
+}
+
+div.highlight .-Color[class*=-BGC17] {
+  background-color: #00005F
+}
+
+div.highlight .-Color[class*=-C18] {
+  color: #000087
+}
+
+div.highlight .-Color[class*=-BGC18] {
+  background-color: #000087
+}
+
+div.highlight .-Color[class*=-C19] {
+  color: #0000AF
+}
+
+div.highlight .-Color[class*=-BGC19] {
+  background-color: #0000AF
+}
+
+div.highlight .-Color[class*=-C20] {
+  color: #0000D7
+}
+
+div.highlight .-Color[class*=-BGC20] {
+  background-color: #0000D7
+}
+
+div.highlight .-Color[class*=-C21] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC21] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C22] {
+  color: #005F00
+}
+
+div.highlight .-Color[class*=-BGC22] {
+  background-color: #005F00
+}
+
+div.highlight .-Color[class*=-C23] {
+  color: #005F5F
+}
+
+div.highlight .-Color[class*=-BGC23] {
+  background-color: #005F5F
+}
+
+div.highlight .-Color[class*=-C24] {
+  color: #005F87
+}
+
+div.highlight .-Color[class*=-BGC24] {
+  background-color: #005F87
+}
+
+div.highlight .-Color[class*=-C25] {
+  color: #005FAF
+}
+
+div.highlight .-Color[class*=-BGC25] {
+  background-color: #005FAF
+}
+
+div.highlight .-Color[class*=-C26] {
+  color: #005FD7
+}
+
+div.highlight .-Color[class*=-BGC26] {
+  background-color: #005FD7
+}
+
+div.highlight .-Color[class*=-C27] {
+  color: #005FFF
+}
+
+div.highlight .-Color[class*=-BGC27] {
+  background-color: #005FFF
+}
+
+div.highlight .-Color[class*=-C28] {
+  color: #008700
+}
+
+div.highlight .-Color[class*=-BGC28] {
+  background-color: #008700
+}
+
+div.highlight .-Color[class*=-C29] {
+  color: #00875F
+}
+
+div.highlight .-Color[class*=-BGC29] {
+  background-color: #00875F
+}
+
+div.highlight .-Color[class*=-C30] {
+  color: #008787
+}
+
+div.highlight .-Color[class*=-BGC30] {
+  background-color: #008787
+}
+
+div.highlight .-Color[class*=-C31] {
+  color: #0087AF
+}
+
+div.highlight .-Color[class*=-BGC31] {
+  background-color: #0087AF
+}
+
+div.highlight .-Color[class*=-C32] {
+  color: #0087D7
+}
+
+div.highlight .-Color[class*=-BGC32] {
+  background-color: #0087D7
+}
+
+div.highlight .-Color[class*=-C33] {
+  color: #0087FF
+}
+
+div.highlight .-Color[class*=-BGC33] {
+  background-color: #0087FF
+}
+
+div.highlight .-Color[class*=-C34] {
+  color: #00AF00
+}
+
+div.highlight .-Color[class*=-BGC34] {
+  background-color: #00AF00
+}
+
+div.highlight .-Color[class*=-C35] {
+  color: #00AF5F
+}
+
+div.highlight .-Color[class*=-BGC35] {
+  background-color: #00AF5F
+}
+
+div.highlight .-Color[class*=-C36] {
+  color: #00AF87
+}
+
+div.highlight .-Color[class*=-BGC36] {
+  background-color: #00AF87
+}
+
+div.highlight .-Color[class*=-C37] {
+  color: #00AFAF
+}
+
+div.highlight .-Color[class*=-BGC37] {
+  background-color: #00AFAF
+}
+
+div.highlight .-Color[class*=-C38] {
+  color: #00AFD7
+}
+
+div.highlight .-Color[class*=-BGC38] {
+  background-color: #00AFD7
+}
+
+div.highlight .-Color[class*=-C39] {
+  color: #00AFFF
+}
+
+div.highlight .-Color[class*=-BGC39] {
+  background-color: #00AFFF
+}
+
+div.highlight .-Color[class*=-C40] {
+  color: #00D700
+}
+
+div.highlight .-Color[class*=-BGC40] {
+  background-color: #00D700
+}
+
+div.highlight .-Color[class*=-C41] {
+  color: #00D75F
+}
+
+div.highlight .-Color[class*=-BGC41] {
+  background-color: #00D75F
+}
+
+div.highlight .-Color[class*=-C42] {
+  color: #00D787
+}
+
+div.highlight .-Color[class*=-BGC42] {
+  background-color: #00D787
+}
+
+div.highlight .-Color[class*=-C43] {
+  color: #00D7AF
+}
+
+div.highlight .-Color[class*=-BGC43] {
+  background-color: #00D7AF
+}
+
+div.highlight .-Color[class*=-C44] {
+  color: #00D7D7
+}
+
+div.highlight .-Color[class*=-BGC44] {
+  background-color: #00D7D7
+}
+
+div.highlight .-Color[class*=-C45] {
+  color: #00D7FF
+}
+
+div.highlight .-Color[class*=-BGC45] {
+  background-color: #00D7FF
+}
+
+div.highlight .-Color[class*=-C46] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC46] {
+  background-color: #00FF00
+}
+
+div.highlight .-Color[class*=-C47] {
+  color: #00FF5F
+}
+
+div.highlight .-Color[class*=-BGC47] {
+  background-color: #00FF5F
+}
+
+div.highlight .-Color[class*=-C48] {
+  color: #00FF87
+}
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+div.highlight .-Color[class*=-BGC48] {
+  background-color: #00FF87
+}
+
+div.highlight .-Color[class*=-C49] {
+  color: #00FFAF
+}
+
+div.highlight .-Color[class*=-BGC49] {
+  background-color: #00FFAF
+}
+
+div.highlight .-Color[class*=-C50] {
+  color: #00FFD7
+}
+
+div.highlight .-Color[class*=-BGC50] {
+  background-color: #00FFD7
+}
+
+div.highlight .-Color[class*=-C51] {
+  color: #00FFFF
+}
+
+div.highlight .-Color[class*=-BGC51] {
+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C52] {
+  color: #5F0000
+}
+
+div.highlight .-Color[class*=-BGC52] {
+  background-color: #5F0000
+}
+
+div.highlight .-Color[class*=-C53] {
+  color: #5F005F
+}
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+div.highlight .-Color[class*=-BGC53] {
+  background-color: #5F005F
+}
+
+div.highlight .-Color[class*=-C54] {
+  color: #5F0087
+}
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+div.highlight .-Color[class*=-BGC54] {
+  background-color: #5F0087
+}
+
+div.highlight .-Color[class*=-C55] {
+  color: #5F00AF
+}
+
+div.highlight .-Color[class*=-BGC55] {
+  background-color: #5F00AF
+}
+
+div.highlight .-Color[class*=-C56] {
+  color: #5F00D7
+}
+
+div.highlight .-Color[class*=-BGC56] {
+  background-color: #5F00D7
+}
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+div.highlight .-Color[class*=-C57] {
+  color: #5F00FF
+}
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+div.highlight .-Color[class*=-BGC57] {
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+}
+
+div.highlight .-Color[class*=-C58] {
+  color: #5F5F00
+}
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+div.highlight .-Color[class*=-BGC58] {
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+}
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+div.highlight .-Color[class*=-C59] {
+  color: #5F5F5F
+}
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+div.highlight .-Color[class*=-BGC59] {
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+}
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+div.highlight .-Color[class*=-C60] {
+  color: #5F5F87
+}
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+div.highlight .-Color[class*=-BGC60] {
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+}
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+  color: #5F5FAF
+}
+
+div.highlight .-Color[class*=-BGC61] {
+  background-color: #5F5FAF
+}
+
+div.highlight .-Color[class*=-C62] {
+  color: #5F5FD7
+}
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+div.highlight .-Color[class*=-BGC62] {
+  background-color: #5F5FD7
+}
+
+div.highlight .-Color[class*=-C63] {
+  color: #5F5FFF
+}
+
+div.highlight .-Color[class*=-BGC63] {
+  background-color: #5F5FFF
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
+
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+}
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+}
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diff --git a/_static/pygments.css b/_static/pygments.css
new file mode 100644
index 00000000..012e6a00
--- /dev/null
+++ b/_static/pygments.css
@@ -0,0 +1,152 @@
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+html[data-theme="light"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight .hll { background-color: #fae4c2 }
+html[data-theme="light"] .highlight { background: #fefefe; color: #080808 }
+html[data-theme="light"] .highlight .c { color: #515151 } /* Comment */
+html[data-theme="light"] .highlight .err { color: #a12236 } /* Error */
+html[data-theme="light"] .highlight .k { color: #6730c5 } /* Keyword */
+html[data-theme="light"] .highlight .l { color: #7f4707 } /* Literal */
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+html[data-theme="dark"] .highlight .err { color: #ffa07a } /* Error */
+html[data-theme="dark"] .highlight .k { color: #dcc6e0 } /* Keyword */
+html[data-theme="dark"] .highlight .l { color: #ffd900 } /* Literal */
+html[data-theme="dark"] .highlight .n { color: #f8f8f2 } /* Name */
+html[data-theme="dark"] .highlight .o { color: #abe338 } /* Operator */
+html[data-theme="dark"] .highlight .p { color: #f8f8f2 } /* Punctuation */
+html[data-theme="dark"] .highlight .ch { color: #ffd900 } /* Comment.Hashbang */
+html[data-theme="dark"] .highlight .cm { color: #ffd900 } /* Comment.Multiline */
+html[data-theme="dark"] .highlight .cp { color: #ffd900 } /* Comment.Preproc */
+html[data-theme="dark"] .highlight .cpf { color: #ffd900 } /* Comment.PreprocFile */
+html[data-theme="dark"] .highlight .c1 { color: #ffd900 } /* Comment.Single */
+html[data-theme="dark"] .highlight .cs { color: #ffd900 } /* Comment.Special */
+html[data-theme="dark"] .highlight .gd { color: #00e0e0 } /* Generic.Deleted */
+html[data-theme="dark"] .highlight .ge { font-style: italic } /* Generic.Emph */
+html[data-theme="dark"] .highlight .gh { color: #00e0e0 } /* Generic.Heading */
+html[data-theme="dark"] .highlight .gs { font-weight: bold } /* Generic.Strong */
+html[data-theme="dark"] .highlight .gu { color: #00e0e0 } /* Generic.Subheading */
+html[data-theme="dark"] .highlight .kc { color: #dcc6e0 } /* Keyword.Constant */
+html[data-theme="dark"] .highlight .kd { color: #dcc6e0 } /* Keyword.Declaration */
+html[data-theme="dark"] .highlight .kn { color: #dcc6e0 } /* Keyword.Namespace */
+html[data-theme="dark"] .highlight .kp { color: #dcc6e0 } /* Keyword.Pseudo */
+html[data-theme="dark"] .highlight .kr { color: #dcc6e0 } /* Keyword.Reserved */
+html[data-theme="dark"] .highlight .kt { color: #ffd900 } /* Keyword.Type */
+html[data-theme="dark"] .highlight .ld { color: #ffd900 } /* Literal.Date */
+html[data-theme="dark"] .highlight .m { color: #ffd900 } /* Literal.Number */
+html[data-theme="dark"] .highlight .s { color: #abe338 } /* Literal.String */
+html[data-theme="dark"] .highlight .na { color: #ffd900 } /* Name.Attribute */
+html[data-theme="dark"] .highlight .nb { color: #ffd900 } /* Name.Builtin */
+html[data-theme="dark"] .highlight .nc { color: #00e0e0 } /* Name.Class */
+html[data-theme="dark"] .highlight .no { color: #00e0e0 } /* Name.Constant */
+html[data-theme="dark"] .highlight .nd { color: #ffd900 } /* Name.Decorator */
+html[data-theme="dark"] .highlight .ni { color: #abe338 } /* Name.Entity */
+html[data-theme="dark"] .highlight .ne { color: #dcc6e0 } /* Name.Exception */
+html[data-theme="dark"] .highlight .nf { color: #00e0e0 } /* Name.Function */
+html[data-theme="dark"] .highlight .nl { color: #ffd900 } /* Name.Label */
+html[data-theme="dark"] .highlight .nn { color: #f8f8f2 } /* Name.Namespace */
+html[data-theme="dark"] .highlight .nx { color: #f8f8f2 } /* Name.Other */
+html[data-theme="dark"] .highlight .py { color: #00e0e0 } /* Name.Property */
+html[data-theme="dark"] .highlight .nt { color: #00e0e0 } /* Name.Tag */
+html[data-theme="dark"] .highlight .nv { color: #ffa07a } /* Name.Variable */
+html[data-theme="dark"] .highlight .ow { color: #dcc6e0 } /* Operator.Word */
+html[data-theme="dark"] .highlight .pm { color: #f8f8f2 } /* Punctuation.Marker */
+html[data-theme="dark"] .highlight .w { color: #f8f8f2 } /* Text.Whitespace */
+html[data-theme="dark"] .highlight .mb { color: #ffd900 } /* Literal.Number.Bin */
+html[data-theme="dark"] .highlight .mf { color: #ffd900 } /* Literal.Number.Float */
+html[data-theme="dark"] .highlight .mh { color: #ffd900 } /* Literal.Number.Hex */
+html[data-theme="dark"] .highlight .mi { color: #ffd900 } /* Literal.Number.Integer */
+html[data-theme="dark"] .highlight .mo { color: #ffd900 } /* Literal.Number.Oct */
+html[data-theme="dark"] .highlight .sa { color: #abe338 } /* Literal.String.Affix */
+html[data-theme="dark"] .highlight .sb { color: #abe338 } /* Literal.String.Backtick */
+html[data-theme="dark"] .highlight .sc { color: #abe338 } /* Literal.String.Char */
+html[data-theme="dark"] .highlight .dl { color: #abe338 } /* Literal.String.Delimiter */
+html[data-theme="dark"] .highlight .sd { color: #abe338 } /* Literal.String.Doc */
+html[data-theme="dark"] .highlight .s2 { color: #abe338 } /* Literal.String.Double */
+html[data-theme="dark"] .highlight .se { color: #abe338 } /* Literal.String.Escape */
+html[data-theme="dark"] .highlight .sh { color: #abe338 } /* Literal.String.Heredoc */
+html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
+html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
+html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
+html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
+html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
+html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
+html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
+html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
+html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
+html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
+html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
+html[data-theme="dark"] .highlight .il { color: #ffd900 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/_static/sbt-webpack-macros.html b/_static/sbt-webpack-macros.html
new file mode 100644
index 00000000..6cbf559f
--- /dev/null
+++ b/_static/sbt-webpack-macros.html
@@ -0,0 +1,11 @@
+<!--
+  All these macros are auto-generated and must **NOT** be edited by hand.
+  See the webpack.config.js file, to learn more about how this is generated.
+-->
+{% macro head_pre_bootstrap() %}
+  <link href="{{ pathto('_static/styles/sphinx-book-theme.css', 1) }}" rel="stylesheet">
+{% endmacro %}
+
+{% macro body_post() %}
+  <script src="{{ pathto('_static/scripts/sphinx-book-theme.js', 1) }}"></script>
+{% endmacro %}
diff --git a/_static/scripts/bootstrap.js b/_static/scripts/bootstrap.js
new file mode 100644
index 00000000..c8178deb
--- /dev/null
+++ b/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
+/*! For license information please see bootstrap.js.LICENSE.txt */
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ve{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=we.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===ti&&this.cycle()}static get Default(){return ri}static get DefaultType(){return ai}static get NAME(){return"carousel"}next(){this._slide(ze)}nextWhenVisible(){!document.hidden&&Bt(this._element)&&this.next()}prev(){this._slide(Re)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?fe.one(this._element,Ke,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void fe.one(this._element,Ke,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?ze:Re;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,Qe,(t=>this._keydown(t))),"hover"===this._config.pause&&(fe.on(this._element,Xe,(()=>this.pause())),fe.on(this._element,Ue,(()=>this._maybeEnableCycle()))),this._config.touch&&je.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of we.find(".carousel-item img",this._element))fe.on(t,Ge,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(qe)),rightCallback:()=>this._slide(this._directionToOrder(Ve)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new je(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=oi[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=we.findOne(ii,this._indicatorsElement);e.classList.remove(ei),e.removeAttribute("aria-current");const i=we.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(ei),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===ze,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const 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.collapse.collapsing").filter((t=>t!==this._element)).map((t=>Ei.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,hi).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(gi),this._element.classList.add(_i),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi,mi),this._element.style[e]="",fe.trigger(this._element,di)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,ui).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(_i),this._element.classList.remove(gi,mi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi),fe.trigger(this._element,fi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(mi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(vi);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(bi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Ei.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,pi,vi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))Ei.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(Ei);const Ai="dropdown",Ti=".bs.dropdown",Ci=".data-api",Oi="ArrowUp",xi="ArrowDown",ki=`hide${Ti}`,Li=`hidden${Ti}`,Si=`show${Ti}`,Di=`shown${Ti}`,$i=`click${Ti}${Ci}`,Ii=`keydown${Ti}${Ci}`,Ni=`keyup${Ti}${Ci}`,Pi="show",Mi='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',ji=`${Mi}.${Pi}`,Fi=".dropdown-menu",Hi=Kt()?"top-end":"top-start",Bi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",zi=Kt()?"bottom-start":"bottom-end",Ri=Kt()?"left-start":"right-start",qi=Kt()?"right-start":"left-start",Vi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Yi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Ki extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Fi)[0]||we.prev(this._element,Fi)[0]||we.findOne(Fi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Vi}static get DefaultType(){return Yi}static get NAME(){return Ai}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,Si,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Pi),this._element.classList.add(Pi),fe.trigger(this._element,Di,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,ki,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Pi),this._element.classList.remove(Pi),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,Li,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ai.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Ft(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Pi)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Ri;if(t.classList.contains("dropstart"))return qi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Bi:Hi:e?zi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===xi,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Ki.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(ji);for(const i of e){const e=Ki.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Oi,xi].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Mi)?this:we.prev(this,Mi)[0]||we.next(this,Mi)[0]||we.findOne(Mi,t.delegateTarget.parentNode),o=Ki.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Ii,Mi,Ki.dataApiKeydownHandler),fe.on(document,Ii,Fi,Ki.dataApiKeydownHandler),fe.on(document,$i,Ki.clearMenus),fe.on(document,Ni,Ki.clearMenus),fe.on(document,$i,Mi,(function(t){t.preventDefault(),Ki.getOrCreateInstance(this).toggle()})),Qt(Ki);const Qi="backdrop",Xi="show",Ui=`mousedown.bs.${Qi}`,Gi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ji={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Zi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Gi}static get DefaultType(){return Ji}static get NAME(){return Qi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Xi),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Xi),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Ui),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Ui,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const tn=".bs.focustrap",en=`focusin${tn}`,nn=`keydown.tab${tn}`,sn="backward",on={autofocus:!0,trapElement:null},rn={autofocus:"boolean",trapElement:"element"};class an extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return on}static get DefaultType(){return rn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,tn),fe.on(document,en,(t=>this._handleFocusin(t))),fe.on(document,nn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,tn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===sn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?sn:"forward")}}const ln=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",cn=".sticky-top",hn="padding-right",dn="margin-right";class un{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,hn,(e=>e+t)),this._setElementAttributes(ln,hn,(e=>e+t)),this._setElementAttributes(cn,dn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,hn),this._resetElementAttributes(ln,hn),this._resetElementAttributes(cn,dn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const fn=".bs.modal",pn=`hide${fn}`,mn=`hidePrevented${fn}`,gn=`hidden${fn}`,_n=`show${fn}`,bn=`shown${fn}`,vn=`resize${fn}`,yn=`click.dismiss${fn}`,wn=`mousedown.dismiss${fn}`,En=`keydown.dismiss${fn}`,An=`click${fn}.data-api`,Tn="modal-open",Cn="show",On="modal-static",xn={backdrop:!0,focus:!0,keyboard:!0},kn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class Ln extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new un,this._addEventListeners()}static get Default(){return xn}static get DefaultType(){return kn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,_n,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(Tn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,pn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(Cn),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,fn),fe.off(this._dialog,fn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Zi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new an({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(Cn),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,bn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,En,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,vn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,wn,(t=>{fe.one(this._element,yn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Tn),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,gn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,mn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(On)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(On),this._queueCallback((()=>{this._element.classList.remove(On),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Ln.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,An,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,_n,(t=>{t.defaultPrevented||fe.one(e,gn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&Ln.getInstance(i).hide(),Ln.getOrCreateInstance(e).toggle(this)})),Ee(Ln),Qt(Ln);const Sn=".bs.offcanvas",Dn=".data-api",$n=`load${Sn}${Dn}`,In="show",Nn="showing",Pn="hiding",Mn=".offcanvas.show",jn=`show${Sn}`,Fn=`shown${Sn}`,Hn=`hide${Sn}`,Bn=`hidePrevented${Sn}`,Wn=`hidden${Sn}`,zn=`resize${Sn}`,Rn=`click${Sn}${Dn}`,qn=`keydown.dismiss${Sn}`,Vn={backdrop:!0,keyboard:!0,scroll:!1},Yn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Kn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Vn}static get DefaultType(){return Yn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,jn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new un).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Nn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(In),this._element.classList.remove(Nn),fe.trigger(this._element,Fn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,Hn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(Pn),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(In,Pn),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new un).reset(),fe.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Zi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Bn)}:null})}_initializeFocusTrap(){return new an({trapElement:this._element})}_addEventListeners(){fe.on(this._element,qn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Bn))}))}static jQueryInterface(t){return this.each((function(){const e=Kn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Rn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Wn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Mn);i&&i!==e&&Kn.getInstance(i).hide(),Kn.getOrCreateInstance(e).toggle(this)})),fe.on(window,$n,(()=>{for(const t of we.find(Mn))Kn.getOrCreateInstance(t).show()})),fe.on(window,zn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Kn.getOrCreateInstance(t).hide()})),Ee(Kn),Qt(Kn);const Qn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Xn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Un=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Gn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Xn.has(i)||Boolean(Un.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Jn={allowList:Qn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Zn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},ts={entry:"(string|element|function|null)",selector:"(string|element)"};class es extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Jn}static get DefaultType(){return Zn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},ts)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Gn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const is=new Set(["sanitize","allowList","sanitizeFn"]),ns="fade",ss="show",os=".tooltip-inner",rs=".modal",as="hide.bs.modal",ls="hover",cs="focus",hs={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ds={allowList:Qn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},us={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class fs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(rs),as,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[cs]=!1,this._activeTrigger[ls]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ns,ss),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ns),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new es({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{[os]:this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ns)}_isShown(){return this.tip&&this.tip.classList.contains(ss)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=hs[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ls?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ls?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?cs:ls]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?cs:ls]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(rs),as,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))is.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".popover-header",ms=".popover-body",gs={...fs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},_s={...fs.DefaultType,content:"(null|string|element|function)"};class bs extends fs{static get Default(){return gs}static get DefaultType(){return _s}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{[ps]:this._getTitle(),[ms]:this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=bs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(bs);const vs=".bs.scrollspy",ys=`activate${vs}`,ws=`click${vs}`,Es=`load${vs}.data-api`,As="active",Ts="[href]",Cs=".nav-link",Os=`${Cs}, .nav-item > ${Cs}, .list-group-item`,xs={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ks={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ls extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xs}static get DefaultType(){return ks}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,ws),fe.on(this._config.target,ws,Ts,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(Ts,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(As),this._activateParents(t),fe.trigger(this._element,ys,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(As);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,Os))t.classList.add(As)}_clearActiveClass(t){t.classList.remove(As);const e=we.find(`${Ts}.${As}`,t);for(const t of e)t.classList.remove(As)}static jQueryInterface(t){return this.each((function(){const e=Ls.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,Es,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ls.getOrCreateInstance(t)})),Qt(Ls);const Ss=".bs.tab",Ds=`hide${Ss}`,$s=`hidden${Ss}`,Is=`show${Ss}`,Ns=`shown${Ss}`,Ps=`click${Ss}`,Ms=`keydown${Ss}`,js=`load${Ss}`,Fs="ArrowLeft",Hs="ArrowRight",Bs="ArrowUp",Ws="ArrowDown",zs="Home",Rs="End",qs="active",Vs="fade",Ys="show",Ks=".dropdown-toggle",Qs=`:not(${Ks})`,Xs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Us=`.nav-link${Qs}, .list-group-item${Qs}, [role="tab"]${Qs}, ${Xs}`,Gs=`.${qs}[data-bs-toggle="tab"], .${qs}[data-bs-toggle="pill"], .${qs}[data-bs-toggle="list"]`;class Js extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ms,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,Ds,{relatedTarget:t}):null;fe.trigger(t,Is,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(qs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ns,{relatedTarget:e})):t.classList.add(Ys)}),t,t.classList.contains(Vs)))}_deactivate(t,e){t&&(t.classList.remove(qs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,$s,{relatedTarget:e})):t.classList.remove(Ys)}),t,t.classList.contains(Vs)))}_keydown(t){if(![Fs,Hs,Bs,Ws,zs,Rs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([zs,Rs].includes(t.key))i=e[t.key===zs?0:e.length-1];else{const n=[Hs,Ws].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Js.getOrCreateInstance(i).show())}_getChildren(){return we.find(Us,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=we.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=we.findOne(t,i);s&&s.classList.toggle(n,e)};n(Ks,qs),n(".dropdown-menu",Ys),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(qs)}_getInnerElement(t){return t.matches(Us)?t:we.findOne(Us,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Js.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,Ps,Xs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Js.getOrCreateInstance(this).show()})),fe.on(window,js,(()=>{for(const t of we.find(Gs))Js.getOrCreateInstance(t)})),Qt(Js);const Zs=".bs.toast",to=`mouseover${Zs}`,eo=`mouseout${Zs}`,io=`focusin${Zs}`,no=`focusout${Zs}`,so=`hide${Zs}`,oo=`hidden${Zs}`,ro=`show${Zs}`,ao=`shown${Zs}`,lo="hide",co="show",ho="showing",uo={animation:"boolean",autohide:"boolean",delay:"number"},fo={animation:!0,autohide:!0,delay:5e3};class po extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return fo}static get DefaultType(){return uo}static get NAME(){return"toast"}show(){fe.trigger(this._element,ro).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(lo),qt(this._element),this._element.classList.add(co,ho),this._queueCallback((()=>{this._element.classList.remove(ho),fe.trigger(this._element,ao),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(fe.trigger(this._element,so).defaultPrevented||(this._element.classList.add(ho),this._queueCallback((()=>{this._element.classList.add(lo),this._element.classList.remove(ho,co),fe.trigger(this._element,oo)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(co),super.dispose()}isShown(){return this._element.classList.contains(co)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,to,(t=>this._onInteraction(t,!0))),fe.on(this._element,eo,(t=>this._onInteraction(t,!1))),fe.on(this._element,io,(t=>this._onInteraction(t,!0))),fe.on(this._element,no,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=po.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}function mo(t){"loading"!=document.readyState?t():document.addEventListener("DOMContentLoaded",t)}Ee(po),Qt(po),mo((function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new fs(t,{delay:{show:500,hide:100}})}))})),mo((function(){document.getElementById("pst-back-to-top").addEventListener("click",(function(){document.body.scrollTop=0,document.documentElement.scrollTop=0}))})),mo((function(){var t=document.getElementById("pst-back-to-top"),e=document.getElementsByClassName("bd-header")[0].getBoundingClientRect();window.addEventListener("scroll",(function(){this.oldScroll>this.scrollY&&this.scrollY>e.bottom?t.style.display="block":t.style.display="none",this.oldScroll=this.scrollY}))})),window.bootstrap=i})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/_static/scripts/bootstrap.js.LICENSE.txt b/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 00000000..28755c2c
--- /dev/null
+++ b/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.3.3 (https://getbootstrap.com/)
+  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/_static/scripts/bootstrap.js.map b/_static/scripts/bootstrap.js.map
new file mode 100644
index 00000000..4a3502ae
--- /dev/null
+++ b/_static/scripts/bootstrap.js.map
@@ -0,0 +1 @@
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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.3.3 (https://getbootstrap.com/)\n  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n    const instanceMap = elementMap.get(element);\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n    instanceMap.set(key, instance);\n  },\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n    return null;\n  },\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key);\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n  }\n  return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n  return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  }\n\n  // Get transition-duration of the element\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n  return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object));\n  }\n  return null;\n};\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])');\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n    if (summary === null) {\n      return false;\n    }\n  }\n  return elementIsVisible;\n};\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n  if (element instanceof ShadowRoot) {\n    return element;\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null;\n  }\n  return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n  return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement);\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n  index += shouldGetNext ? 1 : -1;\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n    return fn.apply(element, [event]);\n  };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string';\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n  return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n    callable = wrapFunction(callable);\n  }\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n  if (!fn) {\n    return;\n  }\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n    const evt = hydrateObj(new Event(event, {\n      bubbles,\n      cancelable: true\n    }), args);\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n    return evt;\n  }\n};\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value;\n        }\n      });\n    }\n  }\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n  if (value === 'false') {\n    return false;\n  }\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n  if (value === '' || value === 'null') {\n    return null;\n  }\n  if (typeof value !== 'string') {\n    return value;\n  }\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n    return attributes;\n  },\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n  static get DefaultType() {\n    return {};\n  }\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    return config;\n  }\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.3';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n    if (!element) {\n      return;\n    }\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n  static get VERSION() {\n    return VERSION;\n  }\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href');\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n  return selector ? selector.split(',').map(sel => parseSelector(sel)).join(',') : null;\n};\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n    return parents;\n  },\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n      previous = previous.previousElementSibling;\n    }\n    return [];\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n      next = next.nextElementSibling;\n    }\n    return [];\n  },\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  },\n  getSelectorFromElement(element) {\n    const selector = getSelector(element);\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null;\n    }\n    return null;\n  },\n  getElementFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.findOne(selector) : null;\n  },\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.find(selector) : [];\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n    if (isDisabled(this)) {\n      return;\n    }\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target);\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove();\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n    this._initEvents();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$c;\n  }\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n  static get NAME() {\n    return NAME$d;\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n    this._handleSwipe();\n    execute(this._config.endCallback);\n  }\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n    if (!direction) {\n      return;\n    }\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n    this._addEventListeners();\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$b;\n  }\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n  static get NAME() {\n    return NAME$c;\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n    this._clearInterval();\n  }\n  cycle() {\n    this._clearInterval();\n    this._updateInterval();\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n    this.cycle();\n  }\n  to(index) {\n    const items = this._getItems();\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n    const activeIndex = this._getItemIndex(this._getActive());\n    if (activeIndex === index) {\n      return;\n    }\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n    this._slide(order, items[index]);\n  }\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause();\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n    const direction = KEY_TO_DIRECTION[event.key];\n    if (direction) {\n      event.preventDefault();\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n    if (!element) {\n      return;\n    }\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n    const activeElement = this._getActive();\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n    if (nextElement === activeElement) {\n      return;\n    }\n    const nextElementIndex = this._getItemIndex(nextElement);\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return;\n    }\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n    this._setActiveIndicatorElement(nextElementIndex);\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n  if (slideIndex) {\n    carousel.to(slideIndex);\n    carousel._maybeEnableCycle();\n    return;\n  }\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n    carousel._maybeEnableCycle();\n    return;\n  }\n  carousel.prev();\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n    this._initializeChildren();\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$a;\n  }\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n  static get NAME() {\n    return NAME$b;\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n    let activeChildren = [];\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n    const dimension = this._getDimension();\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.style[dimension] = 0;\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n    this._queueCallback(complete, this._element, true);\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    const dimension = this._getDimension();\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger);\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n    this._element.style[dimension] = '';\n    this._queueCallback(complete, this._element, true);\n  }\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n    config.parent = getElement(config.parent);\n    return config;\n  }\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element);\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {};\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$9;\n  }\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n  static get NAME() {\n    return NAME$a;\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._createPopper();\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    this._element.focus();\n    this._element.setAttribute('aria-expanded', true);\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    this._completeHide(relatedTarget);\n  }\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    super.dispose();\n  }\n  update() {\n    this._inNavbar = this._detectNavbar();\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.setAttribute('aria-expanded', 'false');\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n  _getConfig(config) {\n    config = super._getConfig(config);\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n    return config;\n  }\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n    let referenceElement = this._element;\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n    const popperConfig = this._getPopperConfig();\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n  _getPlacement() {\n    const parentDropdown = this._parent;\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    };\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n    if (!items.length) {\n      return;\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n      context._completeHide(relatedTarget);\n    }\n  }\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n    event.preventDefault();\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n      instance._selectMenuItem(event);\n      return;\n    }\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$8;\n  }\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n  static get NAME() {\n    return NAME$9;\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._append();\n    const element = this._getElement();\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n    element.classList.add(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n    this._element.remove();\n    this._isAppended = false;\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n      this._element = backdrop;\n    }\n    return this._element;\n  }\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n    const element = this._getElement();\n    this._config.rootElement.append(element);\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$7;\n  }\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n  static get NAME() {\n    return NAME$8;\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n    const elements = SelectorEngine.focusableChildren(trapElement);\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n  hide() {\n    const width = this.getWidth();\n    this._disableOverFlow();\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n  isOverflowing() {\n    return this.getWidth() > 0;\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n    this._element.style.overflow = 'hidden';\n  }\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n      this._saveInitialAttribute(element, styleProperty);\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty);\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$6;\n  }\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n  static get NAME() {\n    return NAME$7;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._isTransitioning = true;\n    this._scrollBar.hide();\n    document.body.classList.add(CLASS_NAME_OPEN);\n    this._adjustDialog();\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = false;\n    this._isTransitioning = true;\n    this._focustrap.deactivate();\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n  dispose() {\n    EventHandler.off(window, EVENT_KEY$4);\n    EventHandler.off(this._dialog, EVENT_KEY$4);\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n  handleUpdate() {\n    this._adjustDialog();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n    this._element.style.display = 'block';\n    this._element.removeAttribute('aria-hidden');\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n          return;\n        }\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n  _hideModal() {\n    this._element.style.display = 'none';\n    this._element.setAttribute('aria-hidden', true);\n    this._element.removeAttribute('aria-modal');\n    this._element.removeAttribute('role');\n    this._isTransitioning = false;\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n      this._resetAdjustments();\n      this._scrollBar.reset();\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY;\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n    this._element.classList.add(CLASS_NAME_STATIC);\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n    this._element.focus();\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const scrollbarWidth = this._scrollBar.getWidth();\n    const isBodyOverflowing = scrollbarWidth > 0;\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](relatedTarget);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  });\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$5;\n  }\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n  static get NAME() {\n    return NAME$6;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._backdrop.show();\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._focustrap.deactivate();\n    this._element.blur();\n    this._isShown = false;\n    this._element.classList.add(CLASS_NAME_HIDING);\n    this._backdrop.hide();\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n      this._element.removeAttribute('aria-modal');\n      this._element.removeAttribute('role');\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n    this._queueCallback(completeCallback, this._element, true);\n  }\n  dispose() {\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n      this.hide();\n    };\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n    });\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  });\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  dd: [],\n  div: [],\n  dl: [],\n  dt: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n    }\n    return true;\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  }\n\n  // Getters\n  static get Default() {\n    return Default$4;\n  }\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n  static get NAME() {\n    return NAME$5;\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n  changeContent(content) {\n    this._checkContent(content);\n    this._config.content = {\n      ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n    const template = templateWrapper.children[0];\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n    return template;\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n    this._checkContent(config.content);\n  }\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n    if (!templateElement) {\n      return;\n    }\n    content = this._resolvePossibleFunction(content);\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n      return;\n    }\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n    templateElement.textContent = content;\n  }\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this]);\n  }\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n    templateElement.textContent = element.textContent;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n    super(element, config);\n\n    // Private\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null;\n\n    // Protected\n    this.tip = null;\n    this._setListeners();\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$3;\n  }\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n  static get NAME() {\n    return NAME$4;\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true;\n  }\n  disable() {\n    this._isEnabled = false;\n  }\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n    this._activeTrigger.click = !this._activeTrigger.click;\n    if (this._isShown()) {\n      this._leave();\n      return;\n    }\n    this._enter();\n  }\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n    this._disposePopper();\n    super.dispose();\n  }\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper();\n    const tip = this._getTipElement();\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n    const {\n      container\n    } = this._config;\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n      if (this._isHovered === false) {\n        this._leave();\n      }\n      this._isHovered = false;\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const tip = this._getTipElement();\n    tip.classList.remove(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n      this._element.removeAttribute('aria-describedby');\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n    return this.tip;\n  }\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml();\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null;\n    }\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n    return tip;\n  }\n  setContent(content) {\n    this._newContent = content;\n    if (this._isShown()) {\n      this._disposePopper();\n      this.show();\n    }\n  }\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n    return this._templateFactory;\n  }\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element]);\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element]);\n  }\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n          context._leave();\n        });\n      }\n    }\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n    if (!title) {\n      return;\n    }\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title');\n  }\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n    this._isHovered = true;\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n    this._isHovered = false;\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n    return config;\n  }\n  _getDelegateConfig() {\n    const config = {};\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value;\n      }\n    }\n    config.selector = false;\n    config.trigger = 'manual';\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config;\n  }\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n      this._popper = null;\n    }\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n  static get NAME() {\n    return NAME$3;\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default$1;\n  }\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n  static get NAME() {\n    return NAME$2;\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables();\n    this._maybeEnableSmoothScroll();\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n  dispose() {\n    this._observer.disconnect();\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body;\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n    return config;\n  }\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height;\n      }\n    });\n  }\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n      this._process(targetElement(entry));\n    };\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n        this._clearActiveClass(targetElement(entry));\n        continue;\n      }\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry);\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return;\n        }\n        continue;\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor);\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n    this._clearActiveClass(this._config.target);\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n    this._activateParents(target);\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst HOME_KEY = 'Home';\nconst END_KEY = 'End';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = `:not(${SELECTOR_DROPDOWN_TOGGLE})`;\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n    if (!this._parent) {\n      return;\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren());\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME$1;\n  }\n\n  // Public\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n    if (this._elemIsActive(innerElem)) {\n      return;\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem();\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n    this._deactivate(active, innerElem);\n    this._activate(innerElem, active);\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.add(CLASS_NAME_ACTIVE);\n    this._activate(SelectorEngine.getElementFromSelector(element)); // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n      this._toggleDropDown(element, true);\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n    this._deactivate(SelectorEngine.getElementFromSelector(element)); // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n      this._toggleDropDown(element, false);\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key)) {\n      return;\n    }\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault();\n    const children = this._getChildren().filter(element => !isDisabled(element));\n    let nextActiveElement;\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1];\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true);\n    }\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n    const isActive = this._elemIsActive(child);\n    const outerElem = this._getOuterElement(child);\n    child.setAttribute('aria-selected', isActive);\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n    this._setAttributeIfNotExists(child, 'role', 'tab');\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child);\n    if (!target) {\n      return;\n    }\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`);\n    }\n  }\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  Tab.getOrCreateInstance(this).show();\n});\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n    this._setListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default;\n  }\n  static get DefaultType() {\n    return DefaultType;\n  }\n  static get NAME() {\n    return NAME;\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._clearTimeout();\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n      this._maybeScheduleHide();\n    };\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n    this._element.classList.add(CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  dispose() {\n    this._clearTimeout();\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n    super.dispose();\n  }\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n    if (isInteracting) {\n      this._clearTimeout();\n      return;\n    }\n    const nextElement = event.relatedTarget;\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n    this._maybeScheduleHide();\n  }\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config](this);\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// TODO: @trallard might end up moving this to the main JS file\n// Import and setup functions to control Bootstrap's behavior.\nimport \"@popperjs/core\";\n// Import all of Bootstrap's JS\nimport * as bootstrap from \"bootstrap\";\nimport { documentReady } from \"./mixin\";\n\n/*******************************************************************************\n * Trigger tooltips\n */\n\n/**\n * Add tooltip to each element with the \"tooltip\" data-bs-toogle class\n */\nfunction TriggerTooltip() {\n  var tooltipTriggerList = [].slice.call(\n    document.querySelectorAll('[data-bs-toggle=\"tooltip\"]'),\n  );\n  tooltipTriggerList.map(function (tooltipTriggerEl) {\n    return new bootstrap.Tooltip(tooltipTriggerEl, {\n      delay: { show: 500, hide: 100 },\n    });\n  });\n}\n\n/*******************************************************************************\n * back 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index 00000000..71955b42
--- /dev/null
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+ * Copyright 2024 Fonticons, Inc.
+ */
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define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","/**\n * Compare [semver](https://semver.org/) version strings to find greater, equal or lesser.\n * This library supports the full semver specification, including comparing versions with different number of digits like `1.0.0`, `1.0`, `1`, and pre-release versions like `1.0.0-alpha`.\n * @param v1 - First version to compare\n * @param v2 - Second version to compare\n * @returns Numeric value compatible with the [Array.sort(fn) interface](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort#Parameters).\n */\nexport const compareVersions = (v1, v2) => {\n    // validate input and split into segments\n    const n1 = validateAndParse(v1);\n    const n2 = validateAndParse(v2);\n    // pop off the patch\n    const p1 = n1.pop();\n    const p2 = n2.pop();\n    // validate numbers\n    const r = compareSegments(n1, n2);\n    if (r !== 0)\n        return r;\n    // validate pre-release\n    if (p1 && p2) {\n        return compareSegments(p1.split('.'), p2.split('.'));\n    }\n    else if (p1 || p2) {\n        return p1 ? -1 : 1;\n    }\n    return 0;\n};\n/**\n * Validate [semver](https://semver.org/) version strings.\n *\n * @param version Version number to validate\n * @returns `true` if the version number is a valid semver version number, `false` otherwise.\n *\n * @example\n * ```\n * validate('1.0.0-rc.1'); // return true\n * validate('1.0-rc.1'); // return false\n * validate('foo'); // return false\n * ```\n */\nexport const validate = (version) => typeof version === 'string' && /^[v\\d]/.test(version) && semver.test(version);\n/**\n * Compare [semver](https://semver.org/) version strings using the specified operator.\n *\n * @param v1 First version to compare\n * @param v2 Second version to compare\n * @param operator Allowed arithmetic operator to use\n * @returns `true` if the comparison between the firstVersion and the secondVersion satisfies the operator, `false` otherwise.\n *\n * @example\n * ```\n * compare('10.1.8', '10.0.4', '>'); // return true\n * compare('10.0.1', '10.0.1', '='); // return true\n * compare('10.1.1', '10.2.2', '<'); // return true\n * compare('10.1.1', '10.2.2', '<='); // return true\n * compare('10.1.1', '10.2.2', '>='); // return false\n * ```\n */\nexport const compare = (v1, v2, operator) => {\n    // validate input operator\n    assertValidOperator(operator);\n    // since result of compareVersions can only be -1 or 0 or 1\n    // a simple map can be used to replace switch\n    const res = compareVersions(v1, v2);\n    return operatorResMap[operator].includes(res);\n};\n/**\n * Match [npm semver](https://docs.npmjs.com/cli/v6/using-npm/semver) version range.\n *\n * @param version Version number to match\n * @param range Range pattern for version\n * @returns `true` if the version number is within the range, `false` otherwise.\n *\n * @example\n * ```\n * satisfies('1.1.0', '^1.0.0'); // return true\n * satisfies('1.1.0', '~1.0.0'); // return false\n * ```\n */\nexport const satisfies = (version, range) => {\n    // if no range operator then \"=\"\n    const m = range.match(/^([<>=~^]+)/);\n    const op = m ? m[1] : '=';\n    // if gt/lt/eq then operator compare\n    if (op !== '^' && op !== '~')\n        return compare(version, range, op);\n    // else range of either \"~\" or \"^\" is assumed\n    const [v1, v2, v3, , vp] = validateAndParse(version);\n    const [r1, r2, r3, , rp] = validateAndParse(range);\n    const v = [v1, v2, v3];\n    const r = [r1, r2 !== null && r2 !== void 0 ? r2 : 'x', r3 !== null && r3 !== void 0 ? r3 : 'x'];\n    // validate pre-release\n    if (rp) {\n        if (!vp)\n            return false;\n        if (compareSegments(v, r) !== 0)\n            return false;\n        if (compareSegments(vp.split('.'), rp.split('.')) === -1)\n            return false;\n    }\n    // first non-zero number\n    const nonZero = r.findIndex((v) => v !== '0') + 1;\n    // pointer to where segments can be >=\n    const i = op === '~' ? 2 : nonZero > 1 ? nonZero : 1;\n    // before pointer must be equal\n    if (compareSegments(v.slice(0, i), r.slice(0, i)) !== 0)\n        return false;\n    // after pointer must be >=\n    if (compareSegments(v.slice(i), r.slice(i)) === -1)\n        return false;\n    return true;\n};\nconst semver = /^[v^~<>=]*?(\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+))?(?:-([\\da-z\\-]+(?:\\.[\\da-z\\-]+)*))?(?:\\+[\\da-z\\-]+(?:\\.[\\da-z\\-]+)*)?)?)?$/i;\nconst validateAndParse = (version) => {\n    if (typeof version !== 'string') {\n        throw new TypeError('Invalid argument expected string');\n    }\n    const match = version.match(semver);\n    if (!match) {\n        throw new Error(`Invalid argument not valid semver ('${version}' received)`);\n    }\n    match.shift();\n    return match;\n};\nconst isWildcard = (s) => s === '*' || s === 'x' || s === 'X';\nconst tryParse = (v) => {\n    const n = parseInt(v, 10);\n    return isNaN(n) ? v : n;\n};\nconst forceType = (a, b) => typeof a !== typeof b ? [String(a), String(b)] : [a, b];\nconst compareStrings = (a, b) => {\n    if (isWildcard(a) || isWildcard(b))\n        return 0;\n    const [ap, bp] = forceType(tryParse(a), tryParse(b));\n    if (ap > bp)\n        return 1;\n    if (ap < bp)\n        return -1;\n    return 0;\n};\nconst compareSegments = (a, b) => {\n    for (let i = 0; i < Math.max(a.length, b.length); i++) {\n        const r = compareStrings(a[i] || '0', b[i] || '0');\n        if (r !== 0)\n            return r;\n    }\n    return 0;\n};\nconst operatorResMap = {\n    '>': [1],\n    '>=': [0, 1],\n    '=': [0],\n    '<=': [-1, 0],\n    '<': [-1],\n};\nconst allowedOperators = Object.keys(operatorResMap);\nconst assertValidOperator = (op) => {\n    if (typeof op !== 'string') {\n        throw new TypeError(`Invalid operator type, expected string but got ${typeof op}`);\n    }\n    if (allowedOperators.indexOf(op) === -1) {\n        throw new Error(`Invalid operator, expected one of ${allowedOperators.join('|')}`);\n    }\n};\n//# sourceMappingURL=index.js.map","// Define the custom behavior of the page\nimport { documentReady } from \"./mixin\";\nimport { compare, validate } from \"compare-versions\";\n\nimport \"../styles/pydata-sphinx-theme.scss\";\n\n/*******************************************************************************\n * Theme interaction\n */\n\nvar prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\");\n\n/**\n * set the the body theme to the one specified by the user browser\n *\n * @param {event} e\n */\nfunction autoTheme(e) {\n  document.documentElement.dataset.theme = prefersDark.matches\n    ? \"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n  // TODO: remove this line after Bootstrap upgrade\n  // v5.3 has a colors mode: https://getbootstrap.com/docs/5.3/customize/color-modes/\n  document.querySelectorAll(\".dropdown-menu\").forEach((el) => {\n    if (theme === \"dark\") {\n      el.classList.add(\"dropdown-menu-dark\");\n    } else {\n      el.classList.remove(\"dropdown-menu-dark\");\n    }\n  });\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10,\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \":not(#pst-search-dialog) > form.bd-search\",\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  const input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  const searchDialog = document.getElementById(\"pst-search-dialog\");\n  const hiddenInput = searchDialog.querySelector(\"input\");\n  if (input === hiddenInput) {\n    if (searchDialog.open) {\n      searchDialog.close();\n    } else {\n      // Note: browsers should focus the input field inside the modal dialog\n      // automatically when it is opened.\n      searchDialog.showModal();\n    }\n  } else {\n    // if the input field is not the hidden one, then toggle its focus state\n\n    if (document.activeElement === input) {\n      input.blur();\n    } else {\n      input.focus();\n      input.select();\n      input.scrollIntoView({ block: \"center\" });\n    }\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if (\n        // Ignore if shift or alt are pressed\n        !event.shiftKey &&\n        !event.altKey &&\n        // On Mac use ⌘, all other OS use Ctrl\n        (useCommandKey\n          ? event.metaKey && !event.ctrlKey\n          : !event.metaKey && event.ctrlKey) &&\n        // Case-insensitive so the shortcut still works with caps lock\n        /^k$/i.test(event.key)\n      ) {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && /Escape/i.test(event.key)) {\n        toggleSearchField();\n      }\n    },\n    true,\n  );\n};\n\n/**\n * If the user is on a Mac, use command (⌘) instead of control (ctrl) key\n *\n * Note: `navigator.platform` is deprecated; however MDN still recommends using\n * it for the one specific use case of detecting whether a keyboard shortcut\n * should use control or command:\n * https://developer.mozilla.org/en-US/docs/Web/API/Navigator/platform#examples\n */\nvar useCommandKey =\n  navigator.platform.indexOf(\"Mac\") === 0 || navigator.platform === \"iPhone\";\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\n\nvar changeSearchShortcutKey = () => {\n  let shortcuts = document.querySelectorAll(\".search-button__kbd-shortcut\");\n  if (useCommandKey) {\n    shortcuts.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\"),\n    );\n  }\n};\n\nconst closeDialogOnBackdropClick = ({\n  currentTarget: dialog,\n  clientX,\n  clientY,\n}) => {\n  if (!dialog.open) {\n    return;\n  }\n\n  // Dialog.getBoundingClientRect() does not include ::backdrop. (This is the\n  // trick that allows us to determine if click was inside or outside of the\n  // dialog: click handler includes backdrop, getBoundingClientRect does not.)\n  const { left, right, top, bottom } = dialog.getBoundingClientRect();\n\n  // 0, 0 means top left\n  const clickWasOutsideDialog =\n    clientX < left || right < clientX || clientY < top || bottom < clientY;\n\n  if (clickWasOutsideDialog) {\n    dialog.close();\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // If user clicks outside the search modal dialog, then close it.\n  const searchDialog = document.getElementById(\"pst-search-dialog\");\n  // Dialog click handler includes clicks on dialog ::backdrop.\n  searchDialog.addEventListener(\"click\", closeDialogOnBackdropClick);\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * path component of URL\n */\nvar getCurrentUrlPath = () => {\n  if (DOCUMENTATION_OPTIONS.BUILDER == \"dirhtml\") {\n    return DOCUMENTATION_OPTIONS.pagename.endsWith(\"index\")\n      ? `${DOCUMENTATION_OPTIONS.pagename.substring(0, DOCUMENTATION_OPTIONS.pagename.length - \"index\".length)}`\n      : `${DOCUMENTATION_OPTIONS.pagename}/`;\n  }\n  return `${DOCUMENTATION_OPTIONS.pagename}.html`;\n};\n\n/**\n * Allow user to dismiss the warning banner about the docs version being dev / old.\n * We store the dismissal date and version, to give us flexibility about making the\n * dismissal last for longer than one browser session, if we decide to do that.\n *\n * @param {event} event the event that trigger the check\n */\nasync function DismissBannerAndStorePref(event) {\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  banner.remove();\n  const version = DOCUMENTATION_OPTIONS.VERSION;\n  const now = new Date();\n  const banner_pref = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  );\n  console.debug(\n    `[PST] Dismissing the version warning banner on ${version} starting ${now}.`,\n  );\n  banner_pref[version] = now;\n  localStorage.setItem(\"pst_banner_pref\", JSON.stringify(banner_pref));\n}\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nasync function checkPageExistsAndRedirect(event) {\n  // ensure we don't follow the initial link\n  event.preventDefault();\n  const currentFilePath = getCurrentUrlPath();\n  let tryUrl = event.currentTarget.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n  try {\n    let head = await fetch(tryUrl, { method: \"HEAD\" });\n    if (head.ok) {\n      location.href = tryUrl; // the page exists, go there\n    } else {\n      location.href = otherDocsHomepage;\n    }\n  } catch (err) {\n    // something went wrong, probably CORS restriction, fallback to other docs homepage\n    location.href = otherDocsHomepage;\n  }\n}\n\n/**\n * Load and parse the version switcher JSON file from an absolute or relative URL.\n *\n * @param {string} url The URL to load version switcher entries from.\n */\nasync function fetchVersionSwitcherJSON(url) {\n  const currentPath = getCurrentUrlPath();\n  // first check if it's a valid URL\n  try {\n    var result = new URL(url);\n  } catch (err) {\n    if (err instanceof TypeError) {\n      // Assume we got a relative path, and fix accordingly.\n      if (window.location.protocol == \"file:\") {\n        // Here instead of returning `null` we work out what the file path would be\n        // anyway (same code path as for served docs), as a convenience to folks who\n        // routinely disable CORS when they boot up their browser.\n        console.info(\n          \"[PST] looks like you're viewing this site from a local filesystem, so \" +\n            \"the version switcher won't work unless you've disabled CORS. See \" +\n            \"https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html\",\n        );\n      }\n      const cutoff = window.location.href.indexOf(currentPath);\n      // cutoff == -1 can happen e.g. on the homepage of locally served docs, where you\n      // get something like http://127.0.0.1:8000/ (no trailing `index.html`)\n      const origin =\n        cutoff == -1\n          ? window.location.href\n          : window.location.href.substring(0, cutoff);\n      result = new URL(url, origin);\n    } else {\n      // something unexpected happened\n      throw err;\n    }\n  }\n  // load and return the JSON\n  const response = await fetch(result);\n  const data = await response.json();\n  return data;\n}\n\n// Populate the version switcher from the JSON data\nfunction populateVersionSwitcher(data, versionSwitcherBtns) {\n  const currentFilePath = getCurrentUrlPath();\n  versionSwitcherBtns.forEach((btn) => {\n    // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n    btn.dataset[\"activeVersionName\"] = \"\";\n    btn.dataset[\"activeVersion\"] = \"\";\n  });\n  // in case there are multiple entries with the same version string, this helps us\n  // decide which entry's `name` to put on the button itself. Without this, it would\n  // always be the *last* version-matching entry; now it will be either the\n  // version-matching entry that is also marked as `\"preferred\": true`, or if that\n  // doesn't exist: the *first* version-matching entry.\n  data = data.map((entry) => {\n    // does this entry match the version that we're currently building/viewing?\n    entry.match =\n      entry.version == DOCUMENTATION_OPTIONS.theme_switcher_version_match;\n    entry.preferred = entry.preferred || false;\n    // if no custom name specified (e.g., \"latest\"), use version string\n    if (!(\"name\" in entry)) {\n      entry.name = entry.version;\n    }\n    return entry;\n  });\n  const hasMatchingPreferredEntry = data\n    .map((entry) => entry.preferred && entry.match)\n    .some(Boolean);\n  var foundMatch = false;\n  // create links to the corresponding page in the other docs versions\n  data.forEach((entry) => {\n    // create the node\n    const anchor = document.createElement(\"a\");\n    anchor.setAttribute(\n      \"class\",\n      \"dropdown-item list-group-item list-group-item-action py-1\",\n    );\n    anchor.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n    anchor.setAttribute(\"role\", \"option\");\n    const span = document.createElement(\"span\");\n    span.textContent = `${entry.name}`;\n    anchor.appendChild(span);\n    // Add dataset values for the version and name in case people want\n    // to apply CSS styling based on this information.\n    anchor.dataset[\"versionName\"] = entry.name;\n    anchor.dataset[\"version\"] = entry.version;\n    // replace dropdown button text with the preferred display name of the\n    // currently-viewed version, rather than using sphinx's {{ version }} variable.\n    // also highlight the dropdown entry for the currently-viewed version's entry\n    let matchesAndIsPreferred = hasMatchingPreferredEntry && entry.preferred;\n    let matchesAndIsFirst =\n      !hasMatchingPreferredEntry && !foundMatch && entry.match;\n    if (matchesAndIsPreferred || matchesAndIsFirst) {\n      anchor.classList.add(\"active\");\n      versionSwitcherBtns.forEach((btn) => {\n        btn.innerText = entry.name;\n        btn.dataset[\"activeVersionName\"] = entry.name;\n        btn.dataset[\"activeVersion\"] = entry.version;\n      });\n      foundMatch = true;\n    }\n    // There may be multiple version-switcher elements, e.g. one\n    // in a slide-over panel displayed on smaller screens.\n    document.querySelectorAll(\".version-switcher__menu\").forEach((menu) => {\n      // we need to clone the node for each menu, but onclick attributes are not\n      // preserved by `.cloneNode()` so we add onclick here after cloning.\n      let node = anchor.cloneNode(true);\n      node.onclick = checkPageExistsAndRedirect;\n      // on click, AJAX calls will check if the linked page exists before\n      // trying to redirect, and if not, will redirect to the homepage\n      // for that version of the docs.\n      menu.append(node);\n    });\n  });\n}\n\n/*******************************************************************************\n * Warning banner when viewing non-stable version of the docs.\n */\n\n/**\n * Show a warning banner when viewing a non-stable version of the docs.\n *\n * adapted 2023-06 from https://mne.tools/versionwarning.js, which was\n * originally adapted 2020-05 from https://scikit-learn.org/versionwarning.js\n *\n * @param {Array} data The version data used to populate the switcher menu.\n */\nfunction showVersionWarningBanner(data) {\n  var version = DOCUMENTATION_OPTIONS.VERSION;\n  // figure out what latest stable version is\n  var preferredEntries = data.filter((entry) => entry.preferred);\n  if (preferredEntries.length !== 1) {\n    const howMany = preferredEntries.length == 0 ? \"No\" : \"Multiple\";\n    console.log(\n      `[PST] ${howMany} versions marked \"preferred\" found in versions JSON, ignoring.`,\n    );\n    return;\n  }\n  const preferredVersion = preferredEntries[0].version;\n  const preferredURL = preferredEntries[0].url;\n  // if already on preferred version, nothing to do\n  const versionsAreComparable = validate(version) && validate(preferredVersion);\n  if (versionsAreComparable && compare(version, preferredVersion, \"=\")) {\n    console.log(\n      \"[PST]: This is the preferred version of the docs, not showing the warning banner.\",\n    );\n    return;\n  }\n  // check if banner has been dismissed recently\n  const dismiss_date_str = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  )[version];\n  if (dismiss_date_str != null) {\n    const dismiss_date = new Date(dismiss_date_str);\n    const now = new Date();\n    const milliseconds_in_a_day = 24 * 60 * 60 * 1000;\n    const days_passed = (now - dismiss_date) / milliseconds_in_a_day;\n    const timeout_in_days = 14;\n    if (days_passed < timeout_in_days) {\n      console.info(\n        `[PST] Suppressing version warning banner; was dismissed ${Math.floor(\n          days_passed,\n        )} day(s) ago`,\n      );\n      return;\n    }\n  }\n\n  // now construct the warning banner\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  const middle = document.createElement(\"div\");\n  const inner = document.createElement(\"div\");\n  const bold = document.createElement(\"strong\");\n  const button = document.createElement(\"a\");\n  const close_btn = document.createElement(\"a\");\n  // these classes exist since pydata-sphinx-theme v0.10.0\n  // the init class is used for animation\n  middle.classList = \"bd-header-announcement__content  ms-auto me-auto\";\n  inner.classList = \"sidebar-message\";\n  button.classList =\n    \"btn text-wrap font-weight-bold ms-3 my-1 align-baseline pst-button-link-to-stable-version\";\n  button.href = `${preferredURL}${getCurrentUrlPath()}`;\n  button.innerText = \"Switch to stable version\";\n  button.onclick = checkPageExistsAndRedirect;\n  close_btn.classList = \"ms-3 my-1 align-baseline\";\n  const close_x = document.createElement(\"i\");\n  close_btn.append(close_x);\n  close_x.classList = \"fa-solid fa-xmark\";\n  close_btn.onclick = DismissBannerAndStorePref;\n  // add the version-dependent text\n  inner.innerText = \"This is documentation for \";\n  const isDev =\n    version.includes(\"dev\") ||\n    version.includes(\"rc\") ||\n    version.includes(\"pre\");\n  const newerThanPreferred =\n    versionsAreComparable && compare(version, preferredVersion, \">\");\n  if (isDev || newerThanPreferred) {\n    bold.innerText = \"an unstable development version\";\n  } else if (versionsAreComparable && compare(version, preferredVersion, \"<\")) {\n    bold.innerText = `an old version (${version})`;\n  } else if (!version) {\n    bold.innerText = \"an unknown version\"; // e.g., an empty string\n  } else {\n    bold.innerText = `version ${version}`;\n  }\n  banner.appendChild(middle);\n  banner.append(close_btn);\n  middle.appendChild(inner);\n  inner.appendChild(bold);\n  inner.appendChild(document.createTextNode(\".\"));\n  inner.appendChild(button);\n  banner.classList.remove(\"d-none\");\n}\n\nasync function fetchAndUseVersions() {\n  // fetch the JSON version data (only once), then use it to populate the version\n  // switcher and maybe show the version warning bar\n  var versionSwitcherBtns = document.querySelectorAll(\n    \".version-switcher__button\",\n  );\n  const hasSwitcherMenu = versionSwitcherBtns.length > 0;\n  const hasVersionsJSON = DOCUMENTATION_OPTIONS.hasOwnProperty(\n    \"theme_switcher_json_url\",\n  );\n  const wantsWarningBanner = DOCUMENTATION_OPTIONS.show_version_warning_banner;\n\n  if (hasVersionsJSON && (hasSwitcherMenu || wantsWarningBanner)) {\n    const data = await fetchVersionSwitcherJSON(\n      DOCUMENTATION_OPTIONS.theme_switcher_json_url,\n    );\n    // TODO: remove the `if(data)` once the `return null` is fixed within fetchVersionSwitcherJSON.\n    // We don't really want the switcher and warning bar to silently not work.\n    if (data) {\n      populateVersionSwitcher(data, versionSwitcherBtns);\n      if (wantsWarningBanner) {\n        showVersionWarningBanner(data);\n      }\n    }\n  }\n}\n\n/*******************************************************************************\n * Sidebar modals (for mobile / narrow screens)\n */\nfunction setupMobileSidebarKeyboardHandlers() {\n  // These are the left and right sidebars for wider screens. We cut and paste\n  // the content from these widescreen sidebars into the mobile dialogs, when\n  // the user clicks the hamburger icon button\n  const primarySidebar = document.getElementById(\"pst-primary-sidebar\");\n  const secondarySidebar = document.getElementById(\"pst-secondary-sidebar\");\n\n  // These are the corresponding left/right <dialog> elements, which are empty\n  // until the user clicks the hamburger icon\n  const primaryDialog = document.getElementById(\"pst-primary-sidebar-modal\");\n  const secondaryDialog = document.getElementById(\n    \"pst-secondary-sidebar-modal\",\n  );\n\n  // These are the hamburger-style buttons in the header nav bar. They only\n  // appear at narrow screen width.\n  const primaryToggle = document.querySelector(\".primary-toggle\");\n  const secondaryToggle = document.querySelector(\".secondary-toggle\");\n\n  // Cut nodes and classes from `from`, paste into/onto `to`\n  const cutAndPasteNodesAndClasses = (from, to) => {\n    Array.from(from.childNodes).forEach((node) => to.appendChild(node));\n    Array.from(from.classList).forEach((cls) => {\n      from.classList.remove(cls);\n      to.classList.add(cls);\n    });\n  };\n\n  // Hook up the ways to open and close the dialog\n  [\n    [primaryToggle, primaryDialog, primarySidebar],\n    [secondaryToggle, secondaryDialog, secondarySidebar],\n  ].forEach(([toggleButton, dialog, sidebar]) => {\n    if (!toggleButton || !dialog || !sidebar) {\n      return;\n    }\n\n    // Clicking the button can only open the sidebar, not close it.\n    // Clicking the button is also the *only* way to open the sidebar.\n    toggleButton.addEventListener(\"click\", (event) => {\n      event.preventDefault();\n      event.stopPropagation();\n\n      // When we open the dialog, we cut and paste the nodes and classes from\n      // the widescreen sidebar into the dialog\n      cutAndPasteNodesAndClasses(sidebar, dialog);\n\n      dialog.showModal();\n    });\n\n    // Listen for clicks on the backdrop in order to close the dialog\n    dialog.addEventListener(\"click\", closeDialogOnBackdropClick);\n\n    // We have to manually attach the escape key because there's some code in\n    // Sphinx's Sphinx_highlight.js that prevents the default behavior of the\n    // escape key\n    dialog.addEventListener(\"keydown\", (event) => {\n      if (event.key === \"Escape\") {\n        event.preventDefault();\n        event.stopPropagation();\n        dialog.close();\n      }\n    });\n\n    // When the dialog is closed, move the nodes (and classes) back to their\n    // original place\n    dialog.addEventListener(\"close\", () => {\n      cutAndPasteNodesAndClasses(dialog, sidebar);\n    });\n  });\n}\n\n/**\n * When the page loads, or the window resizes, or descendant nodes are added or\n * removed from the main element, check all code blocks and Jupyter notebook\n * outputs, and for each one that has scrollable overflow, set tabIndex = 0.\n */\nfunction addTabStopsToScrollableElements() {\n  const updateTabStops = () => {\n    document\n      .querySelectorAll(\n        [\n          // code blocks\n          \"pre\",\n          // NBSphinx notebook output\n          \".nboutput > .output_area\",\n          // Myst-NB\n          \".cell_output > .output\",\n          // ipywidgets\n          \".jp-RenderedHTMLCommon\",\n          // [rST table nodes](https://www.docutils.org/docs/ref/doctree.html#table)\n          \".pst-scrollable-table-container\",\n        ].join(\", \"),\n      )\n      .forEach((el) => {\n        el.tabIndex =\n          el.scrollWidth > el.clientWidth || el.scrollHeight > el.clientHeight\n            ? 0\n            : -1;\n      });\n  };\n  const debouncedUpdateTabStops = debounce(updateTabStops, 300);\n\n  // On window resize\n  window.addEventListener(\"resize\", debouncedUpdateTabStops);\n\n  // The following MutationObserver is for ipywidgets, which take some time to\n  // finish loading and rendering on the page (so even after the \"load\" event is\n  // fired, they still have not finished rendering). Would be nice to replace\n  // the MutationObserver if there is a way to hook into the ipywidgets code to\n  // know when it is done.\n  const mainObserver = new MutationObserver(debouncedUpdateTabStops);\n\n  // On descendant nodes added/removed from main element\n  mainObserver.observe(document.getElementById(\"main-content\"), {\n    subtree: true,\n    childList: true,\n  });\n\n  // On page load (when this function gets called)\n  updateTabStops();\n}\nfunction debounce(callback, wait) {\n  let timeoutId = null;\n  return (...args) => {\n    clearTimeout(timeoutId);\n    timeoutId = setTimeout(() => {\n      callback(...args);\n    }, wait);\n  };\n}\n\n/*******************************************************************************\n * Announcement banner - fetch and load remote HTML\n */\nasync function setupAnnouncementBanner() {\n  const banner = document.querySelector(\".bd-header-announcement\");\n  const { pstAnnouncementUrl } = banner ? banner.dataset : null;\n\n  if (!pstAnnouncementUrl) {\n    return;\n  }\n\n  try {\n    const response = await fetch(pstAnnouncementUrl);\n    if (!response.ok) {\n      throw new Error(\n        `[PST]: HTTP response status not ok: ${response.status} ${response.statusText}`,\n      );\n    }\n    const data = await response.text();\n    if (data.length === 0) {\n      console.log(`[PST]: Empty announcement at: ${pstAnnouncementUrl}`);\n      return;\n    }\n    banner.innerHTML = `<div class=\"bd-header-announcement__content\">${data}</div>`;\n    banner.classList.remove(\"d-none\");\n  } catch (_error) {\n    console.log(`[PST]: Failed to load announcement at: ${pstAnnouncementUrl}`);\n    console.error(_error);\n  }\n}\n\n/*******************************************************************************\n * Reveal (and animate) the banners (version warning, announcement) together\n */\nasync function fetchRevealBannersTogether() {\n  // Wait until finished fetching and loading banners\n  await Promise.allSettled([fetchAndUseVersions(), setupAnnouncementBanner()]);\n\n  // The revealer element should have CSS rules that set height to 0, overflow\n  // to hidden, and an animation transition on the height (unless the user has\n  // turned off animations)\n  const revealer = document.querySelector(\".pst-async-banner-revealer\");\n  if (!revealer) {\n    return;\n  }\n\n  // Remove the d-none (display-none) class to calculate the children heights.\n  revealer.classList.remove(\"d-none\");\n\n  // Add together the heights of the element's children\n  const height = Array.from(revealer.children).reduce(\n    (height, el) => height + el.offsetHeight,\n    0,\n  );\n\n  // Use the calculated height to give the revealer a non-zero height (if\n  // animations allowed, the height change will animate)\n  revealer.style.setProperty(\"height\", `${height}px`);\n\n  // Wait for a bit more than 300ms (the transition duration), then set height\n  // to auto so the banner can resize if the 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\ No newline at end of file
diff --git a/_static/scripts/sphinx-book-theme.js b/_static/scripts/sphinx-book-theme.js
new file mode 100644
index 00000000..481ba0c1
--- /dev/null
+++ b/_static/scripts/sphinx-book-theme.js
@@ -0,0 +1,2 @@
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\ No newline at end of file
diff --git a/_static/scripts/sphinx-book-theme.js.map b/_static/scripts/sphinx-book-theme.js.map
new file mode 100644
index 00000000..2cc145ab
--- /dev/null
+++ b/_static/scripts/sphinx-book-theme.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"scripts/sphinx-book-theme.js","mappings":"mBASA,IAAIA,EAAsBC,IACG,WAAvBC,SAASC,WACXF,IACSC,SAASE,iBAClBF,SAASE,iBAAiB,mBAAoBH,GAE9CC,SAASG,YAAY,sBAAsB,WACd,YAAvBH,SAASC,YAA0BF,GACzC,GACF,EAkKFK,OAAOC,aAtCY,KACjB,IAAIC,EAAQN,SAASO,cAAc,cACrBD,EAAME,mBAGPC,UAAUC,SAAS,wBAC9BJ,EAAMK,mBACJ,WACA,iDAIJC,WAAW,EA2BbR,OAAOS,iBAzJgB,KACrB,IAAIC,EACDd,SAASe,mBAAoD,OAA/Bf,SAASe,mBACvCf,SAASgB,yBAC6B,OAArChB,SAASgB,wBACb,IAAIC,EAASjB,SAASkB,gBACjBJ,GAQHK,QAAQC,IAAI,8BACRpB,SAASqB,eACXrB,SAASqB,iBACArB,SAASsB,sBAClBtB,SAASsB,yBAXXH,QAAQC,IAAI,+BACRH,EAAOM,kBACTN,EAAOM,oBACEN,EAAOO,yBAChBP,EAAOO,0BASX,EA0IF1B,GAzHkB,KAChB,IAAI2B,EAAgB,GACpB,IAwCIC,EAAc,IAAIC,sBAxCA,CAACC,EAASC,KAE9BD,EAAQE,SAASC,IACf,GAAIA,EAAMC,eAERP,EAAcQ,KAAKF,EAAMG,aAGzB,IAAK,IAAIC,EAAK,EAAGA,EAAKV,EAAcW,OAAQD,IAC1C,GAAIV,EAAcU,KAAQJ,EAAMG,OAAQ,CACtCT,EAAcY,OAAOF,EAAI,GACzB,KACF,CAEJ,IAIEV,EAAcW,OAAS,EACzBpC,SAASO,cAAc,4BAA4BE,UAAU6B,IAAI,QAEjEtC,SACGO,cAAc,4BACdE,UAAU8B,OAAO,OACtB,GAYY,CAEZC,WAAY,qBAaVC,EAAiB,GATG,CACtB,aACA,WACA,SACA,iBACA,aACA,UACA,UAGcX,SAASK,IAEvBM,EAAeR,KAEX,IAAIE,IACJ,QAAQA,IACR,IAAIA,EAAGO,QAAQ,IAAK,OACpB,QAAQP,EAAGO,QAAQ,IAAK,OAE3B,IAEH1C,SAAS2C,iBAAiBF,EAAeG,KAAK,OAAOd,SAASK,IAC5DT,EAAYmB,QAAQV,EAAG,IAIJ,IAAIR,sBA1CO,CAACC,EAASC,KAEpCD,EAAQ,GAAGkB,mBAAmBC,EAAI,EACpC/C,SAASgD,KAAKvC,UAAU6B,IAAI,YAE5BtC,SAASgD,KAAKvC,UAAU8B,OAAO,WACjC,IAqCaM,QAAQ7C,SAASO,cAAc,4BAA4B,IAmD5ET,GA1BA,WACE,IAAImD,EAAkB,CACpB,0BACA,aACA,qBACA,sBACA,wBACA,qBACA,qBACA,cACAL,KAAK,KACP5C,SAAS2C,iBAAiBM,GAAiBnB,SAASK,IAClDA,EAAG1B,UAAU6B,IAAI,UAAU,GAE/B,G","sources":["webpack://sphinx_book_theme/./src/sphinx_book_theme/assets/scripts/index.js"],"sourcesContent":["// Import CSS variables\n// ref: https://css-tricks.com/getting-javascript-to-talk-to-css-and-sass/\nimport \"../styles/index.scss\";\n\n/**\n * A helper function to load scripts when the DOM is loaded.\n * This waits for everything to be on the page first before running, since\n * some functionality doesn't behave properly until everything is ready.\n */\nvar sbRunWhenDOMLoaded = (cb) => {\n  if (document.readyState != \"loading\") {\n    cb();\n  } else if (document.addEventListener) {\n    document.addEventListener(\"DOMContentLoaded\", cb);\n  } else {\n    document.attachEvent(\"onreadystatechange\", function () {\n      if (document.readyState == \"complete\") cb();\n    });\n  }\n};\n\n/**\n * Toggle full-screen with button\n *\n * There are some browser-specific hacks in here:\n * - Safari requires a `webkit` prefix, so this uses conditionals to check for that\n *   ref: https://developer.mozilla.org/en-US/docs/Web/API/Fullscreen_API\n */\nvar toggleFullScreen = () => {\n  var isInFullScreen =\n    (document.fullscreenElement && document.fullscreenElement !== null) ||\n    (document.webkitFullscreenElement &&\n      document.webkitFullscreenElement !== null);\n  let docElm = document.documentElement;\n  if (!isInFullScreen) {\n    console.log(\"[SBT]: Entering full screen\");\n    if (docElm.requestFullscreen) {\n      docElm.requestFullscreen();\n    } else if (docElm.webkitRequestFullscreen) {\n      docElm.webkitRequestFullscreen();\n    }\n  } else {\n    console.log(\"[SBT]: Exiting full screen\");\n    if (document.exitFullscreen) {\n      document.exitFullscreen();\n    } else if (document.webkitExitFullscreen) {\n      document.webkitExitFullscreen();\n    }\n  }\n};\n\n/**\n * Manage scrolling behavior. This is primarily two things:\n *\n * 1. Hide the Table of Contents any time sidebar content is on the screen.\n *\n * This will be triggered any time a sidebar item enters or exits the screen.\n * It adds/removes items from an array if they have entered the screen, and\n * removes them when they exit the screen. It hides the TOC if anything is\n * on-screen.\n *\n * ref: https://developer.mozilla.org/en-US/docs/Web/API/Intersection_Observer_API\n *\n * 2. Add a `scrolled` class to <body> to trigger CSS changes.\n */\nvar initTocHide = () => {\n  var onScreenItems = [];\n  let hideTocCallback = (entries, observer) => {\n    // Check whether any sidebar item is displayed\n    entries.forEach((entry) => {\n      if (entry.isIntersecting) {\n        // If an element just came on screen, add it our list\n        onScreenItems.push(entry.target);\n      } else {\n        // Otherwise, if it's in our list then remove it\n        for (let ii = 0; ii < onScreenItems.length; ii++) {\n          if (onScreenItems[ii] === entry.target) {\n            onScreenItems.splice(ii, 1);\n            break;\n          }\n        }\n      }\n    });\n\n    // Hide the TOC if any margin content is displayed on the screen\n    if (onScreenItems.length > 0) {\n      document.querySelector(\"div.bd-sidebar-secondary\").classList.add(\"hide\");\n    } else {\n      document\n        .querySelector(\"div.bd-sidebar-secondary\")\n        .classList.remove(\"hide\");\n    }\n  };\n  let manageScrolledClassOnBody = (entries, observer) => {\n    // The pixel is at the top, so if we're < 0 that it means we've scrolled\n    if (entries[0].boundingClientRect.y < 0) {\n      document.body.classList.add(\"scrolled\");\n    } else {\n      document.body.classList.remove(\"scrolled\");\n    }\n  };\n\n  // Set up the intersection observer to watch all margin content\n  let options = {\n    // Trigger callback when the top of a margin item is 1/3 up the screen\n    rootMargin: \"0px 0px -33% 0px\",\n  };\n  let tocObserver = new IntersectionObserver(hideTocCallback, options);\n  // TODO: deprecate popout after v0.5.0\n  const selectorClasses = [\n    \"marginnote\",\n    \"sidenote\",\n    \"margin\",\n    \"margin-caption\",\n    \"full-width\",\n    \"sidebar\",\n    \"popout\",\n  ];\n  let marginSelector = [];\n  selectorClasses.forEach((ii) => {\n    // Use three permutations of each class name because `tag_` and `_` used to be supported\n    marginSelector.push(\n      ...[\n        `.${ii}`,\n        `.tag_${ii}`,\n        `.${ii.replace(\"-\", \"_\")}`,\n        `.tag_${ii.replace(\"-\", \"_\")}`,\n      ],\n    );\n  });\n  document.querySelectorAll(marginSelector.join(\", \")).forEach((ii) => {\n    tocObserver.observe(ii);\n  });\n\n  // Set up the observer to check if we've scrolled from top of page\n  let scrollObserver = new IntersectionObserver(manageScrolledClassOnBody);\n  scrollObserver.observe(document.querySelector(\".sbt-scroll-pixel-helper\"));\n};\n\n/**\n * Activate Thebe with a custom button click.\n */\nvar initThebeSBT = () => {\n  var title = document.querySelector(\"section h1\");\n  var sibling = title.nextElementSibling;\n  // If the next element after the title isn't a thebe button, add one now.\n  // That way it is initiatlized when thebe is first-clicked and isn't re-added after.\n  if (!sibling.classList.contains(\"thebe-launch-button\")) {\n    title.insertAdjacentHTML(\n      \"afterend\",\n      \"<button class='thebe-launch-button'></button>\",\n    );\n  }\n  // This function is provided by sphinx-thebe\n  initThebe();\n};\n\n/**\n * Add no print class to certain DOM elements\n */\n\nfunction addNoPrint() {\n  var noPrintSelector = [\n    \".bd-header-announcement\",\n    \".bd-header\",\n    \".bd-header-article\",\n    \".bd-sidebar-primary\",\n    \".bd-sidebar-secondary\",\n    \".bd-footer-article\",\n    \".bd-footer-content\",\n    \".bd-footer\",\n  ].join(\",\");\n  document.querySelectorAll(noPrintSelector).forEach((ii) => {\n    ii.classList.add(\"noprint\");\n  });\n}\n\n/**\n * Set up callback functions for UI click actions\n */\nwindow.initThebeSBT = initThebeSBT;\nwindow.toggleFullScreen = toggleFullScreen;\n\n/**\n * Set up functions to load when the DOM is ready\n */\nsbRunWhenDOMLoaded(initTocHide);\nsbRunWhenDOMLoaded(addNoPrint);\n"],"names":["sbRunWhenDOMLoaded","cb","document","readyState","addEventListener","attachEvent","window","initThebeSBT","title","querySelector","nextElementSibling","classList","contains","insertAdjacentHTML","initThebe","toggleFullScreen","isInFullScreen","fullscreenElement","webkitFullscreenElement","docElm","documentElement","console","log","exitFullscreen","webkitExitFullscreen","requestFullscreen","webkitRequestFullscreen","onScreenItems","tocObserver","IntersectionObserver","entries","observer","forEach","entry","isIntersecting","push","target","ii","length","splice","add","remove","rootMargin","marginSelector","replace","querySelectorAll","join","observe","boundingClientRect","y","body","noPrintSelector"],"sourceRoot":""}
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 00000000..7918c3fa
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,574 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 00000000..8a96c69a
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/_static/styles/pydata-sphinx-theme.css b/_static/styles/pydata-sphinx-theme.css
new file mode 100644
index 00000000..d26dd09d
--- /dev/null
+++ b/_static/styles/pydata-sphinx-theme.css
@@ -0,0 +1,32 @@
+@charset "UTF-8";
+/*!
+ * Bootstrap  v5.3.3 (https://getbootstrap.com/)
+ * Copyright 2011-2024 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */:root,[data-bs-theme=light]{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-primary-text-emphasis:#052c65;--bs-secondary-text-emphasis:#2b2f32;--bs-success-text-emphasis:#0a3622;--bs-info-text-emphasis:#055160;--bs-warning-text-emphasis:#664d03;--bs-danger-text-emphasis:#58151c;--bs-light-text-emphasis:#495057;--bs-dark-text-emphasis:#495057;--bs-primary-bg-subtle:#cfe2ff;--bs-secondary-bg-subtle:#e2e3e5;--bs-success-bg-subtle:#d1e7dd;--bs-info-bg-subtle:#cff4fc;--bs-warning-bg-subtle:#fff3cd;--bs-danger-bg-subtle:#f8d7da;--bs-light-bg-subtle:#fcfcfd;--bs-dark-bg-subtle:#ced4da;--bs-primary-border-subtle:#9ec5fe;--bs-secondary-border-subtle:#c4c8cb;--bs-success-border-subtle:#a3cfbb;--bs-info-border-subtle:#9eeaf9;--bs-warning-border-subtle:#ffe69c;--bs-danger-border-subtle:#f1aeb5;--bs-light-border-subtle:#e9ecef;--bs-dark-border-subtle:#adb5bd;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-font-sans-serif:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--bs-font-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier 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0.5rem 1rem rgba(0,0,0,.15);--bs-box-shadow-sm:0 0.125rem 0.25rem rgba(0,0,0,.075);--bs-box-shadow-lg:0 1rem 3rem rgba(0,0,0,.175);--bs-box-shadow-inset:inset 0 1px 2px 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var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 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.page-link{border-bottom-right-radius:var(--bs-pagination-border-radius);border-top-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);border-radius:var(--bs-badge-border-radius);color:var(--bs-badge-color);display:inline-block;font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;padding:var(--bs-badge-padding-y) 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";counter-increment:section}.list-group-item-action{color:var(--bs-list-group-action-color);text-align:inherit;width:100%}.list-group-item-action:focus,.list-group-item-action:hover{background-color:var(--bs-list-group-action-hover-bg);color:var(--bs-list-group-action-hover-color);text-decoration:none;z-index:1}.list-group-item-action:active{background-color:var(--bs-list-group-action-active-bg);color:var(--bs-list-group-action-active-color)}.list-group-item{background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);color:var(--bs-list-group-color);display:block;padding:var(--bs-list-group-item-padding-y) 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--bs-cyan: #0dcaf0;\n  --bs-black: #000;\n  --bs-white: #fff;\n  --bs-gray: #6c757d;\n  --bs-gray-dark: #343a40;\n  --bs-gray-100: #f8f9fa;\n  --bs-gray-200: #e9ecef;\n  --bs-gray-300: #dee2e6;\n  --bs-gray-400: #ced4da;\n  --bs-gray-500: #adb5bd;\n  --bs-gray-600: #6c757d;\n  --bs-gray-700: #495057;\n  --bs-gray-800: #343a40;\n  --bs-gray-900: #212529;\n  --bs-primary: #0d6efd;\n  --bs-secondary: #6c757d;\n  --bs-success: #198754;\n  --bs-info: #0dcaf0;\n  --bs-warning: #ffc107;\n  --bs-danger: #dc3545;\n  --bs-light: #f8f9fa;\n  --bs-dark: #212529;\n  --bs-primary-rgb: 13, 110, 253;\n  --bs-secondary-rgb: 108, 117, 125;\n  --bs-success-rgb: 25, 135, 84;\n  --bs-info-rgb: 13, 202, 240;\n  --bs-warning-rgb: 255, 193, 7;\n  --bs-danger-rgb: 220, 53, 69;\n  --bs-light-rgb: 248, 249, 250;\n  --bs-dark-rgb: 33, 37, 41;\n  --bs-primary-text-emphasis: #052c65;\n  --bs-secondary-text-emphasis: #2b2f32;\n  --bs-success-text-emphasis: #0a3622;\n  --bs-info-text-emphasis: #055160;\n  --bs-warning-text-emphasis: #664d03;\n  --bs-danger-text-emphasis: #58151c;\n  --bs-light-text-emphasis: #495057;\n  --bs-dark-text-emphasis: #495057;\n  --bs-primary-bg-subtle: #cfe2ff;\n  --bs-secondary-bg-subtle: #e2e3e5;\n  --bs-success-bg-subtle: #d1e7dd;\n  --bs-info-bg-subtle: #cff4fc;\n  --bs-warning-bg-subtle: #fff3cd;\n  --bs-danger-bg-subtle: #f8d7da;\n  --bs-light-bg-subtle: #fcfcfd;\n  --bs-dark-bg-subtle: #ced4da;\n  --bs-primary-border-subtle: #9ec5fe;\n  --bs-secondary-border-subtle: #c4c8cb;\n  --bs-success-border-subtle: #a3cfbb;\n  --bs-info-border-subtle: #9eeaf9;\n  --bs-warning-border-subtle: #ffe69c;\n  --bs-danger-border-subtle: #f1aeb5;\n  --bs-light-border-subtle: #e9ecef;\n  --bs-dark-border-subtle: #adb5bd;\n  --bs-white-rgb: 255, 255, 255;\n  --bs-black-rgb: 0, 0, 0;\n  --bs-font-sans-serif: system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\";\n  --bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace;\n  --bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));\n  --bs-body-font-family: var(--bs-font-sans-serif);\n  --bs-body-font-size: 1rem;\n  --bs-body-font-weight: 400;\n  --bs-body-line-height: 1.5;\n  --bs-body-color: #212529;\n  --bs-body-color-rgb: 33, 37, 41;\n  --bs-body-bg: #fff;\n  --bs-body-bg-rgb: 255, 255, 255;\n  --bs-emphasis-color: #000;\n  --bs-emphasis-color-rgb: 0, 0, 0;\n  --bs-secondary-color: rgba(33, 37, 41, 0.75);\n  --bs-secondary-color-rgb: 33, 37, 41;\n  --bs-secondary-bg: #e9ecef;\n  --bs-secondary-bg-rgb: 233, 236, 239;\n  --bs-tertiary-color: rgba(33, 37, 41, 0.5);\n  --bs-tertiary-color-rgb: 33, 37, 41;\n  --bs-tertiary-bg: #f8f9fa;\n  --bs-tertiary-bg-rgb: 248, 249, 250;\n  --bs-heading-color: inherit;\n  --bs-link-color: #0d6efd;\n  --bs-link-color-rgb: 13, 110, 253;\n  --bs-link-decoration: underline;\n  --bs-link-hover-color: #0a58ca;\n  --bs-link-hover-color-rgb: 10, 88, 202;\n  --bs-code-color: #d63384;\n  --bs-highlight-color: #212529;\n  --bs-highlight-bg: #fff3cd;\n  --bs-border-width: 1px;\n  --bs-border-style: solid;\n  --bs-border-color: #dee2e6;\n  --bs-border-color-translucent: rgba(0, 0, 0, 0.175);\n  --bs-border-radius: 0.375rem;\n  --bs-border-radius-sm: 0.25rem;\n  --bs-border-radius-lg: 0.5rem;\n  --bs-border-radius-xl: 1rem;\n  --bs-border-radius-xxl: 2rem;\n  --bs-border-radius-2xl: var(--bs-border-radius-xxl);\n  --bs-border-radius-pill: 50rem;\n  --bs-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);\n  --bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);\n  --bs-box-shadow-lg: 0 1rem 3rem rgba(0, 0, 0, 0.175);\n  --bs-box-shadow-inset: inset 0 1px 2px rgba(0, 0, 0, 0.075);\n  --bs-focus-ring-width: 0.1875rem;\n  --bs-focus-ring-opacity: 1;\n  --bs-focus-ring-color: var(--pst-color-accent);\n  --bs-form-valid-color: #198754;\n  --bs-form-valid-border-color: #198754;\n  --bs-form-invalid-color: #dc3545;\n  --bs-form-invalid-border-color: #dc3545;\n}\n\n[data-bs-theme=dark] {\n  color-scheme: dark;\n  --bs-body-color: #dee2e6;\n  --bs-body-color-rgb: 222, 226, 230;\n  --bs-body-bg: #212529;\n  --bs-body-bg-rgb: 33, 37, 41;\n  --bs-emphasis-color: #fff;\n  --bs-emphasis-color-rgb: 255, 255, 255;\n  --bs-secondary-color: rgba(222, 226, 230, 0.75);\n  --bs-secondary-color-rgb: 222, 226, 230;\n  --bs-secondary-bg: #343a40;\n  --bs-secondary-bg-rgb: 52, 58, 64;\n  --bs-tertiary-color: rgba(222, 226, 230, 0.5);\n  --bs-tertiary-color-rgb: 222, 226, 230;\n  --bs-tertiary-bg: #2b3035;\n  --bs-tertiary-bg-rgb: 43, 48, 53;\n  --bs-primary-text-emphasis: #6ea8fe;\n  --bs-secondary-text-emphasis: #a7acb1;\n  --bs-success-text-emphasis: #75b798;\n  --bs-info-text-emphasis: #6edff6;\n  --bs-warning-text-emphasis: #ffda6a;\n  --bs-danger-text-emphasis: #ea868f;\n  --bs-light-text-emphasis: #f8f9fa;\n  --bs-dark-text-emphasis: #dee2e6;\n  --bs-primary-bg-subtle: #031633;\n  --bs-secondary-bg-subtle: #161719;\n  --bs-success-bg-subtle: #051b11;\n  --bs-info-bg-subtle: #032830;\n  --bs-warning-bg-subtle: #332701;\n  --bs-danger-bg-subtle: #2c0b0e;\n  --bs-light-bg-subtle: #343a40;\n  --bs-dark-bg-subtle: #1a1d20;\n  --bs-primary-border-subtle: #084298;\n  --bs-secondary-border-subtle: #41464b;\n  --bs-success-border-subtle: #0f5132;\n  --bs-info-border-subtle: #087990;\n  --bs-warning-border-subtle: #997404;\n  --bs-danger-border-subtle: #842029;\n  --bs-light-border-subtle: #495057;\n  --bs-dark-border-subtle: #343a40;\n  --bs-heading-color: inherit;\n  --bs-link-color: #6ea8fe;\n  --bs-link-hover-color: #8bb9fe;\n  --bs-link-color-rgb: 110, 168, 254;\n  --bs-link-hover-color-rgb: 139, 185, 254;\n  --bs-code-color: #e685b5;\n  --bs-highlight-color: #dee2e6;\n  --bs-highlight-bg: #664d03;\n  --bs-border-color: #495057;\n  --bs-border-color-translucent: rgba(255, 255, 255, 0.15);\n  --bs-form-valid-color: #75b798;\n  --bs-form-valid-border-color: #75b798;\n  --bs-form-invalid-color: #ea868f;\n  --bs-form-invalid-border-color: #ea868f;\n}\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n@media (prefers-reduced-motion: no-preference) {\n  :root {\n    scroll-behavior: smooth;\n  }\n}\n\nbody {\n  margin: 0;\n  font-family: var(--bs-body-font-family);\n  font-size: var(--bs-body-font-size);\n  font-weight: var(--bs-body-font-weight);\n  line-height: var(--bs-body-line-height);\n  color: var(--bs-body-color);\n  text-align: var(--bs-body-text-align);\n  background-color: var(--bs-body-bg);\n  -webkit-text-size-adjust: 100%;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n\nhr {\n  margin: 1rem 0;\n  color: inherit;\n  border: 0;\n  border-top: var(--bs-border-width) solid;\n  opacity: 0.25;\n}\n\nh6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {\n  margin-top: 0;\n  margin-bottom: 0.5rem;\n  font-weight: 500;\n  line-height: 1.2;\n  color: var(--bs-heading-color);\n}\n\nh1, .h1 {\n  font-size: calc(1.375rem + 1.5vw);\n}\n@media (min-width: 1200px) {\n  h1, .h1 {\n    font-size: 2.5rem;\n  }\n}\n\nh2, .h2 {\n  font-size: calc(1.325rem + 0.9vw);\n}\n@media (min-width: 1200px) {\n  h2, .h2 {\n    font-size: 2rem;\n  }\n}\n\nh3, .h3 {\n  font-size: calc(1.3rem + 0.6vw);\n}\n@media (min-width: 1200px) {\n  h3, .h3 {\n    font-size: 1.75rem;\n  }\n}\n\nh4, .h4 {\n  font-size: calc(1.275rem + 0.3vw);\n}\n@media (min-width: 1200px) {\n  h4, .h4 {\n    font-size: 1.5rem;\n  }\n}\n\nh5, .h5 {\n  font-size: 1.25rem;\n}\n\nh6, .h6 {\n  font-size: 1rem;\n}\n\np {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nabbr[title] {\n  text-decoration: underline dotted;\n  cursor: help;\n  text-decoration-skip-ink: none;\n}\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: 700;\n}\n\ndd {\n  margin-bottom: 0.5rem;\n  margin-left: 0;\n}\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\nb,\nstrong {\n  font-weight: bolder;\n}\n\nsmall, .small {\n  font-size: 0.875em;\n}\n\nmark, .mark {\n  padding: 0.1875em;\n  color: var(--bs-highlight-color);\n  background-color: var(--bs-highlight-bg);\n}\n\nsub,\nsup {\n  position: relative;\n  font-size: 0.75em;\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\nsup {\n  top: -0.5em;\n}\n\na {\n  color: rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 1));\n  text-decoration: underline;\n}\na:hover {\n  --bs-link-color-rgb: var(--bs-link-hover-color-rgb);\n}\n\na:not([href]):not([class]), a:not([href]):not([class]):hover {\n  color: inherit;\n  text-decoration: none;\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--bs-font-monospace);\n  font-size: 1em;\n}\n\npre {\n  display: block;\n  margin-top: 0;\n  margin-bottom: 1rem;\n  overflow: auto;\n  font-size: 0.875em;\n}\npre code {\n  font-size: inherit;\n  color: inherit;\n  word-break: normal;\n}\n\ncode {\n  font-size: 0.875em;\n  color: var(--bs-code-color);\n  word-wrap: break-word;\n}\na > code {\n  color: inherit;\n}\n\nkbd {\n  padding: 0.1875rem 0.375rem;\n  font-size: 0.875em;\n  color: var(--bs-body-bg);\n  background-color: var(--bs-body-color);\n  border-radius: 0.25rem;\n}\nkbd kbd {\n  padding: 0;\n  font-size: 1em;\n}\n\nfigure {\n  margin: 0 0 1rem;\n}\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  color: var(--bs-secondary-color);\n  text-align: left;\n}\n\nth {\n  text-align: inherit;\n  text-align: -webkit-match-parent;\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\nlabel {\n  display: inline-block;\n}\n\nbutton {\n  border-radius: 0;\n}\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0;\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n[role=button] {\n  cursor: pointer;\n}\n\nselect {\n  word-wrap: normal;\n}\nselect:disabled {\n  opacity: 1;\n}\n\n[list]:not([type=date]):not([type=datetime-local]):not([type=month]):not([type=week]):not([type=time])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\nbutton,\n[type=button],\n[type=reset],\n[type=submit] {\n  -webkit-appearance: button;\n}\nbutton:not(:disabled),\n[type=button]:not(:disabled),\n[type=reset]:not(:disabled),\n[type=submit]:not(:disabled) {\n  cursor: pointer;\n}\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\ntextarea {\n  resize: vertical;\n}\n\nfieldset {\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\n\nlegend {\n  float: left;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 0.5rem;\n  font-size: calc(1.275rem + 0.3vw);\n  line-height: inherit;\n}\n@media (min-width: 1200px) {\n  legend {\n    font-size: 1.5rem;\n  }\n}\nlegend + * {\n  clear: left;\n}\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n[type=search] {\n  -webkit-appearance: textfield;\n  outline-offset: -2px;\n}\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n::file-selector-button {\n  font: inherit;\n  -webkit-appearance: button;\n}\n\noutput {\n  display: inline-block;\n}\n\niframe {\n  border: 0;\n}\n\nsummary {\n  display: list-item;\n  cursor: pointer;\n}\n\nprogress {\n  vertical-align: baseline;\n}\n\n[hidden] {\n  display: none !important;\n}\n\n.lead {\n  font-size: 1.25rem;\n  font-weight: 300;\n}\n\n.display-1 {\n  font-size: calc(1.625rem + 4.5vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-1 {\n    font-size: 5rem;\n  }\n}\n\n.display-2 {\n  font-size: calc(1.575rem + 3.9vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-2 {\n    font-size: 4.5rem;\n  }\n}\n\n.display-3 {\n  font-size: calc(1.525rem + 3.3vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-3 {\n    font-size: 4rem;\n  }\n}\n\n.display-4 {\n  font-size: calc(1.475rem + 2.7vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-4 {\n    font-size: 3.5rem;\n  }\n}\n\n.display-5 {\n  font-size: calc(1.425rem + 2.1vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-5 {\n    font-size: 3rem;\n  }\n}\n\n.display-6 {\n  font-size: calc(1.375rem + 1.5vw);\n  font-weight: 300;\n  line-height: 1.2;\n}\n@media (min-width: 1200px) {\n  .display-6 {\n    font-size: 2.5rem;\n  }\n}\n\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n}\n\n.list-inline-item {\n  display: inline-block;\n}\n.list-inline-item:not(:last-child) {\n  margin-right: 0.5rem;\n}\n\n.initialism {\n  font-size: 0.875em;\n  text-transform: uppercase;\n}\n\n.blockquote {\n  margin-bottom: 1rem;\n  font-size: 1.25rem;\n}\n.blockquote > :last-child {\n  margin-bottom: 0;\n}\n\n.blockquote-footer {\n  margin-top: -1rem;\n  margin-bottom: 1rem;\n  font-size: 0.875em;\n  color: #6c757d;\n}\n.blockquote-footer::before {\n  content: \"— \";\n}\n\n.img-fluid {\n  max-width: 100%;\n  height: auto;\n}\n\n.img-thumbnail {\n  padding: 0.25rem;\n  background-color: var(--bs-body-bg);\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n  max-width: 100%;\n  height: auto;\n}\n\n.figure {\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: 0.5rem;\n  line-height: 1;\n}\n\n.figure-caption {\n  font-size: 0.875em;\n  color: var(--bs-secondary-color);\n}\n\n.container,\n.container-fluid,\n.container-xl,\n.container-lg,\n.container-md,\n.container-sm {\n  --bs-gutter-x: 1.5rem;\n  --bs-gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--bs-gutter-x) * 0.5);\n  padding-left: calc(var(--bs-gutter-x) * 0.5);\n  margin-right: auto;\n  margin-left: auto;\n}\n\n@media (min-width: 540px) {\n  .container-sm, .container {\n    max-width: 540px;\n  }\n}\n@media (min-width: 720px) {\n  .container-md, .container-sm, .container {\n    max-width: 720px;\n  }\n}\n@media (min-width: 960px) {\n  .container-lg, .container-md, .container-sm, .container {\n    max-width: 960px;\n  }\n}\n@media (min-width: 1200px) {\n  .container-xl, .container-lg, .container-md, .container-sm, .container {\n    max-width: 1400px;\n  }\n}\n:root {\n  --bs-breakpoint-xs: 0;\n  --bs-breakpoint-sm: 540px;\n  --bs-breakpoint-md: 720px;\n  --bs-breakpoint-lg: 960px;\n  --bs-breakpoint-xl: 1200px;\n}\n\n.row {\n  --bs-gutter-x: 1.5rem;\n  --bs-gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  margin-top: calc(-1 * var(--bs-gutter-y));\n  margin-right: calc(-0.5 * var(--bs-gutter-x));\n  margin-left: calc(-0.5 * var(--bs-gutter-x));\n}\n.row > * {\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%;\n  padding-right: calc(var(--bs-gutter-x) * 0.5);\n  padding-left: calc(var(--bs-gutter-x) * 0.5);\n  margin-top: var(--bs-gutter-y);\n}\n\n.col {\n  flex: 1 0 0%;\n}\n\n.row-cols-auto > * {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n.row-cols-1 > * {\n  flex: 0 0 auto;\n  width: 100%;\n}\n\n.row-cols-2 > * {\n  flex: 0 0 auto;\n  width: 50%;\n}\n\n.row-cols-3 > * {\n  flex: 0 0 auto;\n  width: 33.33333333%;\n}\n\n.row-cols-4 > * {\n  flex: 0 0 auto;\n  width: 25%;\n}\n\n.row-cols-5 > * {\n  flex: 0 0 auto;\n  width: 20%;\n}\n\n.row-cols-6 > * {\n  flex: 0 0 auto;\n  width: 16.66666667%;\n}\n\n.col-auto {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n.col-1 {\n  flex: 0 0 auto;\n  width: 8.33333333%;\n}\n\n.col-2 {\n  flex: 0 0 auto;\n  width: 16.66666667%;\n}\n\n.col-3 {\n  flex: 0 0 auto;\n  width: 25%;\n}\n\n.col-4 {\n  flex: 0 0 auto;\n  width: 33.33333333%;\n}\n\n.col-5 {\n  flex: 0 0 auto;\n  width: 41.66666667%;\n}\n\n.col-6 {\n  flex: 0 0 auto;\n  width: 50%;\n}\n\n.col-7 {\n  flex: 0 0 auto;\n  width: 58.33333333%;\n}\n\n.col-8 {\n  flex: 0 0 auto;\n  width: 66.66666667%;\n}\n\n.col-9 {\n  flex: 0 0 auto;\n  width: 75%;\n}\n\n.col-10 {\n  flex: 0 0 auto;\n  width: 83.33333333%;\n}\n\n.col-11 {\n  flex: 0 0 auto;\n  width: 91.66666667%;\n}\n\n.col-12 {\n  flex: 0 0 auto;\n  width: 100%;\n}\n\n.offset-1 {\n  margin-left: 8.33333333%;\n}\n\n.offset-2 {\n  margin-left: 16.66666667%;\n}\n\n.offset-3 {\n  margin-left: 25%;\n}\n\n.offset-4 {\n  margin-left: 33.33333333%;\n}\n\n.offset-5 {\n  margin-left: 41.66666667%;\n}\n\n.offset-6 {\n  margin-left: 50%;\n}\n\n.offset-7 {\n  margin-left: 58.33333333%;\n}\n\n.offset-8 {\n  margin-left: 66.66666667%;\n}\n\n.offset-9 {\n  margin-left: 75%;\n}\n\n.offset-10 {\n  margin-left: 83.33333333%;\n}\n\n.offset-11 {\n  margin-left: 91.66666667%;\n}\n\n.g-0,\n.gx-0 {\n  --bs-gutter-x: 0;\n}\n\n.g-0,\n.gy-0 {\n  --bs-gutter-y: 0;\n}\n\n.g-1,\n.gx-1 {\n  --bs-gutter-x: 0.25rem;\n}\n\n.g-1,\n.gy-1 {\n  --bs-gutter-y: 0.25rem;\n}\n\n.g-2,\n.gx-2 {\n  --bs-gutter-x: 0.5rem;\n}\n\n.g-2,\n.gy-2 {\n  --bs-gutter-y: 0.5rem;\n}\n\n.g-3,\n.gx-3 {\n  --bs-gutter-x: 1rem;\n}\n\n.g-3,\n.gy-3 {\n  --bs-gutter-y: 1rem;\n}\n\n.g-4,\n.gx-4 {\n  --bs-gutter-x: 1.5rem;\n}\n\n.g-4,\n.gy-4 {\n  --bs-gutter-y: 1.5rem;\n}\n\n.g-5,\n.gx-5 {\n  --bs-gutter-x: 3rem;\n}\n\n.g-5,\n.gy-5 {\n  --bs-gutter-y: 3rem;\n}\n\n@media (min-width: 540px) {\n  .col-sm {\n    flex: 1 0 0%;\n  }\n  .row-cols-sm-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-sm-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-sm-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-sm-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-sm-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-sm-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-sm-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-sm-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-sm-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-sm-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-sm-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-sm-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-sm-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-sm-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-sm-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-sm-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-sm-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-sm-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-sm-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-sm-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-sm-0 {\n    margin-left: 0;\n  }\n  .offset-sm-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-sm-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-sm-3 {\n    margin-left: 25%;\n  }\n  .offset-sm-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-sm-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-sm-6 {\n    margin-left: 50%;\n  }\n  .offset-sm-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-sm-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-sm-9 {\n    margin-left: 75%;\n  }\n  .offset-sm-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-sm-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-sm-0,\n  .gx-sm-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-sm-0,\n  .gy-sm-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-sm-1,\n  .gx-sm-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-sm-1,\n  .gy-sm-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-sm-2,\n  .gx-sm-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-sm-2,\n  .gy-sm-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-sm-3,\n  .gx-sm-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-sm-3,\n  .gy-sm-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-sm-4,\n  .gx-sm-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-sm-4,\n  .gy-sm-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-sm-5,\n  .gx-sm-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-sm-5,\n  .gy-sm-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 720px) {\n  .col-md {\n    flex: 1 0 0%;\n  }\n  .row-cols-md-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-md-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-md-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-md-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-md-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-md-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-md-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-md-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-md-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-md-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-md-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-md-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-md-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-md-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-md-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-md-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-md-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-md-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-md-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-md-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-md-0 {\n    margin-left: 0;\n  }\n  .offset-md-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-md-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-md-3 {\n    margin-left: 25%;\n  }\n  .offset-md-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-md-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-md-6 {\n    margin-left: 50%;\n  }\n  .offset-md-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-md-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-md-9 {\n    margin-left: 75%;\n  }\n  .offset-md-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-md-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-md-0,\n  .gx-md-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-md-0,\n  .gy-md-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-md-1,\n  .gx-md-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-md-1,\n  .gy-md-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-md-2,\n  .gx-md-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-md-2,\n  .gy-md-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-md-3,\n  .gx-md-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-md-3,\n  .gy-md-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-md-4,\n  .gx-md-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-md-4,\n  .gy-md-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-md-5,\n  .gx-md-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-md-5,\n  .gy-md-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 960px) {\n  .col-lg {\n    flex: 1 0 0%;\n  }\n  .row-cols-lg-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-lg-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-lg-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-lg-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-lg-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-lg-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-lg-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-lg-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-lg-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-lg-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-lg-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-lg-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-lg-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-lg-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-lg-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-lg-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-lg-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-lg-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-lg-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-lg-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-lg-0 {\n    margin-left: 0;\n  }\n  .offset-lg-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-lg-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-lg-3 {\n    margin-left: 25%;\n  }\n  .offset-lg-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-lg-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-lg-6 {\n    margin-left: 50%;\n  }\n  .offset-lg-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-lg-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-lg-9 {\n    margin-left: 75%;\n  }\n  .offset-lg-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-lg-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-lg-0,\n  .gx-lg-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-lg-0,\n  .gy-lg-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-lg-1,\n  .gx-lg-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-lg-1,\n  .gy-lg-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-lg-2,\n  .gx-lg-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-lg-2,\n  .gy-lg-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-lg-3,\n  .gx-lg-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-lg-3,\n  .gy-lg-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-lg-4,\n  .gx-lg-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-lg-4,\n  .gy-lg-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-lg-5,\n  .gx-lg-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-lg-5,\n  .gy-lg-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n@media (min-width: 1200px) {\n  .col-xl {\n    flex: 1 0 0%;\n  }\n  .row-cols-xl-auto > * {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .row-cols-xl-1 > * {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .row-cols-xl-2 > * {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .row-cols-xl-3 > * {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .row-cols-xl-4 > * {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .row-cols-xl-5 > * {\n    flex: 0 0 auto;\n    width: 20%;\n  }\n  .row-cols-xl-6 > * {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-xl-auto {\n    flex: 0 0 auto;\n    width: auto;\n  }\n  .col-xl-1 {\n    flex: 0 0 auto;\n    width: 8.33333333%;\n  }\n  .col-xl-2 {\n    flex: 0 0 auto;\n    width: 16.66666667%;\n  }\n  .col-xl-3 {\n    flex: 0 0 auto;\n    width: 25%;\n  }\n  .col-xl-4 {\n    flex: 0 0 auto;\n    width: 33.33333333%;\n  }\n  .col-xl-5 {\n    flex: 0 0 auto;\n    width: 41.66666667%;\n  }\n  .col-xl-6 {\n    flex: 0 0 auto;\n    width: 50%;\n  }\n  .col-xl-7 {\n    flex: 0 0 auto;\n    width: 58.33333333%;\n  }\n  .col-xl-8 {\n    flex: 0 0 auto;\n    width: 66.66666667%;\n  }\n  .col-xl-9 {\n    flex: 0 0 auto;\n    width: 75%;\n  }\n  .col-xl-10 {\n    flex: 0 0 auto;\n    width: 83.33333333%;\n  }\n  .col-xl-11 {\n    flex: 0 0 auto;\n    width: 91.66666667%;\n  }\n  .col-xl-12 {\n    flex: 0 0 auto;\n    width: 100%;\n  }\n  .offset-xl-0 {\n    margin-left: 0;\n  }\n  .offset-xl-1 {\n    margin-left: 8.33333333%;\n  }\n  .offset-xl-2 {\n    margin-left: 16.66666667%;\n  }\n  .offset-xl-3 {\n    margin-left: 25%;\n  }\n  .offset-xl-4 {\n    margin-left: 33.33333333%;\n  }\n  .offset-xl-5 {\n    margin-left: 41.66666667%;\n  }\n  .offset-xl-6 {\n    margin-left: 50%;\n  }\n  .offset-xl-7 {\n    margin-left: 58.33333333%;\n  }\n  .offset-xl-8 {\n    margin-left: 66.66666667%;\n  }\n  .offset-xl-9 {\n    margin-left: 75%;\n  }\n  .offset-xl-10 {\n    margin-left: 83.33333333%;\n  }\n  .offset-xl-11 {\n    margin-left: 91.66666667%;\n  }\n  .g-xl-0,\n  .gx-xl-0 {\n    --bs-gutter-x: 0;\n  }\n  .g-xl-0,\n  .gy-xl-0 {\n    --bs-gutter-y: 0;\n  }\n  .g-xl-1,\n  .gx-xl-1 {\n    --bs-gutter-x: 0.25rem;\n  }\n  .g-xl-1,\n  .gy-xl-1 {\n    --bs-gutter-y: 0.25rem;\n  }\n  .g-xl-2,\n  .gx-xl-2 {\n    --bs-gutter-x: 0.5rem;\n  }\n  .g-xl-2,\n  .gy-xl-2 {\n    --bs-gutter-y: 0.5rem;\n  }\n  .g-xl-3,\n  .gx-xl-3 {\n    --bs-gutter-x: 1rem;\n  }\n  .g-xl-3,\n  .gy-xl-3 {\n    --bs-gutter-y: 1rem;\n  }\n  .g-xl-4,\n  .gx-xl-4 {\n    --bs-gutter-x: 1.5rem;\n  }\n  .g-xl-4,\n  .gy-xl-4 {\n    --bs-gutter-y: 1.5rem;\n  }\n  .g-xl-5,\n  .gx-xl-5 {\n    --bs-gutter-x: 3rem;\n  }\n  .g-xl-5,\n  .gy-xl-5 {\n    --bs-gutter-y: 3rem;\n  }\n}\n.table {\n  --bs-table-color-type: initial;\n  --bs-table-bg-type: initial;\n  --bs-table-color-state: initial;\n  --bs-table-bg-state: initial;\n  --bs-table-color: var(--bs-emphasis-color);\n  --bs-table-bg: var(--bs-body-bg);\n  --bs-table-border-color: var(--bs-border-color);\n  --bs-table-accent-bg: transparent;\n  --bs-table-striped-color: var(--bs-emphasis-color);\n  --bs-table-striped-bg: rgba(var(--bs-emphasis-color-rgb), 0.05);\n  --bs-table-active-color: var(--bs-emphasis-color);\n  --bs-table-active-bg: rgba(var(--bs-emphasis-color-rgb), 0.1);\n  --bs-table-hover-color: var(--bs-emphasis-color);\n  --bs-table-hover-bg: rgba(var(--bs-emphasis-color-rgb), 0.075);\n  width: 100%;\n  margin-bottom: 1rem;\n  vertical-align: top;\n  border-color: var(--bs-table-border-color);\n}\n.table > :not(caption) > * > * {\n  padding: 0.5rem 0.5rem;\n  color: var(--bs-table-color-state, var(--bs-table-color-type, var(--bs-table-color)));\n  background-color: var(--bs-table-bg);\n  border-bottom-width: var(--bs-border-width);\n  box-shadow: inset 0 0 0 9999px var(--bs-table-bg-state, var(--bs-table-bg-type, var(--bs-table-accent-bg)));\n}\n.table > tbody {\n  vertical-align: inherit;\n}\n.table > thead {\n  vertical-align: bottom;\n}\n\n.table-group-divider {\n  border-top: calc(var(--bs-border-width) * 2) solid currentcolor;\n}\n\n.caption-top {\n  caption-side: top;\n}\n\n.table-sm > :not(caption) > * > * {\n  padding: 0.25rem 0.25rem;\n}\n\n.table-bordered > :not(caption) > * {\n  border-width: var(--bs-border-width) 0;\n}\n.table-bordered > :not(caption) > * > * {\n  border-width: 0 var(--bs-border-width);\n}\n\n.table-borderless > :not(caption) > * > * {\n  border-bottom-width: 0;\n}\n.table-borderless > :not(:first-child) {\n  border-top-width: 0;\n}\n\n.table-striped > tbody > tr:nth-of-type(odd) > * {\n  --bs-table-color-type: var(--bs-table-striped-color);\n  --bs-table-bg-type: var(--bs-table-striped-bg);\n}\n\n.table-striped-columns > :not(caption) > tr > :nth-child(even) {\n  --bs-table-color-type: var(--bs-table-striped-color);\n  --bs-table-bg-type: var(--bs-table-striped-bg);\n}\n\n.table-active {\n  --bs-table-color-state: var(--bs-table-active-color);\n  --bs-table-bg-state: var(--bs-table-active-bg);\n}\n\n.table-hover > tbody > tr:hover > * {\n  --bs-table-color-state: var(--bs-table-hover-color);\n  --bs-table-bg-state: var(--bs-table-hover-bg);\n}\n\n.table-primary {\n  --bs-table-color: #000;\n  --bs-table-bg: #cfe2ff;\n  --bs-table-border-color: #a6b5cc;\n  --bs-table-striped-bg: #c5d7f2;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #bacbe6;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #bfd1ec;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-secondary {\n  --bs-table-color: #000;\n  --bs-table-bg: #e2e3e5;\n  --bs-table-border-color: #b5b6b7;\n  --bs-table-striped-bg: #d7d8da;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #cbccce;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #d1d2d4;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-success {\n  --bs-table-color: #000;\n  --bs-table-bg: #d1e7dd;\n  --bs-table-border-color: #a7b9b1;\n  --bs-table-striped-bg: #c7dbd2;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #bcd0c7;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #c1d6cc;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-info {\n  --bs-table-color: #000;\n  --bs-table-bg: #cff4fc;\n  --bs-table-border-color: #a6c3ca;\n  --bs-table-striped-bg: #c5e8ef;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #badce3;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #bfe2e9;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-warning {\n  --bs-table-color: #000;\n  --bs-table-bg: #fff3cd;\n  --bs-table-border-color: #ccc2a4;\n  --bs-table-striped-bg: #f2e7c3;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #e6dbb9;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #ece1be;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-danger {\n  --bs-table-color: #000;\n  --bs-table-bg: #f8d7da;\n  --bs-table-border-color: #c6acae;\n  --bs-table-striped-bg: #eccccf;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #dfc2c4;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #e5c7ca;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-light {\n  --bs-table-color: #000;\n  --bs-table-bg: #f8f9fa;\n  --bs-table-border-color: #c6c7c8;\n  --bs-table-striped-bg: #ecedee;\n  --bs-table-striped-color: #000;\n  --bs-table-active-bg: #dfe0e1;\n  --bs-table-active-color: #000;\n  --bs-table-hover-bg: #e5e6e7;\n  --bs-table-hover-color: #000;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-dark {\n  --bs-table-color: #fff;\n  --bs-table-bg: #212529;\n  --bs-table-border-color: #4d5154;\n  --bs-table-striped-bg: #2c3034;\n  --bs-table-striped-color: #fff;\n  --bs-table-active-bg: #373b3e;\n  --bs-table-active-color: #fff;\n  --bs-table-hover-bg: #323539;\n  --bs-table-hover-color: #fff;\n  color: var(--bs-table-color);\n  border-color: var(--bs-table-border-color);\n}\n\n.table-responsive {\n  overflow-x: auto;\n  -webkit-overflow-scrolling: touch;\n}\n\n@media (max-width: 539.98px) {\n  .table-responsive-sm {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 719.98px) {\n  .table-responsive-md {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 959.98px) {\n  .table-responsive-lg {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n@media (max-width: 1199.98px) {\n  .table-responsive-xl {\n    overflow-x: auto;\n    -webkit-overflow-scrolling: touch;\n  }\n}\n.form-label {\n  margin-bottom: 0.5rem;\n}\n\n.col-form-label {\n  padding-top: calc(0.375rem + var(--bs-border-width));\n  padding-bottom: calc(0.375rem + var(--bs-border-width));\n  margin-bottom: 0;\n  font-size: inherit;\n  line-height: 1.5;\n}\n\n.col-form-label-lg {\n  padding-top: calc(0.5rem + var(--bs-border-width));\n  padding-bottom: calc(0.5rem + var(--bs-border-width));\n  font-size: 1.25rem;\n}\n\n.col-form-label-sm {\n  padding-top: calc(0.25rem + var(--bs-border-width));\n  padding-bottom: calc(0.25rem + var(--bs-border-width));\n  font-size: 0.875rem;\n}\n\n.form-text {\n  margin-top: 0.25rem;\n  font-size: 0.875em;\n  color: var(--bs-secondary-color);\n}\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: 0.375rem 0.75rem;\n  font-size: 1rem;\n  font-weight: 400;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  appearance: none;\n  background-color: var(--bs-body-bg);\n  background-clip: padding-box;\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-control {\n    transition: none;\n  }\n}\n.form-control[type=file] {\n  overflow: hidden;\n}\n.form-control[type=file]:not(:disabled):not([readonly]) {\n  cursor: pointer;\n}\n.form-control:focus {\n  color: var(--bs-body-color);\n  background-color: var(--bs-body-bg);\n  border-color: #86b7fe;\n  outline: 0;\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.form-control::-webkit-date-and-time-value {\n  min-width: 85px;\n  height: 1.5em;\n  margin: 0;\n}\n.form-control::-webkit-datetime-edit {\n  display: block;\n  padding: 0;\n}\n.form-control::placeholder {\n  color: var(--bs-secondary-color);\n  opacity: 1;\n}\n.form-control:disabled {\n  background-color: var(--bs-secondary-bg);\n  opacity: 1;\n}\n.form-control::file-selector-button {\n  padding: 0.375rem 0.75rem;\n  margin: -0.375rem -0.75rem;\n  margin-inline-end: 0.75rem;\n  color: var(--bs-body-color);\n  background-color: var(--bs-tertiary-bg);\n  pointer-events: none;\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n  border-inline-end-width: var(--bs-border-width);\n  border-radius: 0;\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-control::file-selector-button {\n    transition: none;\n  }\n}\n.form-control:hover:not(:disabled):not([readonly])::file-selector-button {\n  background-color: var(--bs-secondary-bg);\n}\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: 0.375rem 0;\n  margin-bottom: 0;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  background-color: transparent;\n  border: solid transparent;\n  border-width: var(--bs-border-width) 0;\n}\n.form-control-plaintext:focus {\n  outline: 0;\n}\n.form-control-plaintext.form-control-sm, .form-control-plaintext.form-control-lg {\n  padding-right: 0;\n  padding-left: 0;\n}\n\n.form-control-sm {\n  min-height: calc(1.5em + 0.5rem + calc(var(--bs-border-width) * 2));\n  padding: 0.25rem 0.5rem;\n  font-size: 0.875rem;\n  border-radius: var(--bs-border-radius-sm);\n}\n.form-control-sm::file-selector-button {\n  padding: 0.25rem 0.5rem;\n  margin: -0.25rem -0.5rem;\n  margin-inline-end: 0.5rem;\n}\n\n.form-control-lg {\n  min-height: calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));\n  padding: 0.5rem 1rem;\n  font-size: 1.25rem;\n  border-radius: var(--bs-border-radius-lg);\n}\n.form-control-lg::file-selector-button {\n  padding: 0.5rem 1rem;\n  margin: -0.5rem -1rem;\n  margin-inline-end: 1rem;\n}\n\ntextarea.form-control {\n  min-height: calc(1.5em + 0.75rem + calc(var(--bs-border-width) * 2));\n}\ntextarea.form-control-sm {\n  min-height: calc(1.5em + 0.5rem + calc(var(--bs-border-width) * 2));\n}\ntextarea.form-control-lg {\n  min-height: calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));\n}\n\n.form-control-color {\n  width: 3rem;\n  height: calc(1.5em + 0.75rem + calc(var(--bs-border-width) * 2));\n  padding: 0.375rem;\n}\n.form-control-color:not(:disabled):not([readonly]) {\n  cursor: pointer;\n}\n.form-control-color::-moz-color-swatch {\n  border: 0 !important;\n  border-radius: var(--bs-border-radius);\n}\n.form-control-color::-webkit-color-swatch {\n  border: 0 !important;\n  border-radius: var(--bs-border-radius);\n}\n.form-control-color.form-control-sm {\n  height: calc(1.5em + 0.5rem + calc(var(--bs-border-width) * 2));\n}\n.form-control-color.form-control-lg {\n  height: calc(1.5em + 1rem + calc(var(--bs-border-width) * 2));\n}\n\n.form-select {\n  --bs-form-select-bg-img: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e\");\n  display: block;\n  width: 100%;\n  padding: 0.375rem 2.25rem 0.375rem 0.75rem;\n  font-size: 1rem;\n  font-weight: 400;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  appearance: none;\n  background-color: var(--bs-body-bg);\n  background-image: var(--bs-form-select-bg-img), var(--bs-form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: right 0.75rem center;\n  background-size: 16px 12px;\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-select {\n    transition: none;\n  }\n}\n.form-select:focus {\n  border-color: #86b7fe;\n  outline: 0;\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.form-select[multiple], .form-select[size]:not([size=\"1\"]) {\n  padding-right: 0.75rem;\n  background-image: none;\n}\n.form-select:disabled {\n  background-color: var(--bs-secondary-bg);\n}\n.form-select:-moz-focusring {\n  color: transparent;\n  text-shadow: 0 0 0 var(--bs-body-color);\n}\n\n.form-select-sm {\n  padding-top: 0.25rem;\n  padding-bottom: 0.25rem;\n  padding-left: 0.5rem;\n  font-size: 0.875rem;\n  border-radius: var(--bs-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  padding-left: 1rem;\n  font-size: 1.25rem;\n  border-radius: var(--bs-border-radius-lg);\n}\n\n[data-bs-theme=dark] .form-select {\n  --bs-form-select-bg-img: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23dee2e6' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e\");\n}\n\n.form-check {\n  display: block;\n  min-height: 1.5rem;\n  padding-left: 1.5em;\n  margin-bottom: 0.125rem;\n}\n.form-check .form-check-input {\n  float: left;\n  margin-left: -1.5em;\n}\n\n.form-check-reverse {\n  padding-right: 1.5em;\n  padding-left: 0;\n  text-align: right;\n}\n.form-check-reverse .form-check-input {\n  float: right;\n  margin-right: -1.5em;\n  margin-left: 0;\n}\n\n.form-check-input {\n  --bs-form-check-bg: var(--bs-body-bg);\n  flex-shrink: 0;\n  width: 1em;\n  height: 1em;\n  margin-top: 0.25em;\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--bs-form-check-bg);\n  background-image: var(--bs-form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  print-color-adjust: exact;\n}\n.form-check-input[type=checkbox] {\n  border-radius: 0.25em;\n}\n.form-check-input[type=radio] {\n  border-radius: 50%;\n}\n.form-check-input:active {\n  filter: brightness(90%);\n}\n.form-check-input:focus {\n  border-color: #86b7fe;\n  outline: 0;\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.form-check-input:checked {\n  background-color: #0d6efd;\n  border-color: #0d6efd;\n}\n.form-check-input:checked[type=checkbox] {\n  --bs-form-check-bg-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/%3e%3c/svg%3e\");\n}\n.form-check-input:checked[type=radio] {\n  --bs-form-check-bg-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='2' fill='%23fff'/%3e%3c/svg%3e\");\n}\n.form-check-input[type=checkbox]:indeterminate {\n  background-color: #0d6efd;\n  border-color: #0d6efd;\n  --bs-form-check-bg-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e\");\n}\n.form-check-input:disabled {\n  pointer-events: none;\n  filter: none;\n  opacity: 0.5;\n}\n.form-check-input[disabled] ~ .form-check-label, .form-check-input:disabled ~ .form-check-label {\n  cursor: default;\n  opacity: 0.5;\n}\n\n.form-switch {\n  padding-left: 2.5em;\n}\n.form-switch .form-check-input {\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%280, 0, 0, 0.25%29'/%3e%3c/svg%3e\");\n  width: 2em;\n  margin-left: -2.5em;\n  background-image: var(--bs-form-switch-bg);\n  background-position: left center;\n  border-radius: 2em;\n  transition: background-position 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-switch .form-check-input {\n    transition: none;\n  }\n}\n.form-switch .form-check-input:focus {\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%2386b7fe'/%3e%3c/svg%3e\");\n}\n.form-switch .form-check-input:checked {\n  background-position: right center;\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e\");\n}\n.form-switch.form-check-reverse {\n  padding-right: 2.5em;\n  padding-left: 0;\n}\n.form-switch.form-check-reverse .form-check-input {\n  margin-right: -2.5em;\n  margin-left: 0;\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: 1rem;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.btn-check[disabled] + .btn, .btn-check:disabled + .btn {\n  pointer-events: none;\n  filter: none;\n  opacity: 0.65;\n}\n\n[data-bs-theme=dark] .form-switch .form-check-input:not(:checked):not(:focus) {\n  --bs-form-switch-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%28255, 255, 255, 0.25%29'/%3e%3c/svg%3e\");\n}\n\n.form-range {\n  width: 100%;\n  height: 1.375rem;\n  padding: 0;\n  appearance: none;\n  background-color: transparent;\n}\n.form-range:focus {\n  outline: 0;\n}\n.form-range:focus::-webkit-slider-thumb {\n  box-shadow: 0 0 0 1px #fff, 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.form-range:focus::-moz-range-thumb {\n  box-shadow: 0 0 0 1px #fff, 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.form-range::-moz-focus-outer {\n  border: 0;\n}\n.form-range::-webkit-slider-thumb {\n  width: 1rem;\n  height: 1rem;\n  margin-top: -0.25rem;\n  appearance: none;\n  background-color: #0d6efd;\n  border: 0;\n  border-radius: 1rem;\n  transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-range::-webkit-slider-thumb {\n    transition: none;\n  }\n}\n.form-range::-webkit-slider-thumb:active {\n  background-color: #b6d4fe;\n}\n.form-range::-webkit-slider-runnable-track {\n  width: 100%;\n  height: 0.5rem;\n  color: transparent;\n  cursor: pointer;\n  background-color: var(--bs-secondary-bg);\n  border-color: transparent;\n  border-radius: 1rem;\n}\n.form-range::-moz-range-thumb {\n  width: 1rem;\n  height: 1rem;\n  appearance: none;\n  background-color: #0d6efd;\n  border: 0;\n  border-radius: 1rem;\n  transition: background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-range::-moz-range-thumb {\n    transition: none;\n  }\n}\n.form-range::-moz-range-thumb:active {\n  background-color: #b6d4fe;\n}\n.form-range::-moz-range-track {\n  width: 100%;\n  height: 0.5rem;\n  color: transparent;\n  cursor: pointer;\n  background-color: var(--bs-secondary-bg);\n  border-color: transparent;\n  border-radius: 1rem;\n}\n.form-range:disabled {\n  pointer-events: none;\n}\n.form-range:disabled::-webkit-slider-thumb {\n  background-color: var(--bs-secondary-color);\n}\n.form-range:disabled::-moz-range-thumb {\n  background-color: var(--bs-secondary-color);\n}\n\n.form-floating {\n  position: relative;\n}\n.form-floating > .form-control,\n.form-floating > .form-control-plaintext,\n.form-floating > .form-select {\n  height: calc(3.5rem + calc(var(--bs-border-width) * 2));\n  min-height: calc(3.5rem + calc(var(--bs-border-width) * 2));\n  line-height: 1.25;\n}\n.form-floating > label {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 2;\n  height: 100%;\n  padding: 1rem 0.75rem;\n  overflow: hidden;\n  text-align: start;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n  pointer-events: none;\n  border: var(--bs-border-width) solid transparent;\n  transform-origin: 0 0;\n  transition: opacity 0.1s ease-in-out, transform 0.1s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .form-floating > label {\n    transition: none;\n  }\n}\n.form-floating > .form-control,\n.form-floating > .form-control-plaintext {\n  padding: 1rem 0.75rem;\n}\n.form-floating > .form-control::placeholder,\n.form-floating > .form-control-plaintext::placeholder {\n  color: transparent;\n}\n.form-floating > .form-control:focus, .form-floating > .form-control:not(:placeholder-shown),\n.form-floating > .form-control-plaintext:focus,\n.form-floating > .form-control-plaintext:not(:placeholder-shown) {\n  padding-top: 1.625rem;\n  padding-bottom: 0.625rem;\n}\n.form-floating > .form-control:-webkit-autofill,\n.form-floating > .form-control-plaintext:-webkit-autofill {\n  padding-top: 1.625rem;\n  padding-bottom: 0.625rem;\n}\n.form-floating > .form-select {\n  padding-top: 1.625rem;\n  padding-bottom: 0.625rem;\n}\n.form-floating > .form-control:focus ~ label,\n.form-floating > .form-control:not(:placeholder-shown) ~ label,\n.form-floating > .form-control-plaintext ~ label,\n.form-floating > .form-select ~ label {\n  color: rgba(var(--bs-body-color-rgb), 0.65);\n  transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem);\n}\n.form-floating > .form-control:focus ~ label::after,\n.form-floating > .form-control:not(:placeholder-shown) ~ label::after,\n.form-floating > .form-control-plaintext ~ label::after,\n.form-floating > .form-select ~ label::after {\n  position: absolute;\n  inset: 1rem 0.375rem;\n  z-index: -1;\n  height: 1.5em;\n  content: \"\";\n  background-color: var(--bs-body-bg);\n  border-radius: var(--bs-border-radius);\n}\n.form-floating > .form-control:-webkit-autofill ~ label {\n  color: rgba(var(--bs-body-color-rgb), 0.65);\n  transform: scale(0.85) translateY(-0.5rem) translateX(0.15rem);\n}\n.form-floating > .form-control-plaintext ~ label {\n  border-width: var(--bs-border-width) 0;\n}\n.form-floating > :disabled ~ label,\n.form-floating > .form-control:disabled ~ label {\n  color: #6c757d;\n}\n.form-floating > :disabled ~ label::after,\n.form-floating > .form-control:disabled ~ label::after {\n  background-color: var(--bs-secondary-bg);\n}\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap;\n  align-items: stretch;\n  width: 100%;\n}\n.input-group > .form-control,\n.input-group > .form-select,\n.input-group > .form-floating {\n  position: relative;\n  flex: 1 1 auto;\n  width: 1%;\n  min-width: 0;\n}\n.input-group > .form-control:focus,\n.input-group > .form-select:focus,\n.input-group > .form-floating:focus-within {\n  z-index: 5;\n}\n.input-group .btn {\n  position: relative;\n  z-index: 2;\n}\n.input-group .btn:focus {\n  z-index: 5;\n}\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: 0.375rem 0.75rem;\n  font-size: 1rem;\n  font-weight: 400;\n  line-height: 1.5;\n  color: var(--bs-body-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--bs-tertiary-bg);\n  border: var(--bs-border-width) solid var(--bs-border-color);\n  border-radius: var(--bs-border-radius);\n}\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: 0.5rem 1rem;\n  font-size: 1.25rem;\n  border-radius: var(--bs-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: 0.25rem 0.5rem;\n  font-size: 0.875rem;\n  border-radius: var(--bs-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: 3rem;\n}\n\n.input-group:not(.has-validation) > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n.input-group:not(.has-validation) > .dropdown-toggle:nth-last-child(n+3),\n.input-group:not(.has-validation) > .form-floating:not(:last-child) > .form-control,\n.input-group:not(.has-validation) > .form-floating:not(:last-child) > .form-select {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group.has-validation > :nth-last-child(n+3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n.input-group.has-validation > .dropdown-toggle:nth-last-child(n+4),\n.input-group.has-validation > .form-floating:nth-last-child(n+3) > .form-control,\n.input-group.has-validation > .form-floating:nth-last-child(n+3) > .form-select {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group > :not(:first-child):not(.dropdown-menu):not(.valid-tooltip):not(.valid-feedback):not(.invalid-tooltip):not(.invalid-feedback) {\n  margin-left: calc(var(--bs-border-width) * -1);\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.input-group > .form-floating:not(:first-child) > .form-control,\n.input-group > .form-floating:not(:first-child) > .form-select {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n\n.valid-feedback {\n  display: none;\n  width: 100%;\n  margin-top: 0.25rem;\n  font-size: 0.875em;\n  color: var(--bs-form-valid-color);\n}\n\n.valid-tooltip {\n  position: absolute;\n  top: 100%;\n  z-index: 5;\n  display: none;\n  max-width: 100%;\n  padding: 0.25rem 0.5rem;\n  margin-top: 0.1rem;\n  font-size: 0.875rem;\n  color: #fff;\n  background-color: var(--bs-success);\n  border-radius: var(--bs-border-radius);\n}\n\n.was-validated :valid ~ .valid-feedback,\n.was-validated :valid ~ .valid-tooltip,\n.is-valid ~ .valid-feedback,\n.is-valid ~ .valid-tooltip {\n  display: block;\n}\n\n.was-validated .form-control:valid, .form-control.is-valid {\n  border-color: var(--bs-form-valid-border-color);\n  padding-right: calc(1.5em + 0.75rem);\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23198754' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e\");\n  background-repeat: no-repeat;\n  background-position: right calc(0.375em + 0.1875rem) center;\n  background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-control:valid:focus, .form-control.is-valid:focus {\n  border-color: var(--bs-form-valid-border-color);\n  box-shadow: 0 0 0 0.1875rem rgba(var(--bs-success-rgb), 1);\n}\n\n.was-validated textarea.form-control:valid, textarea.form-control.is-valid {\n  padding-right: calc(1.5em + 0.75rem);\n  background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem);\n}\n\n.was-validated .form-select:valid, .form-select.is-valid {\n  border-color: var(--bs-form-valid-border-color);\n}\n.was-validated .form-select:valid:not([multiple]):not([size]), .was-validated .form-select:valid:not([multiple])[size=\"1\"], .form-select.is-valid:not([multiple]):not([size]), .form-select.is-valid:not([multiple])[size=\"1\"] {\n  --bs-form-select-bg-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%23198754' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e\");\n  padding-right: 4.125rem;\n  background-position: right 0.75rem center, center right 2.25rem;\n  background-size: 16px 12px, calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-select:valid:focus, .form-select.is-valid:focus {\n  border-color: var(--bs-form-valid-border-color);\n  box-shadow: 0 0 0 0.1875rem rgba(var(--bs-success-rgb), 1);\n}\n\n.was-validated .form-control-color:valid, .form-control-color.is-valid {\n  width: calc(3rem + calc(1.5em + 0.75rem));\n}\n\n.was-validated .form-check-input:valid, .form-check-input.is-valid {\n  border-color: var(--bs-form-valid-border-color);\n}\n.was-validated .form-check-input:valid:checked, .form-check-input.is-valid:checked {\n  background-color: var(--bs-form-valid-color);\n}\n.was-validated .form-check-input:valid:focus, .form-check-input.is-valid:focus {\n  box-shadow: 0 0 0 0.1875rem rgba(var(--bs-success-rgb), 1);\n}\n.was-validated .form-check-input:valid ~ .form-check-label, .form-check-input.is-valid ~ .form-check-label {\n  color: var(--bs-form-valid-color);\n}\n\n.form-check-inline .form-check-input ~ .valid-feedback {\n  margin-left: 0.5em;\n}\n\n.was-validated .input-group > .form-control:not(:focus):valid, .input-group > .form-control:not(:focus).is-valid,\n.was-validated .input-group > .form-select:not(:focus):valid,\n.input-group > .form-select:not(:focus).is-valid,\n.was-validated .input-group > .form-floating:not(:focus-within):valid,\n.input-group > .form-floating:not(:focus-within).is-valid {\n  z-index: 3;\n}\n\n.invalid-feedback {\n  display: none;\n  width: 100%;\n  margin-top: 0.25rem;\n  font-size: 0.875em;\n  color: var(--bs-form-invalid-color);\n}\n\n.invalid-tooltip {\n  position: absolute;\n  top: 100%;\n  z-index: 5;\n  display: none;\n  max-width: 100%;\n  padding: 0.25rem 0.5rem;\n  margin-top: 0.1rem;\n  font-size: 0.875rem;\n  color: #fff;\n  background-color: var(--bs-danger);\n  border-radius: var(--bs-border-radius);\n}\n\n.was-validated :invalid ~ .invalid-feedback,\n.was-validated :invalid ~ .invalid-tooltip,\n.is-invalid ~ .invalid-feedback,\n.is-invalid ~ .invalid-tooltip {\n  display: block;\n}\n\n.was-validated .form-control:invalid, .form-control.is-invalid {\n  border-color: var(--bs-form-invalid-border-color);\n  padding-right: calc(1.5em + 0.75rem);\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23dc3545'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23dc3545' stroke='none'/%3e%3c/svg%3e\");\n  background-repeat: no-repeat;\n  background-position: right calc(0.375em + 0.1875rem) center;\n  background-size: calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-control:invalid:focus, .form-control.is-invalid:focus {\n  border-color: var(--bs-form-invalid-border-color);\n  box-shadow: 0 0 0 0.1875rem rgba(var(--bs-danger-rgb), 1);\n}\n\n.was-validated textarea.form-control:invalid, textarea.form-control.is-invalid {\n  padding-right: calc(1.5em + 0.75rem);\n  background-position: top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem);\n}\n\n.was-validated .form-select:invalid, .form-select.is-invalid {\n  border-color: var(--bs-form-invalid-border-color);\n}\n.was-validated .form-select:invalid:not([multiple]):not([size]), .was-validated .form-select:invalid:not([multiple])[size=\"1\"], .form-select.is-invalid:not([multiple]):not([size]), .form-select.is-invalid:not([multiple])[size=\"1\"] {\n  --bs-form-select-bg-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23dc3545'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23dc3545' stroke='none'/%3e%3c/svg%3e\");\n  padding-right: 4.125rem;\n  background-position: right 0.75rem center, center right 2.25rem;\n  background-size: 16px 12px, calc(0.75em + 0.375rem) calc(0.75em + 0.375rem);\n}\n.was-validated .form-select:invalid:focus, .form-select.is-invalid:focus {\n  border-color: var(--bs-form-invalid-border-color);\n  box-shadow: 0 0 0 0.1875rem rgba(var(--bs-danger-rgb), 1);\n}\n\n.was-validated .form-control-color:invalid, .form-control-color.is-invalid {\n  width: calc(3rem + calc(1.5em + 0.75rem));\n}\n\n.was-validated .form-check-input:invalid, .form-check-input.is-invalid {\n  border-color: var(--bs-form-invalid-border-color);\n}\n.was-validated .form-check-input:invalid:checked, .form-check-input.is-invalid:checked {\n  background-color: var(--bs-form-invalid-color);\n}\n.was-validated .form-check-input:invalid:focus, .form-check-input.is-invalid:focus {\n  box-shadow: 0 0 0 0.1875rem rgba(var(--bs-danger-rgb), 1);\n}\n.was-validated .form-check-input:invalid ~ .form-check-label, .form-check-input.is-invalid ~ .form-check-label {\n  color: var(--bs-form-invalid-color);\n}\n\n.form-check-inline .form-check-input ~ .invalid-feedback {\n  margin-left: 0.5em;\n}\n\n.was-validated .input-group > .form-control:not(:focus):invalid, .input-group > .form-control:not(:focus).is-invalid,\n.was-validated .input-group > .form-select:not(:focus):invalid,\n.input-group > .form-select:not(:focus).is-invalid,\n.was-validated .input-group > .form-floating:not(:focus-within):invalid,\n.input-group > .form-floating:not(:focus-within).is-invalid {\n  z-index: 4;\n}\n\n.btn {\n  --bs-btn-padding-x: 0.75rem;\n  --bs-btn-padding-y: 0.375rem;\n  --bs-btn-font-family: ;\n  --bs-btn-font-size: 1rem;\n  --bs-btn-font-weight: 400;\n  --bs-btn-line-height: 1.5;\n  --bs-btn-color: var(--bs-body-color);\n  --bs-btn-bg: transparent;\n  --bs-btn-border-width: var(--bs-border-width);\n  --bs-btn-border-color: transparent;\n  --bs-btn-border-radius: var(--bs-border-radius);\n  --bs-btn-hover-border-color: transparent;\n  --bs-btn-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n  --bs-btn-disabled-opacity: 0.65;\n  --bs-btn-focus-box-shadow: 0 0 0 0.1875rem rgba(var(--bs-btn-focus-shadow-rgb), .5);\n  display: inline-block;\n  padding: var(--bs-btn-padding-y) var(--bs-btn-padding-x);\n  font-family: var(--bs-btn-font-family);\n  font-size: var(--bs-btn-font-size);\n  font-weight: var(--bs-btn-font-weight);\n  line-height: var(--bs-btn-line-height);\n  color: var(--bs-btn-color);\n  text-align: center;\n  text-decoration: none;\n  vertical-align: middle;\n  cursor: pointer;\n  user-select: none;\n  border: var(--bs-btn-border-width) solid var(--bs-btn-border-color);\n  border-radius: var(--bs-btn-border-radius);\n  background-color: var(--bs-btn-bg);\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .btn {\n    transition: none;\n  }\n}\n.btn:hover {\n  color: var(--bs-btn-hover-color);\n  background-color: var(--bs-btn-hover-bg);\n  border-color: var(--bs-btn-hover-border-color);\n}\n.btn-check + .btn:hover {\n  color: var(--bs-btn-color);\n  background-color: var(--bs-btn-bg);\n  border-color: var(--bs-btn-border-color);\n}\n.btn:focus-visible {\n  color: var(--bs-btn-hover-color);\n  background-color: var(--bs-btn-hover-bg);\n  border-color: var(--bs-btn-hover-border-color);\n  outline: 0;\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn-check:focus-visible + .btn {\n  border-color: var(--bs-btn-hover-border-color);\n  outline: 0;\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn-check:checked + .btn, :not(.btn-check) + .btn:active, .btn:first-child:active, .btn.active, .btn.show {\n  color: var(--bs-btn-active-color);\n  background-color: var(--bs-btn-active-bg);\n  border-color: var(--bs-btn-active-border-color);\n}\n.btn-check:checked + .btn:focus-visible, :not(.btn-check) + .btn:active:focus-visible, .btn:first-child:active:focus-visible, .btn.active:focus-visible, .btn.show:focus-visible {\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn-check:checked:focus-visible + .btn {\n  box-shadow: var(--bs-btn-focus-box-shadow);\n}\n.btn:disabled, .btn.disabled, fieldset:disabled .btn {\n  color: var(--bs-btn-disabled-color);\n  pointer-events: none;\n  background-color: var(--bs-btn-disabled-bg);\n  border-color: var(--bs-btn-disabled-border-color);\n  opacity: var(--bs-btn-disabled-opacity);\n}\n\n.btn-primary {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #0d6efd;\n  --bs-btn-border-color: #0d6efd;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #0b5ed7;\n  --bs-btn-hover-border-color: #0a58ca;\n  --bs-btn-focus-shadow-rgb: 49, 132, 253;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #0a58ca;\n  --bs-btn-active-border-color: #0a53be;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #0d6efd;\n  --bs-btn-disabled-border-color: #0d6efd;\n}\n\n.btn-secondary {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #6c757d;\n  --bs-btn-border-color: #6c757d;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #5c636a;\n  --bs-btn-hover-border-color: #565e64;\n  --bs-btn-focus-shadow-rgb: 130, 138, 145;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #565e64;\n  --bs-btn-active-border-color: #51585e;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #6c757d;\n  --bs-btn-disabled-border-color: #6c757d;\n}\n\n.btn-success {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #198754;\n  --bs-btn-border-color: #198754;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #157347;\n  --bs-btn-hover-border-color: #146c43;\n  --bs-btn-focus-shadow-rgb: 60, 153, 110;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #146c43;\n  --bs-btn-active-border-color: #13653f;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #198754;\n  --bs-btn-disabled-border-color: #198754;\n}\n\n.btn-info {\n  --bs-btn-color: #000;\n  --bs-btn-bg: #0dcaf0;\n  --bs-btn-border-color: #0dcaf0;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #31d2f2;\n  --bs-btn-hover-border-color: #25cff2;\n  --bs-btn-focus-shadow-rgb: 11, 172, 204;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #3dd5f3;\n  --bs-btn-active-border-color: #25cff2;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #000;\n  --bs-btn-disabled-bg: #0dcaf0;\n  --bs-btn-disabled-border-color: #0dcaf0;\n}\n\n.btn-warning {\n  --bs-btn-color: #000;\n  --bs-btn-bg: #ffc107;\n  --bs-btn-border-color: #ffc107;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #ffca2c;\n  --bs-btn-hover-border-color: #ffc720;\n  --bs-btn-focus-shadow-rgb: 217, 164, 6;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #ffcd39;\n  --bs-btn-active-border-color: #ffc720;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #000;\n  --bs-btn-disabled-bg: #ffc107;\n  --bs-btn-disabled-border-color: #ffc107;\n}\n\n.btn-danger {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #dc3545;\n  --bs-btn-border-color: #dc3545;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #bb2d3b;\n  --bs-btn-hover-border-color: #b02a37;\n  --bs-btn-focus-shadow-rgb: 225, 83, 97;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #b02a37;\n  --bs-btn-active-border-color: #a52834;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #dc3545;\n  --bs-btn-disabled-border-color: #dc3545;\n}\n\n.btn-light {\n  --bs-btn-color: #000;\n  --bs-btn-bg: #f8f9fa;\n  --bs-btn-border-color: #f8f9fa;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #d3d4d5;\n  --bs-btn-hover-border-color: #c6c7c8;\n  --bs-btn-focus-shadow-rgb: 211, 212, 213;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #c6c7c8;\n  --bs-btn-active-border-color: #babbbc;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #000;\n  --bs-btn-disabled-bg: #f8f9fa;\n  --bs-btn-disabled-border-color: #f8f9fa;\n}\n\n.btn-dark {\n  --bs-btn-color: #fff;\n  --bs-btn-bg: #212529;\n  --bs-btn-border-color: #212529;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #424649;\n  --bs-btn-hover-border-color: #373b3e;\n  --bs-btn-focus-shadow-rgb: 66, 70, 73;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #4d5154;\n  --bs-btn-active-border-color: #373b3e;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #fff;\n  --bs-btn-disabled-bg: #212529;\n  --bs-btn-disabled-border-color: #212529;\n}\n\n.btn-outline-primary {\n  --bs-btn-color: #0d6efd;\n  --bs-btn-border-color: #0d6efd;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #0d6efd;\n  --bs-btn-hover-border-color: #0d6efd;\n  --bs-btn-focus-shadow-rgb: 13, 110, 253;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #0d6efd;\n  --bs-btn-active-border-color: #0d6efd;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #0d6efd;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #0d6efd;\n  --bs-gradient: none;\n}\n\n.btn-outline-secondary {\n  --bs-btn-color: #6c757d;\n  --bs-btn-border-color: #6c757d;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #6c757d;\n  --bs-btn-hover-border-color: #6c757d;\n  --bs-btn-focus-shadow-rgb: 108, 117, 125;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #6c757d;\n  --bs-btn-active-border-color: #6c757d;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #6c757d;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #6c757d;\n  --bs-gradient: none;\n}\n\n.btn-outline-success {\n  --bs-btn-color: #198754;\n  --bs-btn-border-color: #198754;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #198754;\n  --bs-btn-hover-border-color: #198754;\n  --bs-btn-focus-shadow-rgb: 25, 135, 84;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #198754;\n  --bs-btn-active-border-color: #198754;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #198754;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #198754;\n  --bs-gradient: none;\n}\n\n.btn-outline-info {\n  --bs-btn-color: #0dcaf0;\n  --bs-btn-border-color: #0dcaf0;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #0dcaf0;\n  --bs-btn-hover-border-color: #0dcaf0;\n  --bs-btn-focus-shadow-rgb: 13, 202, 240;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #0dcaf0;\n  --bs-btn-active-border-color: #0dcaf0;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #0dcaf0;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #0dcaf0;\n  --bs-gradient: none;\n}\n\n.btn-outline-warning {\n  --bs-btn-color: #ffc107;\n  --bs-btn-border-color: #ffc107;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #ffc107;\n  --bs-btn-hover-border-color: #ffc107;\n  --bs-btn-focus-shadow-rgb: 255, 193, 7;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #ffc107;\n  --bs-btn-active-border-color: #ffc107;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #ffc107;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #ffc107;\n  --bs-gradient: none;\n}\n\n.btn-outline-danger {\n  --bs-btn-color: #dc3545;\n  --bs-btn-border-color: #dc3545;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #dc3545;\n  --bs-btn-hover-border-color: #dc3545;\n  --bs-btn-focus-shadow-rgb: 220, 53, 69;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #dc3545;\n  --bs-btn-active-border-color: #dc3545;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #dc3545;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #dc3545;\n  --bs-gradient: none;\n}\n\n.btn-outline-light {\n  --bs-btn-color: #f8f9fa;\n  --bs-btn-border-color: #f8f9fa;\n  --bs-btn-hover-color: #000;\n  --bs-btn-hover-bg: #f8f9fa;\n  --bs-btn-hover-border-color: #f8f9fa;\n  --bs-btn-focus-shadow-rgb: 248, 249, 250;\n  --bs-btn-active-color: #000;\n  --bs-btn-active-bg: #f8f9fa;\n  --bs-btn-active-border-color: #f8f9fa;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #f8f9fa;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #f8f9fa;\n  --bs-gradient: none;\n}\n\n.btn-outline-dark {\n  --bs-btn-color: #212529;\n  --bs-btn-border-color: #212529;\n  --bs-btn-hover-color: #fff;\n  --bs-btn-hover-bg: #212529;\n  --bs-btn-hover-border-color: #212529;\n  --bs-btn-focus-shadow-rgb: 33, 37, 41;\n  --bs-btn-active-color: #fff;\n  --bs-btn-active-bg: #212529;\n  --bs-btn-active-border-color: #212529;\n  --bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  --bs-btn-disabled-color: #212529;\n  --bs-btn-disabled-bg: transparent;\n  --bs-btn-disabled-border-color: #212529;\n  --bs-gradient: none;\n}\n\n.btn-link {\n  --bs-btn-font-weight: 400;\n  --bs-btn-color: var(--bs-link-color);\n  --bs-btn-bg: transparent;\n  --bs-btn-border-color: transparent;\n  --bs-btn-hover-color: var(--bs-link-hover-color);\n  --bs-btn-hover-border-color: transparent;\n  --bs-btn-active-color: var(--bs-link-hover-color);\n  --bs-btn-active-border-color: transparent;\n  --bs-btn-disabled-color: #6c757d;\n  --bs-btn-disabled-border-color: transparent;\n  --bs-btn-box-shadow: 0 0 0 #000;\n  --bs-btn-focus-shadow-rgb: 49, 132, 253;\n  text-decoration: underline;\n}\n.btn-link:focus-visible {\n  color: var(--bs-btn-color);\n}\n.btn-link:hover {\n  color: var(--bs-btn-hover-color);\n}\n\n.btn-lg, .btn-group-lg > .btn {\n  --bs-btn-padding-y: 0.5rem;\n  --bs-btn-padding-x: 1rem;\n  --bs-btn-font-size: 1.25rem;\n  --bs-btn-border-radius: var(--bs-border-radius-lg);\n}\n\n.btn-sm, .btn-group-sm > .btn {\n  --bs-btn-padding-y: 0.25rem;\n  --bs-btn-padding-x: 0.5rem;\n  --bs-btn-font-size: 0.875rem;\n  --bs-btn-border-radius: var(--bs-border-radius-sm);\n}\n\n.fade {\n  transition: opacity 0.15s linear;\n}\n@media (prefers-reduced-motion: reduce) {\n  .fade {\n    transition: none;\n  }\n}\n.fade:not(.show) {\n  opacity: 0;\n}\n\n.collapse:not(.show) {\n  display: none;\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  transition: height 0.35s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .collapsing {\n    transition: none;\n  }\n}\n.collapsing.collapse-horizontal {\n  width: 0;\n  height: auto;\n  transition: width 0.35s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .collapsing.collapse-horizontal {\n    transition: none;\n  }\n}\n\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n}\n.dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0.3em solid;\n  border-right: 0.3em solid transparent;\n  border-bottom: 0;\n  border-left: 0.3em solid transparent;\n}\n.dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n\n.dropdown-menu {\n  --bs-dropdown-zindex: 1000;\n  --bs-dropdown-min-width: 10rem;\n  --bs-dropdown-padding-x: 0;\n  --bs-dropdown-padding-y: 0.5rem;\n  --bs-dropdown-spacer: 0.125rem;\n  --bs-dropdown-font-size: 1rem;\n  --bs-dropdown-color: var(--bs-body-color);\n  --bs-dropdown-bg: var(--bs-body-bg);\n  --bs-dropdown-border-color: var(--bs-border-color-translucent);\n  --bs-dropdown-border-radius: var(--bs-border-radius);\n  --bs-dropdown-border-width: var(--bs-border-width);\n  --bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));\n  --bs-dropdown-divider-bg: var(--bs-border-color-translucent);\n  --bs-dropdown-divider-margin-y: 0.5rem;\n  --bs-dropdown-box-shadow: var(--bs-box-shadow);\n  --bs-dropdown-link-color: var(--bs-body-color);\n  --bs-dropdown-link-hover-color: var(--bs-body-color);\n  --bs-dropdown-link-hover-bg: var(--pst-color-surface);\n  --bs-dropdown-link-active-color: #fff;\n  --bs-dropdown-link-active-bg: var(--pst-color-surface);\n  --bs-dropdown-link-disabled-color: var(--bs-tertiary-color);\n  --bs-dropdown-item-padding-x: 1rem;\n  --bs-dropdown-item-padding-y: 0.25rem;\n  --bs-dropdown-header-color: #6c757d;\n  --bs-dropdown-header-padding-x: 1rem;\n  --bs-dropdown-header-padding-y: 0.5rem;\n  position: absolute;\n  z-index: var(--bs-dropdown-zindex);\n  display: none;\n  min-width: var(--bs-dropdown-min-width);\n  padding: var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);\n  margin: 0;\n  font-size: var(--bs-dropdown-font-size);\n  color: var(--bs-dropdown-color);\n  text-align: left;\n  list-style: none;\n  background-color: var(--bs-dropdown-bg);\n  background-clip: padding-box;\n  border: var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);\n  border-radius: var(--bs-dropdown-border-radius);\n}\n.dropdown-menu[data-bs-popper] {\n  top: 100%;\n  left: 0;\n  margin-top: var(--bs-dropdown-spacer);\n}\n\n.dropdown-menu-start {\n  --bs-position: start;\n}\n.dropdown-menu-start[data-bs-popper] {\n  right: auto;\n  left: 0;\n}\n\n.dropdown-menu-end {\n  --bs-position: end;\n}\n.dropdown-menu-end[data-bs-popper] {\n  right: 0;\n  left: auto;\n}\n\n@media (min-width: 540px) {\n  .dropdown-menu-sm-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-sm-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-sm-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-sm-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 720px) {\n  .dropdown-menu-md-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-md-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-md-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-md-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 960px) {\n  .dropdown-menu-lg-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-lg-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-lg-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-lg-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n@media (min-width: 1200px) {\n  .dropdown-menu-xl-start {\n    --bs-position: start;\n  }\n  .dropdown-menu-xl-start[data-bs-popper] {\n    right: auto;\n    left: 0;\n  }\n  .dropdown-menu-xl-end {\n    --bs-position: end;\n  }\n  .dropdown-menu-xl-end[data-bs-popper] {\n    right: 0;\n    left: auto;\n  }\n}\n.dropup .dropdown-menu[data-bs-popper] {\n  top: auto;\n  bottom: 100%;\n  margin-top: 0;\n  margin-bottom: var(--bs-dropdown-spacer);\n}\n.dropup .dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0;\n  border-right: 0.3em solid transparent;\n  border-bottom: 0.3em solid;\n  border-left: 0.3em solid transparent;\n}\n.dropup .dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n\n.dropend .dropdown-menu[data-bs-popper] {\n  top: 0;\n  right: auto;\n  left: 100%;\n  margin-top: 0;\n  margin-left: var(--bs-dropdown-spacer);\n}\n.dropend .dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0.3em solid transparent;\n  border-right: 0;\n  border-bottom: 0.3em solid transparent;\n  border-left: 0.3em solid;\n}\n.dropend .dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n.dropend .dropdown-toggle::after {\n  vertical-align: 0;\n}\n\n.dropstart .dropdown-menu[data-bs-popper] {\n  top: 0;\n  right: 100%;\n  left: auto;\n  margin-top: 0;\n  margin-right: var(--bs-dropdown-spacer);\n}\n.dropstart .dropdown-toggle::after {\n  display: inline-block;\n  margin-left: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n}\n.dropstart .dropdown-toggle::after {\n  display: none;\n}\n.dropstart .dropdown-toggle::before {\n  display: inline-block;\n  margin-right: 0.255em;\n  vertical-align: 0.255em;\n  content: \"\";\n  border-top: 0.3em solid transparent;\n  border-right: 0.3em solid;\n  border-bottom: 0.3em solid transparent;\n}\n.dropstart .dropdown-toggle:empty::after {\n  margin-left: 0;\n}\n.dropstart .dropdown-toggle::before {\n  vertical-align: 0;\n}\n\n.dropdown-divider {\n  height: 0;\n  margin: var(--bs-dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--bs-dropdown-divider-bg);\n  opacity: 1;\n}\n\n.dropdown-item {\n  display: block;\n  width: 100%;\n  padding: var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);\n  clear: both;\n  font-weight: 400;\n  color: var(--bs-dropdown-link-color);\n  text-align: inherit;\n  text-decoration: none;\n  white-space: nowrap;\n  background-color: transparent;\n  border: 0;\n  border-radius: var(--bs-dropdown-item-border-radius, 0);\n}\n.dropdown-item:hover, .dropdown-item:focus {\n  color: var(--bs-dropdown-link-hover-color);\n  background-color: var(--bs-dropdown-link-hover-bg);\n}\n.dropdown-item.active, .dropdown-item:active {\n  color: var(--bs-dropdown-link-active-color);\n  text-decoration: none;\n  background-color: var(--bs-dropdown-link-active-bg);\n}\n.dropdown-item.disabled, .dropdown-item:disabled {\n  color: var(--bs-dropdown-link-disabled-color);\n  pointer-events: none;\n  background-color: transparent;\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n.dropdown-header {\n  display: block;\n  padding: var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);\n  margin-bottom: 0;\n  font-size: 0.875rem;\n  color: var(--bs-dropdown-header-color);\n  white-space: nowrap;\n}\n\n.dropdown-item-text {\n  display: block;\n  padding: var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);\n  color: var(--bs-dropdown-link-color);\n}\n\n.dropdown-menu-dark {\n  --bs-dropdown-color: #dee2e6;\n  --bs-dropdown-bg: #343a40;\n  --bs-dropdown-border-color: var(--bs-border-color-translucent);\n  --bs-dropdown-box-shadow: ;\n  --bs-dropdown-link-color: #dee2e6;\n  --bs-dropdown-link-hover-color: #fff;\n  --bs-dropdown-divider-bg: var(--bs-border-color-translucent);\n  --bs-dropdown-link-hover-bg: var(--pst-color-surface);\n  --bs-dropdown-link-active-color: #fff;\n  --bs-dropdown-link-active-bg: var(--pst-color-surface);\n  --bs-dropdown-link-disabled-color: #adb5bd;\n  --bs-dropdown-header-color: #adb5bd;\n}\n\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  flex: 1 1 auto;\n}\n.btn-group > .btn-check:checked + .btn,\n.btn-group > .btn-check:focus + .btn,\n.btn-group > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn-check:checked + .btn,\n.btn-group-vertical > .btn-check:focus + .btn,\n.btn-group-vertical > .btn:hover,\n.btn-group-vertical > .btn:focus,\n.btn-group-vertical > .btn:active,\n.btn-group-vertical > .btn.active {\n  z-index: 1;\n}\n\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n}\n.btn-toolbar .input-group {\n  width: auto;\n}\n\n.btn-group {\n  border-radius: var(--bs-border-radius);\n}\n.btn-group > :not(.btn-check:first-child) + .btn,\n.btn-group > .btn-group:not(:first-child) {\n  margin-left: calc(var(--bs-border-width) * -1);\n}\n.btn-group > .btn:not(:last-child):not(.dropdown-toggle),\n.btn-group > .btn.dropdown-toggle-split:first-child,\n.btn-group > .btn-group:not(:last-child) > .btn {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn:nth-child(n+3),\n.btn-group > :not(.btn-check) + .btn,\n.btn-group > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n\n.dropdown-toggle-split {\n  padding-right: 0.5625rem;\n  padding-left: 0.5625rem;\n}\n.dropdown-toggle-split::after, .dropup .dropdown-toggle-split::after, .dropend .dropdown-toggle-split::after {\n  margin-left: 0;\n}\n.dropstart .dropdown-toggle-split::before {\n  margin-right: 0;\n}\n\n.btn-sm + .dropdown-toggle-split, .btn-group-sm > .btn + .dropdown-toggle-split {\n  padding-right: 0.375rem;\n  padding-left: 0.375rem;\n}\n\n.btn-lg + .dropdown-toggle-split, .btn-group-lg > .btn + .dropdown-toggle-split {\n  padding-right: 0.75rem;\n  padding-left: 0.75rem;\n}\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group {\n  width: 100%;\n}\n.btn-group-vertical > .btn:not(:first-child),\n.btn-group-vertical > .btn-group:not(:first-child) {\n  margin-top: calc(var(--bs-border-width) * -1);\n}\n.btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle),\n.btn-group-vertical > .btn-group:not(:last-child) > .btn {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn ~ .btn,\n.btn-group-vertical > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n\n.nav {\n  --bs-nav-link-padding-x: 1rem;\n  --bs-nav-link-padding-y: 0.5rem;\n  --bs-nav-link-font-weight: ;\n  --bs-nav-link-color: var(--bs-link-color);\n  --bs-nav-link-hover-color: var(--bs-link-hover-color);\n  --bs-nav-link-disabled-color: var(--bs-secondary-color);\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);\n  font-size: var(--bs-nav-link-font-size);\n  font-weight: var(--bs-nav-link-font-weight);\n  color: var(--bs-nav-link-color);\n  text-decoration: none;\n  background: none;\n  border: 0;\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .nav-link {\n    transition: none;\n  }\n}\n.nav-link:hover, .nav-link:focus {\n  color: var(--bs-nav-link-hover-color);\n}\n.nav-link:focus-visible {\n  outline: 0;\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.nav-link.disabled, .nav-link:disabled {\n  color: var(--bs-nav-link-disabled-color);\n  pointer-events: none;\n  cursor: default;\n}\n\n.nav-tabs {\n  --bs-nav-tabs-border-width: var(--bs-border-width);\n  --bs-nav-tabs-border-color: var(--bs-border-color);\n  --bs-nav-tabs-border-radius: var(--bs-border-radius);\n  --bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);\n  --bs-nav-tabs-link-active-color: var(--bs-emphasis-color);\n  --bs-nav-tabs-link-active-bg: var(--bs-body-bg);\n  --bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);\n  border-bottom: var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color);\n}\n.nav-tabs .nav-link {\n  margin-bottom: calc(-1 * var(--bs-nav-tabs-border-width));\n  border: var(--bs-nav-tabs-border-width) solid transparent;\n  border-top-left-radius: var(--bs-nav-tabs-border-radius);\n  border-top-right-radius: var(--bs-nav-tabs-border-radius);\n}\n.nav-tabs .nav-link:hover, .nav-tabs .nav-link:focus {\n  isolation: isolate;\n  border-color: var(--bs-nav-tabs-link-hover-border-color);\n}\n.nav-tabs .nav-link.active,\n.nav-tabs .nav-item.show .nav-link {\n  color: var(--bs-nav-tabs-link-active-color);\n  background-color: var(--bs-nav-tabs-link-active-bg);\n  border-color: var(--bs-nav-tabs-link-active-border-color);\n}\n.nav-tabs .dropdown-menu {\n  margin-top: calc(-1 * var(--bs-nav-tabs-border-width));\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n\n.nav-pills {\n  --bs-nav-pills-border-radius: var(--bs-border-radius);\n  --bs-nav-pills-link-active-color: #fff;\n  --bs-nav-pills-link-active-bg: #0d6efd;\n}\n.nav-pills .nav-link {\n  border-radius: var(--bs-nav-pills-border-radius);\n}\n.nav-pills .nav-link.active,\n.nav-pills .show > .nav-link {\n  color: var(--bs-nav-pills-link-active-color);\n  background-color: var(--bs-nav-pills-link-active-bg);\n}\n\n.nav-underline {\n  --bs-nav-underline-gap: 1rem;\n  --bs-nav-underline-border-width: 0.125rem;\n  --bs-nav-underline-link-active-color: var(--bs-emphasis-color);\n  gap: var(--bs-nav-underline-gap);\n}\n.nav-underline .nav-link {\n  padding-right: 0;\n  padding-left: 0;\n  border-bottom: var(--bs-nav-underline-border-width) solid transparent;\n}\n.nav-underline .nav-link:hover, .nav-underline .nav-link:focus {\n  border-bottom-color: currentcolor;\n}\n.nav-underline .nav-link.active,\n.nav-underline .show > .nav-link {\n  font-weight: 700;\n  color: var(--bs-nav-underline-link-active-color);\n  border-bottom-color: currentcolor;\n}\n\n.nav-fill > .nav-link,\n.nav-fill .nav-item {\n  flex: 1 1 auto;\n  text-align: center;\n}\n\n.nav-justified > .nav-link,\n.nav-justified .nav-item {\n  flex-basis: 0;\n  flex-grow: 1;\n  text-align: center;\n}\n\n.nav-fill .nav-item .nav-link,\n.nav-justified .nav-item .nav-link {\n  width: 100%;\n}\n\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n\n.navbar {\n  --bs-navbar-padding-x: 0;\n  --bs-navbar-padding-y: 0.5rem;\n  --bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);\n  --bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);\n  --bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);\n  --bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);\n  --bs-navbar-brand-padding-y: 0.3125rem;\n  --bs-navbar-brand-margin-end: 1rem;\n  --bs-navbar-brand-font-size: 1.25rem;\n  --bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);\n  --bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);\n  --bs-navbar-nav-link-padding-x: 0.5rem;\n  --bs-navbar-toggler-padding-y: 0.25rem;\n  --bs-navbar-toggler-padding-x: 0.75rem;\n  --bs-navbar-toggler-font-size: 1.25rem;\n  --bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833, 37, 41, 0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");\n  --bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);\n  --bs-navbar-toggler-border-radius: var(--bs-border-radius);\n  --bs-navbar-toggler-focus-width: 0.1875rem;\n  --bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;\n  position: relative;\n  display: flex;\n  flex-wrap: wrap;\n  align-items: center;\n  justify-content: space-between;\n  padding: var(--bs-navbar-padding-y) var(--bs-navbar-padding-x);\n}\n.navbar > .container,\n.navbar > .container-fluid,\n.navbar > .container-sm,\n.navbar > .container-md,\n.navbar > .container-lg,\n.navbar > .container-xl {\n  display: flex;\n  flex-wrap: inherit;\n  align-items: center;\n  justify-content: space-between;\n}\n.navbar-brand {\n  padding-top: var(--bs-navbar-brand-padding-y);\n  padding-bottom: var(--bs-navbar-brand-padding-y);\n  margin-right: var(--bs-navbar-brand-margin-end);\n  font-size: var(--bs-navbar-brand-font-size);\n  color: var(--bs-navbar-brand-color);\n  text-decoration: none;\n  white-space: nowrap;\n}\n.navbar-brand:hover, .navbar-brand:focus {\n  color: var(--bs-navbar-brand-hover-color);\n}\n\n.navbar-nav {\n  --bs-nav-link-padding-x: 0;\n  --bs-nav-link-padding-y: 0.5rem;\n  --bs-nav-link-font-weight: ;\n  --bs-nav-link-color: var(--bs-navbar-color);\n  --bs-nav-link-hover-color: var(--bs-navbar-hover-color);\n  --bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);\n  display: flex;\n  flex-direction: column;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n.navbar-nav .nav-link.active, .navbar-nav .nav-link.show {\n  color: var(--bs-navbar-active-color);\n}\n.navbar-nav .dropdown-menu {\n  position: static;\n}\n\n.navbar-text {\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  color: var(--bs-navbar-color);\n}\n.navbar-text a,\n.navbar-text a:hover,\n.navbar-text a:focus {\n  color: var(--bs-navbar-active-color);\n}\n\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  align-items: center;\n}\n\n.navbar-toggler {\n  padding: var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);\n  font-size: var(--bs-navbar-toggler-font-size);\n  line-height: 1;\n  color: var(--bs-navbar-color);\n  background-color: transparent;\n  border: var(--bs-border-width) solid var(--bs-navbar-toggler-border-color);\n  border-radius: var(--bs-navbar-toggler-border-radius);\n  transition: var(--bs-navbar-toggler-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .navbar-toggler {\n    transition: none;\n  }\n}\n.navbar-toggler:hover {\n  text-decoration: none;\n}\n.navbar-toggler:focus {\n  text-decoration: none;\n  outline: 0;\n  box-shadow: 0 0 0 var(--bs-navbar-toggler-focus-width);\n}\n\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--bs-navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--bs-scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n@media (min-width: 540px) {\n  .navbar-expand-sm {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-sm .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-sm .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-sm .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-sm .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-sm .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-sm .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-sm .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-sm .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-sm .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 720px) {\n  .navbar-expand-md {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-md .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-md .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-md .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-md .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-md .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-md .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-md .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-md .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-md .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 960px) {\n  .navbar-expand-lg {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-lg .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-lg .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-lg .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-lg .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-lg .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-lg .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-lg .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-lg .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-lg .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n@media (min-width: 1200px) {\n  .navbar-expand-xl {\n    flex-wrap: nowrap;\n    justify-content: flex-start;\n  }\n  .navbar-expand-xl .navbar-nav {\n    flex-direction: row;\n  }\n  .navbar-expand-xl .navbar-nav .dropdown-menu {\n    position: absolute;\n  }\n  .navbar-expand-xl .navbar-nav .nav-link {\n    padding-right: var(--bs-navbar-nav-link-padding-x);\n    padding-left: var(--bs-navbar-nav-link-padding-x);\n  }\n  .navbar-expand-xl .navbar-nav-scroll {\n    overflow: visible;\n  }\n  .navbar-expand-xl .navbar-collapse {\n    display: flex !important;\n    flex-basis: auto;\n  }\n  .navbar-expand-xl .navbar-toggler {\n    display: none;\n  }\n  .navbar-expand-xl .offcanvas {\n    position: static;\n    z-index: auto;\n    flex-grow: 1;\n    width: auto !important;\n    height: auto !important;\n    visibility: visible !important;\n    background-color: transparent !important;\n    border: 0 !important;\n    transform: none !important;\n    transition: none;\n  }\n  .navbar-expand-xl .offcanvas .offcanvas-header {\n    display: none;\n  }\n  .navbar-expand-xl .offcanvas .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n  }\n}\n.navbar-expand {\n  flex-wrap: nowrap;\n  justify-content: flex-start;\n}\n.navbar-expand .navbar-nav {\n  flex-direction: row;\n}\n.navbar-expand .navbar-nav .dropdown-menu {\n  position: absolute;\n}\n.navbar-expand .navbar-nav .nav-link {\n  padding-right: var(--bs-navbar-nav-link-padding-x);\n  padding-left: var(--bs-navbar-nav-link-padding-x);\n}\n.navbar-expand .navbar-nav-scroll {\n  overflow: visible;\n}\n.navbar-expand .navbar-collapse {\n  display: flex !important;\n  flex-basis: auto;\n}\n.navbar-expand .navbar-toggler {\n  display: none;\n}\n.navbar-expand .offcanvas {\n  position: static;\n  z-index: auto;\n  flex-grow: 1;\n  width: auto !important;\n  height: auto !important;\n  visibility: visible !important;\n  background-color: transparent !important;\n  border: 0 !important;\n  transform: none !important;\n  transition: none;\n}\n.navbar-expand .offcanvas .offcanvas-header {\n  display: none;\n}\n.navbar-expand .offcanvas .offcanvas-body {\n  display: flex;\n  flex-grow: 0;\n  padding: 0;\n  overflow-y: visible;\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=dark] {\n  --bs-navbar-color: rgba(255, 255, 255, 0.55);\n  --bs-navbar-hover-color: rgba(255, 255, 255, 0.75);\n  --bs-navbar-disabled-color: rgba(255, 255, 255, 0.25);\n  --bs-navbar-active-color: #fff;\n  --bs-navbar-brand-color: #fff;\n  --bs-navbar-brand-hover-color: #fff;\n  --bs-navbar-toggler-border-color: rgba(255, 255, 255, 0.1);\n  --bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");\n}\n\n[data-bs-theme=dark] .navbar-toggler-icon {\n  --bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");\n}\n\n.card {\n  --bs-card-spacer-y: 1rem;\n  --bs-card-spacer-x: 1rem;\n  --bs-card-title-spacer-y: 0.5rem;\n  --bs-card-title-color: ;\n  --bs-card-subtitle-color: ;\n  --bs-card-border-width: var(--bs-border-width);\n  --bs-card-border-color: var(--bs-border-color-translucent);\n  --bs-card-border-radius: var(--bs-border-radius);\n  --bs-card-box-shadow: ;\n  --bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));\n  --bs-card-cap-padding-y: 0.5rem;\n  --bs-card-cap-padding-x: 1rem;\n  --bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);\n  --bs-card-cap-color: ;\n  --bs-card-height: ;\n  --bs-card-color: ;\n  --bs-card-bg: var(--bs-body-bg);\n  --bs-card-img-overlay-padding: 1rem;\n  --bs-card-group-margin: 0.75rem;\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0;\n  height: var(--bs-card-height);\n  color: var(--bs-body-color);\n  word-wrap: break-word;\n  background-color: var(--bs-card-bg);\n  background-clip: border-box;\n  border: var(--bs-card-border-width) solid var(--bs-card-border-color);\n  border-radius: var(--bs-card-border-radius);\n}\n.card > hr {\n  margin-right: 0;\n  margin-left: 0;\n}\n.card > .list-group {\n  border-top: inherit;\n  border-bottom: inherit;\n}\n.card > .list-group:first-child {\n  border-top-width: 0;\n  border-top-left-radius: var(--bs-card-inner-border-radius);\n  border-top-right-radius: var(--bs-card-inner-border-radius);\n}\n.card > .list-group:last-child {\n  border-bottom-width: 0;\n  border-bottom-right-radius: var(--bs-card-inner-border-radius);\n  border-bottom-left-radius: var(--bs-card-inner-border-radius);\n}\n.card > .card-header + .list-group,\n.card > .list-group + .card-footer {\n  border-top: 0;\n}\n\n.card-body {\n  flex: 1 1 auto;\n  padding: var(--bs-card-spacer-y) var(--bs-card-spacer-x);\n  color: var(--bs-card-color);\n}\n\n.card-title {\n  margin-bottom: var(--bs-card-title-spacer-y);\n  color: var(--bs-card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-0.5 * var(--bs-card-title-spacer-y));\n  margin-bottom: 0;\n  color: var(--bs-card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link + .card-link {\n  margin-left: var(--bs-card-spacer-x);\n}\n\n.card-header {\n  padding: var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);\n  margin-bottom: 0;\n  color: var(--bs-card-cap-color);\n  background-color: var(--bs-card-cap-bg);\n  border-bottom: var(--bs-card-border-width) solid var(--bs-card-border-color);\n}\n.card-header:first-child {\n  border-radius: var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0;\n}\n\n.card-footer {\n  padding: var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);\n  color: var(--bs-card-cap-color);\n  background-color: var(--bs-card-cap-bg);\n  border-top: var(--bs-card-border-width) solid var(--bs-card-border-color);\n}\n.card-footer:last-child {\n  border-radius: 0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius);\n}\n\n.card-header-tabs {\n  margin-right: calc(-0.5 * var(--bs-card-cap-padding-x));\n  margin-bottom: calc(-1 * var(--bs-card-cap-padding-y));\n  margin-left: calc(-0.5 * var(--bs-card-cap-padding-x));\n  border-bottom: 0;\n}\n.card-header-tabs .nav-link.active {\n  background-color: var(--bs-card-bg);\n  border-bottom-color: var(--bs-card-bg);\n}\n\n.card-header-pills {\n  margin-right: calc(-0.5 * var(--bs-card-cap-padding-x));\n  margin-left: calc(-0.5 * var(--bs-card-cap-padding-x));\n}\n\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--bs-card-img-overlay-padding);\n  border-radius: var(--bs-card-inner-border-radius);\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%;\n}\n\n.card-img,\n.card-img-top {\n  border-top-left-radius: var(--bs-card-inner-border-radius);\n  border-top-right-radius: var(--bs-card-inner-border-radius);\n}\n\n.card-img,\n.card-img-bottom {\n  border-bottom-right-radius: var(--bs-card-inner-border-radius);\n  border-bottom-left-radius: var(--bs-card-inner-border-radius);\n}\n\n.card-group > .card {\n  margin-bottom: var(--bs-card-group-margin);\n}\n@media (min-width: 540px) {\n  .card-group {\n    display: flex;\n    flex-flow: row wrap;\n  }\n  .card-group > .card {\n    flex: 1 0 0%;\n    margin-bottom: 0;\n  }\n  .card-group > .card + .card {\n    margin-left: 0;\n    border-left: 0;\n  }\n  .card-group > .card:not(:last-child) {\n    border-top-right-radius: 0;\n    border-bottom-right-radius: 0;\n  }\n  .card-group > .card:not(:last-child) .card-img-top,\n  .card-group > .card:not(:last-child) .card-header {\n    border-top-right-radius: 0;\n  }\n  .card-group > .card:not(:last-child) .card-img-bottom,\n  .card-group > .card:not(:last-child) .card-footer {\n    border-bottom-right-radius: 0;\n  }\n  .card-group > .card:not(:first-child) {\n    border-top-left-radius: 0;\n    border-bottom-left-radius: 0;\n  }\n  .card-group > .card:not(:first-child) .card-img-top,\n  .card-group > .card:not(:first-child) .card-header {\n    border-top-left-radius: 0;\n  }\n  .card-group > .card:not(:first-child) .card-img-bottom,\n  .card-group > .card:not(:first-child) .card-footer {\n    border-bottom-left-radius: 0;\n  }\n}\n\n.accordion {\n  --bs-accordion-color: var(--bs-body-color);\n  --bs-accordion-bg: var(--bs-body-bg);\n  --bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;\n  --bs-accordion-border-color: var(--bs-border-color);\n  --bs-accordion-border-width: var(--bs-border-width);\n  --bs-accordion-border-radius: var(--bs-border-radius);\n  --bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));\n  --bs-accordion-btn-padding-x: 1.25rem;\n  --bs-accordion-btn-padding-y: 1rem;\n  --bs-accordion-btn-color: var(--bs-body-color);\n  --bs-accordion-btn-bg: var(--bs-accordion-bg);\n  --bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23212529' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");\n  --bs-accordion-btn-icon-width: 1.25rem;\n  --bs-accordion-btn-icon-transform: rotate(-180deg);\n  --bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;\n  --bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23052c65' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");\n  --bs-accordion-btn-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  --bs-accordion-body-padding-x: 1.25rem;\n  --bs-accordion-body-padding-y: 1rem;\n  --bs-accordion-active-color: var(--bs-primary-text-emphasis);\n  --bs-accordion-active-bg: var(--bs-primary-bg-subtle);\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);\n  font-size: 1rem;\n  color: var(--bs-accordion-btn-color);\n  text-align: left;\n  background-color: var(--bs-accordion-btn-bg);\n  border: 0;\n  border-radius: 0;\n  overflow-anchor: none;\n  transition: var(--bs-accordion-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .accordion-button {\n    transition: none;\n  }\n}\n.accordion-button:not(.collapsed) {\n  color: var(--bs-accordion-active-color);\n  background-color: var(--bs-accordion-active-bg);\n  box-shadow: inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color);\n}\n.accordion-button:not(.collapsed)::after {\n  background-image: var(--bs-accordion-btn-active-icon);\n  transform: var(--bs-accordion-btn-icon-transform);\n}\n.accordion-button::after {\n  flex-shrink: 0;\n  width: var(--bs-accordion-btn-icon-width);\n  height: var(--bs-accordion-btn-icon-width);\n  margin-left: auto;\n  content: \"\";\n  background-image: var(--bs-accordion-btn-icon);\n  background-repeat: no-repeat;\n  background-size: var(--bs-accordion-btn-icon-width);\n  transition: var(--bs-accordion-btn-icon-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .accordion-button::after {\n    transition: none;\n  }\n}\n.accordion-button:hover {\n  z-index: 2;\n}\n.accordion-button:focus {\n  z-index: 3;\n  outline: 0;\n  box-shadow: var(--bs-accordion-btn-focus-box-shadow);\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--bs-accordion-color);\n  background-color: var(--bs-accordion-bg);\n  border: var(--bs-accordion-border-width) solid var(--bs-accordion-border-color);\n}\n.accordion-item:first-of-type {\n  border-top-left-radius: var(--bs-accordion-border-radius);\n  border-top-right-radius: var(--bs-accordion-border-radius);\n}\n.accordion-item:first-of-type > .accordion-header .accordion-button {\n  border-top-left-radius: var(--bs-accordion-inner-border-radius);\n  border-top-right-radius: var(--bs-accordion-inner-border-radius);\n}\n.accordion-item:not(:first-of-type) {\n  border-top: 0;\n}\n.accordion-item:last-of-type {\n  border-bottom-right-radius: var(--bs-accordion-border-radius);\n  border-bottom-left-radius: var(--bs-accordion-border-radius);\n}\n.accordion-item:last-of-type > .accordion-header .accordion-button.collapsed {\n  border-bottom-right-radius: var(--bs-accordion-inner-border-radius);\n  border-bottom-left-radius: var(--bs-accordion-inner-border-radius);\n}\n.accordion-item:last-of-type > .accordion-collapse {\n  border-bottom-right-radius: var(--bs-accordion-border-radius);\n  border-bottom-left-radius: var(--bs-accordion-border-radius);\n}\n\n.accordion-body {\n  padding: var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x);\n}\n\n.accordion-flush > .accordion-item {\n  border-right: 0;\n  border-left: 0;\n  border-radius: 0;\n}\n.accordion-flush > .accordion-item:first-child {\n  border-top: 0;\n}\n.accordion-flush > .accordion-item:last-child {\n  border-bottom: 0;\n}\n.accordion-flush > .accordion-item > .accordion-header .accordion-button, .accordion-flush > .accordion-item > .accordion-header .accordion-button.collapsed {\n  border-radius: 0;\n}\n.accordion-flush > .accordion-item > .accordion-collapse {\n  border-radius: 0;\n}\n\n[data-bs-theme=dark] .accordion-button::after {\n  --bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");\n  --bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");\n}\n\n.breadcrumb {\n  --bs-breadcrumb-padding-x: 0;\n  --bs-breadcrumb-padding-y: 0;\n  --bs-breadcrumb-margin-bottom: 1rem;\n  --bs-breadcrumb-bg: ;\n  --bs-breadcrumb-border-radius: ;\n  --bs-breadcrumb-divider-color: var(--bs-secondary-color);\n  --bs-breadcrumb-item-padding-x: 0.5rem;\n  --bs-breadcrumb-item-active-color: var(--bs-secondary-color);\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);\n  margin-bottom: var(--bs-breadcrumb-margin-bottom);\n  font-size: var(--bs-breadcrumb-font-size);\n  list-style: none;\n  background-color: var(--bs-breadcrumb-bg);\n  border-radius: var(--bs-breadcrumb-border-radius);\n}\n\n.breadcrumb-item + .breadcrumb-item {\n  padding-left: var(--bs-breadcrumb-item-padding-x);\n}\n.breadcrumb-item + .breadcrumb-item::before {\n  float: left;\n  padding-right: var(--bs-breadcrumb-item-padding-x);\n  color: var(--bs-breadcrumb-divider-color);\n  content: var(--bs-breadcrumb-divider, \"/\") /* rtl: var(--bs-breadcrumb-divider, \"/\") */;\n}\n.breadcrumb-item.active {\n  color: var(--bs-breadcrumb-item-active-color);\n}\n\n.pagination {\n  --bs-pagination-padding-x: 0.75rem;\n  --bs-pagination-padding-y: 0.375rem;\n  --bs-pagination-font-size: 1rem;\n  --bs-pagination-color: var(--bs-link-color);\n  --bs-pagination-bg: var(--bs-body-bg);\n  --bs-pagination-border-width: var(--bs-border-width);\n  --bs-pagination-border-color: var(--bs-border-color);\n  --bs-pagination-border-radius: var(--bs-border-radius);\n  --bs-pagination-hover-color: var(--bs-link-hover-color);\n  --bs-pagination-hover-bg: var(--bs-tertiary-bg);\n  --bs-pagination-hover-border-color: var(--bs-border-color);\n  --bs-pagination-focus-color: var(--bs-link-hover-color);\n  --bs-pagination-focus-bg: var(--bs-secondary-bg);\n  --bs-pagination-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  --bs-pagination-active-color: #fff;\n  --bs-pagination-active-bg: #0d6efd;\n  --bs-pagination-active-border-color: #0d6efd;\n  --bs-pagination-disabled-color: var(--bs-secondary-color);\n  --bs-pagination-disabled-bg: var(--bs-secondary-bg);\n  --bs-pagination-disabled-border-color: var(--bs-border-color);\n  display: flex;\n  padding-left: 0;\n  list-style: none;\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);\n  font-size: var(--bs-pagination-font-size);\n  color: var(--bs-pagination-color);\n  text-decoration: none;\n  background-color: var(--bs-pagination-bg);\n  border: var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);\n  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .page-link {\n    transition: none;\n  }\n}\n.page-link:hover {\n  z-index: 2;\n  color: var(--bs-pagination-hover-color);\n  background-color: var(--bs-pagination-hover-bg);\n  border-color: var(--bs-pagination-hover-border-color);\n}\n.page-link:focus {\n  z-index: 3;\n  color: var(--bs-pagination-focus-color);\n  background-color: var(--bs-pagination-focus-bg);\n  outline: 0;\n  box-shadow: var(--bs-pagination-focus-box-shadow);\n}\n.page-link.active, .active > .page-link {\n  z-index: 3;\n  color: var(--bs-pagination-active-color);\n  background-color: var(--bs-pagination-active-bg);\n  border-color: var(--bs-pagination-active-border-color);\n}\n.page-link.disabled, .disabled > .page-link {\n  color: var(--bs-pagination-disabled-color);\n  pointer-events: none;\n  background-color: var(--bs-pagination-disabled-bg);\n  border-color: var(--bs-pagination-disabled-border-color);\n}\n\n.page-item:not(:first-child) .page-link {\n  margin-left: calc(var(--bs-border-width) * -1);\n}\n.page-item:first-child .page-link {\n  border-top-left-radius: var(--bs-pagination-border-radius);\n  border-bottom-left-radius: var(--bs-pagination-border-radius);\n}\n.page-item:last-child .page-link {\n  border-top-right-radius: var(--bs-pagination-border-radius);\n  border-bottom-right-radius: var(--bs-pagination-border-radius);\n}\n\n.pagination-lg {\n  --bs-pagination-padding-x: 1.5rem;\n  --bs-pagination-padding-y: 0.75rem;\n  --bs-pagination-font-size: 1.25rem;\n  --bs-pagination-border-radius: var(--bs-border-radius-lg);\n}\n\n.pagination-sm {\n  --bs-pagination-padding-x: 0.5rem;\n  --bs-pagination-padding-y: 0.25rem;\n  --bs-pagination-font-size: 0.875rem;\n  --bs-pagination-border-radius: var(--bs-border-radius-sm);\n}\n\n.badge {\n  --bs-badge-padding-x: 0.65em;\n  --bs-badge-padding-y: 0.35em;\n  --bs-badge-font-size: 0.75em;\n  --bs-badge-font-weight: 700;\n  --bs-badge-color: #fff;\n  --bs-badge-border-radius: var(--bs-border-radius);\n  display: inline-block;\n  padding: var(--bs-badge-padding-y) var(--bs-badge-padding-x);\n  font-size: var(--bs-badge-font-size);\n  font-weight: var(--bs-badge-font-weight);\n  line-height: 1;\n  color: var(--bs-badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: var(--bs-badge-border-radius);\n}\n.badge:empty {\n  display: none;\n}\n\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n\n.alert {\n  --bs-alert-bg: transparent;\n  --bs-alert-padding-x: 1rem;\n  --bs-alert-padding-y: 1rem;\n  --bs-alert-margin-bottom: 1rem;\n  --bs-alert-color: inherit;\n  --bs-alert-border-color: transparent;\n  --bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);\n  --bs-alert-border-radius: var(--bs-border-radius);\n  --bs-alert-link-color: inherit;\n  position: relative;\n  padding: var(--bs-alert-padding-y) var(--bs-alert-padding-x);\n  margin-bottom: var(--bs-alert-margin-bottom);\n  color: var(--bs-alert-color);\n  background-color: var(--bs-alert-bg);\n  border: var(--bs-alert-border);\n  border-radius: var(--bs-alert-border-radius);\n}\n\n.alert-heading {\n  color: inherit;\n}\n\n.alert-link {\n  font-weight: 700;\n  color: var(--bs-alert-link-color);\n}\n\n.alert-dismissible {\n  padding-right: 3rem;\n}\n.alert-dismissible .btn-close {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  padding: 1.25rem 1rem;\n}\n\n.alert-primary {\n  --bs-alert-color: var(--bs-primary-text-emphasis);\n  --bs-alert-bg: var(--bs-primary-bg-subtle);\n  --bs-alert-border-color: var(--bs-primary-border-subtle);\n  --bs-alert-link-color: var(--bs-primary-text-emphasis);\n}\n\n.alert-secondary {\n  --bs-alert-color: var(--bs-secondary-text-emphasis);\n  --bs-alert-bg: var(--bs-secondary-bg-subtle);\n  --bs-alert-border-color: var(--bs-secondary-border-subtle);\n  --bs-alert-link-color: var(--bs-secondary-text-emphasis);\n}\n\n.alert-success {\n  --bs-alert-color: var(--bs-success-text-emphasis);\n  --bs-alert-bg: var(--bs-success-bg-subtle);\n  --bs-alert-border-color: var(--bs-success-border-subtle);\n  --bs-alert-link-color: var(--bs-success-text-emphasis);\n}\n\n.alert-info {\n  --bs-alert-color: var(--bs-info-text-emphasis);\n  --bs-alert-bg: var(--bs-info-bg-subtle);\n  --bs-alert-border-color: var(--bs-info-border-subtle);\n  --bs-alert-link-color: var(--bs-info-text-emphasis);\n}\n\n.alert-warning {\n  --bs-alert-color: var(--bs-warning-text-emphasis);\n  --bs-alert-bg: var(--bs-warning-bg-subtle);\n  --bs-alert-border-color: var(--bs-warning-border-subtle);\n  --bs-alert-link-color: var(--bs-warning-text-emphasis);\n}\n\n.alert-danger {\n  --bs-alert-color: var(--bs-danger-text-emphasis);\n  --bs-alert-bg: var(--bs-danger-bg-subtle);\n  --bs-alert-border-color: var(--bs-danger-border-subtle);\n  --bs-alert-link-color: var(--bs-danger-text-emphasis);\n}\n\n.alert-light {\n  --bs-alert-color: var(--bs-light-text-emphasis);\n  --bs-alert-bg: var(--bs-light-bg-subtle);\n  --bs-alert-border-color: var(--bs-light-border-subtle);\n  --bs-alert-link-color: var(--bs-light-text-emphasis);\n}\n\n.alert-dark {\n  --bs-alert-color: var(--bs-dark-text-emphasis);\n  --bs-alert-bg: var(--bs-dark-bg-subtle);\n  --bs-alert-border-color: var(--bs-dark-border-subtle);\n  --bs-alert-link-color: var(--bs-dark-text-emphasis);\n}\n\n@keyframes progress-bar-stripes {\n  0% {\n    background-position-x: 1rem;\n  }\n}\n.progress,\n.progress-stacked {\n  --bs-progress-height: 1rem;\n  --bs-progress-font-size: 0.75rem;\n  --bs-progress-bg: var(--bs-secondary-bg);\n  --bs-progress-border-radius: var(--bs-border-radius);\n  --bs-progress-box-shadow: var(--bs-box-shadow-inset);\n  --bs-progress-bar-color: #fff;\n  --bs-progress-bar-bg: #0d6efd;\n  --bs-progress-bar-transition: width 0.6s ease;\n  display: flex;\n  height: var(--bs-progress-height);\n  overflow: hidden;\n  font-size: var(--bs-progress-font-size);\n  background-color: var(--bs-progress-bg);\n  border-radius: var(--bs-progress-border-radius);\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--bs-progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--bs-progress-bar-bg);\n  transition: var(--bs-progress-bar-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .progress-bar {\n    transition: none;\n  }\n}\n\n.progress-bar-striped {\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-size: var(--bs-progress-height) var(--bs-progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n.progress-bar-animated {\n  animation: 1s linear infinite progress-bar-stripes;\n}\n@media (prefers-reduced-motion: reduce) {\n  .progress-bar-animated {\n    animation: none;\n  }\n}\n\n.list-group {\n  --bs-list-group-color: var(--bs-body-color);\n  --bs-list-group-bg: var(--bs-body-bg);\n  --bs-list-group-border-color: var(--bs-border-color);\n  --bs-list-group-border-width: var(--bs-border-width);\n  --bs-list-group-border-radius: var(--bs-border-radius);\n  --bs-list-group-item-padding-x: 1rem;\n  --bs-list-group-item-padding-y: 0.5rem;\n  --bs-list-group-action-color: var(--bs-secondary-color);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-tertiary-bg);\n  --bs-list-group-action-active-color: var(--bs-body-color);\n  --bs-list-group-action-active-bg: var(--bs-secondary-bg);\n  --bs-list-group-disabled-color: var(--bs-secondary-color);\n  --bs-list-group-disabled-bg: var(--bs-body-bg);\n  --bs-list-group-active-color: #fff;\n  --bs-list-group-active-bg: #0d6efd;\n  --bs-list-group-active-border-color: #0d6efd;\n  display: flex;\n  flex-direction: column;\n  padding-left: 0;\n  margin-bottom: 0;\n  border-radius: var(--bs-list-group-border-radius);\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n}\n.list-group-numbered > .list-group-item::before {\n  content: counters(section, \".\") \". \";\n  counter-increment: section;\n}\n\n.list-group-item-action {\n  width: 100%;\n  color: var(--bs-list-group-action-color);\n  text-align: inherit;\n}\n.list-group-item-action:hover, .list-group-item-action:focus {\n  z-index: 1;\n  color: var(--bs-list-group-action-hover-color);\n  text-decoration: none;\n  background-color: var(--bs-list-group-action-hover-bg);\n}\n.list-group-item-action:active {\n  color: var(--bs-list-group-action-active-color);\n  background-color: var(--bs-list-group-action-active-bg);\n}\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);\n  color: var(--bs-list-group-color);\n  text-decoration: none;\n  background-color: var(--bs-list-group-bg);\n  border: var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);\n}\n.list-group-item:first-child {\n  border-top-left-radius: inherit;\n  border-top-right-radius: inherit;\n}\n.list-group-item:last-child {\n  border-bottom-right-radius: inherit;\n  border-bottom-left-radius: inherit;\n}\n.list-group-item.disabled, .list-group-item:disabled {\n  color: var(--bs-list-group-disabled-color);\n  pointer-events: none;\n  background-color: var(--bs-list-group-disabled-bg);\n}\n.list-group-item.active {\n  z-index: 2;\n  color: var(--bs-list-group-active-color);\n  background-color: var(--bs-list-group-active-bg);\n  border-color: var(--bs-list-group-active-border-color);\n}\n.list-group-item + .list-group-item {\n  border-top-width: 0;\n}\n.list-group-item + .list-group-item.active {\n  margin-top: calc(-1 * var(--bs-list-group-border-width));\n  border-top-width: var(--bs-list-group-border-width);\n}\n\n.list-group-horizontal {\n  flex-direction: row;\n}\n.list-group-horizontal > .list-group-item:first-child:not(:last-child) {\n  border-bottom-left-radius: var(--bs-list-group-border-radius);\n  border-top-right-radius: 0;\n}\n.list-group-horizontal > .list-group-item:last-child:not(:first-child) {\n  border-top-right-radius: var(--bs-list-group-border-radius);\n  border-bottom-left-radius: 0;\n}\n.list-group-horizontal > .list-group-item.active {\n  margin-top: 0;\n}\n.list-group-horizontal > .list-group-item + .list-group-item {\n  border-top-width: var(--bs-list-group-border-width);\n  border-left-width: 0;\n}\n.list-group-horizontal > .list-group-item + .list-group-item.active {\n  margin-left: calc(-1 * var(--bs-list-group-border-width));\n  border-left-width: var(--bs-list-group-border-width);\n}\n\n@media (min-width: 540px) {\n  .list-group-horizontal-sm {\n    flex-direction: row;\n  }\n  .list-group-horizontal-sm > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-sm > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 720px) {\n  .list-group-horizontal-md {\n    flex-direction: row;\n  }\n  .list-group-horizontal-md > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-md > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-md > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-md > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-md > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 960px) {\n  .list-group-horizontal-lg {\n    flex-direction: row;\n  }\n  .list-group-horizontal-lg > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-lg > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n@media (min-width: 1200px) {\n  .list-group-horizontal-xl {\n    flex-direction: row;\n  }\n  .list-group-horizontal-xl > .list-group-item:first-child:not(:last-child) {\n    border-bottom-left-radius: var(--bs-list-group-border-radius);\n    border-top-right-radius: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item:last-child:not(:first-child) {\n    border-top-right-radius: var(--bs-list-group-border-radius);\n    border-bottom-left-radius: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item.active {\n    margin-top: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item + .list-group-item {\n    border-top-width: var(--bs-list-group-border-width);\n    border-left-width: 0;\n  }\n  .list-group-horizontal-xl > .list-group-item + .list-group-item.active {\n    margin-left: calc(-1 * var(--bs-list-group-border-width));\n    border-left-width: var(--bs-list-group-border-width);\n  }\n}\n.list-group-flush {\n  border-radius: 0;\n}\n.list-group-flush > .list-group-item {\n  border-width: 0 0 var(--bs-list-group-border-width);\n}\n.list-group-flush > .list-group-item:last-child {\n  border-bottom-width: 0;\n}\n\n.list-group-item-primary {\n  --bs-list-group-color: var(--bs-primary-text-emphasis);\n  --bs-list-group-bg: var(--bs-primary-bg-subtle);\n  --bs-list-group-border-color: var(--bs-primary-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-primary-border-subtle);\n  --bs-list-group-active-color: var(--bs-primary-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-primary-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-primary-text-emphasis);\n}\n\n.list-group-item-secondary {\n  --bs-list-group-color: var(--bs-secondary-text-emphasis);\n  --bs-list-group-bg: var(--bs-secondary-bg-subtle);\n  --bs-list-group-border-color: var(--bs-secondary-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);\n  --bs-list-group-active-color: var(--bs-secondary-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-secondary-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-secondary-text-emphasis);\n}\n\n.list-group-item-success {\n  --bs-list-group-color: var(--bs-success-text-emphasis);\n  --bs-list-group-bg: var(--bs-success-bg-subtle);\n  --bs-list-group-border-color: var(--bs-success-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-success-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-success-border-subtle);\n  --bs-list-group-active-color: var(--bs-success-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-success-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-success-text-emphasis);\n}\n\n.list-group-item-info {\n  --bs-list-group-color: var(--bs-info-text-emphasis);\n  --bs-list-group-bg: var(--bs-info-bg-subtle);\n  --bs-list-group-border-color: var(--bs-info-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-info-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-info-border-subtle);\n  --bs-list-group-active-color: var(--bs-info-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-info-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-info-text-emphasis);\n}\n\n.list-group-item-warning {\n  --bs-list-group-color: var(--bs-warning-text-emphasis);\n  --bs-list-group-bg: var(--bs-warning-bg-subtle);\n  --bs-list-group-border-color: var(--bs-warning-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-warning-border-subtle);\n  --bs-list-group-active-color: var(--bs-warning-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-warning-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-warning-text-emphasis);\n}\n\n.list-group-item-danger {\n  --bs-list-group-color: var(--bs-danger-text-emphasis);\n  --bs-list-group-bg: var(--bs-danger-bg-subtle);\n  --bs-list-group-border-color: var(--bs-danger-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-danger-border-subtle);\n  --bs-list-group-active-color: var(--bs-danger-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-danger-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-danger-text-emphasis);\n}\n\n.list-group-item-light {\n  --bs-list-group-color: var(--bs-light-text-emphasis);\n  --bs-list-group-bg: var(--bs-light-bg-subtle);\n  --bs-list-group-border-color: var(--bs-light-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-light-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-light-border-subtle);\n  --bs-list-group-active-color: var(--bs-light-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-light-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-light-text-emphasis);\n}\n\n.list-group-item-dark {\n  --bs-list-group-color: var(--bs-dark-text-emphasis);\n  --bs-list-group-bg: var(--bs-dark-bg-subtle);\n  --bs-list-group-border-color: var(--bs-dark-border-subtle);\n  --bs-list-group-action-hover-color: var(--bs-emphasis-color);\n  --bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);\n  --bs-list-group-action-active-color: var(--bs-emphasis-color);\n  --bs-list-group-action-active-bg: var(--bs-dark-border-subtle);\n  --bs-list-group-active-color: var(--bs-dark-bg-subtle);\n  --bs-list-group-active-bg: var(--bs-dark-text-emphasis);\n  --bs-list-group-active-border-color: var(--bs-dark-text-emphasis);\n}\n\n.btn-close {\n  --bs-btn-close-color: #000;\n  --bs-btn-close-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e\");\n  --bs-btn-close-opacity: 0.5;\n  --bs-btn-close-hover-opacity: 0.75;\n  --bs-btn-close-focus-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  --bs-btn-close-focus-opacity: 1;\n  --bs-btn-close-disabled-opacity: 0.25;\n  --bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);\n  box-sizing: content-box;\n  width: 1em;\n  height: 1em;\n  padding: 0.25em 0.25em;\n  color: var(--bs-btn-close-color);\n  background: transparent var(--bs-btn-close-bg) center/1em auto no-repeat;\n  border: 0;\n  border-radius: 0.375rem;\n  opacity: var(--bs-btn-close-opacity);\n}\n.btn-close:hover {\n  color: var(--bs-btn-close-color);\n  text-decoration: none;\n  opacity: var(--bs-btn-close-hover-opacity);\n}\n.btn-close:focus {\n  outline: 0;\n  box-shadow: var(--bs-btn-close-focus-shadow);\n  opacity: var(--bs-btn-close-focus-opacity);\n}\n.btn-close:disabled, .btn-close.disabled {\n  pointer-events: none;\n  user-select: none;\n  opacity: var(--bs-btn-close-disabled-opacity);\n}\n\n.btn-close-white {\n  filter: var(--bs-btn-close-white-filter);\n}\n\n[data-bs-theme=dark] .btn-close {\n  filter: var(--bs-btn-close-white-filter);\n}\n\n.toast {\n  --bs-toast-zindex: 1090;\n  --bs-toast-padding-x: 0.75rem;\n  --bs-toast-padding-y: 0.5rem;\n  --bs-toast-spacing: 1.5rem;\n  --bs-toast-max-width: 350px;\n  --bs-toast-font-size: 0.875rem;\n  --bs-toast-color: ;\n  --bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);\n  --bs-toast-border-width: var(--bs-border-width);\n  --bs-toast-border-color: var(--bs-border-color-translucent);\n  --bs-toast-border-radius: var(--bs-border-radius);\n  --bs-toast-box-shadow: var(--bs-box-shadow);\n  --bs-toast-header-color: var(--bs-secondary-color);\n  --bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);\n  --bs-toast-header-border-color: var(--bs-border-color-translucent);\n  width: var(--bs-toast-max-width);\n  max-width: 100%;\n  font-size: var(--bs-toast-font-size);\n  color: var(--bs-toast-color);\n  pointer-events: auto;\n  background-color: var(--bs-toast-bg);\n  background-clip: padding-box;\n  border: var(--bs-toast-border-width) solid var(--bs-toast-border-color);\n  box-shadow: var(--bs-toast-box-shadow);\n  border-radius: var(--bs-toast-border-radius);\n}\n.toast.showing {\n  opacity: 0;\n}\n.toast:not(.show) {\n  display: none;\n}\n\n.toast-container {\n  --bs-toast-zindex: 1090;\n  position: absolute;\n  z-index: var(--bs-toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n}\n.toast-container > :not(:last-child) {\n  margin-bottom: var(--bs-toast-spacing);\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--bs-toast-padding-y) var(--bs-toast-padding-x);\n  color: var(--bs-toast-header-color);\n  background-color: var(--bs-toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);\n  border-top-left-radius: calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));\n  border-top-right-radius: calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));\n}\n.toast-header .btn-close {\n  margin-right: calc(-0.5 * var(--bs-toast-padding-x));\n  margin-left: var(--bs-toast-padding-x);\n}\n\n.toast-body {\n  padding: var(--bs-toast-padding-x);\n  word-wrap: break-word;\n}\n\n.modal {\n  --bs-modal-zindex: 1055;\n  --bs-modal-width: 500px;\n  --bs-modal-padding: 1rem;\n  --bs-modal-margin: 0.5rem;\n  --bs-modal-color: ;\n  --bs-modal-bg: var(--bs-body-bg);\n  --bs-modal-border-color: var(--bs-border-color-translucent);\n  --bs-modal-border-width: var(--bs-border-width);\n  --bs-modal-border-radius: var(--bs-border-radius-lg);\n  --bs-modal-box-shadow: var(--bs-box-shadow-sm);\n  --bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));\n  --bs-modal-header-padding-x: 1rem;\n  --bs-modal-header-padding-y: 1rem;\n  --bs-modal-header-padding: 1rem 1rem;\n  --bs-modal-header-border-color: var(--bs-border-color);\n  --bs-modal-header-border-width: var(--bs-border-width);\n  --bs-modal-title-line-height: 1.5;\n  --bs-modal-footer-gap: 0.5rem;\n  --bs-modal-footer-bg: ;\n  --bs-modal-footer-border-color: var(--bs-border-color);\n  --bs-modal-footer-border-width: var(--bs-border-width);\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--bs-modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  outline: 0;\n}\n\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--bs-modal-margin);\n  pointer-events: none;\n}\n.modal.fade .modal-dialog {\n  transition: transform 0.3s ease-out;\n  transform: translate(0, -50px);\n}\n@media (prefers-reduced-motion: reduce) {\n  .modal.fade .modal-dialog {\n    transition: none;\n  }\n}\n.modal.show .modal-dialog {\n  transform: none;\n}\n.modal.modal-static .modal-dialog {\n  transform: scale(1.02);\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--bs-modal-margin) * 2);\n}\n.modal-dialog-scrollable .modal-content {\n  max-height: 100%;\n  overflow: hidden;\n}\n.modal-dialog-scrollable .modal-body {\n  overflow-y: auto;\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--bs-modal-margin) * 2);\n}\n\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%;\n  color: var(--bs-modal-color);\n  pointer-events: auto;\n  background-color: var(--bs-modal-bg);\n  background-clip: padding-box;\n  border: var(--bs-modal-border-width) solid var(--bs-modal-border-color);\n  border-radius: var(--bs-modal-border-radius);\n  outline: 0;\n}\n\n.modal-backdrop {\n  --bs-backdrop-zindex: 1050;\n  --bs-backdrop-bg: #000;\n  --bs-backdrop-opacity: 0.5;\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--bs-backdrop-zindex);\n  width: 100vw;\n  height: 100vh;\n  background-color: var(--bs-backdrop-bg);\n}\n.modal-backdrop.fade {\n  opacity: 0;\n}\n.modal-backdrop.show {\n  opacity: var(--bs-backdrop-opacity);\n}\n\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  padding: var(--bs-modal-header-padding);\n  border-bottom: var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);\n  border-top-left-radius: var(--bs-modal-inner-border-radius);\n  border-top-right-radius: var(--bs-modal-inner-border-radius);\n}\n.modal-header .btn-close {\n  padding: calc(var(--bs-modal-header-padding-y) * 0.5) calc(var(--bs-modal-header-padding-x) * 0.5);\n  margin: calc(-0.5 * var(--bs-modal-header-padding-y)) calc(-0.5 * var(--bs-modal-header-padding-x)) calc(-0.5 * var(--bs-modal-header-padding-y)) auto;\n}\n\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--bs-modal-title-line-height);\n}\n\n.modal-body {\n  position: relative;\n  flex: 1 1 auto;\n  padding: var(--bs-modal-padding);\n}\n\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center;\n  justify-content: flex-end;\n  padding: calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap) * 0.5);\n  background-color: var(--bs-modal-footer-bg);\n  border-top: var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);\n  border-bottom-right-radius: var(--bs-modal-inner-border-radius);\n  border-bottom-left-radius: var(--bs-modal-inner-border-radius);\n}\n.modal-footer > * {\n  margin: calc(var(--bs-modal-footer-gap) * 0.5);\n}\n\n@media (min-width: 540px) {\n  .modal {\n    --bs-modal-margin: 1.75rem;\n    --bs-modal-box-shadow: var(--bs-box-shadow);\n  }\n  .modal-dialog {\n    max-width: var(--bs-modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n  .modal-sm {\n    --bs-modal-width: 300px;\n  }\n}\n@media (min-width: 960px) {\n  .modal-lg,\n  .modal-xl {\n    --bs-modal-width: 800px;\n  }\n}\n@media (min-width: 1200px) {\n  .modal-xl {\n    --bs-modal-width: 1140px;\n  }\n}\n.modal-fullscreen {\n  width: 100vw;\n  max-width: none;\n  height: 100%;\n  margin: 0;\n}\n.modal-fullscreen .modal-content {\n  height: 100%;\n  border: 0;\n  border-radius: 0;\n}\n.modal-fullscreen .modal-header,\n.modal-fullscreen .modal-footer {\n  border-radius: 0;\n}\n.modal-fullscreen .modal-body {\n  overflow-y: auto;\n}\n\n@media (max-width: 539.98px) {\n  .modal-fullscreen-sm-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-sm-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-sm-down .modal-header,\n  .modal-fullscreen-sm-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-sm-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 719.98px) {\n  .modal-fullscreen-md-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-md-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-md-down .modal-header,\n  .modal-fullscreen-md-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-md-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 959.98px) {\n  .modal-fullscreen-lg-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-lg-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-lg-down .modal-header,\n  .modal-fullscreen-lg-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-lg-down .modal-body {\n    overflow-y: auto;\n  }\n}\n@media (max-width: 1199.98px) {\n  .modal-fullscreen-xl-down {\n    width: 100vw;\n    max-width: none;\n    height: 100%;\n    margin: 0;\n  }\n  .modal-fullscreen-xl-down .modal-content {\n    height: 100%;\n    border: 0;\n    border-radius: 0;\n  }\n  .modal-fullscreen-xl-down .modal-header,\n  .modal-fullscreen-xl-down .modal-footer {\n    border-radius: 0;\n  }\n  .modal-fullscreen-xl-down .modal-body {\n    overflow-y: auto;\n  }\n}\n.tooltip {\n  --bs-tooltip-zindex: 1080;\n  --bs-tooltip-max-width: 200px;\n  --bs-tooltip-padding-x: 0.5rem;\n  --bs-tooltip-padding-y: 0.25rem;\n  --bs-tooltip-margin: ;\n  --bs-tooltip-font-size: 0.875rem;\n  --bs-tooltip-color: var(--bs-body-bg);\n  --bs-tooltip-bg: var(--bs-emphasis-color);\n  --bs-tooltip-border-radius: var(--bs-border-radius);\n  --bs-tooltip-opacity: 0.9;\n  --bs-tooltip-arrow-width: 0.8rem;\n  --bs-tooltip-arrow-height: 0.4rem;\n  z-index: var(--bs-tooltip-zindex);\n  display: block;\n  margin: var(--bs-tooltip-margin);\n  font-family: var(--bs-font-sans-serif);\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.5;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n  font-size: var(--bs-tooltip-font-size);\n  word-wrap: break-word;\n  opacity: 0;\n}\n.tooltip.show {\n  opacity: var(--bs-tooltip-opacity);\n}\n.tooltip .tooltip-arrow {\n  display: block;\n  width: var(--bs-tooltip-arrow-width);\n  height: var(--bs-tooltip-arrow-height);\n}\n.tooltip .tooltip-arrow::before {\n  position: absolute;\n  content: \"\";\n  border-color: transparent;\n  border-style: solid;\n}\n\n.bs-tooltip-top .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow {\n  bottom: calc(-1 * var(--bs-tooltip-arrow-height));\n}\n.bs-tooltip-top .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before {\n  top: -1px;\n  border-width: var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * 0.5) 0;\n  border-top-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow {\n  left: calc(-1 * var(--bs-tooltip-arrow-height));\n  width: var(--bs-tooltip-arrow-height);\n  height: var(--bs-tooltip-arrow-width);\n}\n.bs-tooltip-end .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before {\n  right: -1px;\n  border-width: calc(var(--bs-tooltip-arrow-width) * 0.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * 0.5) 0;\n  border-right-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:end:ignore */\n.bs-tooltip-bottom .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow {\n  top: calc(-1 * var(--bs-tooltip-arrow-height));\n}\n.bs-tooltip-bottom .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before {\n  bottom: -1px;\n  border-width: 0 calc(var(--bs-tooltip-arrow-width) * 0.5) var(--bs-tooltip-arrow-height);\n  border-bottom-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow, .bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow {\n  right: calc(-1 * var(--bs-tooltip-arrow-height));\n  width: var(--bs-tooltip-arrow-height);\n  height: var(--bs-tooltip-arrow-width);\n}\n.bs-tooltip-start .tooltip-arrow::before, .bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before {\n  left: -1px;\n  border-width: calc(var(--bs-tooltip-arrow-width) * 0.5) 0 calc(var(--bs-tooltip-arrow-width) * 0.5) var(--bs-tooltip-arrow-height);\n  border-left-color: var(--bs-tooltip-bg);\n}\n\n/* rtl:end:ignore */\n.tooltip-inner {\n  max-width: var(--bs-tooltip-max-width);\n  padding: var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);\n  color: var(--bs-tooltip-color);\n  text-align: center;\n  background-color: var(--bs-tooltip-bg);\n  border-radius: var(--bs-tooltip-border-radius);\n}\n\n.popover {\n  --bs-popover-zindex: 1070;\n  --bs-popover-max-width: 276px;\n  --bs-popover-font-size: 0.875rem;\n  --bs-popover-bg: var(--bs-body-bg);\n  --bs-popover-border-width: var(--bs-border-width);\n  --bs-popover-border-color: var(--bs-border-color-translucent);\n  --bs-popover-border-radius: var(--bs-border-radius-lg);\n  --bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));\n  --bs-popover-box-shadow: var(--bs-box-shadow);\n  --bs-popover-header-padding-x: 1rem;\n  --bs-popover-header-padding-y: 0.5rem;\n  --bs-popover-header-font-size: 1rem;\n  --bs-popover-header-color: inherit;\n  --bs-popover-header-bg: var(--bs-secondary-bg);\n  --bs-popover-body-padding-x: 1rem;\n  --bs-popover-body-padding-y: 1rem;\n  --bs-popover-body-color: var(--bs-body-color);\n  --bs-popover-arrow-width: 1rem;\n  --bs-popover-arrow-height: 0.5rem;\n  --bs-popover-arrow-border: var(--bs-popover-border-color);\n  z-index: var(--bs-popover-zindex);\n  display: block;\n  max-width: var(--bs-popover-max-width);\n  font-family: var(--bs-font-sans-serif);\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.5;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n  font-size: var(--bs-popover-font-size);\n  word-wrap: break-word;\n  background-color: var(--bs-popover-bg);\n  background-clip: padding-box;\n  border: var(--bs-popover-border-width) solid var(--bs-popover-border-color);\n  border-radius: var(--bs-popover-border-radius);\n}\n.popover .popover-arrow {\n  display: block;\n  width: var(--bs-popover-arrow-width);\n  height: var(--bs-popover-arrow-height);\n}\n.popover .popover-arrow::before, .popover .popover-arrow::after {\n  position: absolute;\n  display: block;\n  content: \"\";\n  border-color: transparent;\n  border-style: solid;\n  border-width: 0;\n}\n\n.bs-popover-top > .popover-arrow, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow {\n  bottom: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n}\n.bs-popover-top > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::before, .bs-popover-top > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::after {\n  border-width: var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width) * 0.5) 0;\n}\n.bs-popover-top > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::before {\n  bottom: 0;\n  border-top-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-top > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=top] > .popover-arrow::after {\n  bottom: var(--bs-popover-border-width);\n  border-top-color: var(--bs-popover-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end > .popover-arrow, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow {\n  left: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n  width: var(--bs-popover-arrow-height);\n  height: var(--bs-popover-arrow-width);\n}\n.bs-popover-end > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::before, .bs-popover-end > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::after {\n  border-width: calc(var(--bs-popover-arrow-width) * 0.5) var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width) * 0.5) 0;\n}\n.bs-popover-end > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::before {\n  left: 0;\n  border-right-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-end > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=right] > .popover-arrow::after {\n  left: var(--bs-popover-border-width);\n  border-right-color: var(--bs-popover-bg);\n}\n\n/* rtl:end:ignore */\n.bs-popover-bottom > .popover-arrow, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow {\n  top: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n}\n.bs-popover-bottom > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::before, .bs-popover-bottom > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::after {\n  border-width: 0 calc(var(--bs-popover-arrow-width) * 0.5) var(--bs-popover-arrow-height);\n}\n.bs-popover-bottom > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::before {\n  top: 0;\n  border-bottom-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-bottom > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=bottom] > .popover-arrow::after {\n  top: var(--bs-popover-border-width);\n  border-bottom-color: var(--bs-popover-bg);\n}\n.bs-popover-bottom .popover-header::before, .bs-popover-auto[data-popper-placement^=bottom] .popover-header::before {\n  position: absolute;\n  top: 0;\n  left: 50%;\n  display: block;\n  width: var(--bs-popover-arrow-width);\n  margin-left: calc(-0.5 * var(--bs-popover-arrow-width));\n  content: \"\";\n  border-bottom: var(--bs-popover-border-width) solid var(--bs-popover-header-bg);\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start > .popover-arrow, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow {\n  right: calc(-1 * (var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));\n  width: var(--bs-popover-arrow-height);\n  height: var(--bs-popover-arrow-width);\n}\n.bs-popover-start > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::before, .bs-popover-start > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::after {\n  border-width: calc(var(--bs-popover-arrow-width) * 0.5) 0 calc(var(--bs-popover-arrow-width) * 0.5) var(--bs-popover-arrow-height);\n}\n.bs-popover-start > .popover-arrow::before, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::before {\n  right: 0;\n  border-left-color: var(--bs-popover-arrow-border);\n}\n.bs-popover-start > .popover-arrow::after, .bs-popover-auto[data-popper-placement^=left] > .popover-arrow::after {\n  right: var(--bs-popover-border-width);\n  border-left-color: var(--bs-popover-bg);\n}\n\n/* rtl:end:ignore */\n.popover-header {\n  padding: var(--bs-popover-header-padding-y) var(--bs-popover-header-padding-x);\n  margin-bottom: 0;\n  font-size: var(--bs-popover-header-font-size);\n  color: var(--bs-popover-header-color);\n  background-color: var(--bs-popover-header-bg);\n  border-bottom: var(--bs-popover-border-width) solid var(--bs-popover-border-color);\n  border-top-left-radius: var(--bs-popover-inner-border-radius);\n  border-top-right-radius: var(--bs-popover-inner-border-radius);\n}\n.popover-header:empty {\n  display: none;\n}\n\n.popover-body {\n  padding: var(--bs-popover-body-padding-y) var(--bs-popover-body-padding-x);\n  color: var(--bs-popover-body-color);\n}\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n}\n.carousel-inner::after {\n  display: block;\n  clear: both;\n  content: \"\";\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  transition: transform 0.6s ease-in-out;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-item {\n    transition: none;\n  }\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n.carousel-fade .carousel-item {\n  opacity: 0;\n  transition-property: opacity;\n  transform: none;\n}\n.carousel-fade .carousel-item.active,\n.carousel-fade .carousel-item-next.carousel-item-start,\n.carousel-fade .carousel-item-prev.carousel-item-end {\n  z-index: 1;\n  opacity: 1;\n}\n.carousel-fade .active.carousel-item-start,\n.carousel-fade .active.carousel-item-end {\n  z-index: 0;\n  opacity: 0;\n  transition: opacity 0s 0.6s;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-fade .active.carousel-item-start,\n  .carousel-fade .active.carousel-item-end {\n    transition: none;\n  }\n}\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  width: 15%;\n  padding: 0;\n  color: #fff;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: 0.5;\n  transition: opacity 0.15s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-control-prev,\n  .carousel-control-next {\n    transition: none;\n  }\n}\n.carousel-control-prev:hover, .carousel-control-prev:focus,\n.carousel-control-next:hover,\n.carousel-control-next:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  opacity: 0.9;\n}\n\n.carousel-control-prev {\n  left: 0;\n}\n\n.carousel-control-next {\n  right: 0;\n}\n\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: 2rem;\n  height: 2rem;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n.carousel-control-prev-icon {\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e\") /*rtl:url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\")*/;\n}\n\n.carousel-control-next-icon {\n  background-image: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\") /*rtl:url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e\")*/;\n}\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  margin-right: 15%;\n  margin-bottom: 1rem;\n  margin-left: 15%;\n}\n.carousel-indicators [data-bs-target] {\n  box-sizing: content-box;\n  flex: 0 1 auto;\n  width: 30px;\n  height: 3px;\n  padding: 0;\n  margin-right: 3px;\n  margin-left: 3px;\n  text-indent: -999px;\n  cursor: pointer;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 0;\n  border-top: 10px solid transparent;\n  border-bottom: 10px solid transparent;\n  opacity: 0.5;\n  transition: opacity 0.6s ease;\n}\n@media (prefers-reduced-motion: reduce) {\n  .carousel-indicators [data-bs-target] {\n    transition: none;\n  }\n}\n.carousel-indicators .active {\n  opacity: 1;\n}\n\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 1.25rem;\n  left: 15%;\n  padding-top: 1.25rem;\n  padding-bottom: 1.25rem;\n  color: #fff;\n  text-align: center;\n}\n\n.carousel-dark .carousel-control-prev-icon,\n.carousel-dark .carousel-control-next-icon {\n  filter: invert(1) grayscale(100);\n}\n.carousel-dark .carousel-indicators [data-bs-target] {\n  background-color: #000;\n}\n.carousel-dark .carousel-caption {\n  color: #000;\n}\n\n[data-bs-theme=dark] .carousel .carousel-control-prev-icon,\n[data-bs-theme=dark] .carousel .carousel-control-next-icon, [data-bs-theme=dark].carousel .carousel-control-prev-icon,\n[data-bs-theme=dark].carousel .carousel-control-next-icon {\n  filter: invert(1) grayscale(100);\n}\n[data-bs-theme=dark] .carousel .carousel-indicators [data-bs-target], [data-bs-theme=dark].carousel .carousel-indicators [data-bs-target] {\n  background-color: #000;\n}\n[data-bs-theme=dark] .carousel .carousel-caption, [data-bs-theme=dark].carousel .carousel-caption {\n  color: #000;\n}\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--bs-spinner-width);\n  height: var(--bs-spinner-height);\n  vertical-align: var(--bs-spinner-vertical-align);\n  border-radius: 50%;\n  animation: var(--bs-spinner-animation-speed) linear infinite var(--bs-spinner-animation-name);\n}\n\n@keyframes spinner-border {\n  to {\n    transform: rotate(360deg) /* rtl:ignore */;\n  }\n}\n.spinner-border {\n  --bs-spinner-width: 2rem;\n  --bs-spinner-height: 2rem;\n  --bs-spinner-vertical-align: -0.125em;\n  --bs-spinner-border-width: 0.25em;\n  --bs-spinner-animation-speed: 0.75s;\n  --bs-spinner-animation-name: spinner-border;\n  border: var(--bs-spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  --bs-spinner-width: 1rem;\n  --bs-spinner-height: 1rem;\n  --bs-spinner-border-width: 0.2em;\n}\n\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n.spinner-grow {\n  --bs-spinner-width: 2rem;\n  --bs-spinner-height: 2rem;\n  --bs-spinner-vertical-align: -0.125em;\n  --bs-spinner-animation-speed: 0.75s;\n  --bs-spinner-animation-name: spinner-grow;\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --bs-spinner-width: 1rem;\n  --bs-spinner-height: 1rem;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .spinner-border,\n  .spinner-grow {\n    --bs-spinner-animation-speed: 1.5s;\n  }\n}\n.offcanvas, .offcanvas-xl, .offcanvas-lg, .offcanvas-md, .offcanvas-sm {\n  --bs-offcanvas-zindex: 1045;\n  --bs-offcanvas-width: 400px;\n  --bs-offcanvas-height: 30vh;\n  --bs-offcanvas-padding-x: 1rem;\n  --bs-offcanvas-padding-y: 1rem;\n  --bs-offcanvas-color: var(--bs-body-color);\n  --bs-offcanvas-bg: var(--bs-body-bg);\n  --bs-offcanvas-border-width: var(--bs-border-width);\n  --bs-offcanvas-border-color: var(--bs-border-color-translucent);\n  --bs-offcanvas-box-shadow: var(--bs-box-shadow-sm);\n  --bs-offcanvas-transition: transform 0.3s ease-in-out;\n  --bs-offcanvas-title-line-height: 1.5;\n}\n\n@media (max-width: 539.98px) {\n  .offcanvas-sm {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 539.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-sm {\n    transition: none;\n  }\n}\n@media (max-width: 539.98px) {\n  .offcanvas-sm.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-sm.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-sm.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-sm.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-sm.showing, .offcanvas-sm.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-sm.showing, .offcanvas-sm.hiding, .offcanvas-sm.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 540px) {\n  .offcanvas-sm {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-sm .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-sm .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 719.98px) {\n  .offcanvas-md {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 719.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-md {\n    transition: none;\n  }\n}\n@media (max-width: 719.98px) {\n  .offcanvas-md.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-md.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-md.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-md.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-md.showing, .offcanvas-md.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-md.showing, .offcanvas-md.hiding, .offcanvas-md.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 720px) {\n  .offcanvas-md {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-md .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-md .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 959.98px) {\n  .offcanvas-lg {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 959.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-lg {\n    transition: none;\n  }\n}\n@media (max-width: 959.98px) {\n  .offcanvas-lg.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-lg.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-lg.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-lg.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-lg.showing, .offcanvas-lg.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-lg.showing, .offcanvas-lg.hiding, .offcanvas-lg.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 960px) {\n  .offcanvas-lg {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-lg .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-lg .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n@media (max-width: 1199.98px) {\n  .offcanvas-xl {\n    position: fixed;\n    bottom: 0;\n    z-index: var(--bs-offcanvas-zindex);\n    display: flex;\n    flex-direction: column;\n    max-width: 100%;\n    color: var(--bs-offcanvas-color);\n    visibility: hidden;\n    background-color: var(--bs-offcanvas-bg);\n    background-clip: padding-box;\n    outline: 0;\n    transition: var(--bs-offcanvas-transition);\n  }\n}\n@media (max-width: 1199.98px) and (prefers-reduced-motion: reduce) {\n  .offcanvas-xl {\n    transition: none;\n  }\n}\n@media (max-width: 1199.98px) {\n  .offcanvas-xl.offcanvas-start {\n    top: 0;\n    left: 0;\n    width: var(--bs-offcanvas-width);\n    border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(-100%);\n  }\n  .offcanvas-xl.offcanvas-end {\n    top: 0;\n    right: 0;\n    width: var(--bs-offcanvas-width);\n    border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateX(100%);\n  }\n  .offcanvas-xl.offcanvas-top {\n    top: 0;\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(-100%);\n  }\n  .offcanvas-xl.offcanvas-bottom {\n    right: 0;\n    left: 0;\n    height: var(--bs-offcanvas-height);\n    max-height: 100%;\n    border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n    transform: translateY(100%);\n  }\n  .offcanvas-xl.showing, .offcanvas-xl.show:not(.hiding) {\n    transform: none;\n  }\n  .offcanvas-xl.showing, .offcanvas-xl.hiding, .offcanvas-xl.show {\n    visibility: visible;\n  }\n}\n@media (min-width: 1200px) {\n  .offcanvas-xl {\n    --bs-offcanvas-height: auto;\n    --bs-offcanvas-border-width: 0;\n    background-color: transparent !important;\n  }\n  .offcanvas-xl .offcanvas-header {\n    display: none;\n  }\n  .offcanvas-xl .offcanvas-body {\n    display: flex;\n    flex-grow: 0;\n    padding: 0;\n    overflow-y: visible;\n    background-color: transparent !important;\n  }\n}\n\n.offcanvas {\n  position: fixed;\n  bottom: 0;\n  z-index: var(--bs-offcanvas-zindex);\n  display: flex;\n  flex-direction: column;\n  max-width: 100%;\n  color: var(--bs-offcanvas-color);\n  visibility: hidden;\n  background-color: var(--bs-offcanvas-bg);\n  background-clip: padding-box;\n  outline: 0;\n  transition: var(--bs-offcanvas-transition);\n}\n@media (prefers-reduced-motion: reduce) {\n  .offcanvas {\n    transition: none;\n  }\n}\n.offcanvas.offcanvas-start {\n  top: 0;\n  left: 0;\n  width: var(--bs-offcanvas-width);\n  border-right: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateX(-100%);\n}\n.offcanvas.offcanvas-end {\n  top: 0;\n  right: 0;\n  width: var(--bs-offcanvas-width);\n  border-left: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateX(100%);\n}\n.offcanvas.offcanvas-top {\n  top: 0;\n  right: 0;\n  left: 0;\n  height: var(--bs-offcanvas-height);\n  max-height: 100%;\n  border-bottom: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateY(-100%);\n}\n.offcanvas.offcanvas-bottom {\n  right: 0;\n  left: 0;\n  height: var(--bs-offcanvas-height);\n  max-height: 100%;\n  border-top: var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);\n  transform: translateY(100%);\n}\n.offcanvas.showing, .offcanvas.show:not(.hiding) {\n  transform: none;\n}\n.offcanvas.showing, .offcanvas.hiding, .offcanvas.show {\n  visibility: visible;\n}\n\n.offcanvas-backdrop {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: 1040;\n  width: 100vw;\n  height: 100vh;\n  background-color: #000;\n}\n.offcanvas-backdrop.fade {\n  opacity: 0;\n}\n.offcanvas-backdrop.show {\n  opacity: 0.5;\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  padding: var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);\n}\n.offcanvas-header .btn-close {\n  padding: calc(var(--bs-offcanvas-padding-y) * 0.5) calc(var(--bs-offcanvas-padding-x) * 0.5);\n  margin: calc(-0.5 * var(--bs-offcanvas-padding-y)) calc(-0.5 * var(--bs-offcanvas-padding-x)) calc(-0.5 * var(--bs-offcanvas-padding-y)) auto;\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--bs-offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);\n  overflow-y: auto;\n}\n\n.placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: 0.5;\n}\n.placeholder.btn::before {\n  display: inline-block;\n  content: \"\";\n}\n\n.placeholder-xs {\n  min-height: 0.6em;\n}\n\n.placeholder-sm {\n  min-height: 0.8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n.placeholder-glow .placeholder {\n  animation: placeholder-glow 2s ease-in-out infinite;\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: 0.2;\n  }\n}\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n.clearfix::after {\n  display: block;\n  clear: both;\n  content: \"\";\n}\n\n.text-bg-primary {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-primary-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-secondary {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-secondary-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-success {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-success-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-info {\n  color: #000 !important;\n  background-color: RGBA(var(--bs-info-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-warning {\n  color: #000 !important;\n  background-color: RGBA(var(--bs-warning-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-danger {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-danger-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-light {\n  color: #000 !important;\n  background-color: RGBA(var(--bs-light-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.text-bg-dark {\n  color: #fff !important;\n  background-color: RGBA(var(--bs-dark-rgb), var(--bs-bg-opacity, 1)) !important;\n}\n\n.link-primary {\n  color: RGBA(var(--bs-primary-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-primary-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-primary:hover, .link-primary:focus {\n  color: RGBA(10, 88, 202, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(10, 88, 202, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-secondary {\n  color: RGBA(var(--bs-secondary-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-secondary-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-secondary:hover, .link-secondary:focus {\n  color: RGBA(86, 94, 100, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(86, 94, 100, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-success {\n  color: RGBA(var(--bs-success-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-success-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-success:hover, .link-success:focus {\n  color: RGBA(20, 108, 67, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(20, 108, 67, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-info {\n  color: RGBA(var(--bs-info-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-info-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-info:hover, .link-info:focus {\n  color: RGBA(61, 213, 243, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(61, 213, 243, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-warning {\n  color: RGBA(var(--bs-warning-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-warning-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-warning:hover, .link-warning:focus {\n  color: RGBA(255, 205, 57, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(255, 205, 57, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-danger {\n  color: RGBA(var(--bs-danger-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-danger-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-danger:hover, .link-danger:focus {\n  color: RGBA(176, 42, 55, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(176, 42, 55, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-light {\n  color: RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-light:hover, .link-light:focus {\n  color: RGBA(249, 250, 251, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(249, 250, 251, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-dark {\n  color: RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-dark:hover, .link-dark:focus {\n  color: RGBA(26, 30, 33, var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(26, 30, 33, var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-body-emphasis {\n  color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;\n  text-decoration-color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n.link-body-emphasis:hover, .link-body-emphasis:focus {\n  color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 0.75)) !important;\n  text-decoration-color: RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 0.75)) !important;\n}\n\n.focus-ring:focus {\n  outline: 0;\n  box-shadow: var(--bs-focus-ring-x, 0) var(--bs-focus-ring-y, 0) var(--bs-focus-ring-blur, 0) var(--bs-focus-ring-width) var(--bs-focus-ring-color);\n}\n\n.icon-link {\n  display: inline-flex;\n  gap: 0.375rem;\n  align-items: center;\n  text-decoration-color: rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 0.5));\n  text-underline-offset: 0.25em;\n  backface-visibility: hidden;\n}\n.icon-link > .bi {\n  flex-shrink: 0;\n  width: 1em;\n  height: 1em;\n  fill: currentcolor;\n  transition: 0.2s ease-in-out transform;\n}\n@media (prefers-reduced-motion: reduce) {\n  .icon-link > .bi {\n    transition: none;\n  }\n}\n\n.icon-link-hover:hover > .bi, .icon-link-hover:focus-visible > .bi {\n  transform: var(--bs-icon-link-transform, translate3d(0.25em, 0, 0));\n}\n\n.ratio {\n  position: relative;\n  width: 100%;\n}\n.ratio::before {\n  display: block;\n  padding-top: var(--bs-aspect-ratio);\n  content: \"\";\n}\n.ratio > * {\n  position: absolute;\n  top: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n}\n\n.ratio-1x1 {\n  --bs-aspect-ratio: 100%;\n}\n\n.ratio-4x3 {\n  --bs-aspect-ratio: 75%;\n}\n\n.ratio-16x9 {\n  --bs-aspect-ratio: 56.25%;\n}\n\n.ratio-21x9 {\n  --bs-aspect-ratio: 42.8571428571%;\n}\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1030;\n}\n\n.sticky-top {\n  position: sticky;\n  top: 0;\n  z-index: 1020;\n}\n\n.sticky-bottom {\n  position: sticky;\n  bottom: 0;\n  z-index: 1020;\n}\n\n@media (min-width: 540px) {\n  .sticky-sm-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-sm-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 720px) {\n  .sticky-md-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-md-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 960px) {\n  .sticky-lg-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-lg-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n@media (min-width: 1200px) {\n  .sticky-xl-top {\n    position: sticky;\n    top: 0;\n    z-index: 1020;\n  }\n  .sticky-xl-bottom {\n    position: sticky;\n    bottom: 0;\n    z-index: 1020;\n  }\n}\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important;\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n}\n.visually-hidden:not(caption),\n.visually-hidden-focusable:not(:focus):not(:focus-within):not(caption) {\n  position: absolute !important;\n}\n\n.stretched-link::after {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1;\n  content: \"\";\n}\n\n.text-truncate {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n\n.vr {\n  display: inline-block;\n  align-self: stretch;\n  width: var(--bs-border-width);\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: 0.25;\n}\n\n.align-baseline {\n  vertical-align: baseline !important;\n}\n\n.align-top {\n  vertical-align: top !important;\n}\n\n.align-middle {\n  vertical-align: middle !important;\n}\n\n.align-bottom {\n  vertical-align: bottom !important;\n}\n\n.align-text-bottom {\n  vertical-align: text-bottom !important;\n}\n\n.align-text-top {\n  vertical-align: text-top !important;\n}\n\n.float-start {\n  float: left !important;\n}\n\n.float-end {\n  float: right !important;\n}\n\n.float-none {\n  float: none !important;\n}\n\n.object-fit-contain {\n  object-fit: contain !important;\n}\n\n.object-fit-cover {\n  object-fit: cover !important;\n}\n\n.object-fit-fill {\n  object-fit: fill !important;\n}\n\n.object-fit-scale {\n  object-fit: scale-down !important;\n}\n\n.object-fit-none {\n  object-fit: none !important;\n}\n\n.opacity-0 {\n  opacity: 0 !important;\n}\n\n.opacity-25 {\n  opacity: 0.25 !important;\n}\n\n.opacity-50 {\n  opacity: 0.5 !important;\n}\n\n.opacity-75 {\n  opacity: 0.75 !important;\n}\n\n.opacity-100 {\n  opacity: 1 !important;\n}\n\n.overflow-auto {\n  overflow: auto !important;\n}\n\n.overflow-hidden {\n  overflow: hidden !important;\n}\n\n.overflow-visible {\n  overflow: visible !important;\n}\n\n.overflow-scroll {\n  overflow: scroll !important;\n}\n\n.overflow-x-auto {\n  overflow-x: auto !important;\n}\n\n.overflow-x-hidden {\n  overflow-x: hidden !important;\n}\n\n.overflow-x-visible {\n  overflow-x: visible !important;\n}\n\n.overflow-x-scroll {\n  overflow-x: scroll !important;\n}\n\n.overflow-y-auto {\n  overflow-y: auto !important;\n}\n\n.overflow-y-hidden {\n  overflow-y: hidden !important;\n}\n\n.overflow-y-visible {\n  overflow-y: visible !important;\n}\n\n.overflow-y-scroll {\n  overflow-y: scroll !important;\n}\n\n.d-inline {\n  display: inline !important;\n}\n\n.d-inline-block {\n  display: inline-block !important;\n}\n\n.d-block {\n  display: block !important;\n}\n\n.d-grid {\n  display: grid !important;\n}\n\n.d-inline-grid {\n  display: inline-grid !important;\n}\n\n.d-table {\n  display: table !important;\n}\n\n.d-table-row {\n  display: table-row !important;\n}\n\n.d-table-cell {\n  display: table-cell !important;\n}\n\n.d-flex {\n  display: flex !important;\n}\n\n.d-inline-flex {\n  display: inline-flex !important;\n}\n\n.d-none {\n  display: none !important;\n}\n\n.shadow {\n  box-shadow: var(--bs-box-shadow) !important;\n}\n\n.shadow-sm {\n  box-shadow: var(--bs-box-shadow-sm) !important;\n}\n\n.shadow-lg {\n  box-shadow: var(--bs-box-shadow-lg) !important;\n}\n\n.shadow-none {\n  box-shadow: none !important;\n}\n\n.focus-ring-primary {\n  --bs-focus-ring-color: rgba(var(--bs-primary-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-secondary {\n  --bs-focus-ring-color: rgba(var(--bs-secondary-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-success {\n  --bs-focus-ring-color: rgba(var(--bs-success-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-info {\n  --bs-focus-ring-color: rgba(var(--bs-info-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-warning {\n  --bs-focus-ring-color: rgba(var(--bs-warning-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-danger {\n  --bs-focus-ring-color: rgba(var(--bs-danger-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-light {\n  --bs-focus-ring-color: rgba(var(--bs-light-rgb), var(--bs-focus-ring-opacity));\n}\n\n.focus-ring-dark {\n  --bs-focus-ring-color: rgba(var(--bs-dark-rgb), var(--bs-focus-ring-opacity));\n}\n\n.position-static {\n  position: static !important;\n}\n\n.position-relative {\n  position: relative !important;\n}\n\n.position-absolute {\n  position: absolute !important;\n}\n\n.position-fixed {\n  position: fixed !important;\n}\n\n.position-sticky {\n  position: sticky !important;\n}\n\n.top-0 {\n  top: 0 !important;\n}\n\n.top-50 {\n  top: 50% !important;\n}\n\n.top-100 {\n  top: 100% !important;\n}\n\n.bottom-0 {\n  bottom: 0 !important;\n}\n\n.bottom-50 {\n  bottom: 50% !important;\n}\n\n.bottom-100 {\n  bottom: 100% !important;\n}\n\n.start-0 {\n  left: 0 !important;\n}\n\n.start-50 {\n  left: 50% !important;\n}\n\n.start-100 {\n  left: 100% !important;\n}\n\n.end-0 {\n  right: 0 !important;\n}\n\n.end-50 {\n  right: 50% !important;\n}\n\n.end-100 {\n  right: 100% !important;\n}\n\n.translate-middle {\n  transform: translate(-50%, -50%) !important;\n}\n\n.translate-middle-x {\n  transform: translateX(-50%) !important;\n}\n\n.translate-middle-y {\n  transform: translateY(-50%) !important;\n}\n\n.border {\n  border: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-0 {\n  border: 0 !important;\n}\n\n.border-top {\n  border-top: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-top-0 {\n  border-top: 0 !important;\n}\n\n.border-end {\n  border-right: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-end-0 {\n  border-right: 0 !important;\n}\n\n.border-bottom {\n  border-bottom: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-bottom-0 {\n  border-bottom: 0 !important;\n}\n\n.border-start {\n  border-left: var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important;\n}\n\n.border-start-0 {\n  border-left: 0 !important;\n}\n\n.border-primary {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-primary-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-secondary {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-secondary-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-success {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-success-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-info {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-info-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-warning {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-warning-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-danger {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-danger-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-light {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-light-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-dark {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-dark-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-black {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-black-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-white {\n  --bs-border-opacity: 1;\n  border-color: rgba(var(--bs-white-rgb), var(--bs-border-opacity)) !important;\n}\n\n.border-primary-subtle {\n  border-color: var(--bs-primary-border-subtle) !important;\n}\n\n.border-secondary-subtle {\n  border-color: var(--bs-secondary-border-subtle) !important;\n}\n\n.border-success-subtle {\n  border-color: var(--bs-success-border-subtle) !important;\n}\n\n.border-info-subtle {\n  border-color: var(--bs-info-border-subtle) !important;\n}\n\n.border-warning-subtle {\n  border-color: var(--bs-warning-border-subtle) !important;\n}\n\n.border-danger-subtle {\n  border-color: var(--bs-danger-border-subtle) !important;\n}\n\n.border-light-subtle {\n  border-color: var(--bs-light-border-subtle) !important;\n}\n\n.border-dark-subtle {\n  border-color: var(--bs-dark-border-subtle) !important;\n}\n\n.border-1 {\n  border-width: 1px !important;\n}\n\n.border-2 {\n  border-width: 2px !important;\n}\n\n.border-3 {\n  border-width: 3px !important;\n}\n\n.border-4 {\n  border-width: 4px !important;\n}\n\n.border-5 {\n  border-width: 5px !important;\n}\n\n.border-opacity-10 {\n  --bs-border-opacity: 0.1;\n}\n\n.border-opacity-25 {\n  --bs-border-opacity: 0.25;\n}\n\n.border-opacity-50 {\n  --bs-border-opacity: 0.5;\n}\n\n.border-opacity-75 {\n  --bs-border-opacity: 0.75;\n}\n\n.border-opacity-100 {\n  --bs-border-opacity: 1;\n}\n\n.w-25 {\n  width: 25% !important;\n}\n\n.w-50 {\n  width: 50% !important;\n}\n\n.w-75 {\n  width: 75% !important;\n}\n\n.w-100 {\n  width: 100% !important;\n}\n\n.w-auto {\n  width: auto !important;\n}\n\n.mw-100 {\n  max-width: 100% !important;\n}\n\n.vw-100 {\n  width: 100vw !important;\n}\n\n.min-vw-100 {\n  min-width: 100vw !important;\n}\n\n.h-25 {\n  height: 25% !important;\n}\n\n.h-50 {\n  height: 50% !important;\n}\n\n.h-75 {\n  height: 75% !important;\n}\n\n.h-100 {\n  height: 100% !important;\n}\n\n.h-auto {\n  height: auto !important;\n}\n\n.mh-100 {\n  max-height: 100% !important;\n}\n\n.vh-100 {\n  height: 100vh !important;\n}\n\n.min-vh-100 {\n  min-height: 100vh !important;\n}\n\n.flex-fill {\n  flex: 1 1 auto !important;\n}\n\n.flex-row {\n  flex-direction: row !important;\n}\n\n.flex-column {\n  flex-direction: column !important;\n}\n\n.flex-row-reverse {\n  flex-direction: row-reverse !important;\n}\n\n.flex-column-reverse {\n  flex-direction: column-reverse !important;\n}\n\n.flex-grow-0 {\n  flex-grow: 0 !important;\n}\n\n.flex-grow-1 {\n  flex-grow: 1 !important;\n}\n\n.flex-shrink-0 {\n  flex-shrink: 0 !important;\n}\n\n.flex-shrink-1 {\n  flex-shrink: 1 !important;\n}\n\n.flex-wrap {\n  flex-wrap: wrap !important;\n}\n\n.flex-nowrap {\n  flex-wrap: nowrap !important;\n}\n\n.flex-wrap-reverse {\n  flex-wrap: wrap-reverse !important;\n}\n\n.justify-content-start {\n  justify-content: flex-start !important;\n}\n\n.justify-content-end {\n  justify-content: flex-end !important;\n}\n\n.justify-content-center {\n  justify-content: center !important;\n}\n\n.justify-content-between {\n  justify-content: space-between !important;\n}\n\n.justify-content-around {\n  justify-content: space-around !important;\n}\n\n.justify-content-evenly {\n  justify-content: space-evenly !important;\n}\n\n.align-items-start {\n  align-items: flex-start !important;\n}\n\n.align-items-end {\n  align-items: flex-end !important;\n}\n\n.align-items-center {\n  align-items: center !important;\n}\n\n.align-items-baseline {\n  align-items: baseline !important;\n}\n\n.align-items-stretch {\n  align-items: stretch !important;\n}\n\n.align-content-start {\n  align-content: flex-start !important;\n}\n\n.align-content-end {\n  align-content: flex-end !important;\n}\n\n.align-content-center {\n  align-content: center !important;\n}\n\n.align-content-between {\n  align-content: space-between !important;\n}\n\n.align-content-around {\n  align-content: space-around !important;\n}\n\n.align-content-stretch {\n  align-content: stretch !important;\n}\n\n.align-self-auto {\n  align-self: auto !important;\n}\n\n.align-self-start {\n  align-self: flex-start !important;\n}\n\n.align-self-end {\n  align-self: flex-end !important;\n}\n\n.align-self-center {\n  align-self: center !important;\n}\n\n.align-self-baseline {\n  align-self: baseline !important;\n}\n\n.align-self-stretch {\n  align-self: stretch !important;\n}\n\n.order-first {\n  order: -1 !important;\n}\n\n.order-0 {\n  order: 0 !important;\n}\n\n.order-1 {\n  order: 1 !important;\n}\n\n.order-2 {\n  order: 2 !important;\n}\n\n.order-3 {\n  order: 3 !important;\n}\n\n.order-4 {\n  order: 4 !important;\n}\n\n.order-5 {\n  order: 5 !important;\n}\n\n.order-last {\n  order: 6 !important;\n}\n\n.m-0 {\n  margin: 0 !important;\n}\n\n.m-1 {\n  margin: 0.25rem !important;\n}\n\n.m-2 {\n  margin: 0.5rem !important;\n}\n\n.m-3 {\n  margin: 1rem !important;\n}\n\n.m-4 {\n  margin: 1.5rem !important;\n}\n\n.m-5 {\n  margin: 3rem !important;\n}\n\n.m-auto {\n  margin: auto !important;\n}\n\n.mx-0 {\n  margin-right: 0 !important;\n  margin-left: 0 !important;\n}\n\n.mx-1 {\n  margin-right: 0.25rem !important;\n  margin-left: 0.25rem !important;\n}\n\n.mx-2 {\n  margin-right: 0.5rem !important;\n  margin-left: 0.5rem !important;\n}\n\n.mx-3 {\n  margin-right: 1rem !important;\n  margin-left: 1rem !important;\n}\n\n.mx-4 {\n  margin-right: 1.5rem !important;\n  margin-left: 1.5rem !important;\n}\n\n.mx-5 {\n  margin-right: 3rem !important;\n  margin-left: 3rem !important;\n}\n\n.mx-auto {\n  margin-right: auto !important;\n  margin-left: auto !important;\n}\n\n.my-0 {\n  margin-top: 0 !important;\n  margin-bottom: 0 !important;\n}\n\n.my-1 {\n  margin-top: 0.25rem !important;\n  margin-bottom: 0.25rem !important;\n}\n\n.my-2 {\n  margin-top: 0.5rem !important;\n  margin-bottom: 0.5rem !important;\n}\n\n.my-3 {\n  margin-top: 1rem !important;\n  margin-bottom: 1rem !important;\n}\n\n.my-4 {\n  margin-top: 1.5rem !important;\n  margin-bottom: 1.5rem !important;\n}\n\n.my-5 {\n  margin-top: 3rem !important;\n  margin-bottom: 3rem !important;\n}\n\n.my-auto {\n  margin-top: auto !important;\n  margin-bottom: auto !important;\n}\n\n.mt-0 {\n  margin-top: 0 !important;\n}\n\n.mt-1 {\n  margin-top: 0.25rem !important;\n}\n\n.mt-2 {\n  margin-top: 0.5rem !important;\n}\n\n.mt-3 {\n  margin-top: 1rem !important;\n}\n\n.mt-4 {\n  margin-top: 1.5rem !important;\n}\n\n.mt-5 {\n  margin-top: 3rem !important;\n}\n\n.mt-auto {\n  margin-top: auto !important;\n}\n\n.me-0 {\n  margin-right: 0 !important;\n}\n\n.me-1 {\n  margin-right: 0.25rem !important;\n}\n\n.me-2 {\n  margin-right: 0.5rem !important;\n}\n\n.me-3 {\n  margin-right: 1rem !important;\n}\n\n.me-4 {\n  margin-right: 1.5rem !important;\n}\n\n.me-5 {\n  margin-right: 3rem !important;\n}\n\n.me-auto {\n  margin-right: auto !important;\n}\n\n.mb-0 {\n  margin-bottom: 0 !important;\n}\n\n.mb-1 {\n  margin-bottom: 0.25rem !important;\n}\n\n.mb-2 {\n  margin-bottom: 0.5rem !important;\n}\n\n.mb-3 {\n  margin-bottom: 1rem !important;\n}\n\n.mb-4 {\n  margin-bottom: 1.5rem !important;\n}\n\n.mb-5 {\n  margin-bottom: 3rem !important;\n}\n\n.mb-auto {\n  margin-bottom: auto !important;\n}\n\n.ms-0 {\n  margin-left: 0 !important;\n}\n\n.ms-1 {\n  margin-left: 0.25rem !important;\n}\n\n.ms-2 {\n  margin-left: 0.5rem !important;\n}\n\n.ms-3 {\n  margin-left: 1rem !important;\n}\n\n.ms-4 {\n  margin-left: 1.5rem !important;\n}\n\n.ms-5 {\n  margin-left: 3rem !important;\n}\n\n.ms-auto {\n  margin-left: auto !important;\n}\n\n.p-0 {\n  padding: 0 !important;\n}\n\n.p-1 {\n  padding: 0.25rem !important;\n}\n\n.p-2 {\n  padding: 0.5rem !important;\n}\n\n.p-3 {\n  padding: 1rem !important;\n}\n\n.p-4 {\n  padding: 1.5rem !important;\n}\n\n.p-5 {\n  padding: 3rem !important;\n}\n\n.px-0 {\n  padding-right: 0 !important;\n  padding-left: 0 !important;\n}\n\n.px-1 {\n  padding-right: 0.25rem !important;\n  padding-left: 0.25rem !important;\n}\n\n.px-2 {\n  padding-right: 0.5rem !important;\n  padding-left: 0.5rem !important;\n}\n\n.px-3 {\n  padding-right: 1rem !important;\n  padding-left: 1rem !important;\n}\n\n.px-4 {\n  padding-right: 1.5rem !important;\n  padding-left: 1.5rem !important;\n}\n\n.px-5 {\n  padding-right: 3rem !important;\n  padding-left: 3rem !important;\n}\n\n.py-0 {\n  padding-top: 0 !important;\n  padding-bottom: 0 !important;\n}\n\n.py-1 {\n  padding-top: 0.25rem !important;\n  padding-bottom: 0.25rem !important;\n}\n\n.py-2 {\n  padding-top: 0.5rem !important;\n  padding-bottom: 0.5rem !important;\n}\n\n.py-3 {\n  padding-top: 1rem !important;\n  padding-bottom: 1rem !important;\n}\n\n.py-4 {\n  padding-top: 1.5rem !important;\n  padding-bottom: 1.5rem !important;\n}\n\n.py-5 {\n  padding-top: 3rem !important;\n  padding-bottom: 3rem !important;\n}\n\n.pt-0 {\n  padding-top: 0 !important;\n}\n\n.pt-1 {\n  padding-top: 0.25rem !important;\n}\n\n.pt-2 {\n  padding-top: 0.5rem !important;\n}\n\n.pt-3 {\n  padding-top: 1rem !important;\n}\n\n.pt-4 {\n  padding-top: 1.5rem !important;\n}\n\n.pt-5 {\n  padding-top: 3rem !important;\n}\n\n.pe-0 {\n  padding-right: 0 !important;\n}\n\n.pe-1 {\n  padding-right: 0.25rem !important;\n}\n\n.pe-2 {\n  padding-right: 0.5rem !important;\n}\n\n.pe-3 {\n  padding-right: 1rem !important;\n}\n\n.pe-4 {\n  padding-right: 1.5rem !important;\n}\n\n.pe-5 {\n  padding-right: 3rem !important;\n}\n\n.pb-0 {\n  padding-bottom: 0 !important;\n}\n\n.pb-1 {\n  padding-bottom: 0.25rem !important;\n}\n\n.pb-2 {\n  padding-bottom: 0.5rem !important;\n}\n\n.pb-3 {\n  padding-bottom: 1rem !important;\n}\n\n.pb-4 {\n  padding-bottom: 1.5rem !important;\n}\n\n.pb-5 {\n  padding-bottom: 3rem !important;\n}\n\n.ps-0 {\n  padding-left: 0 !important;\n}\n\n.ps-1 {\n  padding-left: 0.25rem !important;\n}\n\n.ps-2 {\n  padding-left: 0.5rem !important;\n}\n\n.ps-3 {\n  padding-left: 1rem !important;\n}\n\n.ps-4 {\n  padding-left: 1.5rem !important;\n}\n\n.ps-5 {\n  padding-left: 3rem !important;\n}\n\n.gap-0 {\n  gap: 0 !important;\n}\n\n.gap-1 {\n  gap: 0.25rem !important;\n}\n\n.gap-2 {\n  gap: 0.5rem !important;\n}\n\n.gap-3 {\n  gap: 1rem !important;\n}\n\n.gap-4 {\n  gap: 1.5rem !important;\n}\n\n.gap-5 {\n  gap: 3rem !important;\n}\n\n.row-gap-0 {\n  row-gap: 0 !important;\n}\n\n.row-gap-1 {\n  row-gap: 0.25rem !important;\n}\n\n.row-gap-2 {\n  row-gap: 0.5rem !important;\n}\n\n.row-gap-3 {\n  row-gap: 1rem !important;\n}\n\n.row-gap-4 {\n  row-gap: 1.5rem !important;\n}\n\n.row-gap-5 {\n  row-gap: 3rem !important;\n}\n\n.column-gap-0 {\n  column-gap: 0 !important;\n}\n\n.column-gap-1 {\n  column-gap: 0.25rem !important;\n}\n\n.column-gap-2 {\n  column-gap: 0.5rem !important;\n}\n\n.column-gap-3 {\n  column-gap: 1rem !important;\n}\n\n.column-gap-4 {\n  column-gap: 1.5rem !important;\n}\n\n.column-gap-5 {\n  column-gap: 3rem !important;\n}\n\n.font-monospace {\n  font-family: var(--bs-font-monospace) !important;\n}\n\n.fs-1 {\n  font-size: calc(1.375rem + 1.5vw) !important;\n}\n\n.fs-2 {\n  font-size: calc(1.325rem + 0.9vw) !important;\n}\n\n.fs-3 {\n  font-size: calc(1.3rem + 0.6vw) !important;\n}\n\n.fs-4 {\n  font-size: calc(1.275rem + 0.3vw) !important;\n}\n\n.fs-5 {\n  font-size: 1.25rem !important;\n}\n\n.fs-6 {\n  font-size: 1rem !important;\n}\n\n.fst-italic {\n  font-style: italic !important;\n}\n\n.fst-normal {\n  font-style: normal !important;\n}\n\n.fw-lighter {\n  font-weight: lighter !important;\n}\n\n.fw-light {\n  font-weight: 300 !important;\n}\n\n.fw-normal {\n  font-weight: 400 !important;\n}\n\n.fw-medium {\n  font-weight: 500 !important;\n}\n\n.fw-semibold {\n  font-weight: 600 !important;\n}\n\n.fw-bold {\n  font-weight: 700 !important;\n}\n\n.fw-bolder {\n  font-weight: bolder !important;\n}\n\n.lh-1 {\n  line-height: 1 !important;\n}\n\n.lh-sm {\n  line-height: 1.25 !important;\n}\n\n.lh-base {\n  line-height: 1.5 !important;\n}\n\n.lh-lg {\n  line-height: 2 !important;\n}\n\n.text-start {\n  text-align: left !important;\n}\n\n.text-end {\n  text-align: right !important;\n}\n\n.text-center {\n  text-align: center !important;\n}\n\n.text-decoration-none {\n  text-decoration: none !important;\n}\n\n.text-decoration-underline {\n  text-decoration: underline !important;\n}\n\n.text-decoration-line-through {\n  text-decoration: line-through !important;\n}\n\n.text-lowercase {\n  text-transform: lowercase !important;\n}\n\n.text-uppercase {\n  text-transform: uppercase !important;\n}\n\n.text-capitalize {\n  text-transform: capitalize !important;\n}\n\n.text-wrap {\n  white-space: normal !important;\n}\n\n.text-nowrap {\n  white-space: nowrap !important;\n}\n\n/* rtl:begin:remove */\n.text-break {\n  word-wrap: break-word !important;\n  word-break: break-word !important;\n}\n\n/* rtl:end:remove */\n.text-primary {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-primary-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-secondary {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-secondary-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-success {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-success-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-info {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-info-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-warning {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-warning-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-danger {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-danger-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-light {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-light-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-dark {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-dark-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-black {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-black-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-white {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-white-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-body {\n  --bs-text-opacity: 1;\n  color: rgba(var(--bs-body-color-rgb), var(--bs-text-opacity)) !important;\n}\n\n.text-muted {\n  --bs-text-opacity: 1;\n  color: var(--bs-secondary-color) !important;\n}\n\n.text-black-50 {\n  --bs-text-opacity: 1;\n  color: rgba(0, 0, 0, 0.5) !important;\n}\n\n.text-white-50 {\n  --bs-text-opacity: 1;\n  color: rgba(255, 255, 255, 0.5) !important;\n}\n\n.text-body-secondary {\n  --bs-text-opacity: 1;\n  color: var(--bs-secondary-color) !important;\n}\n\n.text-body-tertiary {\n  --bs-text-opacity: 1;\n  color: var(--bs-tertiary-color) !important;\n}\n\n.text-body-emphasis {\n  --bs-text-opacity: 1;\n  color: var(--bs-emphasis-color) !important;\n}\n\n.text-reset {\n  --bs-text-opacity: 1;\n  color: inherit !important;\n}\n\n.text-opacity-25 {\n  --bs-text-opacity: 0.25;\n}\n\n.text-opacity-50 {\n  --bs-text-opacity: 0.5;\n}\n\n.text-opacity-75 {\n  --bs-text-opacity: 0.75;\n}\n\n.text-opacity-100 {\n  --bs-text-opacity: 1;\n}\n\n.text-primary-emphasis {\n  color: var(--bs-primary-text-emphasis) !important;\n}\n\n.text-secondary-emphasis {\n  color: var(--bs-secondary-text-emphasis) !important;\n}\n\n.text-success-emphasis {\n  color: var(--bs-success-text-emphasis) !important;\n}\n\n.text-info-emphasis {\n  color: var(--bs-info-text-emphasis) !important;\n}\n\n.text-warning-emphasis {\n  color: var(--bs-warning-text-emphasis) !important;\n}\n\n.text-danger-emphasis {\n  color: var(--bs-danger-text-emphasis) !important;\n}\n\n.text-light-emphasis {\n  color: var(--bs-light-text-emphasis) !important;\n}\n\n.text-dark-emphasis {\n  color: var(--bs-dark-text-emphasis) !important;\n}\n\n.link-opacity-10 {\n  --bs-link-opacity: 0.1;\n}\n\n.link-opacity-10-hover:hover {\n  --bs-link-opacity: 0.1;\n}\n\n.link-opacity-25 {\n  --bs-link-opacity: 0.25;\n}\n\n.link-opacity-25-hover:hover {\n  --bs-link-opacity: 0.25;\n}\n\n.link-opacity-50 {\n  --bs-link-opacity: 0.5;\n}\n\n.link-opacity-50-hover:hover {\n  --bs-link-opacity: 0.5;\n}\n\n.link-opacity-75 {\n  --bs-link-opacity: 0.75;\n}\n\n.link-opacity-75-hover:hover {\n  --bs-link-opacity: 0.75;\n}\n\n.link-opacity-100 {\n  --bs-link-opacity: 1;\n}\n\n.link-opacity-100-hover:hover {\n  --bs-link-opacity: 1;\n}\n\n.link-offset-1 {\n  text-underline-offset: 0.125em !important;\n}\n\n.link-offset-1-hover:hover {\n  text-underline-offset: 0.125em !important;\n}\n\n.link-offset-2 {\n  text-underline-offset: 0.25em !important;\n}\n\n.link-offset-2-hover:hover {\n  text-underline-offset: 0.25em !important;\n}\n\n.link-offset-3 {\n  text-underline-offset: 0.375em !important;\n}\n\n.link-offset-3-hover:hover {\n  text-underline-offset: 0.375em !important;\n}\n\n.link-underline-primary {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-primary-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-secondary {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-secondary-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-success {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-success-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-info {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-info-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-warning {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-warning-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-danger {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-danger-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-light {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-light-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline-dark {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-dark-rgb), var(--bs-link-underline-opacity)) !important;\n}\n\n.link-underline {\n  --bs-link-underline-opacity: 1;\n  text-decoration-color: rgba(var(--bs-link-color-rgb), var(--bs-link-underline-opacity, 1)) !important;\n}\n\n.link-underline-opacity-0 {\n  --bs-link-underline-opacity: 0;\n}\n\n.link-underline-opacity-0-hover:hover {\n  --bs-link-underline-opacity: 0;\n}\n\n.link-underline-opacity-10 {\n  --bs-link-underline-opacity: 0.1;\n}\n\n.link-underline-opacity-10-hover:hover {\n  --bs-link-underline-opacity: 0.1;\n}\n\n.link-underline-opacity-25 {\n  --bs-link-underline-opacity: 0.25;\n}\n\n.link-underline-opacity-25-hover:hover {\n  --bs-link-underline-opacity: 0.25;\n}\n\n.link-underline-opacity-50 {\n  --bs-link-underline-opacity: 0.5;\n}\n\n.link-underline-opacity-50-hover:hover {\n  --bs-link-underline-opacity: 0.5;\n}\n\n.link-underline-opacity-75 {\n  --bs-link-underline-opacity: 0.75;\n}\n\n.link-underline-opacity-75-hover:hover {\n  --bs-link-underline-opacity: 0.75;\n}\n\n.link-underline-opacity-100 {\n  --bs-link-underline-opacity: 1;\n}\n\n.link-underline-opacity-100-hover:hover {\n  --bs-link-underline-opacity: 1;\n}\n\n.bg-primary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-primary-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-secondary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-secondary-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-success {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-success-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-info {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-info-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-warning {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-warning-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-danger {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-danger-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-light {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-light-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-dark {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-dark-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-black {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-black-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-white {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-white-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-body {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-body-bg-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-transparent {\n  --bs-bg-opacity: 1;\n  background-color: transparent !important;\n}\n\n.bg-body-secondary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-secondary-bg-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-body-tertiary {\n  --bs-bg-opacity: 1;\n  background-color: rgba(var(--bs-tertiary-bg-rgb), var(--bs-bg-opacity)) !important;\n}\n\n.bg-opacity-10 {\n  --bs-bg-opacity: 0.1;\n}\n\n.bg-opacity-25 {\n  --bs-bg-opacity: 0.25;\n}\n\n.bg-opacity-50 {\n  --bs-bg-opacity: 0.5;\n}\n\n.bg-opacity-75 {\n  --bs-bg-opacity: 0.75;\n}\n\n.bg-opacity-100 {\n  --bs-bg-opacity: 1;\n}\n\n.bg-primary-subtle {\n  background-color: var(--bs-primary-bg-subtle) !important;\n}\n\n.bg-secondary-subtle {\n  background-color: var(--bs-secondary-bg-subtle) !important;\n}\n\n.bg-success-subtle {\n  background-color: var(--bs-success-bg-subtle) !important;\n}\n\n.bg-info-subtle {\n  background-color: var(--bs-info-bg-subtle) !important;\n}\n\n.bg-warning-subtle {\n  background-color: var(--bs-warning-bg-subtle) !important;\n}\n\n.bg-danger-subtle {\n  background-color: var(--bs-danger-bg-subtle) !important;\n}\n\n.bg-light-subtle {\n  background-color: var(--bs-light-bg-subtle) !important;\n}\n\n.bg-dark-subtle {\n  background-color: var(--bs-dark-bg-subtle) !important;\n}\n\n.bg-gradient {\n  background-image: var(--bs-gradient) !important;\n}\n\n.user-select-all {\n  user-select: all !important;\n}\n\n.user-select-auto {\n  user-select: auto !important;\n}\n\n.user-select-none {\n  user-select: none !important;\n}\n\n.pe-none {\n  pointer-events: none !important;\n}\n\n.pe-auto {\n  pointer-events: auto !important;\n}\n\n.rounded {\n  border-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-0 {\n  border-radius: 0 !important;\n}\n\n.rounded-1 {\n  border-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-2 {\n  border-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-3 {\n  border-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-4 {\n  border-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-5 {\n  border-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-circle {\n  border-radius: 50% !important;\n}\n\n.rounded-pill {\n  border-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-top {\n  border-top-left-radius: var(--bs-border-radius) !important;\n  border-top-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-top-0 {\n  border-top-left-radius: 0 !important;\n  border-top-right-radius: 0 !important;\n}\n\n.rounded-top-1 {\n  border-top-left-radius: var(--bs-border-radius-sm) !important;\n  border-top-right-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-top-2 {\n  border-top-left-radius: var(--bs-border-radius) !important;\n  border-top-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-top-3 {\n  border-top-left-radius: var(--bs-border-radius-lg) !important;\n  border-top-right-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-top-4 {\n  border-top-left-radius: var(--bs-border-radius-xl) !important;\n  border-top-right-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-top-5 {\n  border-top-left-radius: var(--bs-border-radius-xxl) !important;\n  border-top-right-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-top-circle {\n  border-top-left-radius: 50% !important;\n  border-top-right-radius: 50% !important;\n}\n\n.rounded-top-pill {\n  border-top-left-radius: var(--bs-border-radius-pill) !important;\n  border-top-right-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-end {\n  border-top-right-radius: var(--bs-border-radius) !important;\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-end-0 {\n  border-top-right-radius: 0 !important;\n  border-bottom-right-radius: 0 !important;\n}\n\n.rounded-end-1 {\n  border-top-right-radius: var(--bs-border-radius-sm) !important;\n  border-bottom-right-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-end-2 {\n  border-top-right-radius: var(--bs-border-radius) !important;\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-end-3 {\n  border-top-right-radius: var(--bs-border-radius-lg) !important;\n  border-bottom-right-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-end-4 {\n  border-top-right-radius: var(--bs-border-radius-xl) !important;\n  border-bottom-right-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-end-5 {\n  border-top-right-radius: var(--bs-border-radius-xxl) !important;\n  border-bottom-right-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-end-circle {\n  border-top-right-radius: 50% !important;\n  border-bottom-right-radius: 50% !important;\n}\n\n.rounded-end-pill {\n  border-top-right-radius: var(--bs-border-radius-pill) !important;\n  border-bottom-right-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-bottom {\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-bottom-0 {\n  border-bottom-right-radius: 0 !important;\n  border-bottom-left-radius: 0 !important;\n}\n\n.rounded-bottom-1 {\n  border-bottom-right-radius: var(--bs-border-radius-sm) !important;\n  border-bottom-left-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-bottom-2 {\n  border-bottom-right-radius: var(--bs-border-radius) !important;\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-bottom-3 {\n  border-bottom-right-radius: var(--bs-border-radius-lg) !important;\n  border-bottom-left-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-bottom-4 {\n  border-bottom-right-radius: var(--bs-border-radius-xl) !important;\n  border-bottom-left-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-bottom-5 {\n  border-bottom-right-radius: var(--bs-border-radius-xxl) !important;\n  border-bottom-left-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-bottom-circle {\n  border-bottom-right-radius: 50% !important;\n  border-bottom-left-radius: 50% !important;\n}\n\n.rounded-bottom-pill {\n  border-bottom-right-radius: var(--bs-border-radius-pill) !important;\n  border-bottom-left-radius: var(--bs-border-radius-pill) !important;\n}\n\n.rounded-start {\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n  border-top-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-start-0 {\n  border-bottom-left-radius: 0 !important;\n  border-top-left-radius: 0 !important;\n}\n\n.rounded-start-1 {\n  border-bottom-left-radius: var(--bs-border-radius-sm) !important;\n  border-top-left-radius: var(--bs-border-radius-sm) !important;\n}\n\n.rounded-start-2 {\n  border-bottom-left-radius: var(--bs-border-radius) !important;\n  border-top-left-radius: var(--bs-border-radius) !important;\n}\n\n.rounded-start-3 {\n  border-bottom-left-radius: var(--bs-border-radius-lg) !important;\n  border-top-left-radius: var(--bs-border-radius-lg) !important;\n}\n\n.rounded-start-4 {\n  border-bottom-left-radius: var(--bs-border-radius-xl) !important;\n  border-top-left-radius: var(--bs-border-radius-xl) !important;\n}\n\n.rounded-start-5 {\n  border-bottom-left-radius: var(--bs-border-radius-xxl) !important;\n  border-top-left-radius: var(--bs-border-radius-xxl) !important;\n}\n\n.rounded-start-circle {\n  border-bottom-left-radius: 50% !important;\n  border-top-left-radius: 50% !important;\n}\n\n.rounded-start-pill {\n  border-bottom-left-radius: var(--bs-border-radius-pill) !important;\n  border-top-left-radius: var(--bs-border-radius-pill) !important;\n}\n\n.visible {\n  visibility: visible !important;\n}\n\n.invisible {\n  visibility: hidden !important;\n}\n\n.z-n1 {\n  z-index: -1 !important;\n}\n\n.z-0 {\n  z-index: 0 !important;\n}\n\n.z-1 {\n  z-index: 1 !important;\n}\n\n.z-2 {\n  z-index: 2 !important;\n}\n\n.z-3 {\n  z-index: 3 !important;\n}\n\n@media (min-width: 540px) {\n  .float-sm-start {\n    float: left !important;\n  }\n  .float-sm-end {\n    float: right !important;\n  }\n  .float-sm-none {\n    float: none !important;\n  }\n  .object-fit-sm-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-sm-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-sm-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-sm-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-sm-none {\n    object-fit: none !important;\n  }\n  .d-sm-inline {\n    display: inline !important;\n  }\n  .d-sm-inline-block {\n    display: inline-block !important;\n  }\n  .d-sm-block {\n    display: block !important;\n  }\n  .d-sm-grid {\n    display: grid !important;\n  }\n  .d-sm-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-sm-table {\n    display: table !important;\n  }\n  .d-sm-table-row {\n    display: table-row !important;\n  }\n  .d-sm-table-cell {\n    display: table-cell !important;\n  }\n  .d-sm-flex {\n    display: flex !important;\n  }\n  .d-sm-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-sm-none {\n    display: none !important;\n  }\n  .flex-sm-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-sm-row {\n    flex-direction: row !important;\n  }\n  .flex-sm-column {\n    flex-direction: column !important;\n  }\n  .flex-sm-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-sm-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-sm-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-sm-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-sm-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-sm-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-sm-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-sm-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-sm-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-sm-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-sm-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-sm-center {\n    justify-content: center !important;\n  }\n  .justify-content-sm-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-sm-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-sm-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-sm-start {\n    align-items: flex-start !important;\n  }\n  .align-items-sm-end {\n    align-items: flex-end !important;\n  }\n  .align-items-sm-center {\n    align-items: center !important;\n  }\n  .align-items-sm-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-sm-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-sm-start {\n    align-content: flex-start !important;\n  }\n  .align-content-sm-end {\n    align-content: flex-end !important;\n  }\n  .align-content-sm-center {\n    align-content: center !important;\n  }\n  .align-content-sm-between {\n    align-content: space-between !important;\n  }\n  .align-content-sm-around {\n    align-content: space-around !important;\n  }\n  .align-content-sm-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-sm-auto {\n    align-self: auto !important;\n  }\n  .align-self-sm-start {\n    align-self: flex-start !important;\n  }\n  .align-self-sm-end {\n    align-self: flex-end !important;\n  }\n  .align-self-sm-center {\n    align-self: center !important;\n  }\n  .align-self-sm-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-sm-stretch {\n    align-self: stretch !important;\n  }\n  .order-sm-first {\n    order: -1 !important;\n  }\n  .order-sm-0 {\n    order: 0 !important;\n  }\n  .order-sm-1 {\n    order: 1 !important;\n  }\n  .order-sm-2 {\n    order: 2 !important;\n  }\n  .order-sm-3 {\n    order: 3 !important;\n  }\n  .order-sm-4 {\n    order: 4 !important;\n  }\n  .order-sm-5 {\n    order: 5 !important;\n  }\n  .order-sm-last {\n    order: 6 !important;\n  }\n  .m-sm-0 {\n    margin: 0 !important;\n  }\n  .m-sm-1 {\n    margin: 0.25rem !important;\n  }\n  .m-sm-2 {\n    margin: 0.5rem !important;\n  }\n  .m-sm-3 {\n    margin: 1rem !important;\n  }\n  .m-sm-4 {\n    margin: 1.5rem !important;\n  }\n  .m-sm-5 {\n    margin: 3rem !important;\n  }\n  .m-sm-auto {\n    margin: auto !important;\n  }\n  .mx-sm-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-sm-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-sm-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-sm-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-sm-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-sm-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-sm-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-sm-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-sm-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-sm-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-sm-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-sm-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-sm-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-sm-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-sm-0 {\n    margin-top: 0 !important;\n  }\n  .mt-sm-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-sm-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-sm-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-sm-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-sm-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-sm-auto {\n    margin-top: auto !important;\n  }\n  .me-sm-0 {\n    margin-right: 0 !important;\n  }\n  .me-sm-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-sm-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-sm-3 {\n    margin-right: 1rem !important;\n  }\n  .me-sm-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-sm-5 {\n    margin-right: 3rem !important;\n  }\n  .me-sm-auto {\n    margin-right: auto !important;\n  }\n  .mb-sm-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-sm-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-sm-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-sm-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-sm-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-sm-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-sm-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-sm-0 {\n    margin-left: 0 !important;\n  }\n  .ms-sm-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-sm-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-sm-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-sm-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-sm-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-sm-auto {\n    margin-left: auto !important;\n  }\n  .p-sm-0 {\n    padding: 0 !important;\n  }\n  .p-sm-1 {\n    padding: 0.25rem !important;\n  }\n  .p-sm-2 {\n    padding: 0.5rem !important;\n  }\n  .p-sm-3 {\n    padding: 1rem !important;\n  }\n  .p-sm-4 {\n    padding: 1.5rem !important;\n  }\n  .p-sm-5 {\n    padding: 3rem !important;\n  }\n  .px-sm-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-sm-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-sm-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-sm-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-sm-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-sm-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-sm-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-sm-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-sm-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-sm-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-sm-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-sm-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-sm-0 {\n    padding-top: 0 !important;\n  }\n  .pt-sm-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-sm-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-sm-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-sm-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-sm-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-sm-0 {\n    padding-right: 0 !important;\n  }\n  .pe-sm-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-sm-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-sm-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-sm-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-sm-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-sm-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-sm-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-sm-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-sm-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-sm-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-sm-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-sm-0 {\n    padding-left: 0 !important;\n  }\n  .ps-sm-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-sm-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-sm-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-sm-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-sm-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-sm-0 {\n    gap: 0 !important;\n  }\n  .gap-sm-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-sm-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-sm-3 {\n    gap: 1rem !important;\n  }\n  .gap-sm-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-sm-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-sm-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-sm-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-sm-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-sm-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-sm-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-sm-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-sm-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-sm-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-sm-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-sm-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-sm-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-sm-5 {\n    column-gap: 3rem !important;\n  }\n  .text-sm-start {\n    text-align: left !important;\n  }\n  .text-sm-end {\n    text-align: right !important;\n  }\n  .text-sm-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 720px) {\n  .float-md-start {\n    float: left !important;\n  }\n  .float-md-end {\n    float: right !important;\n  }\n  .float-md-none {\n    float: none !important;\n  }\n  .object-fit-md-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-md-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-md-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-md-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-md-none {\n    object-fit: none !important;\n  }\n  .d-md-inline {\n    display: inline !important;\n  }\n  .d-md-inline-block {\n    display: inline-block !important;\n  }\n  .d-md-block {\n    display: block !important;\n  }\n  .d-md-grid {\n    display: grid !important;\n  }\n  .d-md-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-md-table {\n    display: table !important;\n  }\n  .d-md-table-row {\n    display: table-row !important;\n  }\n  .d-md-table-cell {\n    display: table-cell !important;\n  }\n  .d-md-flex {\n    display: flex !important;\n  }\n  .d-md-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-md-none {\n    display: none !important;\n  }\n  .flex-md-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-md-row {\n    flex-direction: row !important;\n  }\n  .flex-md-column {\n    flex-direction: column !important;\n  }\n  .flex-md-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-md-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-md-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-md-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-md-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-md-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-md-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-md-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-md-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-md-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-md-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-md-center {\n    justify-content: center !important;\n  }\n  .justify-content-md-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-md-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-md-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-md-start {\n    align-items: flex-start !important;\n  }\n  .align-items-md-end {\n    align-items: flex-end !important;\n  }\n  .align-items-md-center {\n    align-items: center !important;\n  }\n  .align-items-md-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-md-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-md-start {\n    align-content: flex-start !important;\n  }\n  .align-content-md-end {\n    align-content: flex-end !important;\n  }\n  .align-content-md-center {\n    align-content: center !important;\n  }\n  .align-content-md-between {\n    align-content: space-between !important;\n  }\n  .align-content-md-around {\n    align-content: space-around !important;\n  }\n  .align-content-md-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-md-auto {\n    align-self: auto !important;\n  }\n  .align-self-md-start {\n    align-self: flex-start !important;\n  }\n  .align-self-md-end {\n    align-self: flex-end !important;\n  }\n  .align-self-md-center {\n    align-self: center !important;\n  }\n  .align-self-md-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-md-stretch {\n    align-self: stretch !important;\n  }\n  .order-md-first {\n    order: -1 !important;\n  }\n  .order-md-0 {\n    order: 0 !important;\n  }\n  .order-md-1 {\n    order: 1 !important;\n  }\n  .order-md-2 {\n    order: 2 !important;\n  }\n  .order-md-3 {\n    order: 3 !important;\n  }\n  .order-md-4 {\n    order: 4 !important;\n  }\n  .order-md-5 {\n    order: 5 !important;\n  }\n  .order-md-last {\n    order: 6 !important;\n  }\n  .m-md-0 {\n    margin: 0 !important;\n  }\n  .m-md-1 {\n    margin: 0.25rem !important;\n  }\n  .m-md-2 {\n    margin: 0.5rem !important;\n  }\n  .m-md-3 {\n    margin: 1rem !important;\n  }\n  .m-md-4 {\n    margin: 1.5rem !important;\n  }\n  .m-md-5 {\n    margin: 3rem !important;\n  }\n  .m-md-auto {\n    margin: auto !important;\n  }\n  .mx-md-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-md-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-md-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-md-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-md-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-md-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-md-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-md-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-md-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-md-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-md-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-md-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-md-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-md-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-md-0 {\n    margin-top: 0 !important;\n  }\n  .mt-md-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-md-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-md-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-md-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-md-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-md-auto {\n    margin-top: auto !important;\n  }\n  .me-md-0 {\n    margin-right: 0 !important;\n  }\n  .me-md-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-md-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-md-3 {\n    margin-right: 1rem !important;\n  }\n  .me-md-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-md-5 {\n    margin-right: 3rem !important;\n  }\n  .me-md-auto {\n    margin-right: auto !important;\n  }\n  .mb-md-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-md-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-md-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-md-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-md-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-md-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-md-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-md-0 {\n    margin-left: 0 !important;\n  }\n  .ms-md-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-md-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-md-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-md-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-md-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-md-auto {\n    margin-left: auto !important;\n  }\n  .p-md-0 {\n    padding: 0 !important;\n  }\n  .p-md-1 {\n    padding: 0.25rem !important;\n  }\n  .p-md-2 {\n    padding: 0.5rem !important;\n  }\n  .p-md-3 {\n    padding: 1rem !important;\n  }\n  .p-md-4 {\n    padding: 1.5rem !important;\n  }\n  .p-md-5 {\n    padding: 3rem !important;\n  }\n  .px-md-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-md-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-md-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-md-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-md-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-md-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-md-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-md-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-md-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-md-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-md-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-md-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-md-0 {\n    padding-top: 0 !important;\n  }\n  .pt-md-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-md-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-md-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-md-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-md-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-md-0 {\n    padding-right: 0 !important;\n  }\n  .pe-md-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-md-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-md-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-md-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-md-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-md-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-md-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-md-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-md-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-md-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-md-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-md-0 {\n    padding-left: 0 !important;\n  }\n  .ps-md-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-md-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-md-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-md-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-md-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-md-0 {\n    gap: 0 !important;\n  }\n  .gap-md-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-md-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-md-3 {\n    gap: 1rem !important;\n  }\n  .gap-md-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-md-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-md-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-md-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-md-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-md-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-md-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-md-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-md-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-md-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-md-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-md-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-md-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-md-5 {\n    column-gap: 3rem !important;\n  }\n  .text-md-start {\n    text-align: left !important;\n  }\n  .text-md-end {\n    text-align: right !important;\n  }\n  .text-md-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 960px) {\n  .float-lg-start {\n    float: left !important;\n  }\n  .float-lg-end {\n    float: right !important;\n  }\n  .float-lg-none {\n    float: none !important;\n  }\n  .object-fit-lg-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-lg-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-lg-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-lg-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-lg-none {\n    object-fit: none !important;\n  }\n  .d-lg-inline {\n    display: inline !important;\n  }\n  .d-lg-inline-block {\n    display: inline-block !important;\n  }\n  .d-lg-block {\n    display: block !important;\n  }\n  .d-lg-grid {\n    display: grid !important;\n  }\n  .d-lg-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-lg-table {\n    display: table !important;\n  }\n  .d-lg-table-row {\n    display: table-row !important;\n  }\n  .d-lg-table-cell {\n    display: table-cell !important;\n  }\n  .d-lg-flex {\n    display: flex !important;\n  }\n  .d-lg-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-lg-none {\n    display: none !important;\n  }\n  .flex-lg-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-lg-row {\n    flex-direction: row !important;\n  }\n  .flex-lg-column {\n    flex-direction: column !important;\n  }\n  .flex-lg-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-lg-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-lg-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-lg-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-lg-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-lg-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-lg-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-lg-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-lg-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-lg-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-lg-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-lg-center {\n    justify-content: center !important;\n  }\n  .justify-content-lg-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-lg-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-lg-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-lg-start {\n    align-items: flex-start !important;\n  }\n  .align-items-lg-end {\n    align-items: flex-end !important;\n  }\n  .align-items-lg-center {\n    align-items: center !important;\n  }\n  .align-items-lg-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-lg-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-lg-start {\n    align-content: flex-start !important;\n  }\n  .align-content-lg-end {\n    align-content: flex-end !important;\n  }\n  .align-content-lg-center {\n    align-content: center !important;\n  }\n  .align-content-lg-between {\n    align-content: space-between !important;\n  }\n  .align-content-lg-around {\n    align-content: space-around !important;\n  }\n  .align-content-lg-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-lg-auto {\n    align-self: auto !important;\n  }\n  .align-self-lg-start {\n    align-self: flex-start !important;\n  }\n  .align-self-lg-end {\n    align-self: flex-end !important;\n  }\n  .align-self-lg-center {\n    align-self: center !important;\n  }\n  .align-self-lg-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-lg-stretch {\n    align-self: stretch !important;\n  }\n  .order-lg-first {\n    order: -1 !important;\n  }\n  .order-lg-0 {\n    order: 0 !important;\n  }\n  .order-lg-1 {\n    order: 1 !important;\n  }\n  .order-lg-2 {\n    order: 2 !important;\n  }\n  .order-lg-3 {\n    order: 3 !important;\n  }\n  .order-lg-4 {\n    order: 4 !important;\n  }\n  .order-lg-5 {\n    order: 5 !important;\n  }\n  .order-lg-last {\n    order: 6 !important;\n  }\n  .m-lg-0 {\n    margin: 0 !important;\n  }\n  .m-lg-1 {\n    margin: 0.25rem !important;\n  }\n  .m-lg-2 {\n    margin: 0.5rem !important;\n  }\n  .m-lg-3 {\n    margin: 1rem !important;\n  }\n  .m-lg-4 {\n    margin: 1.5rem !important;\n  }\n  .m-lg-5 {\n    margin: 3rem !important;\n  }\n  .m-lg-auto {\n    margin: auto !important;\n  }\n  .mx-lg-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-lg-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-lg-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-lg-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-lg-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-lg-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-lg-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-lg-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-lg-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-lg-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-lg-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-lg-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-lg-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-lg-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-lg-0 {\n    margin-top: 0 !important;\n  }\n  .mt-lg-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-lg-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-lg-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-lg-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-lg-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-lg-auto {\n    margin-top: auto !important;\n  }\n  .me-lg-0 {\n    margin-right: 0 !important;\n  }\n  .me-lg-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-lg-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-lg-3 {\n    margin-right: 1rem !important;\n  }\n  .me-lg-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-lg-5 {\n    margin-right: 3rem !important;\n  }\n  .me-lg-auto {\n    margin-right: auto !important;\n  }\n  .mb-lg-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-lg-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-lg-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-lg-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-lg-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-lg-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-lg-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-lg-0 {\n    margin-left: 0 !important;\n  }\n  .ms-lg-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-lg-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-lg-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-lg-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-lg-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-lg-auto {\n    margin-left: auto !important;\n  }\n  .p-lg-0 {\n    padding: 0 !important;\n  }\n  .p-lg-1 {\n    padding: 0.25rem !important;\n  }\n  .p-lg-2 {\n    padding: 0.5rem !important;\n  }\n  .p-lg-3 {\n    padding: 1rem !important;\n  }\n  .p-lg-4 {\n    padding: 1.5rem !important;\n  }\n  .p-lg-5 {\n    padding: 3rem !important;\n  }\n  .px-lg-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-lg-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-lg-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-lg-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-lg-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-lg-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-lg-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-lg-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-lg-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-lg-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-lg-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-lg-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-lg-0 {\n    padding-top: 0 !important;\n  }\n  .pt-lg-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-lg-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-lg-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-lg-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-lg-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-lg-0 {\n    padding-right: 0 !important;\n  }\n  .pe-lg-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-lg-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-lg-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-lg-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-lg-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-lg-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-lg-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-lg-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-lg-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-lg-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-lg-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-lg-0 {\n    padding-left: 0 !important;\n  }\n  .ps-lg-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-lg-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-lg-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-lg-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-lg-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-lg-0 {\n    gap: 0 !important;\n  }\n  .gap-lg-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-lg-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-lg-3 {\n    gap: 1rem !important;\n  }\n  .gap-lg-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-lg-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-lg-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-lg-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-lg-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-lg-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-lg-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-lg-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-lg-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-lg-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-lg-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-lg-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-lg-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-lg-5 {\n    column-gap: 3rem !important;\n  }\n  .text-lg-start {\n    text-align: left !important;\n  }\n  .text-lg-end {\n    text-align: right !important;\n  }\n  .text-lg-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 1200px) {\n  .float-xl-start {\n    float: left !important;\n  }\n  .float-xl-end {\n    float: right !important;\n  }\n  .float-xl-none {\n    float: none !important;\n  }\n  .object-fit-xl-contain {\n    object-fit: contain !important;\n  }\n  .object-fit-xl-cover {\n    object-fit: cover !important;\n  }\n  .object-fit-xl-fill {\n    object-fit: fill !important;\n  }\n  .object-fit-xl-scale {\n    object-fit: scale-down !important;\n  }\n  .object-fit-xl-none {\n    object-fit: none !important;\n  }\n  .d-xl-inline {\n    display: inline !important;\n  }\n  .d-xl-inline-block {\n    display: inline-block !important;\n  }\n  .d-xl-block {\n    display: block !important;\n  }\n  .d-xl-grid {\n    display: grid !important;\n  }\n  .d-xl-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-xl-table {\n    display: table !important;\n  }\n  .d-xl-table-row {\n    display: table-row !important;\n  }\n  .d-xl-table-cell {\n    display: table-cell !important;\n  }\n  .d-xl-flex {\n    display: flex !important;\n  }\n  .d-xl-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-xl-none {\n    display: none !important;\n  }\n  .flex-xl-fill {\n    flex: 1 1 auto !important;\n  }\n  .flex-xl-row {\n    flex-direction: row !important;\n  }\n  .flex-xl-column {\n    flex-direction: column !important;\n  }\n  .flex-xl-row-reverse {\n    flex-direction: row-reverse !important;\n  }\n  .flex-xl-column-reverse {\n    flex-direction: column-reverse !important;\n  }\n  .flex-xl-grow-0 {\n    flex-grow: 0 !important;\n  }\n  .flex-xl-grow-1 {\n    flex-grow: 1 !important;\n  }\n  .flex-xl-shrink-0 {\n    flex-shrink: 0 !important;\n  }\n  .flex-xl-shrink-1 {\n    flex-shrink: 1 !important;\n  }\n  .flex-xl-wrap {\n    flex-wrap: wrap !important;\n  }\n  .flex-xl-nowrap {\n    flex-wrap: nowrap !important;\n  }\n  .flex-xl-wrap-reverse {\n    flex-wrap: wrap-reverse !important;\n  }\n  .justify-content-xl-start {\n    justify-content: flex-start !important;\n  }\n  .justify-content-xl-end {\n    justify-content: flex-end !important;\n  }\n  .justify-content-xl-center {\n    justify-content: center !important;\n  }\n  .justify-content-xl-between {\n    justify-content: space-between !important;\n  }\n  .justify-content-xl-around {\n    justify-content: space-around !important;\n  }\n  .justify-content-xl-evenly {\n    justify-content: space-evenly !important;\n  }\n  .align-items-xl-start {\n    align-items: flex-start !important;\n  }\n  .align-items-xl-end {\n    align-items: flex-end !important;\n  }\n  .align-items-xl-center {\n    align-items: center !important;\n  }\n  .align-items-xl-baseline {\n    align-items: baseline !important;\n  }\n  .align-items-xl-stretch {\n    align-items: stretch !important;\n  }\n  .align-content-xl-start {\n    align-content: flex-start !important;\n  }\n  .align-content-xl-end {\n    align-content: flex-end !important;\n  }\n  .align-content-xl-center {\n    align-content: center !important;\n  }\n  .align-content-xl-between {\n    align-content: space-between !important;\n  }\n  .align-content-xl-around {\n    align-content: space-around !important;\n  }\n  .align-content-xl-stretch {\n    align-content: stretch !important;\n  }\n  .align-self-xl-auto {\n    align-self: auto !important;\n  }\n  .align-self-xl-start {\n    align-self: flex-start !important;\n  }\n  .align-self-xl-end {\n    align-self: flex-end !important;\n  }\n  .align-self-xl-center {\n    align-self: center !important;\n  }\n  .align-self-xl-baseline {\n    align-self: baseline !important;\n  }\n  .align-self-xl-stretch {\n    align-self: stretch !important;\n  }\n  .order-xl-first {\n    order: -1 !important;\n  }\n  .order-xl-0 {\n    order: 0 !important;\n  }\n  .order-xl-1 {\n    order: 1 !important;\n  }\n  .order-xl-2 {\n    order: 2 !important;\n  }\n  .order-xl-3 {\n    order: 3 !important;\n  }\n  .order-xl-4 {\n    order: 4 !important;\n  }\n  .order-xl-5 {\n    order: 5 !important;\n  }\n  .order-xl-last {\n    order: 6 !important;\n  }\n  .m-xl-0 {\n    margin: 0 !important;\n  }\n  .m-xl-1 {\n    margin: 0.25rem !important;\n  }\n  .m-xl-2 {\n    margin: 0.5rem !important;\n  }\n  .m-xl-3 {\n    margin: 1rem !important;\n  }\n  .m-xl-4 {\n    margin: 1.5rem !important;\n  }\n  .m-xl-5 {\n    margin: 3rem !important;\n  }\n  .m-xl-auto {\n    margin: auto !important;\n  }\n  .mx-xl-0 {\n    margin-right: 0 !important;\n    margin-left: 0 !important;\n  }\n  .mx-xl-1 {\n    margin-right: 0.25rem !important;\n    margin-left: 0.25rem !important;\n  }\n  .mx-xl-2 {\n    margin-right: 0.5rem !important;\n    margin-left: 0.5rem !important;\n  }\n  .mx-xl-3 {\n    margin-right: 1rem !important;\n    margin-left: 1rem !important;\n  }\n  .mx-xl-4 {\n    margin-right: 1.5rem !important;\n    margin-left: 1.5rem !important;\n  }\n  .mx-xl-5 {\n    margin-right: 3rem !important;\n    margin-left: 3rem !important;\n  }\n  .mx-xl-auto {\n    margin-right: auto !important;\n    margin-left: auto !important;\n  }\n  .my-xl-0 {\n    margin-top: 0 !important;\n    margin-bottom: 0 !important;\n  }\n  .my-xl-1 {\n    margin-top: 0.25rem !important;\n    margin-bottom: 0.25rem !important;\n  }\n  .my-xl-2 {\n    margin-top: 0.5rem !important;\n    margin-bottom: 0.5rem !important;\n  }\n  .my-xl-3 {\n    margin-top: 1rem !important;\n    margin-bottom: 1rem !important;\n  }\n  .my-xl-4 {\n    margin-top: 1.5rem !important;\n    margin-bottom: 1.5rem !important;\n  }\n  .my-xl-5 {\n    margin-top: 3rem !important;\n    margin-bottom: 3rem !important;\n  }\n  .my-xl-auto {\n    margin-top: auto !important;\n    margin-bottom: auto !important;\n  }\n  .mt-xl-0 {\n    margin-top: 0 !important;\n  }\n  .mt-xl-1 {\n    margin-top: 0.25rem !important;\n  }\n  .mt-xl-2 {\n    margin-top: 0.5rem !important;\n  }\n  .mt-xl-3 {\n    margin-top: 1rem !important;\n  }\n  .mt-xl-4 {\n    margin-top: 1.5rem !important;\n  }\n  .mt-xl-5 {\n    margin-top: 3rem !important;\n  }\n  .mt-xl-auto {\n    margin-top: auto !important;\n  }\n  .me-xl-0 {\n    margin-right: 0 !important;\n  }\n  .me-xl-1 {\n    margin-right: 0.25rem !important;\n  }\n  .me-xl-2 {\n    margin-right: 0.5rem !important;\n  }\n  .me-xl-3 {\n    margin-right: 1rem !important;\n  }\n  .me-xl-4 {\n    margin-right: 1.5rem !important;\n  }\n  .me-xl-5 {\n    margin-right: 3rem !important;\n  }\n  .me-xl-auto {\n    margin-right: auto !important;\n  }\n  .mb-xl-0 {\n    margin-bottom: 0 !important;\n  }\n  .mb-xl-1 {\n    margin-bottom: 0.25rem !important;\n  }\n  .mb-xl-2 {\n    margin-bottom: 0.5rem !important;\n  }\n  .mb-xl-3 {\n    margin-bottom: 1rem !important;\n  }\n  .mb-xl-4 {\n    margin-bottom: 1.5rem !important;\n  }\n  .mb-xl-5 {\n    margin-bottom: 3rem !important;\n  }\n  .mb-xl-auto {\n    margin-bottom: auto !important;\n  }\n  .ms-xl-0 {\n    margin-left: 0 !important;\n  }\n  .ms-xl-1 {\n    margin-left: 0.25rem !important;\n  }\n  .ms-xl-2 {\n    margin-left: 0.5rem !important;\n  }\n  .ms-xl-3 {\n    margin-left: 1rem !important;\n  }\n  .ms-xl-4 {\n    margin-left: 1.5rem !important;\n  }\n  .ms-xl-5 {\n    margin-left: 3rem !important;\n  }\n  .ms-xl-auto {\n    margin-left: auto !important;\n  }\n  .p-xl-0 {\n    padding: 0 !important;\n  }\n  .p-xl-1 {\n    padding: 0.25rem !important;\n  }\n  .p-xl-2 {\n    padding: 0.5rem !important;\n  }\n  .p-xl-3 {\n    padding: 1rem !important;\n  }\n  .p-xl-4 {\n    padding: 1.5rem !important;\n  }\n  .p-xl-5 {\n    padding: 3rem !important;\n  }\n  .px-xl-0 {\n    padding-right: 0 !important;\n    padding-left: 0 !important;\n  }\n  .px-xl-1 {\n    padding-right: 0.25rem !important;\n    padding-left: 0.25rem !important;\n  }\n  .px-xl-2 {\n    padding-right: 0.5rem !important;\n    padding-left: 0.5rem !important;\n  }\n  .px-xl-3 {\n    padding-right: 1rem !important;\n    padding-left: 1rem !important;\n  }\n  .px-xl-4 {\n    padding-right: 1.5rem !important;\n    padding-left: 1.5rem !important;\n  }\n  .px-xl-5 {\n    padding-right: 3rem !important;\n    padding-left: 3rem !important;\n  }\n  .py-xl-0 {\n    padding-top: 0 !important;\n    padding-bottom: 0 !important;\n  }\n  .py-xl-1 {\n    padding-top: 0.25rem !important;\n    padding-bottom: 0.25rem !important;\n  }\n  .py-xl-2 {\n    padding-top: 0.5rem !important;\n    padding-bottom: 0.5rem !important;\n  }\n  .py-xl-3 {\n    padding-top: 1rem !important;\n    padding-bottom: 1rem !important;\n  }\n  .py-xl-4 {\n    padding-top: 1.5rem !important;\n    padding-bottom: 1.5rem !important;\n  }\n  .py-xl-5 {\n    padding-top: 3rem !important;\n    padding-bottom: 3rem !important;\n  }\n  .pt-xl-0 {\n    padding-top: 0 !important;\n  }\n  .pt-xl-1 {\n    padding-top: 0.25rem !important;\n  }\n  .pt-xl-2 {\n    padding-top: 0.5rem !important;\n  }\n  .pt-xl-3 {\n    padding-top: 1rem !important;\n  }\n  .pt-xl-4 {\n    padding-top: 1.5rem !important;\n  }\n  .pt-xl-5 {\n    padding-top: 3rem !important;\n  }\n  .pe-xl-0 {\n    padding-right: 0 !important;\n  }\n  .pe-xl-1 {\n    padding-right: 0.25rem !important;\n  }\n  .pe-xl-2 {\n    padding-right: 0.5rem !important;\n  }\n  .pe-xl-3 {\n    padding-right: 1rem !important;\n  }\n  .pe-xl-4 {\n    padding-right: 1.5rem !important;\n  }\n  .pe-xl-5 {\n    padding-right: 3rem !important;\n  }\n  .pb-xl-0 {\n    padding-bottom: 0 !important;\n  }\n  .pb-xl-1 {\n    padding-bottom: 0.25rem !important;\n  }\n  .pb-xl-2 {\n    padding-bottom: 0.5rem !important;\n  }\n  .pb-xl-3 {\n    padding-bottom: 1rem !important;\n  }\n  .pb-xl-4 {\n    padding-bottom: 1.5rem !important;\n  }\n  .pb-xl-5 {\n    padding-bottom: 3rem !important;\n  }\n  .ps-xl-0 {\n    padding-left: 0 !important;\n  }\n  .ps-xl-1 {\n    padding-left: 0.25rem !important;\n  }\n  .ps-xl-2 {\n    padding-left: 0.5rem !important;\n  }\n  .ps-xl-3 {\n    padding-left: 1rem !important;\n  }\n  .ps-xl-4 {\n    padding-left: 1.5rem !important;\n  }\n  .ps-xl-5 {\n    padding-left: 3rem !important;\n  }\n  .gap-xl-0 {\n    gap: 0 !important;\n  }\n  .gap-xl-1 {\n    gap: 0.25rem !important;\n  }\n  .gap-xl-2 {\n    gap: 0.5rem !important;\n  }\n  .gap-xl-3 {\n    gap: 1rem !important;\n  }\n  .gap-xl-4 {\n    gap: 1.5rem !important;\n  }\n  .gap-xl-5 {\n    gap: 3rem !important;\n  }\n  .row-gap-xl-0 {\n    row-gap: 0 !important;\n  }\n  .row-gap-xl-1 {\n    row-gap: 0.25rem !important;\n  }\n  .row-gap-xl-2 {\n    row-gap: 0.5rem !important;\n  }\n  .row-gap-xl-3 {\n    row-gap: 1rem !important;\n  }\n  .row-gap-xl-4 {\n    row-gap: 1.5rem !important;\n  }\n  .row-gap-xl-5 {\n    row-gap: 3rem !important;\n  }\n  .column-gap-xl-0 {\n    column-gap: 0 !important;\n  }\n  .column-gap-xl-1 {\n    column-gap: 0.25rem !important;\n  }\n  .column-gap-xl-2 {\n    column-gap: 0.5rem !important;\n  }\n  .column-gap-xl-3 {\n    column-gap: 1rem !important;\n  }\n  .column-gap-xl-4 {\n    column-gap: 1.5rem !important;\n  }\n  .column-gap-xl-5 {\n    column-gap: 3rem !important;\n  }\n  .text-xl-start {\n    text-align: left !important;\n  }\n  .text-xl-end {\n    text-align: right !important;\n  }\n  .text-xl-center {\n    text-align: center !important;\n  }\n}\n@media (min-width: 1200px) {\n  .fs-1 {\n    font-size: 2.5rem !important;\n  }\n  .fs-2 {\n    font-size: 2rem !important;\n  }\n  .fs-3 {\n    font-size: 1.75rem !important;\n  }\n  .fs-4 {\n    font-size: 1.5rem !important;\n  }\n}\n@media print {\n  .d-print-inline {\n    display: inline !important;\n  }\n  .d-print-inline-block {\n    display: inline-block !important;\n  }\n  .d-print-block {\n    display: block !important;\n  }\n  .d-print-grid {\n    display: grid !important;\n  }\n  .d-print-inline-grid {\n    display: inline-grid !important;\n  }\n  .d-print-table {\n    display: table !important;\n  }\n  .d-print-table-row {\n    display: table-row !important;\n  }\n  .d-print-table-cell {\n    display: table-cell !important;\n  }\n  .d-print-flex {\n    display: flex !important;\n  }\n  .d-print-inline-flex {\n    display: inline-flex !important;\n  }\n  .d-print-none {\n    display: none !important;\n  }\n}\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n.fa {\n  font-family: var(--fa-style-family, \"Font Awesome 6 Free\");\n  font-weight: var(--fa-style, 900);\n}\n\n.fa,\n.fa-classic,\n.fa-sharp,\n.fas,\n.fa-solid,\n.far,\n.fa-regular,\n.fab,\n.fa-brands {\n  -moz-osx-font-smoothing: grayscale;\n  -webkit-font-smoothing: antialiased;\n  display: var(--fa-display, inline-block);\n  font-style: normal;\n  font-variant: normal;\n  line-height: 1;\n  text-rendering: auto;\n}\n\n.fas,\n.fa-classic,\n.fa-solid,\n.far,\n.fa-regular {\n  font-family: \"Font Awesome 6 Free\";\n}\n\n.fab,\n.fa-brands {\n  font-family: \"Font Awesome 6 Brands\";\n}\n\n.fa-1x {\n  font-size: 1em;\n}\n\n.fa-2x {\n  font-size: 2em;\n}\n\n.fa-3x {\n  font-size: 3em;\n}\n\n.fa-4x {\n  font-size: 4em;\n}\n\n.fa-5x {\n  font-size: 5em;\n}\n\n.fa-6x {\n  font-size: 6em;\n}\n\n.fa-7x {\n  font-size: 7em;\n}\n\n.fa-8x {\n  font-size: 8em;\n}\n\n.fa-9x {\n  font-size: 9em;\n}\n\n.fa-10x {\n  font-size: 10em;\n}\n\n.fa-2xs {\n  font-size: 0.625em;\n  line-height: 0.1em;\n  vertical-align: 0.225em;\n}\n\n.fa-xs {\n  font-size: 0.75em;\n  line-height: 0.0833333337em;\n  vertical-align: 0.125em;\n}\n\n.fa-sm {\n  font-size: 0.875em;\n  line-height: 0.0714285718em;\n  vertical-align: 0.0535714295em;\n}\n\n.fa-lg {\n  font-size: 1.25em;\n  line-height: 0.05em;\n  vertical-align: -0.075em;\n}\n\n.fa-xl {\n  font-size: 1.5em;\n  line-height: 0.0416666682em;\n  vertical-align: -0.125em;\n}\n\n.fa-2xl {\n  font-size: 2em;\n  line-height: 0.03125em;\n  vertical-align: -0.1875em;\n}\n\n.fa-fw {\n  text-align: center;\n  width: 1.25em;\n}\n\n.fa-ul {\n  list-style-type: none;\n  margin-left: var(--fa-li-margin, 2.5em);\n  padding-left: 0;\n}\n.fa-ul > li {\n  position: relative;\n}\n\n.fa-li {\n  left: calc(var(--fa-li-width, 2em) * -1);\n  position: absolute;\n  text-align: center;\n  width: var(--fa-li-width, 2em);\n  line-height: inherit;\n}\n\n.fa-border {\n  border-color: var(--fa-border-color, #eee);\n  border-radius: var(--fa-border-radius, 0.1em);\n  border-style: var(--fa-border-style, solid);\n  border-width: var(--fa-border-width, 0.08em);\n  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em);\n}\n\n.fa-pull-left {\n  float: left;\n  margin-right: var(--fa-pull-margin, 0.3em);\n}\n\n.fa-pull-right {\n  float: right;\n  margin-left: var(--fa-pull-margin, 0.3em);\n}\n\n.fa-beat {\n  animation-name: fa-beat;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, ease-in-out);\n}\n\n.fa-bounce {\n  animation-name: fa-bounce;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));\n}\n\n.fa-fade {\n  animation-name: fa-fade;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));\n}\n\n.fa-beat-fade {\n  animation-name: fa-beat-fade;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));\n}\n\n.fa-flip {\n  animation-name: fa-flip;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, ease-in-out);\n}\n\n.fa-shake {\n  animation-name: fa-shake;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, linear);\n}\n\n.fa-spin {\n  animation-name: fa-spin;\n  animation-delay: var(--fa-animation-delay, 0s);\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 2s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, linear);\n}\n\n.fa-spin-reverse {\n  --fa-animation-direction: reverse;\n}\n\n.fa-pulse,\n.fa-spin-pulse {\n  animation-name: fa-spin;\n  animation-direction: var(--fa-animation-direction, normal);\n  animation-duration: var(--fa-animation-duration, 1s);\n  animation-iteration-count: var(--fa-animation-iteration-count, infinite);\n  animation-timing-function: var(--fa-animation-timing, steps(8));\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .fa-beat,\n  .fa-bounce,\n  .fa-fade,\n  .fa-beat-fade,\n  .fa-flip,\n  .fa-pulse,\n  .fa-shake,\n  .fa-spin,\n  .fa-spin-pulse {\n    animation-delay: -1ms;\n    animation-duration: 1ms;\n    animation-iteration-count: 1;\n    transition-delay: 0s;\n    transition-duration: 0s;\n  }\n}\n@keyframes fa-beat {\n  0%, 90% {\n    transform: scale(1);\n  }\n  45% {\n    transform: scale(var(--fa-beat-scale, 1.25));\n  }\n}\n@keyframes fa-bounce {\n  0% {\n    transform: scale(1, 1) translateY(0);\n  }\n  10% {\n    transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);\n  }\n  30% {\n    transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));\n  }\n  50% {\n    transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);\n  }\n  57% {\n    transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));\n  }\n  64% {\n    transform: scale(1, 1) translateY(0);\n  }\n  100% {\n    transform: scale(1, 1) translateY(0);\n  }\n}\n@keyframes fa-fade {\n  50% {\n    opacity: var(--fa-fade-opacity, 0.4);\n  }\n}\n@keyframes fa-beat-fade {\n  0%, 100% {\n    opacity: var(--fa-beat-fade-opacity, 0.4);\n    transform: scale(1);\n  }\n  50% {\n    opacity: 1;\n    transform: scale(var(--fa-beat-fade-scale, 1.125));\n  }\n}\n@keyframes fa-flip {\n  50% {\n    transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));\n  }\n}\n@keyframes fa-shake {\n  0% {\n    transform: rotate(-15deg);\n  }\n  4% {\n    transform: rotate(15deg);\n  }\n  8%, 24% {\n    transform: rotate(-18deg);\n  }\n  12%, 28% {\n    transform: rotate(18deg);\n  }\n  16% {\n    transform: rotate(-22deg);\n  }\n  20% {\n    transform: rotate(22deg);\n  }\n  32% {\n    transform: rotate(-12deg);\n  }\n  36% {\n    transform: rotate(12deg);\n  }\n  40%, 100% {\n    transform: rotate(0deg);\n  }\n}\n@keyframes fa-spin {\n  0% {\n    transform: rotate(0deg);\n  }\n  100% {\n    transform: rotate(360deg);\n  }\n}\n.fa-rotate-90 {\n  transform: rotate(90deg);\n}\n\n.fa-rotate-180 {\n  transform: rotate(180deg);\n}\n\n.fa-rotate-270 {\n  transform: rotate(270deg);\n}\n\n.fa-flip-horizontal {\n  transform: scale(-1, 1);\n}\n\n.fa-flip-vertical {\n  transform: scale(1, -1);\n}\n\n.fa-flip-both,\n.fa-flip-horizontal.fa-flip-vertical {\n  transform: scale(-1, -1);\n}\n\n.fa-rotate-by {\n  transform: rotate(var(--fa-rotate-angle, 0));\n}\n\n.fa-stack {\n  display: inline-block;\n  height: 2em;\n  line-height: 2em;\n  position: relative;\n  vertical-align: middle;\n  width: 2.5em;\n}\n\n.fa-stack-1x,\n.fa-stack-2x {\n  left: 0;\n  position: absolute;\n  text-align: center;\n  width: 100%;\n  z-index: var(--fa-stack-z-index, auto);\n}\n\n.fa-stack-1x {\n  line-height: inherit;\n}\n\n.fa-stack-2x {\n  font-size: 2em;\n}\n\n.fa-inverse {\n  color: var(--fa-inverse, #fff);\n}\n\n/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen\nreaders do not read off random characters that represent icons */\n.fa-0::before {\n  content: \"\\30 \";\n}\n\n.fa-1::before {\n  content: \"\\31 \";\n}\n\n.fa-2::before {\n  content: \"\\32 \";\n}\n\n.fa-3::before {\n  content: \"\\33 \";\n}\n\n.fa-4::before {\n  content: \"\\34 \";\n}\n\n.fa-5::before {\n  content: \"\\35 \";\n}\n\n.fa-6::before {\n  content: \"\\36 \";\n}\n\n.fa-7::before {\n  content: \"\\37 \";\n}\n\n.fa-8::before {\n  content: \"\\38 \";\n}\n\n.fa-9::before {\n  content: \"\\39 \";\n}\n\n.fa-fill-drip::before {\n  content: \"\\f576\";\n}\n\n.fa-arrows-to-circle::before {\n  content: \"\\e4bd\";\n}\n\n.fa-circle-chevron-right::before {\n  content: \"\\f138\";\n}\n\n.fa-chevron-circle-right::before {\n  content: \"\\f138\";\n}\n\n.fa-at::before {\n  content: \"\\@\";\n}\n\n.fa-trash-can::before {\n  content: \"\\f2ed\";\n}\n\n.fa-trash-alt::before {\n  content: \"\\f2ed\";\n}\n\n.fa-text-height::before {\n  content: \"\\f034\";\n}\n\n.fa-user-xmark::before {\n  content: \"\\f235\";\n}\n\n.fa-user-times::before {\n  content: \"\\f235\";\n}\n\n.fa-stethoscope::before {\n  content: \"\\f0f1\";\n}\n\n.fa-message::before {\n  content: \"\\f27a\";\n}\n\n.fa-comment-alt::before {\n  content: \"\\f27a\";\n}\n\n.fa-info::before {\n  content: \"\\f129\";\n}\n\n.fa-down-left-and-up-right-to-center::before {\n  content: \"\\f422\";\n}\n\n.fa-compress-alt::before {\n  content: \"\\f422\";\n}\n\n.fa-explosion::before {\n  content: \"\\e4e9\";\n}\n\n.fa-file-lines::before {\n  content: \"\\f15c\";\n}\n\n.fa-file-alt::before {\n  content: \"\\f15c\";\n}\n\n.fa-file-text::before {\n  content: \"\\f15c\";\n}\n\n.fa-wave-square::before {\n  content: \"\\f83e\";\n}\n\n.fa-ring::before {\n  content: \"\\f70b\";\n}\n\n.fa-building-un::before {\n  content: \"\\e4d9\";\n}\n\n.fa-dice-three::before {\n  content: \"\\f527\";\n}\n\n.fa-calendar-days::before {\n  content: \"\\f073\";\n}\n\n.fa-calendar-alt::before {\n  content: \"\\f073\";\n}\n\n.fa-anchor-circle-check::before {\n  content: \"\\e4aa\";\n}\n\n.fa-building-circle-arrow-right::before {\n  content: \"\\e4d1\";\n}\n\n.fa-volleyball::before {\n  content: \"\\f45f\";\n}\n\n.fa-volleyball-ball::before {\n  content: \"\\f45f\";\n}\n\n.fa-arrows-up-to-line::before {\n  content: \"\\e4c2\";\n}\n\n.fa-sort-down::before {\n  content: \"\\f0dd\";\n}\n\n.fa-sort-desc::before {\n  content: \"\\f0dd\";\n}\n\n.fa-circle-minus::before {\n  content: \"\\f056\";\n}\n\n.fa-minus-circle::before {\n  content: \"\\f056\";\n}\n\n.fa-door-open::before {\n  content: \"\\f52b\";\n}\n\n.fa-right-from-bracket::before {\n  content: \"\\f2f5\";\n}\n\n.fa-sign-out-alt::before {\n  content: \"\\f2f5\";\n}\n\n.fa-atom::before {\n  content: \"\\f5d2\";\n}\n\n.fa-soap::before {\n  content: \"\\e06e\";\n}\n\n.fa-icons::before {\n  content: \"\\f86d\";\n}\n\n.fa-heart-music-camera-bolt::before {\n  content: \"\\f86d\";\n}\n\n.fa-microphone-lines-slash::before {\n  content: \"\\f539\";\n}\n\n.fa-microphone-alt-slash::before {\n  content: \"\\f539\";\n}\n\n.fa-bridge-circle-check::before {\n  content: \"\\e4c9\";\n}\n\n.fa-pump-medical::before {\n  content: \"\\e06a\";\n}\n\n.fa-fingerprint::before {\n  content: \"\\f577\";\n}\n\n.fa-hand-point-right::before {\n  content: \"\\f0a4\";\n}\n\n.fa-magnifying-glass-location::before {\n  content: \"\\f689\";\n}\n\n.fa-search-location::before {\n  content: \"\\f689\";\n}\n\n.fa-forward-step::before {\n  content: \"\\f051\";\n}\n\n.fa-step-forward::before {\n  content: \"\\f051\";\n}\n\n.fa-face-smile-beam::before {\n  content: \"\\f5b8\";\n}\n\n.fa-smile-beam::before {\n  content: \"\\f5b8\";\n}\n\n.fa-flag-checkered::before {\n  content: \"\\f11e\";\n}\n\n.fa-football::before {\n  content: \"\\f44e\";\n}\n\n.fa-football-ball::before {\n  content: \"\\f44e\";\n}\n\n.fa-school-circle-exclamation::before {\n  content: \"\\e56c\";\n}\n\n.fa-crop::before {\n  content: \"\\f125\";\n}\n\n.fa-angles-down::before {\n  content: \"\\f103\";\n}\n\n.fa-angle-double-down::before {\n  content: \"\\f103\";\n}\n\n.fa-users-rectangle::before {\n  content: \"\\e594\";\n}\n\n.fa-people-roof::before {\n  content: \"\\e537\";\n}\n\n.fa-people-line::before {\n  content: \"\\e534\";\n}\n\n.fa-beer-mug-empty::before {\n  content: \"\\f0fc\";\n}\n\n.fa-beer::before {\n  content: \"\\f0fc\";\n}\n\n.fa-diagram-predecessor::before {\n  content: \"\\e477\";\n}\n\n.fa-arrow-up-long::before {\n  content: \"\\f176\";\n}\n\n.fa-long-arrow-up::before {\n  content: \"\\f176\";\n}\n\n.fa-fire-flame-simple::before {\n  content: \"\\f46a\";\n}\n\n.fa-burn::before {\n  content: \"\\f46a\";\n}\n\n.fa-person::before {\n  content: \"\\f183\";\n}\n\n.fa-male::before {\n  content: \"\\f183\";\n}\n\n.fa-laptop::before {\n  content: \"\\f109\";\n}\n\n.fa-file-csv::before {\n  content: \"\\f6dd\";\n}\n\n.fa-menorah::before {\n  content: \"\\f676\";\n}\n\n.fa-truck-plane::before {\n  content: \"\\e58f\";\n}\n\n.fa-record-vinyl::before {\n  content: \"\\f8d9\";\n}\n\n.fa-face-grin-stars::before {\n  content: \"\\f587\";\n}\n\n.fa-grin-stars::before {\n  content: \"\\f587\";\n}\n\n.fa-bong::before {\n  content: \"\\f55c\";\n}\n\n.fa-spaghetti-monster-flying::before {\n  content: \"\\f67b\";\n}\n\n.fa-pastafarianism::before {\n  content: \"\\f67b\";\n}\n\n.fa-arrow-down-up-across-line::before {\n  content: \"\\e4af\";\n}\n\n.fa-spoon::before {\n  content: \"\\f2e5\";\n}\n\n.fa-utensil-spoon::before {\n  content: \"\\f2e5\";\n}\n\n.fa-jar-wheat::before {\n  content: \"\\e517\";\n}\n\n.fa-envelopes-bulk::before {\n  content: \"\\f674\";\n}\n\n.fa-mail-bulk::before {\n  content: \"\\f674\";\n}\n\n.fa-file-circle-exclamation::before {\n  content: \"\\e4eb\";\n}\n\n.fa-circle-h::before {\n  content: \"\\f47e\";\n}\n\n.fa-hospital-symbol::before {\n  content: \"\\f47e\";\n}\n\n.fa-pager::before {\n  content: \"\\f815\";\n}\n\n.fa-address-book::before {\n  content: \"\\f2b9\";\n}\n\n.fa-contact-book::before {\n  content: \"\\f2b9\";\n}\n\n.fa-strikethrough::before {\n  content: \"\\f0cc\";\n}\n\n.fa-k::before {\n  content: \"K\";\n}\n\n.fa-landmark-flag::before {\n  content: \"\\e51c\";\n}\n\n.fa-pencil::before {\n  content: \"\\f303\";\n}\n\n.fa-pencil-alt::before {\n  content: \"\\f303\";\n}\n\n.fa-backward::before {\n  content: \"\\f04a\";\n}\n\n.fa-caret-right::before {\n  content: \"\\f0da\";\n}\n\n.fa-comments::before {\n  content: \"\\f086\";\n}\n\n.fa-paste::before {\n  content: \"\\f0ea\";\n}\n\n.fa-file-clipboard::before {\n  content: \"\\f0ea\";\n}\n\n.fa-code-pull-request::before {\n  content: \"\\e13c\";\n}\n\n.fa-clipboard-list::before {\n  content: \"\\f46d\";\n}\n\n.fa-truck-ramp-box::before {\n  content: \"\\f4de\";\n}\n\n.fa-truck-loading::before {\n  content: \"\\f4de\";\n}\n\n.fa-user-check::before {\n  content: \"\\f4fc\";\n}\n\n.fa-vial-virus::before {\n  content: \"\\e597\";\n}\n\n.fa-sheet-plastic::before {\n  content: \"\\e571\";\n}\n\n.fa-blog::before {\n  content: \"\\f781\";\n}\n\n.fa-user-ninja::before {\n  content: \"\\f504\";\n}\n\n.fa-person-arrow-up-from-line::before {\n  content: \"\\e539\";\n}\n\n.fa-scroll-torah::before {\n  content: \"\\f6a0\";\n}\n\n.fa-torah::before {\n  content: \"\\f6a0\";\n}\n\n.fa-broom-ball::before {\n  content: \"\\f458\";\n}\n\n.fa-quidditch::before {\n  content: \"\\f458\";\n}\n\n.fa-quidditch-broom-ball::before {\n  content: \"\\f458\";\n}\n\n.fa-toggle-off::before {\n  content: \"\\f204\";\n}\n\n.fa-box-archive::before {\n  content: \"\\f187\";\n}\n\n.fa-archive::before {\n  content: \"\\f187\";\n}\n\n.fa-person-drowning::before {\n  content: \"\\e545\";\n}\n\n.fa-arrow-down-9-1::before {\n  content: \"\\f886\";\n}\n\n.fa-sort-numeric-desc::before {\n  content: \"\\f886\";\n}\n\n.fa-sort-numeric-down-alt::before {\n  content: \"\\f886\";\n}\n\n.fa-face-grin-tongue-squint::before {\n  content: \"\\f58a\";\n}\n\n.fa-grin-tongue-squint::before {\n  content: \"\\f58a\";\n}\n\n.fa-spray-can::before {\n  content: \"\\f5bd\";\n}\n\n.fa-truck-monster::before {\n  content: \"\\f63b\";\n}\n\n.fa-w::before {\n  content: \"W\";\n}\n\n.fa-earth-africa::before {\n  content: \"\\f57c\";\n}\n\n.fa-globe-africa::before {\n  content: \"\\f57c\";\n}\n\n.fa-rainbow::before {\n  content: \"\\f75b\";\n}\n\n.fa-circle-notch::before {\n  content: \"\\f1ce\";\n}\n\n.fa-tablet-screen-button::before {\n  content: \"\\f3fa\";\n}\n\n.fa-tablet-alt::before {\n  content: \"\\f3fa\";\n}\n\n.fa-paw::before {\n  content: \"\\f1b0\";\n}\n\n.fa-cloud::before {\n  content: \"\\f0c2\";\n}\n\n.fa-trowel-bricks::before {\n  content: \"\\e58a\";\n}\n\n.fa-face-flushed::before {\n  content: \"\\f579\";\n}\n\n.fa-flushed::before {\n  content: \"\\f579\";\n}\n\n.fa-hospital-user::before {\n  content: \"\\f80d\";\n}\n\n.fa-tent-arrow-left-right::before {\n  content: \"\\e57f\";\n}\n\n.fa-gavel::before {\n  content: \"\\f0e3\";\n}\n\n.fa-legal::before {\n  content: \"\\f0e3\";\n}\n\n.fa-binoculars::before {\n  content: \"\\f1e5\";\n}\n\n.fa-microphone-slash::before {\n  content: \"\\f131\";\n}\n\n.fa-box-tissue::before {\n  content: \"\\e05b\";\n}\n\n.fa-motorcycle::before {\n  content: \"\\f21c\";\n}\n\n.fa-bell-concierge::before {\n  content: \"\\f562\";\n}\n\n.fa-concierge-bell::before {\n  content: \"\\f562\";\n}\n\n.fa-pen-ruler::before {\n  content: \"\\f5ae\";\n}\n\n.fa-pencil-ruler::before {\n  content: \"\\f5ae\";\n}\n\n.fa-people-arrows::before {\n  content: \"\\e068\";\n}\n\n.fa-people-arrows-left-right::before {\n  content: \"\\e068\";\n}\n\n.fa-mars-and-venus-burst::before {\n  content: \"\\e523\";\n}\n\n.fa-square-caret-right::before {\n  content: \"\\f152\";\n}\n\n.fa-caret-square-right::before {\n  content: \"\\f152\";\n}\n\n.fa-scissors::before {\n  content: \"\\f0c4\";\n}\n\n.fa-cut::before {\n  content: \"\\f0c4\";\n}\n\n.fa-sun-plant-wilt::before {\n  content: \"\\e57a\";\n}\n\n.fa-toilets-portable::before {\n  content: \"\\e584\";\n}\n\n.fa-hockey-puck::before {\n  content: \"\\f453\";\n}\n\n.fa-table::before {\n  content: \"\\f0ce\";\n}\n\n.fa-magnifying-glass-arrow-right::before {\n  content: \"\\e521\";\n}\n\n.fa-tachograph-digital::before {\n  content: \"\\f566\";\n}\n\n.fa-digital-tachograph::before {\n  content: \"\\f566\";\n}\n\n.fa-users-slash::before {\n  content: \"\\e073\";\n}\n\n.fa-clover::before {\n  content: \"\\e139\";\n}\n\n.fa-reply::before {\n  content: \"\\f3e5\";\n}\n\n.fa-mail-reply::before {\n  content: \"\\f3e5\";\n}\n\n.fa-star-and-crescent::before {\n  content: \"\\f699\";\n}\n\n.fa-house-fire::before {\n  content: \"\\e50c\";\n}\n\n.fa-square-minus::before {\n  content: \"\\f146\";\n}\n\n.fa-minus-square::before {\n  content: \"\\f146\";\n}\n\n.fa-helicopter::before {\n  content: \"\\f533\";\n}\n\n.fa-compass::before {\n  content: \"\\f14e\";\n}\n\n.fa-square-caret-down::before {\n  content: \"\\f150\";\n}\n\n.fa-caret-square-down::before {\n  content: \"\\f150\";\n}\n\n.fa-file-circle-question::before {\n  content: \"\\e4ef\";\n}\n\n.fa-laptop-code::before {\n  content: \"\\f5fc\";\n}\n\n.fa-swatchbook::before {\n  content: \"\\f5c3\";\n}\n\n.fa-prescription-bottle::before {\n  content: \"\\f485\";\n}\n\n.fa-bars::before {\n  content: \"\\f0c9\";\n}\n\n.fa-navicon::before {\n  content: \"\\f0c9\";\n}\n\n.fa-people-group::before {\n  content: \"\\e533\";\n}\n\n.fa-hourglass-end::before {\n  content: \"\\f253\";\n}\n\n.fa-hourglass-3::before {\n  content: \"\\f253\";\n}\n\n.fa-heart-crack::before {\n  content: \"\\f7a9\";\n}\n\n.fa-heart-broken::before {\n  content: \"\\f7a9\";\n}\n\n.fa-square-up-right::before {\n  content: \"\\f360\";\n}\n\n.fa-external-link-square-alt::before {\n  content: \"\\f360\";\n}\n\n.fa-face-kiss-beam::before {\n  content: \"\\f597\";\n}\n\n.fa-kiss-beam::before {\n  content: \"\\f597\";\n}\n\n.fa-film::before {\n  content: \"\\f008\";\n}\n\n.fa-ruler-horizontal::before {\n  content: \"\\f547\";\n}\n\n.fa-people-robbery::before {\n  content: \"\\e536\";\n}\n\n.fa-lightbulb::before {\n  content: \"\\f0eb\";\n}\n\n.fa-caret-left::before {\n  content: \"\\f0d9\";\n}\n\n.fa-circle-exclamation::before {\n  content: \"\\f06a\";\n}\n\n.fa-exclamation-circle::before {\n  content: \"\\f06a\";\n}\n\n.fa-school-circle-xmark::before {\n  content: \"\\e56d\";\n}\n\n.fa-arrow-right-from-bracket::before {\n  content: \"\\f08b\";\n}\n\n.fa-sign-out::before {\n  content: \"\\f08b\";\n}\n\n.fa-circle-chevron-down::before {\n  content: \"\\f13a\";\n}\n\n.fa-chevron-circle-down::before {\n  content: \"\\f13a\";\n}\n\n.fa-unlock-keyhole::before {\n  content: \"\\f13e\";\n}\n\n.fa-unlock-alt::before {\n  content: \"\\f13e\";\n}\n\n.fa-cloud-showers-heavy::before {\n  content: \"\\f740\";\n}\n\n.fa-headphones-simple::before {\n  content: \"\\f58f\";\n}\n\n.fa-headphones-alt::before {\n  content: \"\\f58f\";\n}\n\n.fa-sitemap::before {\n  content: \"\\f0e8\";\n}\n\n.fa-circle-dollar-to-slot::before {\n  content: \"\\f4b9\";\n}\n\n.fa-donate::before {\n  content: \"\\f4b9\";\n}\n\n.fa-memory::before {\n  content: \"\\f538\";\n}\n\n.fa-road-spikes::before {\n  content: \"\\e568\";\n}\n\n.fa-fire-burner::before {\n  content: \"\\e4f1\";\n}\n\n.fa-flag::before {\n  content: \"\\f024\";\n}\n\n.fa-hanukiah::before {\n  content: \"\\f6e6\";\n}\n\n.fa-feather::before {\n  content: \"\\f52d\";\n}\n\n.fa-volume-low::before {\n  content: \"\\f027\";\n}\n\n.fa-volume-down::before {\n  content: \"\\f027\";\n}\n\n.fa-comment-slash::before {\n  content: \"\\f4b3\";\n}\n\n.fa-cloud-sun-rain::before {\n  content: \"\\f743\";\n}\n\n.fa-compress::before {\n  content: \"\\f066\";\n}\n\n.fa-wheat-awn::before {\n  content: \"\\e2cd\";\n}\n\n.fa-wheat-alt::before {\n  content: \"\\e2cd\";\n}\n\n.fa-ankh::before {\n  content: \"\\f644\";\n}\n\n.fa-hands-holding-child::before {\n  content: \"\\e4fa\";\n}\n\n.fa-asterisk::before {\n  content: \"\\*\";\n}\n\n.fa-square-check::before {\n  content: \"\\f14a\";\n}\n\n.fa-check-square::before {\n  content: \"\\f14a\";\n}\n\n.fa-peseta-sign::before {\n  content: \"\\e221\";\n}\n\n.fa-heading::before {\n  content: \"\\f1dc\";\n}\n\n.fa-header::before {\n  content: \"\\f1dc\";\n}\n\n.fa-ghost::before {\n  content: \"\\f6e2\";\n}\n\n.fa-list::before {\n  content: \"\\f03a\";\n}\n\n.fa-list-squares::before {\n  content: \"\\f03a\";\n}\n\n.fa-square-phone-flip::before {\n  content: \"\\f87b\";\n}\n\n.fa-phone-square-alt::before {\n  content: \"\\f87b\";\n}\n\n.fa-cart-plus::before {\n  content: \"\\f217\";\n}\n\n.fa-gamepad::before {\n  content: \"\\f11b\";\n}\n\n.fa-circle-dot::before {\n  content: \"\\f192\";\n}\n\n.fa-dot-circle::before {\n  content: \"\\f192\";\n}\n\n.fa-face-dizzy::before {\n  content: \"\\f567\";\n}\n\n.fa-dizzy::before {\n  content: \"\\f567\";\n}\n\n.fa-egg::before {\n  content: \"\\f7fb\";\n}\n\n.fa-house-medical-circle-xmark::before {\n  content: \"\\e513\";\n}\n\n.fa-campground::before {\n  content: \"\\f6bb\";\n}\n\n.fa-folder-plus::before {\n  content: \"\\f65e\";\n}\n\n.fa-futbol::before {\n  content: \"\\f1e3\";\n}\n\n.fa-futbol-ball::before {\n  content: \"\\f1e3\";\n}\n\n.fa-soccer-ball::before {\n  content: \"\\f1e3\";\n}\n\n.fa-paintbrush::before {\n  content: \"\\f1fc\";\n}\n\n.fa-paint-brush::before {\n  content: \"\\f1fc\";\n}\n\n.fa-lock::before {\n  content: \"\\f023\";\n}\n\n.fa-gas-pump::before {\n  content: \"\\f52f\";\n}\n\n.fa-hot-tub-person::before {\n  content: \"\\f593\";\n}\n\n.fa-hot-tub::before {\n  content: \"\\f593\";\n}\n\n.fa-map-location::before {\n  content: \"\\f59f\";\n}\n\n.fa-map-marked::before {\n  content: \"\\f59f\";\n}\n\n.fa-house-flood-water::before {\n  content: \"\\e50e\";\n}\n\n.fa-tree::before {\n  content: \"\\f1bb\";\n}\n\n.fa-bridge-lock::before {\n  content: \"\\e4cc\";\n}\n\n.fa-sack-dollar::before {\n  content: \"\\f81d\";\n}\n\n.fa-pen-to-square::before {\n  content: \"\\f044\";\n}\n\n.fa-edit::before {\n  content: \"\\f044\";\n}\n\n.fa-car-side::before {\n  content: \"\\f5e4\";\n}\n\n.fa-share-nodes::before {\n  content: \"\\f1e0\";\n}\n\n.fa-share-alt::before {\n  content: \"\\f1e0\";\n}\n\n.fa-heart-circle-minus::before {\n  content: \"\\e4ff\";\n}\n\n.fa-hourglass-half::before {\n  content: \"\\f252\";\n}\n\n.fa-hourglass-2::before {\n  content: \"\\f252\";\n}\n\n.fa-microscope::before {\n  content: \"\\f610\";\n}\n\n.fa-sink::before {\n  content: \"\\e06d\";\n}\n\n.fa-bag-shopping::before {\n  content: \"\\f290\";\n}\n\n.fa-shopping-bag::before {\n  content: \"\\f290\";\n}\n\n.fa-arrow-down-z-a::before {\n  content: \"\\f881\";\n}\n\n.fa-sort-alpha-desc::before {\n  content: \"\\f881\";\n}\n\n.fa-sort-alpha-down-alt::before {\n  content: \"\\f881\";\n}\n\n.fa-mitten::before {\n  content: \"\\f7b5\";\n}\n\n.fa-person-rays::before {\n  content: \"\\e54d\";\n}\n\n.fa-users::before {\n  content: \"\\f0c0\";\n}\n\n.fa-eye-slash::before {\n  content: \"\\f070\";\n}\n\n.fa-flask-vial::before {\n  content: \"\\e4f3\";\n}\n\n.fa-hand::before {\n  content: \"\\f256\";\n}\n\n.fa-hand-paper::before {\n  content: \"\\f256\";\n}\n\n.fa-om::before {\n  content: \"\\f679\";\n}\n\n.fa-worm::before {\n  content: \"\\e599\";\n}\n\n.fa-house-circle-xmark::before {\n  content: \"\\e50b\";\n}\n\n.fa-plug::before {\n  content: \"\\f1e6\";\n}\n\n.fa-chevron-up::before {\n  content: \"\\f077\";\n}\n\n.fa-hand-spock::before {\n  content: \"\\f259\";\n}\n\n.fa-stopwatch::before {\n  content: \"\\f2f2\";\n}\n\n.fa-face-kiss::before {\n  content: \"\\f596\";\n}\n\n.fa-kiss::before {\n  content: \"\\f596\";\n}\n\n.fa-bridge-circle-xmark::before {\n  content: \"\\e4cb\";\n}\n\n.fa-face-grin-tongue::before {\n  content: \"\\f589\";\n}\n\n.fa-grin-tongue::before {\n  content: \"\\f589\";\n}\n\n.fa-chess-bishop::before {\n  content: \"\\f43a\";\n}\n\n.fa-face-grin-wink::before {\n  content: \"\\f58c\";\n}\n\n.fa-grin-wink::before {\n  content: \"\\f58c\";\n}\n\n.fa-ear-deaf::before {\n  content: \"\\f2a4\";\n}\n\n.fa-deaf::before {\n  content: \"\\f2a4\";\n}\n\n.fa-deafness::before {\n  content: \"\\f2a4\";\n}\n\n.fa-hard-of-hearing::before {\n  content: \"\\f2a4\";\n}\n\n.fa-road-circle-check::before {\n  content: \"\\e564\";\n}\n\n.fa-dice-five::before {\n  content: \"\\f523\";\n}\n\n.fa-square-rss::before {\n  content: \"\\f143\";\n}\n\n.fa-rss-square::before {\n  content: \"\\f143\";\n}\n\n.fa-land-mine-on::before {\n  content: \"\\e51b\";\n}\n\n.fa-i-cursor::before {\n  content: \"\\f246\";\n}\n\n.fa-stamp::before {\n  content: \"\\f5bf\";\n}\n\n.fa-stairs::before {\n  content: \"\\e289\";\n}\n\n.fa-i::before {\n  content: \"I\";\n}\n\n.fa-hryvnia-sign::before {\n  content: \"\\f6f2\";\n}\n\n.fa-hryvnia::before {\n  content: \"\\f6f2\";\n}\n\n.fa-pills::before {\n  content: \"\\f484\";\n}\n\n.fa-face-grin-wide::before {\n  content: \"\\f581\";\n}\n\n.fa-grin-alt::before {\n  content: \"\\f581\";\n}\n\n.fa-tooth::before {\n  content: \"\\f5c9\";\n}\n\n.fa-v::before {\n  content: \"V\";\n}\n\n.fa-bangladeshi-taka-sign::before {\n  content: \"\\e2e6\";\n}\n\n.fa-bicycle::before {\n  content: \"\\f206\";\n}\n\n.fa-staff-snake::before {\n  content: \"\\e579\";\n}\n\n.fa-rod-asclepius::before {\n  content: \"\\e579\";\n}\n\n.fa-rod-snake::before {\n  content: \"\\e579\";\n}\n\n.fa-staff-aesculapius::before {\n  content: \"\\e579\";\n}\n\n.fa-head-side-cough-slash::before {\n  content: \"\\e062\";\n}\n\n.fa-truck-medical::before {\n  content: \"\\f0f9\";\n}\n\n.fa-ambulance::before {\n  content: \"\\f0f9\";\n}\n\n.fa-wheat-awn-circle-exclamation::before {\n  content: \"\\e598\";\n}\n\n.fa-snowman::before {\n  content: \"\\f7d0\";\n}\n\n.fa-mortar-pestle::before {\n  content: \"\\f5a7\";\n}\n\n.fa-road-barrier::before {\n  content: \"\\e562\";\n}\n\n.fa-school::before {\n  content: \"\\f549\";\n}\n\n.fa-igloo::before {\n  content: \"\\f7ae\";\n}\n\n.fa-joint::before {\n  content: \"\\f595\";\n}\n\n.fa-angle-right::before {\n  content: \"\\f105\";\n}\n\n.fa-horse::before {\n  content: \"\\f6f0\";\n}\n\n.fa-q::before {\n  content: \"Q\";\n}\n\n.fa-g::before {\n  content: \"G\";\n}\n\n.fa-notes-medical::before {\n  content: \"\\f481\";\n}\n\n.fa-temperature-half::before {\n  content: \"\\f2c9\";\n}\n\n.fa-temperature-2::before {\n  content: \"\\f2c9\";\n}\n\n.fa-thermometer-2::before {\n  content: \"\\f2c9\";\n}\n\n.fa-thermometer-half::before {\n  content: \"\\f2c9\";\n}\n\n.fa-dong-sign::before {\n  content: \"\\e169\";\n}\n\n.fa-capsules::before {\n  content: \"\\f46b\";\n}\n\n.fa-poo-storm::before {\n  content: \"\\f75a\";\n}\n\n.fa-poo-bolt::before {\n  content: \"\\f75a\";\n}\n\n.fa-face-frown-open::before {\n  content: \"\\f57a\";\n}\n\n.fa-frown-open::before {\n  content: \"\\f57a\";\n}\n\n.fa-hand-point-up::before {\n  content: \"\\f0a6\";\n}\n\n.fa-money-bill::before {\n  content: \"\\f0d6\";\n}\n\n.fa-bookmark::before {\n  content: \"\\f02e\";\n}\n\n.fa-align-justify::before {\n  content: \"\\f039\";\n}\n\n.fa-umbrella-beach::before {\n  content: \"\\f5ca\";\n}\n\n.fa-helmet-un::before {\n  content: \"\\e503\";\n}\n\n.fa-bullseye::before {\n  content: \"\\f140\";\n}\n\n.fa-bacon::before {\n  content: \"\\f7e5\";\n}\n\n.fa-hand-point-down::before {\n  content: \"\\f0a7\";\n}\n\n.fa-arrow-up-from-bracket::before {\n  content: \"\\e09a\";\n}\n\n.fa-folder::before {\n  content: \"\\f07b\";\n}\n\n.fa-folder-blank::before {\n  content: \"\\f07b\";\n}\n\n.fa-file-waveform::before {\n  content: \"\\f478\";\n}\n\n.fa-file-medical-alt::before {\n  content: \"\\f478\";\n}\n\n.fa-radiation::before {\n  content: \"\\f7b9\";\n}\n\n.fa-chart-simple::before {\n  content: \"\\e473\";\n}\n\n.fa-mars-stroke::before {\n  content: \"\\f229\";\n}\n\n.fa-vial::before {\n  content: \"\\f492\";\n}\n\n.fa-gauge::before {\n  content: \"\\f624\";\n}\n\n.fa-dashboard::before {\n  content: \"\\f624\";\n}\n\n.fa-gauge-med::before {\n  content: \"\\f624\";\n}\n\n.fa-tachometer-alt-average::before {\n  content: \"\\f624\";\n}\n\n.fa-wand-magic-sparkles::before {\n  content: \"\\e2ca\";\n}\n\n.fa-magic-wand-sparkles::before {\n  content: \"\\e2ca\";\n}\n\n.fa-e::before {\n  content: \"E\";\n}\n\n.fa-pen-clip::before {\n  content: \"\\f305\";\n}\n\n.fa-pen-alt::before {\n  content: \"\\f305\";\n}\n\n.fa-bridge-circle-exclamation::before {\n  content: \"\\e4ca\";\n}\n\n.fa-user::before {\n  content: \"\\f007\";\n}\n\n.fa-school-circle-check::before {\n  content: \"\\e56b\";\n}\n\n.fa-dumpster::before {\n  content: \"\\f793\";\n}\n\n.fa-van-shuttle::before {\n  content: \"\\f5b6\";\n}\n\n.fa-shuttle-van::before {\n  content: \"\\f5b6\";\n}\n\n.fa-building-user::before {\n  content: \"\\e4da\";\n}\n\n.fa-square-caret-left::before {\n  content: \"\\f191\";\n}\n\n.fa-caret-square-left::before {\n  content: \"\\f191\";\n}\n\n.fa-highlighter::before {\n  content: \"\\f591\";\n}\n\n.fa-key::before {\n  content: \"\\f084\";\n}\n\n.fa-bullhorn::before {\n  content: \"\\f0a1\";\n}\n\n.fa-globe::before {\n  content: \"\\f0ac\";\n}\n\n.fa-synagogue::before {\n  content: \"\\f69b\";\n}\n\n.fa-person-half-dress::before {\n  content: \"\\e548\";\n}\n\n.fa-road-bridge::before {\n  content: \"\\e563\";\n}\n\n.fa-location-arrow::before {\n  content: \"\\f124\";\n}\n\n.fa-c::before {\n  content: \"C\";\n}\n\n.fa-tablet-button::before {\n  content: \"\\f10a\";\n}\n\n.fa-building-lock::before {\n  content: \"\\e4d6\";\n}\n\n.fa-pizza-slice::before {\n  content: \"\\f818\";\n}\n\n.fa-money-bill-wave::before {\n  content: \"\\f53a\";\n}\n\n.fa-chart-area::before {\n  content: \"\\f1fe\";\n}\n\n.fa-area-chart::before {\n  content: \"\\f1fe\";\n}\n\n.fa-house-flag::before {\n  content: \"\\e50d\";\n}\n\n.fa-person-circle-minus::before {\n  content: \"\\e540\";\n}\n\n.fa-ban::before {\n  content: \"\\f05e\";\n}\n\n.fa-cancel::before {\n  content: \"\\f05e\";\n}\n\n.fa-camera-rotate::before {\n  content: \"\\e0d8\";\n}\n\n.fa-spray-can-sparkles::before {\n  content: \"\\f5d0\";\n}\n\n.fa-air-freshener::before {\n  content: \"\\f5d0\";\n}\n\n.fa-star::before {\n  content: \"\\f005\";\n}\n\n.fa-repeat::before {\n  content: \"\\f363\";\n}\n\n.fa-cross::before {\n  content: \"\\f654\";\n}\n\n.fa-box::before {\n  content: \"\\f466\";\n}\n\n.fa-venus-mars::before {\n  content: \"\\f228\";\n}\n\n.fa-arrow-pointer::before {\n  content: \"\\f245\";\n}\n\n.fa-mouse-pointer::before {\n  content: \"\\f245\";\n}\n\n.fa-maximize::before {\n  content: \"\\f31e\";\n}\n\n.fa-expand-arrows-alt::before {\n  content: \"\\f31e\";\n}\n\n.fa-charging-station::before {\n  content: \"\\f5e7\";\n}\n\n.fa-shapes::before {\n  content: \"\\f61f\";\n}\n\n.fa-triangle-circle-square::before {\n  content: \"\\f61f\";\n}\n\n.fa-shuffle::before {\n  content: \"\\f074\";\n}\n\n.fa-random::before {\n  content: \"\\f074\";\n}\n\n.fa-person-running::before {\n  content: \"\\f70c\";\n}\n\n.fa-running::before {\n  content: \"\\f70c\";\n}\n\n.fa-mobile-retro::before {\n  content: \"\\e527\";\n}\n\n.fa-grip-lines-vertical::before {\n  content: \"\\f7a5\";\n}\n\n.fa-spider::before {\n  content: \"\\f717\";\n}\n\n.fa-hands-bound::before {\n  content: \"\\e4f9\";\n}\n\n.fa-file-invoice-dollar::before {\n  content: \"\\f571\";\n}\n\n.fa-plane-circle-exclamation::before {\n  content: \"\\e556\";\n}\n\n.fa-x-ray::before {\n  content: \"\\f497\";\n}\n\n.fa-spell-check::before {\n  content: \"\\f891\";\n}\n\n.fa-slash::before {\n  content: \"\\f715\";\n}\n\n.fa-computer-mouse::before {\n  content: \"\\f8cc\";\n}\n\n.fa-mouse::before {\n  content: \"\\f8cc\";\n}\n\n.fa-arrow-right-to-bracket::before {\n  content: \"\\f090\";\n}\n\n.fa-sign-in::before {\n  content: \"\\f090\";\n}\n\n.fa-shop-slash::before {\n  content: \"\\e070\";\n}\n\n.fa-store-alt-slash::before {\n  content: \"\\e070\";\n}\n\n.fa-server::before {\n  content: \"\\f233\";\n}\n\n.fa-virus-covid-slash::before {\n  content: \"\\e4a9\";\n}\n\n.fa-shop-lock::before {\n  content: \"\\e4a5\";\n}\n\n.fa-hourglass-start::before {\n  content: \"\\f251\";\n}\n\n.fa-hourglass-1::before {\n  content: \"\\f251\";\n}\n\n.fa-blender-phone::before {\n  content: \"\\f6b6\";\n}\n\n.fa-building-wheat::before {\n  content: \"\\e4db\";\n}\n\n.fa-person-breastfeeding::before {\n  content: \"\\e53a\";\n}\n\n.fa-right-to-bracket::before {\n  content: \"\\f2f6\";\n}\n\n.fa-sign-in-alt::before {\n  content: \"\\f2f6\";\n}\n\n.fa-venus::before {\n  content: \"\\f221\";\n}\n\n.fa-passport::before {\n  content: \"\\f5ab\";\n}\n\n.fa-heart-pulse::before {\n  content: \"\\f21e\";\n}\n\n.fa-heartbeat::before {\n  content: \"\\f21e\";\n}\n\n.fa-people-carry-box::before {\n  content: \"\\f4ce\";\n}\n\n.fa-people-carry::before {\n  content: \"\\f4ce\";\n}\n\n.fa-temperature-high::before {\n  content: \"\\f769\";\n}\n\n.fa-microchip::before {\n  content: \"\\f2db\";\n}\n\n.fa-crown::before {\n  content: \"\\f521\";\n}\n\n.fa-weight-hanging::before {\n  content: \"\\f5cd\";\n}\n\n.fa-xmarks-lines::before {\n  content: \"\\e59a\";\n}\n\n.fa-file-prescription::before {\n  content: \"\\f572\";\n}\n\n.fa-weight-scale::before {\n  content: \"\\f496\";\n}\n\n.fa-weight::before {\n  content: \"\\f496\";\n}\n\n.fa-user-group::before {\n  content: \"\\f500\";\n}\n\n.fa-user-friends::before {\n  content: \"\\f500\";\n}\n\n.fa-arrow-up-a-z::before {\n  content: \"\\f15e\";\n}\n\n.fa-sort-alpha-up::before {\n  content: \"\\f15e\";\n}\n\n.fa-chess-knight::before {\n  content: \"\\f441\";\n}\n\n.fa-face-laugh-squint::before {\n  content: \"\\f59b\";\n}\n\n.fa-laugh-squint::before {\n  content: \"\\f59b\";\n}\n\n.fa-wheelchair::before {\n  content: \"\\f193\";\n}\n\n.fa-circle-arrow-up::before {\n  content: \"\\f0aa\";\n}\n\n.fa-arrow-circle-up::before {\n  content: \"\\f0aa\";\n}\n\n.fa-toggle-on::before {\n  content: \"\\f205\";\n}\n\n.fa-person-walking::before {\n  content: \"\\f554\";\n}\n\n.fa-walking::before {\n  content: \"\\f554\";\n}\n\n.fa-l::before {\n  content: \"L\";\n}\n\n.fa-fire::before {\n  content: \"\\f06d\";\n}\n\n.fa-bed-pulse::before {\n  content: \"\\f487\";\n}\n\n.fa-procedures::before {\n  content: \"\\f487\";\n}\n\n.fa-shuttle-space::before {\n  content: \"\\f197\";\n}\n\n.fa-space-shuttle::before {\n  content: \"\\f197\";\n}\n\n.fa-face-laugh::before {\n  content: \"\\f599\";\n}\n\n.fa-laugh::before {\n  content: \"\\f599\";\n}\n\n.fa-folder-open::before {\n  content: \"\\f07c\";\n}\n\n.fa-heart-circle-plus::before {\n  content: \"\\e500\";\n}\n\n.fa-code-fork::before {\n  content: \"\\e13b\";\n}\n\n.fa-city::before {\n  content: \"\\f64f\";\n}\n\n.fa-microphone-lines::before {\n  content: \"\\f3c9\";\n}\n\n.fa-microphone-alt::before {\n  content: \"\\f3c9\";\n}\n\n.fa-pepper-hot::before {\n  content: \"\\f816\";\n}\n\n.fa-unlock::before {\n  content: \"\\f09c\";\n}\n\n.fa-colon-sign::before {\n  content: \"\\e140\";\n}\n\n.fa-headset::before {\n  content: \"\\f590\";\n}\n\n.fa-store-slash::before {\n  content: \"\\e071\";\n}\n\n.fa-road-circle-xmark::before {\n  content: \"\\e566\";\n}\n\n.fa-user-minus::before {\n  content: \"\\f503\";\n}\n\n.fa-mars-stroke-up::before {\n  content: \"\\f22a\";\n}\n\n.fa-mars-stroke-v::before {\n  content: \"\\f22a\";\n}\n\n.fa-champagne-glasses::before {\n  content: \"\\f79f\";\n}\n\n.fa-glass-cheers::before {\n  content: \"\\f79f\";\n}\n\n.fa-clipboard::before {\n  content: \"\\f328\";\n}\n\n.fa-house-circle-exclamation::before {\n  content: \"\\e50a\";\n}\n\n.fa-file-arrow-up::before {\n  content: \"\\f574\";\n}\n\n.fa-file-upload::before {\n  content: \"\\f574\";\n}\n\n.fa-wifi::before {\n  content: \"\\f1eb\";\n}\n\n.fa-wifi-3::before {\n  content: \"\\f1eb\";\n}\n\n.fa-wifi-strong::before {\n  content: \"\\f1eb\";\n}\n\n.fa-bath::before {\n  content: \"\\f2cd\";\n}\n\n.fa-bathtub::before {\n  content: \"\\f2cd\";\n}\n\n.fa-underline::before {\n  content: \"\\f0cd\";\n}\n\n.fa-user-pen::before {\n  content: \"\\f4ff\";\n}\n\n.fa-user-edit::before {\n  content: \"\\f4ff\";\n}\n\n.fa-signature::before {\n  content: \"\\f5b7\";\n}\n\n.fa-stroopwafel::before {\n  content: \"\\f551\";\n}\n\n.fa-bold::before {\n  content: \"\\f032\";\n}\n\n.fa-anchor-lock::before {\n  content: \"\\e4ad\";\n}\n\n.fa-building-ngo::before {\n  content: \"\\e4d7\";\n}\n\n.fa-manat-sign::before {\n  content: \"\\e1d5\";\n}\n\n.fa-not-equal::before {\n  content: \"\\f53e\";\n}\n\n.fa-border-top-left::before {\n  content: \"\\f853\";\n}\n\n.fa-border-style::before {\n  content: \"\\f853\";\n}\n\n.fa-map-location-dot::before {\n  content: \"\\f5a0\";\n}\n\n.fa-map-marked-alt::before {\n  content: \"\\f5a0\";\n}\n\n.fa-jedi::before {\n  content: \"\\f669\";\n}\n\n.fa-square-poll-vertical::before {\n  content: \"\\f681\";\n}\n\n.fa-poll::before {\n  content: \"\\f681\";\n}\n\n.fa-mug-hot::before {\n  content: \"\\f7b6\";\n}\n\n.fa-car-battery::before {\n  content: \"\\f5df\";\n}\n\n.fa-battery-car::before {\n  content: \"\\f5df\";\n}\n\n.fa-gift::before {\n  content: \"\\f06b\";\n}\n\n.fa-dice-two::before {\n  content: \"\\f528\";\n}\n\n.fa-chess-queen::before {\n  content: \"\\f445\";\n}\n\n.fa-glasses::before {\n  content: \"\\f530\";\n}\n\n.fa-chess-board::before {\n  content: \"\\f43c\";\n}\n\n.fa-building-circle-check::before {\n  content: \"\\e4d2\";\n}\n\n.fa-person-chalkboard::before {\n  content: \"\\e53d\";\n}\n\n.fa-mars-stroke-right::before {\n  content: \"\\f22b\";\n}\n\n.fa-mars-stroke-h::before {\n  content: \"\\f22b\";\n}\n\n.fa-hand-back-fist::before {\n  content: \"\\f255\";\n}\n\n.fa-hand-rock::before {\n  content: \"\\f255\";\n}\n\n.fa-square-caret-up::before {\n  content: \"\\f151\";\n}\n\n.fa-caret-square-up::before {\n  content: \"\\f151\";\n}\n\n.fa-cloud-showers-water::before {\n  content: \"\\e4e4\";\n}\n\n.fa-chart-bar::before {\n  content: \"\\f080\";\n}\n\n.fa-bar-chart::before {\n  content: \"\\f080\";\n}\n\n.fa-hands-bubbles::before {\n  content: \"\\e05e\";\n}\n\n.fa-hands-wash::before {\n  content: \"\\e05e\";\n}\n\n.fa-less-than-equal::before {\n  content: \"\\f537\";\n}\n\n.fa-train::before {\n  content: \"\\f238\";\n}\n\n.fa-eye-low-vision::before {\n  content: \"\\f2a8\";\n}\n\n.fa-low-vision::before {\n  content: \"\\f2a8\";\n}\n\n.fa-crow::before {\n  content: \"\\f520\";\n}\n\n.fa-sailboat::before {\n  content: \"\\e445\";\n}\n\n.fa-window-restore::before {\n  content: \"\\f2d2\";\n}\n\n.fa-square-plus::before {\n  content: \"\\f0fe\";\n}\n\n.fa-plus-square::before {\n  content: \"\\f0fe\";\n}\n\n.fa-torii-gate::before {\n  content: \"\\f6a1\";\n}\n\n.fa-frog::before {\n  content: \"\\f52e\";\n}\n\n.fa-bucket::before {\n  content: \"\\e4cf\";\n}\n\n.fa-image::before {\n  content: \"\\f03e\";\n}\n\n.fa-microphone::before {\n  content: \"\\f130\";\n}\n\n.fa-cow::before {\n  content: \"\\f6c8\";\n}\n\n.fa-caret-up::before {\n  content: \"\\f0d8\";\n}\n\n.fa-screwdriver::before {\n  content: \"\\f54a\";\n}\n\n.fa-folder-closed::before {\n  content: \"\\e185\";\n}\n\n.fa-house-tsunami::before {\n  content: \"\\e515\";\n}\n\n.fa-square-nfi::before {\n  content: \"\\e576\";\n}\n\n.fa-arrow-up-from-ground-water::before {\n  content: \"\\e4b5\";\n}\n\n.fa-martini-glass::before {\n  content: \"\\f57b\";\n}\n\n.fa-glass-martini-alt::before {\n  content: \"\\f57b\";\n}\n\n.fa-rotate-left::before {\n  content: \"\\f2ea\";\n}\n\n.fa-rotate-back::before {\n  content: \"\\f2ea\";\n}\n\n.fa-rotate-backward::before {\n  content: \"\\f2ea\";\n}\n\n.fa-undo-alt::before {\n  content: \"\\f2ea\";\n}\n\n.fa-table-columns::before {\n  content: \"\\f0db\";\n}\n\n.fa-columns::before {\n  content: \"\\f0db\";\n}\n\n.fa-lemon::before {\n  content: \"\\f094\";\n}\n\n.fa-head-side-mask::before {\n  content: \"\\e063\";\n}\n\n.fa-handshake::before {\n  content: \"\\f2b5\";\n}\n\n.fa-gem::before {\n  content: \"\\f3a5\";\n}\n\n.fa-dolly::before {\n  content: \"\\f472\";\n}\n\n.fa-dolly-box::before {\n  content: \"\\f472\";\n}\n\n.fa-smoking::before {\n  content: \"\\f48d\";\n}\n\n.fa-minimize::before {\n  content: \"\\f78c\";\n}\n\n.fa-compress-arrows-alt::before {\n  content: \"\\f78c\";\n}\n\n.fa-monument::before {\n  content: \"\\f5a6\";\n}\n\n.fa-snowplow::before {\n  content: \"\\f7d2\";\n}\n\n.fa-angles-right::before {\n  content: \"\\f101\";\n}\n\n.fa-angle-double-right::before {\n  content: \"\\f101\";\n}\n\n.fa-cannabis::before {\n  content: \"\\f55f\";\n}\n\n.fa-circle-play::before {\n  content: \"\\f144\";\n}\n\n.fa-play-circle::before {\n  content: \"\\f144\";\n}\n\n.fa-tablets::before {\n  content: \"\\f490\";\n}\n\n.fa-ethernet::before {\n  content: \"\\f796\";\n}\n\n.fa-euro-sign::before {\n  content: \"\\f153\";\n}\n\n.fa-eur::before {\n  content: \"\\f153\";\n}\n\n.fa-euro::before {\n  content: \"\\f153\";\n}\n\n.fa-chair::before {\n  content: \"\\f6c0\";\n}\n\n.fa-circle-check::before {\n  content: \"\\f058\";\n}\n\n.fa-check-circle::before {\n  content: \"\\f058\";\n}\n\n.fa-circle-stop::before {\n  content: \"\\f28d\";\n}\n\n.fa-stop-circle::before {\n  content: \"\\f28d\";\n}\n\n.fa-compass-drafting::before {\n  content: \"\\f568\";\n}\n\n.fa-drafting-compass::before {\n  content: \"\\f568\";\n}\n\n.fa-plate-wheat::before {\n  content: \"\\e55a\";\n}\n\n.fa-icicles::before {\n  content: \"\\f7ad\";\n}\n\n.fa-person-shelter::before {\n  content: \"\\e54f\";\n}\n\n.fa-neuter::before {\n  content: \"\\f22c\";\n}\n\n.fa-id-badge::before {\n  content: \"\\f2c1\";\n}\n\n.fa-marker::before {\n  content: \"\\f5a1\";\n}\n\n.fa-face-laugh-beam::before {\n  content: \"\\f59a\";\n}\n\n.fa-laugh-beam::before {\n  content: \"\\f59a\";\n}\n\n.fa-helicopter-symbol::before {\n  content: \"\\e502\";\n}\n\n.fa-universal-access::before {\n  content: \"\\f29a\";\n}\n\n.fa-circle-chevron-up::before {\n  content: \"\\f139\";\n}\n\n.fa-chevron-circle-up::before {\n  content: \"\\f139\";\n}\n\n.fa-lari-sign::before {\n  content: \"\\e1c8\";\n}\n\n.fa-volcano::before {\n  content: \"\\f770\";\n}\n\n.fa-person-walking-dashed-line-arrow-right::before {\n  content: \"\\e553\";\n}\n\n.fa-sterling-sign::before {\n  content: \"\\f154\";\n}\n\n.fa-gbp::before {\n  content: \"\\f154\";\n}\n\n.fa-pound-sign::before {\n  content: \"\\f154\";\n}\n\n.fa-viruses::before {\n  content: \"\\e076\";\n}\n\n.fa-square-person-confined::before {\n  content: \"\\e577\";\n}\n\n.fa-user-tie::before {\n  content: \"\\f508\";\n}\n\n.fa-arrow-down-long::before {\n  content: \"\\f175\";\n}\n\n.fa-long-arrow-down::before {\n  content: \"\\f175\";\n}\n\n.fa-tent-arrow-down-to-line::before {\n  content: \"\\e57e\";\n}\n\n.fa-certificate::before {\n  content: \"\\f0a3\";\n}\n\n.fa-reply-all::before {\n  content: \"\\f122\";\n}\n\n.fa-mail-reply-all::before {\n  content: \"\\f122\";\n}\n\n.fa-suitcase::before {\n  content: \"\\f0f2\";\n}\n\n.fa-person-skating::before {\n  content: \"\\f7c5\";\n}\n\n.fa-skating::before {\n  content: \"\\f7c5\";\n}\n\n.fa-filter-circle-dollar::before {\n  content: \"\\f662\";\n}\n\n.fa-funnel-dollar::before {\n  content: \"\\f662\";\n}\n\n.fa-camera-retro::before {\n  content: \"\\f083\";\n}\n\n.fa-circle-arrow-down::before {\n  content: \"\\f0ab\";\n}\n\n.fa-arrow-circle-down::before {\n  content: \"\\f0ab\";\n}\n\n.fa-file-import::before {\n  content: \"\\f56f\";\n}\n\n.fa-arrow-right-to-file::before {\n  content: \"\\f56f\";\n}\n\n.fa-square-arrow-up-right::before {\n  content: \"\\f14c\";\n}\n\n.fa-external-link-square::before {\n  content: \"\\f14c\";\n}\n\n.fa-box-open::before {\n  content: \"\\f49e\";\n}\n\n.fa-scroll::before {\n  content: \"\\f70e\";\n}\n\n.fa-spa::before {\n  content: \"\\f5bb\";\n}\n\n.fa-location-pin-lock::before {\n  content: \"\\e51f\";\n}\n\n.fa-pause::before {\n  content: \"\\f04c\";\n}\n\n.fa-hill-avalanche::before {\n  content: \"\\e507\";\n}\n\n.fa-temperature-empty::before {\n  content: \"\\f2cb\";\n}\n\n.fa-temperature-0::before {\n  content: \"\\f2cb\";\n}\n\n.fa-thermometer-0::before {\n  content: \"\\f2cb\";\n}\n\n.fa-thermometer-empty::before {\n  content: \"\\f2cb\";\n}\n\n.fa-bomb::before {\n  content: \"\\f1e2\";\n}\n\n.fa-registered::before {\n  content: \"\\f25d\";\n}\n\n.fa-address-card::before {\n  content: \"\\f2bb\";\n}\n\n.fa-contact-card::before {\n  content: \"\\f2bb\";\n}\n\n.fa-vcard::before {\n  content: \"\\f2bb\";\n}\n\n.fa-scale-unbalanced-flip::before {\n  content: \"\\f516\";\n}\n\n.fa-balance-scale-right::before {\n  content: \"\\f516\";\n}\n\n.fa-subscript::before {\n  content: \"\\f12c\";\n}\n\n.fa-diamond-turn-right::before {\n  content: \"\\f5eb\";\n}\n\n.fa-directions::before {\n  content: \"\\f5eb\";\n}\n\n.fa-burst::before {\n  content: \"\\e4dc\";\n}\n\n.fa-house-laptop::before {\n  content: \"\\e066\";\n}\n\n.fa-laptop-house::before {\n  content: \"\\e066\";\n}\n\n.fa-face-tired::before {\n  content: \"\\f5c8\";\n}\n\n.fa-tired::before {\n  content: \"\\f5c8\";\n}\n\n.fa-money-bills::before {\n  content: \"\\e1f3\";\n}\n\n.fa-smog::before {\n  content: \"\\f75f\";\n}\n\n.fa-crutch::before {\n  content: \"\\f7f7\";\n}\n\n.fa-cloud-arrow-up::before {\n  content: \"\\f0ee\";\n}\n\n.fa-cloud-upload::before {\n  content: \"\\f0ee\";\n}\n\n.fa-cloud-upload-alt::before {\n  content: \"\\f0ee\";\n}\n\n.fa-palette::before {\n  content: \"\\f53f\";\n}\n\n.fa-arrows-turn-right::before {\n  content: \"\\e4c0\";\n}\n\n.fa-vest::before {\n  content: \"\\e085\";\n}\n\n.fa-ferry::before {\n  content: \"\\e4ea\";\n}\n\n.fa-arrows-down-to-people::before {\n  content: \"\\e4b9\";\n}\n\n.fa-seedling::before {\n  content: \"\\f4d8\";\n}\n\n.fa-sprout::before {\n  content: \"\\f4d8\";\n}\n\n.fa-left-right::before {\n  content: \"\\f337\";\n}\n\n.fa-arrows-alt-h::before {\n  content: \"\\f337\";\n}\n\n.fa-boxes-packing::before {\n  content: \"\\e4c7\";\n}\n\n.fa-circle-arrow-left::before {\n  content: \"\\f0a8\";\n}\n\n.fa-arrow-circle-left::before {\n  content: \"\\f0a8\";\n}\n\n.fa-group-arrows-rotate::before {\n  content: \"\\e4f6\";\n}\n\n.fa-bowl-food::before {\n  content: \"\\e4c6\";\n}\n\n.fa-candy-cane::before {\n  content: \"\\f786\";\n}\n\n.fa-arrow-down-wide-short::before {\n  content: \"\\f160\";\n}\n\n.fa-sort-amount-asc::before {\n  content: \"\\f160\";\n}\n\n.fa-sort-amount-down::before {\n  content: \"\\f160\";\n}\n\n.fa-cloud-bolt::before {\n  content: \"\\f76c\";\n}\n\n.fa-thunderstorm::before {\n  content: \"\\f76c\";\n}\n\n.fa-text-slash::before {\n  content: \"\\f87d\";\n}\n\n.fa-remove-format::before {\n  content: \"\\f87d\";\n}\n\n.fa-face-smile-wink::before {\n  content: \"\\f4da\";\n}\n\n.fa-smile-wink::before {\n  content: \"\\f4da\";\n}\n\n.fa-file-word::before {\n  content: \"\\f1c2\";\n}\n\n.fa-file-powerpoint::before {\n  content: \"\\f1c4\";\n}\n\n.fa-arrows-left-right::before {\n  content: \"\\f07e\";\n}\n\n.fa-arrows-h::before {\n  content: \"\\f07e\";\n}\n\n.fa-house-lock::before {\n  content: \"\\e510\";\n}\n\n.fa-cloud-arrow-down::before {\n  content: \"\\f0ed\";\n}\n\n.fa-cloud-download::before {\n  content: \"\\f0ed\";\n}\n\n.fa-cloud-download-alt::before {\n  content: \"\\f0ed\";\n}\n\n.fa-children::before {\n  content: \"\\e4e1\";\n}\n\n.fa-chalkboard::before {\n  content: \"\\f51b\";\n}\n\n.fa-blackboard::before {\n  content: \"\\f51b\";\n}\n\n.fa-user-large-slash::before {\n  content: \"\\f4fa\";\n}\n\n.fa-user-alt-slash::before {\n  content: \"\\f4fa\";\n}\n\n.fa-envelope-open::before {\n  content: \"\\f2b6\";\n}\n\n.fa-handshake-simple-slash::before {\n  content: \"\\e05f\";\n}\n\n.fa-handshake-alt-slash::before {\n  content: \"\\e05f\";\n}\n\n.fa-mattress-pillow::before {\n  content: \"\\e525\";\n}\n\n.fa-guarani-sign::before {\n  content: \"\\e19a\";\n}\n\n.fa-arrows-rotate::before {\n  content: \"\\f021\";\n}\n\n.fa-refresh::before {\n  content: \"\\f021\";\n}\n\n.fa-sync::before {\n  content: \"\\f021\";\n}\n\n.fa-fire-extinguisher::before {\n  content: \"\\f134\";\n}\n\n.fa-cruzeiro-sign::before {\n  content: \"\\e152\";\n}\n\n.fa-greater-than-equal::before {\n  content: \"\\f532\";\n}\n\n.fa-shield-halved::before {\n  content: \"\\f3ed\";\n}\n\n.fa-shield-alt::before {\n  content: \"\\f3ed\";\n}\n\n.fa-book-atlas::before {\n  content: \"\\f558\";\n}\n\n.fa-atlas::before {\n  content: \"\\f558\";\n}\n\n.fa-virus::before {\n  content: \"\\e074\";\n}\n\n.fa-envelope-circle-check::before {\n  content: \"\\e4e8\";\n}\n\n.fa-layer-group::before {\n  content: \"\\f5fd\";\n}\n\n.fa-arrows-to-dot::before {\n  content: \"\\e4be\";\n}\n\n.fa-archway::before {\n  content: \"\\f557\";\n}\n\n.fa-heart-circle-check::before {\n  content: \"\\e4fd\";\n}\n\n.fa-house-chimney-crack::before {\n  content: \"\\f6f1\";\n}\n\n.fa-house-damage::before {\n  content: \"\\f6f1\";\n}\n\n.fa-file-zipper::before {\n  content: \"\\f1c6\";\n}\n\n.fa-file-archive::before {\n  content: \"\\f1c6\";\n}\n\n.fa-square::before {\n  content: \"\\f0c8\";\n}\n\n.fa-martini-glass-empty::before {\n  content: \"\\f000\";\n}\n\n.fa-glass-martini::before {\n  content: \"\\f000\";\n}\n\n.fa-couch::before {\n  content: \"\\f4b8\";\n}\n\n.fa-cedi-sign::before {\n  content: \"\\e0df\";\n}\n\n.fa-italic::before {\n  content: \"\\f033\";\n}\n\n.fa-table-cells-column-lock::before {\n  content: \"\\e678\";\n}\n\n.fa-church::before {\n  content: \"\\f51d\";\n}\n\n.fa-comments-dollar::before {\n  content: \"\\f653\";\n}\n\n.fa-democrat::before {\n  content: \"\\f747\";\n}\n\n.fa-z::before {\n  content: \"Z\";\n}\n\n.fa-person-skiing::before {\n  content: \"\\f7c9\";\n}\n\n.fa-skiing::before {\n  content: \"\\f7c9\";\n}\n\n.fa-road-lock::before {\n  content: \"\\e567\";\n}\n\n.fa-a::before {\n  content: \"A\";\n}\n\n.fa-temperature-arrow-down::before {\n  content: \"\\e03f\";\n}\n\n.fa-temperature-down::before {\n  content: \"\\e03f\";\n}\n\n.fa-feather-pointed::before {\n  content: \"\\f56b\";\n}\n\n.fa-feather-alt::before {\n  content: \"\\f56b\";\n}\n\n.fa-p::before {\n  content: \"P\";\n}\n\n.fa-snowflake::before {\n  content: \"\\f2dc\";\n}\n\n.fa-newspaper::before {\n  content: \"\\f1ea\";\n}\n\n.fa-rectangle-ad::before {\n  content: \"\\f641\";\n}\n\n.fa-ad::before {\n  content: \"\\f641\";\n}\n\n.fa-circle-arrow-right::before {\n  content: \"\\f0a9\";\n}\n\n.fa-arrow-circle-right::before {\n  content: \"\\f0a9\";\n}\n\n.fa-filter-circle-xmark::before {\n  content: \"\\e17b\";\n}\n\n.fa-locust::before {\n  content: \"\\e520\";\n}\n\n.fa-sort::before {\n  content: \"\\f0dc\";\n}\n\n.fa-unsorted::before {\n  content: \"\\f0dc\";\n}\n\n.fa-list-ol::before {\n  content: \"\\f0cb\";\n}\n\n.fa-list-1-2::before {\n  content: \"\\f0cb\";\n}\n\n.fa-list-numeric::before {\n  content: \"\\f0cb\";\n}\n\n.fa-person-dress-burst::before {\n  content: \"\\e544\";\n}\n\n.fa-money-check-dollar::before {\n  content: \"\\f53d\";\n}\n\n.fa-money-check-alt::before {\n  content: \"\\f53d\";\n}\n\n.fa-vector-square::before {\n  content: \"\\f5cb\";\n}\n\n.fa-bread-slice::before {\n  content: \"\\f7ec\";\n}\n\n.fa-language::before {\n  content: \"\\f1ab\";\n}\n\n.fa-face-kiss-wink-heart::before {\n  content: \"\\f598\";\n}\n\n.fa-kiss-wink-heart::before {\n  content: \"\\f598\";\n}\n\n.fa-filter::before {\n  content: \"\\f0b0\";\n}\n\n.fa-question::before {\n  content: \"\\?\";\n}\n\n.fa-file-signature::before {\n  content: \"\\f573\";\n}\n\n.fa-up-down-left-right::before {\n  content: \"\\f0b2\";\n}\n\n.fa-arrows-alt::before {\n  content: \"\\f0b2\";\n}\n\n.fa-house-chimney-user::before {\n  content: \"\\e065\";\n}\n\n.fa-hand-holding-heart::before {\n  content: \"\\f4be\";\n}\n\n.fa-puzzle-piece::before {\n  content: \"\\f12e\";\n}\n\n.fa-money-check::before {\n  content: \"\\f53c\";\n}\n\n.fa-star-half-stroke::before {\n  content: \"\\f5c0\";\n}\n\n.fa-star-half-alt::before {\n  content: \"\\f5c0\";\n}\n\n.fa-code::before {\n  content: \"\\f121\";\n}\n\n.fa-whiskey-glass::before {\n  content: \"\\f7a0\";\n}\n\n.fa-glass-whiskey::before {\n  content: \"\\f7a0\";\n}\n\n.fa-building-circle-exclamation::before {\n  content: \"\\e4d3\";\n}\n\n.fa-magnifying-glass-chart::before {\n  content: \"\\e522\";\n}\n\n.fa-arrow-up-right-from-square::before {\n  content: \"\\f08e\";\n}\n\n.fa-external-link::before {\n  content: \"\\f08e\";\n}\n\n.fa-cubes-stacked::before {\n  content: \"\\e4e6\";\n}\n\n.fa-won-sign::before {\n  content: \"\\f159\";\n}\n\n.fa-krw::before {\n  content: \"\\f159\";\n}\n\n.fa-won::before {\n  content: \"\\f159\";\n}\n\n.fa-virus-covid::before {\n  content: \"\\e4a8\";\n}\n\n.fa-austral-sign::before {\n  content: \"\\e0a9\";\n}\n\n.fa-f::before {\n  content: \"F\";\n}\n\n.fa-leaf::before {\n  content: \"\\f06c\";\n}\n\n.fa-road::before {\n  content: \"\\f018\";\n}\n\n.fa-taxi::before {\n  content: \"\\f1ba\";\n}\n\n.fa-cab::before {\n  content: \"\\f1ba\";\n}\n\n.fa-person-circle-plus::before {\n  content: \"\\e541\";\n}\n\n.fa-chart-pie::before {\n  content: \"\\f200\";\n}\n\n.fa-pie-chart::before {\n  content: \"\\f200\";\n}\n\n.fa-bolt-lightning::before {\n  content: \"\\e0b7\";\n}\n\n.fa-sack-xmark::before {\n  content: \"\\e56a\";\n}\n\n.fa-file-excel::before {\n  content: \"\\f1c3\";\n}\n\n.fa-file-contract::before {\n  content: \"\\f56c\";\n}\n\n.fa-fish-fins::before {\n  content: \"\\e4f2\";\n}\n\n.fa-building-flag::before {\n  content: \"\\e4d5\";\n}\n\n.fa-face-grin-beam::before {\n  content: \"\\f582\";\n}\n\n.fa-grin-beam::before {\n  content: \"\\f582\";\n}\n\n.fa-object-ungroup::before {\n  content: \"\\f248\";\n}\n\n.fa-poop::before {\n  content: \"\\f619\";\n}\n\n.fa-location-pin::before {\n  content: \"\\f041\";\n}\n\n.fa-map-marker::before {\n  content: \"\\f041\";\n}\n\n.fa-kaaba::before {\n  content: \"\\f66b\";\n}\n\n.fa-toilet-paper::before {\n  content: \"\\f71e\";\n}\n\n.fa-helmet-safety::before {\n  content: \"\\f807\";\n}\n\n.fa-hard-hat::before {\n  content: \"\\f807\";\n}\n\n.fa-hat-hard::before {\n  content: \"\\f807\";\n}\n\n.fa-eject::before {\n  content: \"\\f052\";\n}\n\n.fa-circle-right::before {\n  content: \"\\f35a\";\n}\n\n.fa-arrow-alt-circle-right::before {\n  content: \"\\f35a\";\n}\n\n.fa-plane-circle-check::before {\n  content: \"\\e555\";\n}\n\n.fa-face-rolling-eyes::before {\n  content: \"\\f5a5\";\n}\n\n.fa-meh-rolling-eyes::before {\n  content: \"\\f5a5\";\n}\n\n.fa-object-group::before {\n  content: \"\\f247\";\n}\n\n.fa-chart-line::before {\n  content: \"\\f201\";\n}\n\n.fa-line-chart::before {\n  content: \"\\f201\";\n}\n\n.fa-mask-ventilator::before {\n  content: \"\\e524\";\n}\n\n.fa-arrow-right::before {\n  content: \"\\f061\";\n}\n\n.fa-signs-post::before {\n  content: \"\\f277\";\n}\n\n.fa-map-signs::before {\n  content: \"\\f277\";\n}\n\n.fa-cash-register::before {\n  content: \"\\f788\";\n}\n\n.fa-person-circle-question::before {\n  content: \"\\e542\";\n}\n\n.fa-h::before {\n  content: \"H\";\n}\n\n.fa-tarp::before {\n  content: \"\\e57b\";\n}\n\n.fa-screwdriver-wrench::before {\n  content: \"\\f7d9\";\n}\n\n.fa-tools::before {\n  content: \"\\f7d9\";\n}\n\n.fa-arrows-to-eye::before {\n  content: \"\\e4bf\";\n}\n\n.fa-plug-circle-bolt::before {\n  content: \"\\e55b\";\n}\n\n.fa-heart::before {\n  content: \"\\f004\";\n}\n\n.fa-mars-and-venus::before {\n  content: \"\\f224\";\n}\n\n.fa-house-user::before {\n  content: \"\\e1b0\";\n}\n\n.fa-home-user::before {\n  content: \"\\e1b0\";\n}\n\n.fa-dumpster-fire::before {\n  content: \"\\f794\";\n}\n\n.fa-house-crack::before {\n  content: \"\\e3b1\";\n}\n\n.fa-martini-glass-citrus::before {\n  content: \"\\f561\";\n}\n\n.fa-cocktail::before {\n  content: \"\\f561\";\n}\n\n.fa-face-surprise::before {\n  content: \"\\f5c2\";\n}\n\n.fa-surprise::before {\n  content: \"\\f5c2\";\n}\n\n.fa-bottle-water::before {\n  content: \"\\e4c5\";\n}\n\n.fa-circle-pause::before {\n  content: \"\\f28b\";\n}\n\n.fa-pause-circle::before {\n  content: \"\\f28b\";\n}\n\n.fa-toilet-paper-slash::before {\n  content: \"\\e072\";\n}\n\n.fa-apple-whole::before {\n  content: \"\\f5d1\";\n}\n\n.fa-apple-alt::before {\n  content: \"\\f5d1\";\n}\n\n.fa-kitchen-set::before {\n  content: \"\\e51a\";\n}\n\n.fa-r::before {\n  content: \"R\";\n}\n\n.fa-temperature-quarter::before {\n  content: \"\\f2ca\";\n}\n\n.fa-temperature-1::before {\n  content: \"\\f2ca\";\n}\n\n.fa-thermometer-1::before {\n  content: \"\\f2ca\";\n}\n\n.fa-thermometer-quarter::before {\n  content: \"\\f2ca\";\n}\n\n.fa-cube::before {\n  content: \"\\f1b2\";\n}\n\n.fa-bitcoin-sign::before {\n  content: \"\\e0b4\";\n}\n\n.fa-shield-dog::before {\n  content: \"\\e573\";\n}\n\n.fa-solar-panel::before {\n  content: \"\\f5ba\";\n}\n\n.fa-lock-open::before {\n  content: \"\\f3c1\";\n}\n\n.fa-elevator::before {\n  content: \"\\e16d\";\n}\n\n.fa-money-bill-transfer::before {\n  content: \"\\e528\";\n}\n\n.fa-money-bill-trend-up::before {\n  content: \"\\e529\";\n}\n\n.fa-house-flood-water-circle-arrow-right::before {\n  content: \"\\e50f\";\n}\n\n.fa-square-poll-horizontal::before {\n  content: \"\\f682\";\n}\n\n.fa-poll-h::before {\n  content: \"\\f682\";\n}\n\n.fa-circle::before {\n  content: \"\\f111\";\n}\n\n.fa-backward-fast::before {\n  content: \"\\f049\";\n}\n\n.fa-fast-backward::before {\n  content: \"\\f049\";\n}\n\n.fa-recycle::before {\n  content: \"\\f1b8\";\n}\n\n.fa-user-astronaut::before {\n  content: \"\\f4fb\";\n}\n\n.fa-plane-slash::before {\n  content: \"\\e069\";\n}\n\n.fa-trademark::before {\n  content: \"\\f25c\";\n}\n\n.fa-basketball::before {\n  content: \"\\f434\";\n}\n\n.fa-basketball-ball::before {\n  content: \"\\f434\";\n}\n\n.fa-satellite-dish::before {\n  content: \"\\f7c0\";\n}\n\n.fa-circle-up::before {\n  content: \"\\f35b\";\n}\n\n.fa-arrow-alt-circle-up::before {\n  content: \"\\f35b\";\n}\n\n.fa-mobile-screen-button::before {\n  content: \"\\f3cd\";\n}\n\n.fa-mobile-alt::before {\n  content: \"\\f3cd\";\n}\n\n.fa-volume-high::before {\n  content: \"\\f028\";\n}\n\n.fa-volume-up::before {\n  content: \"\\f028\";\n}\n\n.fa-users-rays::before {\n  content: \"\\e593\";\n}\n\n.fa-wallet::before {\n  content: \"\\f555\";\n}\n\n.fa-clipboard-check::before {\n  content: \"\\f46c\";\n}\n\n.fa-file-audio::before {\n  content: \"\\f1c7\";\n}\n\n.fa-burger::before {\n  content: \"\\f805\";\n}\n\n.fa-hamburger::before {\n  content: \"\\f805\";\n}\n\n.fa-wrench::before {\n  content: \"\\f0ad\";\n}\n\n.fa-bugs::before {\n  content: \"\\e4d0\";\n}\n\n.fa-rupee-sign::before {\n  content: \"\\f156\";\n}\n\n.fa-rupee::before {\n  content: \"\\f156\";\n}\n\n.fa-file-image::before {\n  content: \"\\f1c5\";\n}\n\n.fa-circle-question::before {\n  content: \"\\f059\";\n}\n\n.fa-question-circle::before {\n  content: \"\\f059\";\n}\n\n.fa-plane-departure::before {\n  content: \"\\f5b0\";\n}\n\n.fa-handshake-slash::before {\n  content: \"\\e060\";\n}\n\n.fa-book-bookmark::before {\n  content: \"\\e0bb\";\n}\n\n.fa-code-branch::before {\n  content: \"\\f126\";\n}\n\n.fa-hat-cowboy::before {\n  content: \"\\f8c0\";\n}\n\n.fa-bridge::before {\n  content: \"\\e4c8\";\n}\n\n.fa-phone-flip::before {\n  content: \"\\f879\";\n}\n\n.fa-phone-alt::before {\n  content: \"\\f879\";\n}\n\n.fa-truck-front::before {\n  content: \"\\e2b7\";\n}\n\n.fa-cat::before {\n  content: \"\\f6be\";\n}\n\n.fa-anchor-circle-exclamation::before {\n  content: \"\\e4ab\";\n}\n\n.fa-truck-field::before {\n  content: \"\\e58d\";\n}\n\n.fa-route::before {\n  content: \"\\f4d7\";\n}\n\n.fa-clipboard-question::before {\n  content: \"\\e4e3\";\n}\n\n.fa-panorama::before {\n  content: \"\\e209\";\n}\n\n.fa-comment-medical::before {\n  content: \"\\f7f5\";\n}\n\n.fa-teeth-open::before {\n  content: \"\\f62f\";\n}\n\n.fa-file-circle-minus::before {\n  content: \"\\e4ed\";\n}\n\n.fa-tags::before {\n  content: \"\\f02c\";\n}\n\n.fa-wine-glass::before {\n  content: \"\\f4e3\";\n}\n\n.fa-forward-fast::before {\n  content: \"\\f050\";\n}\n\n.fa-fast-forward::before {\n  content: \"\\f050\";\n}\n\n.fa-face-meh-blank::before {\n  content: \"\\f5a4\";\n}\n\n.fa-meh-blank::before {\n  content: \"\\f5a4\";\n}\n\n.fa-square-parking::before {\n  content: \"\\f540\";\n}\n\n.fa-parking::before {\n  content: \"\\f540\";\n}\n\n.fa-house-signal::before {\n  content: \"\\e012\";\n}\n\n.fa-bars-progress::before {\n  content: \"\\f828\";\n}\n\n.fa-tasks-alt::before {\n  content: \"\\f828\";\n}\n\n.fa-faucet-drip::before {\n  content: \"\\e006\";\n}\n\n.fa-cart-flatbed::before {\n  content: \"\\f474\";\n}\n\n.fa-dolly-flatbed::before {\n  content: \"\\f474\";\n}\n\n.fa-ban-smoking::before {\n  content: \"\\f54d\";\n}\n\n.fa-smoking-ban::before {\n  content: \"\\f54d\";\n}\n\n.fa-terminal::before {\n  content: \"\\f120\";\n}\n\n.fa-mobile-button::before {\n  content: \"\\f10b\";\n}\n\n.fa-house-medical-flag::before {\n  content: \"\\e514\";\n}\n\n.fa-basket-shopping::before {\n  content: \"\\f291\";\n}\n\n.fa-shopping-basket::before {\n  content: \"\\f291\";\n}\n\n.fa-tape::before {\n  content: \"\\f4db\";\n}\n\n.fa-bus-simple::before {\n  content: \"\\f55e\";\n}\n\n.fa-bus-alt::before {\n  content: \"\\f55e\";\n}\n\n.fa-eye::before {\n  content: \"\\f06e\";\n}\n\n.fa-face-sad-cry::before {\n  content: \"\\f5b3\";\n}\n\n.fa-sad-cry::before {\n  content: \"\\f5b3\";\n}\n\n.fa-audio-description::before {\n  content: \"\\f29e\";\n}\n\n.fa-person-military-to-person::before {\n  content: \"\\e54c\";\n}\n\n.fa-file-shield::before {\n  content: \"\\e4f0\";\n}\n\n.fa-user-slash::before {\n  content: \"\\f506\";\n}\n\n.fa-pen::before {\n  content: \"\\f304\";\n}\n\n.fa-tower-observation::before {\n  content: \"\\e586\";\n}\n\n.fa-file-code::before {\n  content: \"\\f1c9\";\n}\n\n.fa-signal::before {\n  content: \"\\f012\";\n}\n\n.fa-signal-5::before {\n  content: \"\\f012\";\n}\n\n.fa-signal-perfect::before {\n  content: \"\\f012\";\n}\n\n.fa-bus::before {\n  content: \"\\f207\";\n}\n\n.fa-heart-circle-xmark::before {\n  content: \"\\e501\";\n}\n\n.fa-house-chimney::before {\n  content: \"\\e3af\";\n}\n\n.fa-home-lg::before {\n  content: \"\\e3af\";\n}\n\n.fa-window-maximize::before {\n  content: \"\\f2d0\";\n}\n\n.fa-face-frown::before {\n  content: \"\\f119\";\n}\n\n.fa-frown::before {\n  content: \"\\f119\";\n}\n\n.fa-prescription::before {\n  content: \"\\f5b1\";\n}\n\n.fa-shop::before {\n  content: \"\\f54f\";\n}\n\n.fa-store-alt::before {\n  content: \"\\f54f\";\n}\n\n.fa-floppy-disk::before {\n  content: \"\\f0c7\";\n}\n\n.fa-save::before {\n  content: \"\\f0c7\";\n}\n\n.fa-vihara::before {\n  content: \"\\f6a7\";\n}\n\n.fa-scale-unbalanced::before {\n  content: \"\\f515\";\n}\n\n.fa-balance-scale-left::before {\n  content: \"\\f515\";\n}\n\n.fa-sort-up::before {\n  content: \"\\f0de\";\n}\n\n.fa-sort-asc::before {\n  content: \"\\f0de\";\n}\n\n.fa-comment-dots::before {\n  content: \"\\f4ad\";\n}\n\n.fa-commenting::before {\n  content: \"\\f4ad\";\n}\n\n.fa-plant-wilt::before {\n  content: \"\\e5aa\";\n}\n\n.fa-diamond::before {\n  content: \"\\f219\";\n}\n\n.fa-face-grin-squint::before {\n  content: \"\\f585\";\n}\n\n.fa-grin-squint::before {\n  content: \"\\f585\";\n}\n\n.fa-hand-holding-dollar::before {\n  content: \"\\f4c0\";\n}\n\n.fa-hand-holding-usd::before {\n  content: \"\\f4c0\";\n}\n\n.fa-bacterium::before {\n  content: \"\\e05a\";\n}\n\n.fa-hand-pointer::before {\n  content: \"\\f25a\";\n}\n\n.fa-drum-steelpan::before {\n  content: \"\\f56a\";\n}\n\n.fa-hand-scissors::before {\n  content: \"\\f257\";\n}\n\n.fa-hands-praying::before {\n  content: \"\\f684\";\n}\n\n.fa-praying-hands::before {\n  content: \"\\f684\";\n}\n\n.fa-arrow-rotate-right::before {\n  content: \"\\f01e\";\n}\n\n.fa-arrow-right-rotate::before {\n  content: \"\\f01e\";\n}\n\n.fa-arrow-rotate-forward::before {\n  content: \"\\f01e\";\n}\n\n.fa-redo::before {\n  content: \"\\f01e\";\n}\n\n.fa-biohazard::before {\n  content: \"\\f780\";\n}\n\n.fa-location-crosshairs::before {\n  content: \"\\f601\";\n}\n\n.fa-location::before {\n  content: \"\\f601\";\n}\n\n.fa-mars-double::before {\n  content: \"\\f227\";\n}\n\n.fa-child-dress::before {\n  content: \"\\e59c\";\n}\n\n.fa-users-between-lines::before {\n  content: \"\\e591\";\n}\n\n.fa-lungs-virus::before {\n  content: \"\\e067\";\n}\n\n.fa-face-grin-tears::before {\n  content: \"\\f588\";\n}\n\n.fa-grin-tears::before {\n  content: \"\\f588\";\n}\n\n.fa-phone::before {\n  content: \"\\f095\";\n}\n\n.fa-calendar-xmark::before {\n  content: \"\\f273\";\n}\n\n.fa-calendar-times::before {\n  content: \"\\f273\";\n}\n\n.fa-child-reaching::before {\n  content: \"\\e59d\";\n}\n\n.fa-head-side-virus::before {\n  content: \"\\e064\";\n}\n\n.fa-user-gear::before {\n  content: \"\\f4fe\";\n}\n\n.fa-user-cog::before {\n  content: \"\\f4fe\";\n}\n\n.fa-arrow-up-1-9::before {\n  content: \"\\f163\";\n}\n\n.fa-sort-numeric-up::before {\n  content: \"\\f163\";\n}\n\n.fa-door-closed::before {\n  content: \"\\f52a\";\n}\n\n.fa-shield-virus::before {\n  content: \"\\e06c\";\n}\n\n.fa-dice-six::before {\n  content: \"\\f526\";\n}\n\n.fa-mosquito-net::before {\n  content: \"\\e52c\";\n}\n\n.fa-bridge-water::before {\n  content: \"\\e4ce\";\n}\n\n.fa-person-booth::before {\n  content: \"\\f756\";\n}\n\n.fa-text-width::before {\n  content: \"\\f035\";\n}\n\n.fa-hat-wizard::before {\n  content: \"\\f6e8\";\n}\n\n.fa-pen-fancy::before {\n  content: \"\\f5ac\";\n}\n\n.fa-person-digging::before {\n  content: \"\\f85e\";\n}\n\n.fa-digging::before {\n  content: \"\\f85e\";\n}\n\n.fa-trash::before {\n  content: \"\\f1f8\";\n}\n\n.fa-gauge-simple::before {\n  content: \"\\f629\";\n}\n\n.fa-gauge-simple-med::before {\n  content: \"\\f629\";\n}\n\n.fa-tachometer-average::before {\n  content: \"\\f629\";\n}\n\n.fa-book-medical::before {\n  content: \"\\f7e6\";\n}\n\n.fa-poo::before {\n  content: \"\\f2fe\";\n}\n\n.fa-quote-right::before {\n  content: \"\\f10e\";\n}\n\n.fa-quote-right-alt::before {\n  content: \"\\f10e\";\n}\n\n.fa-shirt::before {\n  content: \"\\f553\";\n}\n\n.fa-t-shirt::before {\n  content: \"\\f553\";\n}\n\n.fa-tshirt::before {\n  content: \"\\f553\";\n}\n\n.fa-cubes::before {\n  content: \"\\f1b3\";\n}\n\n.fa-divide::before {\n  content: \"\\f529\";\n}\n\n.fa-tenge-sign::before {\n  content: \"\\f7d7\";\n}\n\n.fa-tenge::before {\n  content: \"\\f7d7\";\n}\n\n.fa-headphones::before {\n  content: \"\\f025\";\n}\n\n.fa-hands-holding::before {\n  content: \"\\f4c2\";\n}\n\n.fa-hands-clapping::before {\n  content: \"\\e1a8\";\n}\n\n.fa-republican::before {\n  content: \"\\f75e\";\n}\n\n.fa-arrow-left::before {\n  content: \"\\f060\";\n}\n\n.fa-person-circle-xmark::before {\n  content: \"\\e543\";\n}\n\n.fa-ruler::before {\n  content: \"\\f545\";\n}\n\n.fa-align-left::before {\n  content: \"\\f036\";\n}\n\n.fa-dice-d6::before {\n  content: \"\\f6d1\";\n}\n\n.fa-restroom::before {\n  content: \"\\f7bd\";\n}\n\n.fa-j::before {\n  content: \"J\";\n}\n\n.fa-users-viewfinder::before {\n  content: \"\\e595\";\n}\n\n.fa-file-video::before {\n  content: \"\\f1c8\";\n}\n\n.fa-up-right-from-square::before {\n  content: \"\\f35d\";\n}\n\n.fa-external-link-alt::before {\n  content: \"\\f35d\";\n}\n\n.fa-table-cells::before {\n  content: \"\\f00a\";\n}\n\n.fa-th::before {\n  content: \"\\f00a\";\n}\n\n.fa-file-pdf::before {\n  content: \"\\f1c1\";\n}\n\n.fa-book-bible::before {\n  content: \"\\f647\";\n}\n\n.fa-bible::before {\n  content: \"\\f647\";\n}\n\n.fa-o::before {\n  content: \"O\";\n}\n\n.fa-suitcase-medical::before {\n  content: \"\\f0fa\";\n}\n\n.fa-medkit::before {\n  content: \"\\f0fa\";\n}\n\n.fa-user-secret::before {\n  content: \"\\f21b\";\n}\n\n.fa-otter::before {\n  content: \"\\f700\";\n}\n\n.fa-person-dress::before {\n  content: \"\\f182\";\n}\n\n.fa-female::before {\n  content: \"\\f182\";\n}\n\n.fa-comment-dollar::before {\n  content: \"\\f651\";\n}\n\n.fa-business-time::before {\n  content: \"\\f64a\";\n}\n\n.fa-briefcase-clock::before {\n  content: \"\\f64a\";\n}\n\n.fa-table-cells-large::before {\n  content: \"\\f009\";\n}\n\n.fa-th-large::before {\n  content: \"\\f009\";\n}\n\n.fa-book-tanakh::before {\n  content: \"\\f827\";\n}\n\n.fa-tanakh::before {\n  content: \"\\f827\";\n}\n\n.fa-phone-volume::before {\n  content: \"\\f2a0\";\n}\n\n.fa-volume-control-phone::before {\n  content: \"\\f2a0\";\n}\n\n.fa-hat-cowboy-side::before {\n  content: \"\\f8c1\";\n}\n\n.fa-clipboard-user::before {\n  content: \"\\f7f3\";\n}\n\n.fa-child::before {\n  content: \"\\f1ae\";\n}\n\n.fa-lira-sign::before {\n  content: \"\\f195\";\n}\n\n.fa-satellite::before {\n  content: \"\\f7bf\";\n}\n\n.fa-plane-lock::before {\n  content: \"\\e558\";\n}\n\n.fa-tag::before {\n  content: \"\\f02b\";\n}\n\n.fa-comment::before {\n  content: \"\\f075\";\n}\n\n.fa-cake-candles::before {\n  content: \"\\f1fd\";\n}\n\n.fa-birthday-cake::before {\n  content: \"\\f1fd\";\n}\n\n.fa-cake::before {\n  content: \"\\f1fd\";\n}\n\n.fa-envelope::before {\n  content: \"\\f0e0\";\n}\n\n.fa-angles-up::before {\n  content: \"\\f102\";\n}\n\n.fa-angle-double-up::before {\n  content: \"\\f102\";\n}\n\n.fa-paperclip::before {\n  content: \"\\f0c6\";\n}\n\n.fa-arrow-right-to-city::before {\n  content: \"\\e4b3\";\n}\n\n.fa-ribbon::before {\n  content: \"\\f4d6\";\n}\n\n.fa-lungs::before {\n  content: \"\\f604\";\n}\n\n.fa-arrow-up-9-1::before {\n  content: \"\\f887\";\n}\n\n.fa-sort-numeric-up-alt::before {\n  content: \"\\f887\";\n}\n\n.fa-litecoin-sign::before {\n  content: \"\\e1d3\";\n}\n\n.fa-border-none::before {\n  content: \"\\f850\";\n}\n\n.fa-circle-nodes::before {\n  content: \"\\e4e2\";\n}\n\n.fa-parachute-box::before {\n  content: \"\\f4cd\";\n}\n\n.fa-indent::before {\n  content: \"\\f03c\";\n}\n\n.fa-truck-field-un::before {\n  content: \"\\e58e\";\n}\n\n.fa-hourglass::before {\n  content: \"\\f254\";\n}\n\n.fa-hourglass-empty::before {\n  content: \"\\f254\";\n}\n\n.fa-mountain::before {\n  content: \"\\f6fc\";\n}\n\n.fa-user-doctor::before {\n  content: \"\\f0f0\";\n}\n\n.fa-user-md::before {\n  content: \"\\f0f0\";\n}\n\n.fa-circle-info::before {\n  content: \"\\f05a\";\n}\n\n.fa-info-circle::before {\n  content: \"\\f05a\";\n}\n\n.fa-cloud-meatball::before {\n  content: \"\\f73b\";\n}\n\n.fa-camera::before {\n  content: \"\\f030\";\n}\n\n.fa-camera-alt::before {\n  content: \"\\f030\";\n}\n\n.fa-square-virus::before {\n  content: \"\\e578\";\n}\n\n.fa-meteor::before {\n  content: \"\\f753\";\n}\n\n.fa-car-on::before {\n  content: \"\\e4dd\";\n}\n\n.fa-sleigh::before {\n  content: \"\\f7cc\";\n}\n\n.fa-arrow-down-1-9::before {\n  content: \"\\f162\";\n}\n\n.fa-sort-numeric-asc::before {\n  content: \"\\f162\";\n}\n\n.fa-sort-numeric-down::before {\n  content: \"\\f162\";\n}\n\n.fa-hand-holding-droplet::before {\n  content: \"\\f4c1\";\n}\n\n.fa-hand-holding-water::before {\n  content: \"\\f4c1\";\n}\n\n.fa-water::before {\n  content: \"\\f773\";\n}\n\n.fa-calendar-check::before {\n  content: \"\\f274\";\n}\n\n.fa-braille::before {\n  content: \"\\f2a1\";\n}\n\n.fa-prescription-bottle-medical::before {\n  content: \"\\f486\";\n}\n\n.fa-prescription-bottle-alt::before {\n  content: \"\\f486\";\n}\n\n.fa-landmark::before {\n  content: \"\\f66f\";\n}\n\n.fa-truck::before {\n  content: \"\\f0d1\";\n}\n\n.fa-crosshairs::before {\n  content: \"\\f05b\";\n}\n\n.fa-person-cane::before {\n  content: \"\\e53c\";\n}\n\n.fa-tent::before {\n  content: \"\\e57d\";\n}\n\n.fa-vest-patches::before {\n  content: \"\\e086\";\n}\n\n.fa-check-double::before {\n  content: \"\\f560\";\n}\n\n.fa-arrow-down-a-z::before {\n  content: \"\\f15d\";\n}\n\n.fa-sort-alpha-asc::before {\n  content: \"\\f15d\";\n}\n\n.fa-sort-alpha-down::before {\n  content: \"\\f15d\";\n}\n\n.fa-money-bill-wheat::before {\n  content: \"\\e52a\";\n}\n\n.fa-cookie::before {\n  content: \"\\f563\";\n}\n\n.fa-arrow-rotate-left::before {\n  content: \"\\f0e2\";\n}\n\n.fa-arrow-left-rotate::before {\n  content: \"\\f0e2\";\n}\n\n.fa-arrow-rotate-back::before {\n  content: \"\\f0e2\";\n}\n\n.fa-arrow-rotate-backward::before {\n  content: \"\\f0e2\";\n}\n\n.fa-undo::before {\n  content: \"\\f0e2\";\n}\n\n.fa-hard-drive::before {\n  content: \"\\f0a0\";\n}\n\n.fa-hdd::before {\n  content: \"\\f0a0\";\n}\n\n.fa-face-grin-squint-tears::before {\n  content: \"\\f586\";\n}\n\n.fa-grin-squint-tears::before {\n  content: \"\\f586\";\n}\n\n.fa-dumbbell::before {\n  content: \"\\f44b\";\n}\n\n.fa-rectangle-list::before {\n  content: \"\\f022\";\n}\n\n.fa-list-alt::before {\n  content: \"\\f022\";\n}\n\n.fa-tarp-droplet::before {\n  content: \"\\e57c\";\n}\n\n.fa-house-medical-circle-check::before {\n  content: \"\\e511\";\n}\n\n.fa-person-skiing-nordic::before {\n  content: \"\\f7ca\";\n}\n\n.fa-skiing-nordic::before {\n  content: \"\\f7ca\";\n}\n\n.fa-calendar-plus::before {\n  content: \"\\f271\";\n}\n\n.fa-plane-arrival::before {\n  content: \"\\f5af\";\n}\n\n.fa-circle-left::before {\n  content: \"\\f359\";\n}\n\n.fa-arrow-alt-circle-left::before {\n  content: \"\\f359\";\n}\n\n.fa-train-subway::before {\n  content: \"\\f239\";\n}\n\n.fa-subway::before {\n  content: \"\\f239\";\n}\n\n.fa-chart-gantt::before {\n  content: \"\\e0e4\";\n}\n\n.fa-indian-rupee-sign::before {\n  content: \"\\e1bc\";\n}\n\n.fa-indian-rupee::before {\n  content: \"\\e1bc\";\n}\n\n.fa-inr::before {\n  content: \"\\e1bc\";\n}\n\n.fa-crop-simple::before {\n  content: \"\\f565\";\n}\n\n.fa-crop-alt::before {\n  content: \"\\f565\";\n}\n\n.fa-money-bill-1::before {\n  content: \"\\f3d1\";\n}\n\n.fa-money-bill-alt::before {\n  content: \"\\f3d1\";\n}\n\n.fa-left-long::before {\n  content: \"\\f30a\";\n}\n\n.fa-long-arrow-alt-left::before {\n  content: \"\\f30a\";\n}\n\n.fa-dna::before {\n  content: \"\\f471\";\n}\n\n.fa-virus-slash::before {\n  content: \"\\e075\";\n}\n\n.fa-minus::before {\n  content: \"\\f068\";\n}\n\n.fa-subtract::before {\n  content: \"\\f068\";\n}\n\n.fa-chess::before {\n  content: \"\\f439\";\n}\n\n.fa-arrow-left-long::before {\n  content: \"\\f177\";\n}\n\n.fa-long-arrow-left::before {\n  content: \"\\f177\";\n}\n\n.fa-plug-circle-check::before {\n  content: \"\\e55c\";\n}\n\n.fa-street-view::before {\n  content: \"\\f21d\";\n}\n\n.fa-franc-sign::before {\n  content: \"\\e18f\";\n}\n\n.fa-volume-off::before {\n  content: \"\\f026\";\n}\n\n.fa-hands-asl-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-american-sign-language-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-asl-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-hands-american-sign-language-interpreting::before {\n  content: \"\\f2a3\";\n}\n\n.fa-gear::before {\n  content: \"\\f013\";\n}\n\n.fa-cog::before {\n  content: \"\\f013\";\n}\n\n.fa-droplet-slash::before {\n  content: \"\\f5c7\";\n}\n\n.fa-tint-slash::before {\n  content: \"\\f5c7\";\n}\n\n.fa-mosque::before {\n  content: \"\\f678\";\n}\n\n.fa-mosquito::before {\n  content: \"\\e52b\";\n}\n\n.fa-star-of-david::before {\n  content: \"\\f69a\";\n}\n\n.fa-person-military-rifle::before {\n  content: \"\\e54b\";\n}\n\n.fa-cart-shopping::before {\n  content: \"\\f07a\";\n}\n\n.fa-shopping-cart::before {\n  content: \"\\f07a\";\n}\n\n.fa-vials::before {\n  content: \"\\f493\";\n}\n\n.fa-plug-circle-plus::before {\n  content: \"\\e55f\";\n}\n\n.fa-place-of-worship::before {\n  content: \"\\f67f\";\n}\n\n.fa-grip-vertical::before {\n  content: \"\\f58e\";\n}\n\n.fa-arrow-turn-up::before {\n  content: \"\\f148\";\n}\n\n.fa-level-up::before {\n  content: \"\\f148\";\n}\n\n.fa-u::before {\n  content: \"U\";\n}\n\n.fa-square-root-variable::before {\n  content: \"\\f698\";\n}\n\n.fa-square-root-alt::before {\n  content: \"\\f698\";\n}\n\n.fa-clock::before {\n  content: \"\\f017\";\n}\n\n.fa-clock-four::before {\n  content: \"\\f017\";\n}\n\n.fa-backward-step::before {\n  content: \"\\f048\";\n}\n\n.fa-step-backward::before {\n  content: \"\\f048\";\n}\n\n.fa-pallet::before {\n  content: \"\\f482\";\n}\n\n.fa-faucet::before {\n  content: \"\\e005\";\n}\n\n.fa-baseball-bat-ball::before {\n  content: \"\\f432\";\n}\n\n.fa-s::before {\n  content: \"S\";\n}\n\n.fa-timeline::before {\n  content: \"\\e29c\";\n}\n\n.fa-keyboard::before {\n  content: \"\\f11c\";\n}\n\n.fa-caret-down::before {\n  content: \"\\f0d7\";\n}\n\n.fa-house-chimney-medical::before {\n  content: \"\\f7f2\";\n}\n\n.fa-clinic-medical::before {\n  content: \"\\f7f2\";\n}\n\n.fa-temperature-three-quarters::before {\n  content: \"\\f2c8\";\n}\n\n.fa-temperature-3::before {\n  content: \"\\f2c8\";\n}\n\n.fa-thermometer-3::before {\n  content: \"\\f2c8\";\n}\n\n.fa-thermometer-three-quarters::before {\n  content: \"\\f2c8\";\n}\n\n.fa-mobile-screen::before {\n  content: \"\\f3cf\";\n}\n\n.fa-mobile-android-alt::before {\n  content: \"\\f3cf\";\n}\n\n.fa-plane-up::before {\n  content: \"\\e22d\";\n}\n\n.fa-piggy-bank::before {\n  content: \"\\f4d3\";\n}\n\n.fa-battery-half::before {\n  content: \"\\f242\";\n}\n\n.fa-battery-3::before {\n  content: \"\\f242\";\n}\n\n.fa-mountain-city::before {\n  content: \"\\e52e\";\n}\n\n.fa-coins::before {\n  content: \"\\f51e\";\n}\n\n.fa-khanda::before {\n  content: \"\\f66d\";\n}\n\n.fa-sliders::before {\n  content: \"\\f1de\";\n}\n\n.fa-sliders-h::before {\n  content: \"\\f1de\";\n}\n\n.fa-folder-tree::before {\n  content: \"\\f802\";\n}\n\n.fa-network-wired::before {\n  content: \"\\f6ff\";\n}\n\n.fa-map-pin::before {\n  content: \"\\f276\";\n}\n\n.fa-hamsa::before {\n  content: \"\\f665\";\n}\n\n.fa-cent-sign::before {\n  content: \"\\e3f5\";\n}\n\n.fa-flask::before {\n  content: \"\\f0c3\";\n}\n\n.fa-person-pregnant::before {\n  content: \"\\e31e\";\n}\n\n.fa-wand-sparkles::before {\n  content: \"\\f72b\";\n}\n\n.fa-ellipsis-vertical::before {\n  content: \"\\f142\";\n}\n\n.fa-ellipsis-v::before {\n  content: \"\\f142\";\n}\n\n.fa-ticket::before {\n  content: \"\\f145\";\n}\n\n.fa-power-off::before {\n  content: \"\\f011\";\n}\n\n.fa-right-long::before {\n  content: \"\\f30b\";\n}\n\n.fa-long-arrow-alt-right::before {\n  content: \"\\f30b\";\n}\n\n.fa-flag-usa::before {\n  content: \"\\f74d\";\n}\n\n.fa-laptop-file::before {\n  content: \"\\e51d\";\n}\n\n.fa-tty::before {\n  content: \"\\f1e4\";\n}\n\n.fa-teletype::before {\n  content: \"\\f1e4\";\n}\n\n.fa-diagram-next::before {\n  content: \"\\e476\";\n}\n\n.fa-person-rifle::before {\n  content: \"\\e54e\";\n}\n\n.fa-house-medical-circle-exclamation::before {\n  content: \"\\e512\";\n}\n\n.fa-closed-captioning::before {\n  content: \"\\f20a\";\n}\n\n.fa-person-hiking::before {\n  content: \"\\f6ec\";\n}\n\n.fa-hiking::before {\n  content: \"\\f6ec\";\n}\n\n.fa-venus-double::before {\n  content: \"\\f226\";\n}\n\n.fa-images::before {\n  content: \"\\f302\";\n}\n\n.fa-calculator::before {\n  content: \"\\f1ec\";\n}\n\n.fa-people-pulling::before {\n  content: \"\\e535\";\n}\n\n.fa-n::before {\n  content: \"N\";\n}\n\n.fa-cable-car::before {\n  content: \"\\f7da\";\n}\n\n.fa-tram::before {\n  content: \"\\f7da\";\n}\n\n.fa-cloud-rain::before {\n  content: \"\\f73d\";\n}\n\n.fa-building-circle-xmark::before {\n  content: \"\\e4d4\";\n}\n\n.fa-ship::before {\n  content: \"\\f21a\";\n}\n\n.fa-arrows-down-to-line::before {\n  content: \"\\e4b8\";\n}\n\n.fa-download::before {\n  content: \"\\f019\";\n}\n\n.fa-face-grin::before {\n  content: \"\\f580\";\n}\n\n.fa-grin::before {\n  content: \"\\f580\";\n}\n\n.fa-delete-left::before {\n  content: \"\\f55a\";\n}\n\n.fa-backspace::before {\n  content: \"\\f55a\";\n}\n\n.fa-eye-dropper::before {\n  content: \"\\f1fb\";\n}\n\n.fa-eye-dropper-empty::before {\n  content: \"\\f1fb\";\n}\n\n.fa-eyedropper::before {\n  content: \"\\f1fb\";\n}\n\n.fa-file-circle-check::before {\n  content: \"\\e5a0\";\n}\n\n.fa-forward::before {\n  content: \"\\f04e\";\n}\n\n.fa-mobile::before {\n  content: \"\\f3ce\";\n}\n\n.fa-mobile-android::before {\n  content: \"\\f3ce\";\n}\n\n.fa-mobile-phone::before {\n  content: \"\\f3ce\";\n}\n\n.fa-face-meh::before {\n  content: \"\\f11a\";\n}\n\n.fa-meh::before {\n  content: \"\\f11a\";\n}\n\n.fa-align-center::before {\n  content: \"\\f037\";\n}\n\n.fa-book-skull::before {\n  content: \"\\f6b7\";\n}\n\n.fa-book-dead::before {\n  content: \"\\f6b7\";\n}\n\n.fa-id-card::before {\n  content: \"\\f2c2\";\n}\n\n.fa-drivers-license::before {\n  content: \"\\f2c2\";\n}\n\n.fa-outdent::before {\n  content: \"\\f03b\";\n}\n\n.fa-dedent::before {\n  content: \"\\f03b\";\n}\n\n.fa-heart-circle-exclamation::before {\n  content: \"\\e4fe\";\n}\n\n.fa-house::before {\n  content: \"\\f015\";\n}\n\n.fa-home::before {\n  content: \"\\f015\";\n}\n\n.fa-home-alt::before {\n  content: \"\\f015\";\n}\n\n.fa-home-lg-alt::before {\n  content: \"\\f015\";\n}\n\n.fa-calendar-week::before {\n  content: \"\\f784\";\n}\n\n.fa-laptop-medical::before {\n  content: \"\\f812\";\n}\n\n.fa-b::before {\n  content: \"B\";\n}\n\n.fa-file-medical::before {\n  content: \"\\f477\";\n}\n\n.fa-dice-one::before {\n  content: \"\\f525\";\n}\n\n.fa-kiwi-bird::before {\n  content: \"\\f535\";\n}\n\n.fa-arrow-right-arrow-left::before {\n  content: \"\\f0ec\";\n}\n\n.fa-exchange::before {\n  content: \"\\f0ec\";\n}\n\n.fa-rotate-right::before {\n  content: \"\\f2f9\";\n}\n\n.fa-redo-alt::before {\n  content: \"\\f2f9\";\n}\n\n.fa-rotate-forward::before {\n  content: \"\\f2f9\";\n}\n\n.fa-utensils::before {\n  content: \"\\f2e7\";\n}\n\n.fa-cutlery::before {\n  content: \"\\f2e7\";\n}\n\n.fa-arrow-up-wide-short::before {\n  content: \"\\f161\";\n}\n\n.fa-sort-amount-up::before {\n  content: \"\\f161\";\n}\n\n.fa-mill-sign::before {\n  content: \"\\e1ed\";\n}\n\n.fa-bowl-rice::before {\n  content: \"\\e2eb\";\n}\n\n.fa-skull::before {\n  content: \"\\f54c\";\n}\n\n.fa-tower-broadcast::before {\n  content: \"\\f519\";\n}\n\n.fa-broadcast-tower::before {\n  content: \"\\f519\";\n}\n\n.fa-truck-pickup::before {\n  content: \"\\f63c\";\n}\n\n.fa-up-long::before {\n  content: \"\\f30c\";\n}\n\n.fa-long-arrow-alt-up::before {\n  content: \"\\f30c\";\n}\n\n.fa-stop::before {\n  content: \"\\f04d\";\n}\n\n.fa-code-merge::before {\n  content: \"\\f387\";\n}\n\n.fa-upload::before {\n  content: \"\\f093\";\n}\n\n.fa-hurricane::before {\n  content: \"\\f751\";\n}\n\n.fa-mound::before {\n  content: \"\\e52d\";\n}\n\n.fa-toilet-portable::before {\n  content: \"\\e583\";\n}\n\n.fa-compact-disc::before {\n  content: \"\\f51f\";\n}\n\n.fa-file-arrow-down::before {\n  content: \"\\f56d\";\n}\n\n.fa-file-download::before {\n  content: \"\\f56d\";\n}\n\n.fa-caravan::before {\n  content: \"\\f8ff\";\n}\n\n.fa-shield-cat::before {\n  content: \"\\e572\";\n}\n\n.fa-bolt::before {\n  content: \"\\f0e7\";\n}\n\n.fa-zap::before {\n  content: \"\\f0e7\";\n}\n\n.fa-glass-water::before {\n  content: \"\\e4f4\";\n}\n\n.fa-oil-well::before {\n  content: \"\\e532\";\n}\n\n.fa-vault::before {\n  content: \"\\e2c5\";\n}\n\n.fa-mars::before {\n  content: \"\\f222\";\n}\n\n.fa-toilet::before {\n  content: \"\\f7d8\";\n}\n\n.fa-plane-circle-xmark::before {\n  content: \"\\e557\";\n}\n\n.fa-yen-sign::before {\n  content: \"\\f157\";\n}\n\n.fa-cny::before {\n  content: \"\\f157\";\n}\n\n.fa-jpy::before {\n  content: \"\\f157\";\n}\n\n.fa-rmb::before {\n  content: \"\\f157\";\n}\n\n.fa-yen::before {\n  content: \"\\f157\";\n}\n\n.fa-ruble-sign::before {\n  content: \"\\f158\";\n}\n\n.fa-rouble::before {\n  content: \"\\f158\";\n}\n\n.fa-rub::before {\n  content: \"\\f158\";\n}\n\n.fa-ruble::before {\n  content: \"\\f158\";\n}\n\n.fa-sun::before {\n  content: \"\\f185\";\n}\n\n.fa-guitar::before {\n  content: \"\\f7a6\";\n}\n\n.fa-face-laugh-wink::before {\n  content: \"\\f59c\";\n}\n\n.fa-laugh-wink::before {\n  content: \"\\f59c\";\n}\n\n.fa-horse-head::before {\n  content: \"\\f7ab\";\n}\n\n.fa-bore-hole::before {\n  content: \"\\e4c3\";\n}\n\n.fa-industry::before {\n  content: \"\\f275\";\n}\n\n.fa-circle-down::before {\n  content: \"\\f358\";\n}\n\n.fa-arrow-alt-circle-down::before {\n  content: \"\\f358\";\n}\n\n.fa-arrows-turn-to-dots::before {\n  content: \"\\e4c1\";\n}\n\n.fa-florin-sign::before {\n  content: \"\\e184\";\n}\n\n.fa-arrow-down-short-wide::before {\n  content: \"\\f884\";\n}\n\n.fa-sort-amount-desc::before {\n  content: \"\\f884\";\n}\n\n.fa-sort-amount-down-alt::before {\n  content: \"\\f884\";\n}\n\n.fa-less-than::before {\n  content: \"\\<\";\n}\n\n.fa-angle-down::before {\n  content: \"\\f107\";\n}\n\n.fa-car-tunnel::before {\n  content: \"\\e4de\";\n}\n\n.fa-head-side-cough::before {\n  content: \"\\e061\";\n}\n\n.fa-grip-lines::before {\n  content: \"\\f7a4\";\n}\n\n.fa-thumbs-down::before {\n  content: \"\\f165\";\n}\n\n.fa-user-lock::before {\n  content: \"\\f502\";\n}\n\n.fa-arrow-right-long::before {\n  content: \"\\f178\";\n}\n\n.fa-long-arrow-right::before {\n  content: \"\\f178\";\n}\n\n.fa-anchor-circle-xmark::before {\n  content: \"\\e4ac\";\n}\n\n.fa-ellipsis::before {\n  content: \"\\f141\";\n}\n\n.fa-ellipsis-h::before {\n  content: \"\\f141\";\n}\n\n.fa-chess-pawn::before {\n  content: \"\\f443\";\n}\n\n.fa-kit-medical::before {\n  content: \"\\f479\";\n}\n\n.fa-first-aid::before {\n  content: \"\\f479\";\n}\n\n.fa-person-through-window::before {\n  content: \"\\e5a9\";\n}\n\n.fa-toolbox::before {\n  content: \"\\f552\";\n}\n\n.fa-hands-holding-circle::before {\n  content: \"\\e4fb\";\n}\n\n.fa-bug::before {\n  content: \"\\f188\";\n}\n\n.fa-credit-card::before {\n  content: \"\\f09d\";\n}\n\n.fa-credit-card-alt::before {\n  content: \"\\f09d\";\n}\n\n.fa-car::before {\n  content: \"\\f1b9\";\n}\n\n.fa-automobile::before {\n  content: \"\\f1b9\";\n}\n\n.fa-hand-holding-hand::before {\n  content: \"\\e4f7\";\n}\n\n.fa-book-open-reader::before {\n  content: \"\\f5da\";\n}\n\n.fa-book-reader::before {\n  content: \"\\f5da\";\n}\n\n.fa-mountain-sun::before {\n  content: \"\\e52f\";\n}\n\n.fa-arrows-left-right-to-line::before {\n  content: \"\\e4ba\";\n}\n\n.fa-dice-d20::before {\n  content: \"\\f6cf\";\n}\n\n.fa-truck-droplet::before {\n  content: \"\\e58c\";\n}\n\n.fa-file-circle-xmark::before {\n  content: \"\\e5a1\";\n}\n\n.fa-temperature-arrow-up::before {\n  content: \"\\e040\";\n}\n\n.fa-temperature-up::before {\n  content: \"\\e040\";\n}\n\n.fa-medal::before {\n  content: \"\\f5a2\";\n}\n\n.fa-bed::before {\n  content: \"\\f236\";\n}\n\n.fa-square-h::before {\n  content: \"\\f0fd\";\n}\n\n.fa-h-square::before {\n  content: \"\\f0fd\";\n}\n\n.fa-podcast::before {\n  content: \"\\f2ce\";\n}\n\n.fa-temperature-full::before {\n  content: \"\\f2c7\";\n}\n\n.fa-temperature-4::before {\n  content: \"\\f2c7\";\n}\n\n.fa-thermometer-4::before {\n  content: \"\\f2c7\";\n}\n\n.fa-thermometer-full::before {\n  content: \"\\f2c7\";\n}\n\n.fa-bell::before {\n  content: \"\\f0f3\";\n}\n\n.fa-superscript::before {\n  content: \"\\f12b\";\n}\n\n.fa-plug-circle-xmark::before {\n  content: \"\\e560\";\n}\n\n.fa-star-of-life::before {\n  content: \"\\f621\";\n}\n\n.fa-phone-slash::before {\n  content: \"\\f3dd\";\n}\n\n.fa-paint-roller::before {\n  content: \"\\f5aa\";\n}\n\n.fa-handshake-angle::before {\n  content: \"\\f4c4\";\n}\n\n.fa-hands-helping::before {\n  content: \"\\f4c4\";\n}\n\n.fa-location-dot::before {\n  content: \"\\f3c5\";\n}\n\n.fa-map-marker-alt::before {\n  content: \"\\f3c5\";\n}\n\n.fa-file::before {\n  content: \"\\f15b\";\n}\n\n.fa-greater-than::before {\n  content: \"\\>\";\n}\n\n.fa-person-swimming::before {\n  content: \"\\f5c4\";\n}\n\n.fa-swimmer::before {\n  content: \"\\f5c4\";\n}\n\n.fa-arrow-down::before {\n  content: \"\\f063\";\n}\n\n.fa-droplet::before {\n  content: \"\\f043\";\n}\n\n.fa-tint::before {\n  content: \"\\f043\";\n}\n\n.fa-eraser::before {\n  content: \"\\f12d\";\n}\n\n.fa-earth-americas::before {\n  content: \"\\f57d\";\n}\n\n.fa-earth::before {\n  content: \"\\f57d\";\n}\n\n.fa-earth-america::before {\n  content: \"\\f57d\";\n}\n\n.fa-globe-americas::before {\n  content: \"\\f57d\";\n}\n\n.fa-person-burst::before {\n  content: \"\\e53b\";\n}\n\n.fa-dove::before {\n  content: \"\\f4ba\";\n}\n\n.fa-battery-empty::before {\n  content: \"\\f244\";\n}\n\n.fa-battery-0::before {\n  content: \"\\f244\";\n}\n\n.fa-socks::before {\n  content: \"\\f696\";\n}\n\n.fa-inbox::before {\n  content: \"\\f01c\";\n}\n\n.fa-section::before {\n  content: \"\\e447\";\n}\n\n.fa-gauge-high::before {\n  content: \"\\f625\";\n}\n\n.fa-tachometer-alt::before {\n  content: \"\\f625\";\n}\n\n.fa-tachometer-alt-fast::before {\n  content: \"\\f625\";\n}\n\n.fa-envelope-open-text::before {\n  content: \"\\f658\";\n}\n\n.fa-hospital::before {\n  content: \"\\f0f8\";\n}\n\n.fa-hospital-alt::before {\n  content: \"\\f0f8\";\n}\n\n.fa-hospital-wide::before {\n  content: \"\\f0f8\";\n}\n\n.fa-wine-bottle::before {\n  content: \"\\f72f\";\n}\n\n.fa-chess-rook::before {\n  content: \"\\f447\";\n}\n\n.fa-bars-staggered::before {\n  content: \"\\f550\";\n}\n\n.fa-reorder::before {\n  content: \"\\f550\";\n}\n\n.fa-stream::before {\n  content: \"\\f550\";\n}\n\n.fa-dharmachakra::before {\n  content: \"\\f655\";\n}\n\n.fa-hotdog::before {\n  content: \"\\f80f\";\n}\n\n.fa-person-walking-with-cane::before {\n  content: \"\\f29d\";\n}\n\n.fa-blind::before {\n  content: \"\\f29d\";\n}\n\n.fa-drum::before {\n  content: \"\\f569\";\n}\n\n.fa-ice-cream::before {\n  content: \"\\f810\";\n}\n\n.fa-heart-circle-bolt::before {\n  content: \"\\e4fc\";\n}\n\n.fa-fax::before {\n  content: \"\\f1ac\";\n}\n\n.fa-paragraph::before {\n  content: \"\\f1dd\";\n}\n\n.fa-check-to-slot::before {\n  content: \"\\f772\";\n}\n\n.fa-vote-yea::before {\n  content: \"\\f772\";\n}\n\n.fa-star-half::before {\n  content: \"\\f089\";\n}\n\n.fa-boxes-stacked::before {\n  content: \"\\f468\";\n}\n\n.fa-boxes::before {\n  content: \"\\f468\";\n}\n\n.fa-boxes-alt::before {\n  content: \"\\f468\";\n}\n\n.fa-link::before {\n  content: \"\\f0c1\";\n}\n\n.fa-chain::before {\n  content: \"\\f0c1\";\n}\n\n.fa-ear-listen::before {\n  content: \"\\f2a2\";\n}\n\n.fa-assistive-listening-systems::before {\n  content: \"\\f2a2\";\n}\n\n.fa-tree-city::before {\n  content: \"\\e587\";\n}\n\n.fa-play::before {\n  content: \"\\f04b\";\n}\n\n.fa-font::before {\n  content: \"\\f031\";\n}\n\n.fa-table-cells-row-lock::before {\n  content: \"\\e67a\";\n}\n\n.fa-rupiah-sign::before {\n  content: \"\\e23d\";\n}\n\n.fa-magnifying-glass::before {\n  content: \"\\f002\";\n}\n\n.fa-search::before {\n  content: \"\\f002\";\n}\n\n.fa-table-tennis-paddle-ball::before {\n  content: \"\\f45d\";\n}\n\n.fa-ping-pong-paddle-ball::before {\n  content: \"\\f45d\";\n}\n\n.fa-table-tennis::before {\n  content: \"\\f45d\";\n}\n\n.fa-person-dots-from-line::before {\n  content: \"\\f470\";\n}\n\n.fa-diagnoses::before {\n  content: \"\\f470\";\n}\n\n.fa-trash-can-arrow-up::before {\n  content: \"\\f82a\";\n}\n\n.fa-trash-restore-alt::before {\n  content: \"\\f82a\";\n}\n\n.fa-naira-sign::before {\n  content: \"\\e1f6\";\n}\n\n.fa-cart-arrow-down::before {\n  content: \"\\f218\";\n}\n\n.fa-walkie-talkie::before {\n  content: \"\\f8ef\";\n}\n\n.fa-file-pen::before {\n  content: \"\\f31c\";\n}\n\n.fa-file-edit::before {\n  content: \"\\f31c\";\n}\n\n.fa-receipt::before {\n  content: \"\\f543\";\n}\n\n.fa-square-pen::before {\n  content: \"\\f14b\";\n}\n\n.fa-pen-square::before {\n  content: \"\\f14b\";\n}\n\n.fa-pencil-square::before {\n  content: \"\\f14b\";\n}\n\n.fa-suitcase-rolling::before {\n  content: \"\\f5c1\";\n}\n\n.fa-person-circle-exclamation::before {\n  content: \"\\e53f\";\n}\n\n.fa-chevron-down::before {\n  content: \"\\f078\";\n}\n\n.fa-battery-full::before {\n  content: \"\\f240\";\n}\n\n.fa-battery::before {\n  content: \"\\f240\";\n}\n\n.fa-battery-5::before {\n  content: \"\\f240\";\n}\n\n.fa-skull-crossbones::before {\n  content: \"\\f714\";\n}\n\n.fa-code-compare::before {\n  content: \"\\e13a\";\n}\n\n.fa-list-ul::before {\n  content: \"\\f0ca\";\n}\n\n.fa-list-dots::before {\n  content: \"\\f0ca\";\n}\n\n.fa-school-lock::before {\n  content: \"\\e56f\";\n}\n\n.fa-tower-cell::before {\n  content: \"\\e585\";\n}\n\n.fa-down-long::before {\n  content: \"\\f309\";\n}\n\n.fa-long-arrow-alt-down::before {\n  content: \"\\f309\";\n}\n\n.fa-ranking-star::before {\n  content: \"\\e561\";\n}\n\n.fa-chess-king::before {\n  content: \"\\f43f\";\n}\n\n.fa-person-harassing::before {\n  content: \"\\e549\";\n}\n\n.fa-brazilian-real-sign::before {\n  content: \"\\e46c\";\n}\n\n.fa-landmark-dome::before {\n  content: \"\\f752\";\n}\n\n.fa-landmark-alt::before {\n  content: \"\\f752\";\n}\n\n.fa-arrow-up::before {\n  content: \"\\f062\";\n}\n\n.fa-tv::before {\n  content: \"\\f26c\";\n}\n\n.fa-television::before {\n  content: \"\\f26c\";\n}\n\n.fa-tv-alt::before {\n  content: \"\\f26c\";\n}\n\n.fa-shrimp::before {\n  content: \"\\e448\";\n}\n\n.fa-list-check::before {\n  content: \"\\f0ae\";\n}\n\n.fa-tasks::before {\n  content: \"\\f0ae\";\n}\n\n.fa-jug-detergent::before {\n  content: \"\\e519\";\n}\n\n.fa-circle-user::before {\n  content: \"\\f2bd\";\n}\n\n.fa-user-circle::before {\n  content: \"\\f2bd\";\n}\n\n.fa-user-shield::before {\n  content: \"\\f505\";\n}\n\n.fa-wind::before {\n  content: \"\\f72e\";\n}\n\n.fa-car-burst::before {\n  content: \"\\f5e1\";\n}\n\n.fa-car-crash::before {\n  content: \"\\f5e1\";\n}\n\n.fa-y::before {\n  content: \"Y\";\n}\n\n.fa-person-snowboarding::before {\n  content: \"\\f7ce\";\n}\n\n.fa-snowboarding::before {\n  content: \"\\f7ce\";\n}\n\n.fa-truck-fast::before {\n  content: \"\\f48b\";\n}\n\n.fa-shipping-fast::before {\n  content: \"\\f48b\";\n}\n\n.fa-fish::before {\n  content: \"\\f578\";\n}\n\n.fa-user-graduate::before {\n  content: \"\\f501\";\n}\n\n.fa-circle-half-stroke::before {\n  content: \"\\f042\";\n}\n\n.fa-adjust::before {\n  content: \"\\f042\";\n}\n\n.fa-clapperboard::before {\n  content: \"\\e131\";\n}\n\n.fa-circle-radiation::before {\n  content: \"\\f7ba\";\n}\n\n.fa-radiation-alt::before {\n  content: \"\\f7ba\";\n}\n\n.fa-baseball::before {\n  content: \"\\f433\";\n}\n\n.fa-baseball-ball::before {\n  content: \"\\f433\";\n}\n\n.fa-jet-fighter-up::before {\n  content: \"\\e518\";\n}\n\n.fa-diagram-project::before {\n  content: \"\\f542\";\n}\n\n.fa-project-diagram::before {\n  content: \"\\f542\";\n}\n\n.fa-copy::before {\n  content: \"\\f0c5\";\n}\n\n.fa-volume-xmark::before {\n  content: \"\\f6a9\";\n}\n\n.fa-volume-mute::before {\n  content: \"\\f6a9\";\n}\n\n.fa-volume-times::before {\n  content: \"\\f6a9\";\n}\n\n.fa-hand-sparkles::before {\n  content: \"\\e05d\";\n}\n\n.fa-grip::before {\n  content: \"\\f58d\";\n}\n\n.fa-grip-horizontal::before {\n  content: \"\\f58d\";\n}\n\n.fa-share-from-square::before {\n  content: \"\\f14d\";\n}\n\n.fa-share-square::before {\n  content: \"\\f14d\";\n}\n\n.fa-child-combatant::before {\n  content: \"\\e4e0\";\n}\n\n.fa-child-rifle::before {\n  content: \"\\e4e0\";\n}\n\n.fa-gun::before {\n  content: \"\\e19b\";\n}\n\n.fa-square-phone::before {\n  content: \"\\f098\";\n}\n\n.fa-phone-square::before {\n  content: \"\\f098\";\n}\n\n.fa-plus::before {\n  content: \"\\+\";\n}\n\n.fa-add::before {\n  content: \"\\+\";\n}\n\n.fa-expand::before {\n  content: \"\\f065\";\n}\n\n.fa-computer::before {\n  content: \"\\e4e5\";\n}\n\n.fa-xmark::before {\n  content: \"\\f00d\";\n}\n\n.fa-close::before {\n  content: \"\\f00d\";\n}\n\n.fa-multiply::before {\n  content: \"\\f00d\";\n}\n\n.fa-remove::before {\n  content: \"\\f00d\";\n}\n\n.fa-times::before {\n  content: \"\\f00d\";\n}\n\n.fa-arrows-up-down-left-right::before {\n  content: \"\\f047\";\n}\n\n.fa-arrows::before {\n  content: \"\\f047\";\n}\n\n.fa-chalkboard-user::before {\n  content: \"\\f51c\";\n}\n\n.fa-chalkboard-teacher::before {\n  content: \"\\f51c\";\n}\n\n.fa-peso-sign::before {\n  content: \"\\e222\";\n}\n\n.fa-building-shield::before {\n  content: \"\\e4d8\";\n}\n\n.fa-baby::before {\n  content: \"\\f77c\";\n}\n\n.fa-users-line::before {\n  content: \"\\e592\";\n}\n\n.fa-quote-left::before {\n  content: \"\\f10d\";\n}\n\n.fa-quote-left-alt::before {\n  content: \"\\f10d\";\n}\n\n.fa-tractor::before {\n  content: \"\\f722\";\n}\n\n.fa-trash-arrow-up::before {\n  content: \"\\f829\";\n}\n\n.fa-trash-restore::before {\n  content: \"\\f829\";\n}\n\n.fa-arrow-down-up-lock::before {\n  content: \"\\e4b0\";\n}\n\n.fa-lines-leaning::before {\n  content: \"\\e51e\";\n}\n\n.fa-ruler-combined::before {\n  content: \"\\f546\";\n}\n\n.fa-copyright::before {\n  content: \"\\f1f9\";\n}\n\n.fa-equals::before {\n  content: \"\\=\";\n}\n\n.fa-blender::before {\n  content: \"\\f517\";\n}\n\n.fa-teeth::before {\n  content: \"\\f62e\";\n}\n\n.fa-shekel-sign::before {\n  content: \"\\f20b\";\n}\n\n.fa-ils::before {\n  content: \"\\f20b\";\n}\n\n.fa-shekel::before {\n  content: \"\\f20b\";\n}\n\n.fa-sheqel::before {\n  content: \"\\f20b\";\n}\n\n.fa-sheqel-sign::before {\n  content: \"\\f20b\";\n}\n\n.fa-map::before {\n  content: \"\\f279\";\n}\n\n.fa-rocket::before {\n  content: \"\\f135\";\n}\n\n.fa-photo-film::before {\n  content: \"\\f87c\";\n}\n\n.fa-photo-video::before {\n  content: \"\\f87c\";\n}\n\n.fa-folder-minus::before {\n  content: \"\\f65d\";\n}\n\n.fa-store::before {\n  content: \"\\f54e\";\n}\n\n.fa-arrow-trend-up::before {\n  content: \"\\e098\";\n}\n\n.fa-plug-circle-minus::before {\n  content: \"\\e55e\";\n}\n\n.fa-sign-hanging::before {\n  content: \"\\f4d9\";\n}\n\n.fa-sign::before {\n  content: \"\\f4d9\";\n}\n\n.fa-bezier-curve::before {\n  content: \"\\f55b\";\n}\n\n.fa-bell-slash::before {\n  content: \"\\f1f6\";\n}\n\n.fa-tablet::before {\n  content: \"\\f3fb\";\n}\n\n.fa-tablet-android::before {\n  content: \"\\f3fb\";\n}\n\n.fa-school-flag::before {\n  content: \"\\e56e\";\n}\n\n.fa-fill::before {\n  content: \"\\f575\";\n}\n\n.fa-angle-up::before {\n  content: \"\\f106\";\n}\n\n.fa-drumstick-bite::before {\n  content: \"\\f6d7\";\n}\n\n.fa-holly-berry::before {\n  content: \"\\f7aa\";\n}\n\n.fa-chevron-left::before {\n  content: \"\\f053\";\n}\n\n.fa-bacteria::before {\n  content: \"\\e059\";\n}\n\n.fa-hand-lizard::before {\n  content: \"\\f258\";\n}\n\n.fa-notdef::before {\n  content: \"\\e1fe\";\n}\n\n.fa-disease::before {\n  content: \"\\f7fa\";\n}\n\n.fa-briefcase-medical::before {\n  content: \"\\f469\";\n}\n\n.fa-genderless::before {\n  content: \"\\f22d\";\n}\n\n.fa-chevron-right::before {\n  content: \"\\f054\";\n}\n\n.fa-retweet::before {\n  content: \"\\f079\";\n}\n\n.fa-car-rear::before {\n  content: \"\\f5de\";\n}\n\n.fa-car-alt::before {\n  content: \"\\f5de\";\n}\n\n.fa-pump-soap::before {\n  content: \"\\e06b\";\n}\n\n.fa-video-slash::before {\n  content: \"\\f4e2\";\n}\n\n.fa-battery-quarter::before {\n  content: \"\\f243\";\n}\n\n.fa-battery-2::before {\n  content: \"\\f243\";\n}\n\n.fa-radio::before {\n  content: \"\\f8d7\";\n}\n\n.fa-baby-carriage::before {\n  content: \"\\f77d\";\n}\n\n.fa-carriage-baby::before {\n  content: \"\\f77d\";\n}\n\n.fa-traffic-light::before {\n  content: \"\\f637\";\n}\n\n.fa-thermometer::before {\n  content: \"\\f491\";\n}\n\n.fa-vr-cardboard::before {\n  content: \"\\f729\";\n}\n\n.fa-hand-middle-finger::before {\n  content: \"\\f806\";\n}\n\n.fa-percent::before {\n  content: \"\\%\";\n}\n\n.fa-percentage::before {\n  content: \"\\%\";\n}\n\n.fa-truck-moving::before {\n  content: \"\\f4df\";\n}\n\n.fa-glass-water-droplet::before {\n  content: \"\\e4f5\";\n}\n\n.fa-display::before {\n  content: \"\\e163\";\n}\n\n.fa-face-smile::before {\n  content: \"\\f118\";\n}\n\n.fa-smile::before {\n  content: \"\\f118\";\n}\n\n.fa-thumbtack::before {\n  content: \"\\f08d\";\n}\n\n.fa-thumb-tack::before {\n  content: \"\\f08d\";\n}\n\n.fa-trophy::before {\n  content: \"\\f091\";\n}\n\n.fa-person-praying::before {\n  content: \"\\f683\";\n}\n\n.fa-pray::before {\n  content: \"\\f683\";\n}\n\n.fa-hammer::before {\n  content: \"\\f6e3\";\n}\n\n.fa-hand-peace::before {\n  content: \"\\f25b\";\n}\n\n.fa-rotate::before {\n  content: \"\\f2f1\";\n}\n\n.fa-sync-alt::before {\n  content: \"\\f2f1\";\n}\n\n.fa-spinner::before {\n  content: \"\\f110\";\n}\n\n.fa-robot::before {\n  content: \"\\f544\";\n}\n\n.fa-peace::before {\n  content: \"\\f67c\";\n}\n\n.fa-gears::before {\n  content: \"\\f085\";\n}\n\n.fa-cogs::before {\n  content: \"\\f085\";\n}\n\n.fa-warehouse::before {\n  content: \"\\f494\";\n}\n\n.fa-arrow-up-right-dots::before {\n  content: \"\\e4b7\";\n}\n\n.fa-splotch::before {\n  content: \"\\f5bc\";\n}\n\n.fa-face-grin-hearts::before {\n  content: \"\\f584\";\n}\n\n.fa-grin-hearts::before {\n  content: \"\\f584\";\n}\n\n.fa-dice-four::before {\n  content: \"\\f524\";\n}\n\n.fa-sim-card::before {\n  content: \"\\f7c4\";\n}\n\n.fa-transgender::before {\n  content: \"\\f225\";\n}\n\n.fa-transgender-alt::before {\n  content: \"\\f225\";\n}\n\n.fa-mercury::before {\n  content: \"\\f223\";\n}\n\n.fa-arrow-turn-down::before {\n  content: \"\\f149\";\n}\n\n.fa-level-down::before {\n  content: \"\\f149\";\n}\n\n.fa-person-falling-burst::before {\n  content: \"\\e547\";\n}\n\n.fa-award::before {\n  content: \"\\f559\";\n}\n\n.fa-ticket-simple::before {\n  content: \"\\f3ff\";\n}\n\n.fa-ticket-alt::before {\n  content: \"\\f3ff\";\n}\n\n.fa-building::before {\n  content: \"\\f1ad\";\n}\n\n.fa-angles-left::before {\n  content: \"\\f100\";\n}\n\n.fa-angle-double-left::before {\n  content: \"\\f100\";\n}\n\n.fa-qrcode::before {\n  content: \"\\f029\";\n}\n\n.fa-clock-rotate-left::before {\n  content: \"\\f1da\";\n}\n\n.fa-history::before {\n  content: \"\\f1da\";\n}\n\n.fa-face-grin-beam-sweat::before {\n  content: \"\\f583\";\n}\n\n.fa-grin-beam-sweat::before {\n  content: \"\\f583\";\n}\n\n.fa-file-export::before {\n  content: \"\\f56e\";\n}\n\n.fa-arrow-right-from-file::before {\n  content: \"\\f56e\";\n}\n\n.fa-shield::before {\n  content: \"\\f132\";\n}\n\n.fa-shield-blank::before {\n  content: \"\\f132\";\n}\n\n.fa-arrow-up-short-wide::before {\n  content: \"\\f885\";\n}\n\n.fa-sort-amount-up-alt::before {\n  content: \"\\f885\";\n}\n\n.fa-house-medical::before {\n  content: \"\\e3b2\";\n}\n\n.fa-golf-ball-tee::before {\n  content: \"\\f450\";\n}\n\n.fa-golf-ball::before {\n  content: \"\\f450\";\n}\n\n.fa-circle-chevron-left::before {\n  content: \"\\f137\";\n}\n\n.fa-chevron-circle-left::before {\n  content: \"\\f137\";\n}\n\n.fa-house-chimney-window::before {\n  content: \"\\e00d\";\n}\n\n.fa-pen-nib::before {\n  content: \"\\f5ad\";\n}\n\n.fa-tent-arrow-turn-left::before {\n  content: \"\\e580\";\n}\n\n.fa-tents::before {\n  content: \"\\e582\";\n}\n\n.fa-wand-magic::before {\n  content: \"\\f0d0\";\n}\n\n.fa-magic::before {\n  content: \"\\f0d0\";\n}\n\n.fa-dog::before {\n  content: \"\\f6d3\";\n}\n\n.fa-carrot::before {\n  content: \"\\f787\";\n}\n\n.fa-moon::before {\n  content: \"\\f186\";\n}\n\n.fa-wine-glass-empty::before {\n  content: \"\\f5ce\";\n}\n\n.fa-wine-glass-alt::before {\n  content: \"\\f5ce\";\n}\n\n.fa-cheese::before {\n  content: \"\\f7ef\";\n}\n\n.fa-yin-yang::before {\n  content: \"\\f6ad\";\n}\n\n.fa-music::before {\n  content: \"\\f001\";\n}\n\n.fa-code-commit::before {\n  content: \"\\f386\";\n}\n\n.fa-temperature-low::before {\n  content: \"\\f76b\";\n}\n\n.fa-person-biking::before {\n  content: \"\\f84a\";\n}\n\n.fa-biking::before {\n  content: \"\\f84a\";\n}\n\n.fa-broom::before {\n  content: \"\\f51a\";\n}\n\n.fa-shield-heart::before {\n  content: \"\\e574\";\n}\n\n.fa-gopuram::before {\n  content: \"\\f664\";\n}\n\n.fa-earth-oceania::before {\n  content: \"\\e47b\";\n}\n\n.fa-globe-oceania::before {\n  content: \"\\e47b\";\n}\n\n.fa-square-xmark::before {\n  content: \"\\f2d3\";\n}\n\n.fa-times-square::before {\n  content: \"\\f2d3\";\n}\n\n.fa-xmark-square::before {\n  content: \"\\f2d3\";\n}\n\n.fa-hashtag::before {\n  content: \"\\#\";\n}\n\n.fa-up-right-and-down-left-from-center::before {\n  content: \"\\f424\";\n}\n\n.fa-expand-alt::before {\n  content: \"\\f424\";\n}\n\n.fa-oil-can::before {\n  content: \"\\f613\";\n}\n\n.fa-t::before {\n  content: \"T\";\n}\n\n.fa-hippo::before {\n  content: \"\\f6ed\";\n}\n\n.fa-chart-column::before {\n  content: \"\\e0e3\";\n}\n\n.fa-infinity::before {\n  content: \"\\f534\";\n}\n\n.fa-vial-circle-check::before {\n  content: \"\\e596\";\n}\n\n.fa-person-arrow-down-to-line::before {\n  content: \"\\e538\";\n}\n\n.fa-voicemail::before {\n  content: \"\\f897\";\n}\n\n.fa-fan::before {\n  content: \"\\f863\";\n}\n\n.fa-person-walking-luggage::before {\n  content: \"\\e554\";\n}\n\n.fa-up-down::before {\n  content: \"\\f338\";\n}\n\n.fa-arrows-alt-v::before {\n  content: \"\\f338\";\n}\n\n.fa-cloud-moon-rain::before {\n  content: \"\\f73c\";\n}\n\n.fa-calendar::before {\n  content: \"\\f133\";\n}\n\n.fa-trailer::before {\n  content: \"\\e041\";\n}\n\n.fa-bahai::before {\n  content: \"\\f666\";\n}\n\n.fa-haykal::before {\n  content: \"\\f666\";\n}\n\n.fa-sd-card::before {\n  content: \"\\f7c2\";\n}\n\n.fa-dragon::before {\n  content: \"\\f6d5\";\n}\n\n.fa-shoe-prints::before {\n  content: \"\\f54b\";\n}\n\n.fa-circle-plus::before {\n  content: \"\\f055\";\n}\n\n.fa-plus-circle::before {\n  content: \"\\f055\";\n}\n\n.fa-face-grin-tongue-wink::before {\n  content: \"\\f58b\";\n}\n\n.fa-grin-tongue-wink::before {\n  content: \"\\f58b\";\n}\n\n.fa-hand-holding::before {\n  content: \"\\f4bd\";\n}\n\n.fa-plug-circle-exclamation::before {\n  content: \"\\e55d\";\n}\n\n.fa-link-slash::before {\n  content: \"\\f127\";\n}\n\n.fa-chain-broken::before {\n  content: \"\\f127\";\n}\n\n.fa-chain-slash::before {\n  content: \"\\f127\";\n}\n\n.fa-unlink::before {\n  content: \"\\f127\";\n}\n\n.fa-clone::before {\n  content: \"\\f24d\";\n}\n\n.fa-person-walking-arrow-loop-left::before {\n  content: \"\\e551\";\n}\n\n.fa-arrow-up-z-a::before {\n  content: \"\\f882\";\n}\n\n.fa-sort-alpha-up-alt::before {\n  content: \"\\f882\";\n}\n\n.fa-fire-flame-curved::before {\n  content: \"\\f7e4\";\n}\n\n.fa-fire-alt::before {\n  content: \"\\f7e4\";\n}\n\n.fa-tornado::before {\n  content: \"\\f76f\";\n}\n\n.fa-file-circle-plus::before {\n  content: \"\\e494\";\n}\n\n.fa-book-quran::before {\n  content: \"\\f687\";\n}\n\n.fa-quran::before {\n  content: \"\\f687\";\n}\n\n.fa-anchor::before {\n  content: \"\\f13d\";\n}\n\n.fa-border-all::before {\n  content: \"\\f84c\";\n}\n\n.fa-face-angry::before {\n  content: \"\\f556\";\n}\n\n.fa-angry::before {\n  content: \"\\f556\";\n}\n\n.fa-cookie-bite::before {\n  content: \"\\f564\";\n}\n\n.fa-arrow-trend-down::before {\n  content: \"\\e097\";\n}\n\n.fa-rss::before {\n  content: \"\\f09e\";\n}\n\n.fa-feed::before {\n  content: \"\\f09e\";\n}\n\n.fa-draw-polygon::before {\n  content: \"\\f5ee\";\n}\n\n.fa-scale-balanced::before {\n  content: \"\\f24e\";\n}\n\n.fa-balance-scale::before {\n  content: \"\\f24e\";\n}\n\n.fa-gauge-simple-high::before {\n  content: \"\\f62a\";\n}\n\n.fa-tachometer::before {\n  content: \"\\f62a\";\n}\n\n.fa-tachometer-fast::before {\n  content: \"\\f62a\";\n}\n\n.fa-shower::before {\n  content: \"\\f2cc\";\n}\n\n.fa-desktop::before {\n  content: \"\\f390\";\n}\n\n.fa-desktop-alt::before {\n  content: \"\\f390\";\n}\n\n.fa-m::before {\n  content: \"M\";\n}\n\n.fa-table-list::before {\n  content: \"\\f00b\";\n}\n\n.fa-th-list::before {\n  content: \"\\f00b\";\n}\n\n.fa-comment-sms::before {\n  content: \"\\f7cd\";\n}\n\n.fa-sms::before {\n  content: \"\\f7cd\";\n}\n\n.fa-book::before {\n  content: \"\\f02d\";\n}\n\n.fa-user-plus::before {\n  content: \"\\f234\";\n}\n\n.fa-check::before {\n  content: \"\\f00c\";\n}\n\n.fa-battery-three-quarters::before {\n  content: \"\\f241\";\n}\n\n.fa-battery-4::before {\n  content: \"\\f241\";\n}\n\n.fa-house-circle-check::before {\n  content: \"\\e509\";\n}\n\n.fa-angle-left::before {\n  content: \"\\f104\";\n}\n\n.fa-diagram-successor::before {\n  content: \"\\e47a\";\n}\n\n.fa-truck-arrow-right::before {\n  content: \"\\e58b\";\n}\n\n.fa-arrows-split-up-and-left::before {\n  content: \"\\e4bc\";\n}\n\n.fa-hand-fist::before {\n  content: \"\\f6de\";\n}\n\n.fa-fist-raised::before {\n  content: \"\\f6de\";\n}\n\n.fa-cloud-moon::before {\n  content: \"\\f6c3\";\n}\n\n.fa-briefcase::before {\n  content: \"\\f0b1\";\n}\n\n.fa-person-falling::before {\n  content: \"\\e546\";\n}\n\n.fa-image-portrait::before {\n  content: \"\\f3e0\";\n}\n\n.fa-portrait::before {\n  content: \"\\f3e0\";\n}\n\n.fa-user-tag::before {\n  content: \"\\f507\";\n}\n\n.fa-rug::before {\n  content: \"\\e569\";\n}\n\n.fa-earth-europe::before {\n  content: \"\\f7a2\";\n}\n\n.fa-globe-europe::before {\n  content: \"\\f7a2\";\n}\n\n.fa-cart-flatbed-suitcase::before {\n  content: \"\\f59d\";\n}\n\n.fa-luggage-cart::before {\n  content: \"\\f59d\";\n}\n\n.fa-rectangle-xmark::before {\n  content: \"\\f410\";\n}\n\n.fa-rectangle-times::before {\n  content: \"\\f410\";\n}\n\n.fa-times-rectangle::before {\n  content: \"\\f410\";\n}\n\n.fa-window-close::before {\n  content: \"\\f410\";\n}\n\n.fa-baht-sign::before {\n  content: \"\\e0ac\";\n}\n\n.fa-book-open::before {\n  content: \"\\f518\";\n}\n\n.fa-book-journal-whills::before {\n  content: \"\\f66a\";\n}\n\n.fa-journal-whills::before {\n  content: \"\\f66a\";\n}\n\n.fa-handcuffs::before {\n  content: \"\\e4f8\";\n}\n\n.fa-triangle-exclamation::before {\n  content: \"\\f071\";\n}\n\n.fa-exclamation-triangle::before {\n  content: \"\\f071\";\n}\n\n.fa-warning::before {\n  content: \"\\f071\";\n}\n\n.fa-database::before {\n  content: \"\\f1c0\";\n}\n\n.fa-share::before {\n  content: \"\\f064\";\n}\n\n.fa-mail-forward::before {\n  content: \"\\f064\";\n}\n\n.fa-bottle-droplet::before {\n  content: \"\\e4c4\";\n}\n\n.fa-mask-face::before {\n  content: \"\\e1d7\";\n}\n\n.fa-hill-rockslide::before {\n  content: \"\\e508\";\n}\n\n.fa-right-left::before {\n  content: \"\\f362\";\n}\n\n.fa-exchange-alt::before {\n  content: \"\\f362\";\n}\n\n.fa-paper-plane::before {\n  content: \"\\f1d8\";\n}\n\n.fa-road-circle-exclamation::before {\n  content: \"\\e565\";\n}\n\n.fa-dungeon::before {\n  content: \"\\f6d9\";\n}\n\n.fa-align-right::before {\n  content: \"\\f038\";\n}\n\n.fa-money-bill-1-wave::before {\n  content: \"\\f53b\";\n}\n\n.fa-money-bill-wave-alt::before {\n  content: \"\\f53b\";\n}\n\n.fa-life-ring::before {\n  content: \"\\f1cd\";\n}\n\n.fa-hands::before {\n  content: \"\\f2a7\";\n}\n\n.fa-sign-language::before {\n  content: \"\\f2a7\";\n}\n\n.fa-signing::before {\n  content: \"\\f2a7\";\n}\n\n.fa-calendar-day::before {\n  content: \"\\f783\";\n}\n\n.fa-water-ladder::before {\n  content: \"\\f5c5\";\n}\n\n.fa-ladder-water::before {\n  content: \"\\f5c5\";\n}\n\n.fa-swimming-pool::before {\n  content: \"\\f5c5\";\n}\n\n.fa-arrows-up-down::before {\n  content: \"\\f07d\";\n}\n\n.fa-arrows-v::before {\n  content: \"\\f07d\";\n}\n\n.fa-face-grimace::before {\n  content: \"\\f57f\";\n}\n\n.fa-grimace::before {\n  content: \"\\f57f\";\n}\n\n.fa-wheelchair-move::before {\n  content: \"\\e2ce\";\n}\n\n.fa-wheelchair-alt::before {\n  content: \"\\e2ce\";\n}\n\n.fa-turn-down::before {\n  content: \"\\f3be\";\n}\n\n.fa-level-down-alt::before {\n  content: \"\\f3be\";\n}\n\n.fa-person-walking-arrow-right::before {\n  content: \"\\e552\";\n}\n\n.fa-square-envelope::before {\n  content: \"\\f199\";\n}\n\n.fa-envelope-square::before {\n  content: \"\\f199\";\n}\n\n.fa-dice::before {\n  content: \"\\f522\";\n}\n\n.fa-bowling-ball::before {\n  content: \"\\f436\";\n}\n\n.fa-brain::before {\n  content: \"\\f5dc\";\n}\n\n.fa-bandage::before {\n  content: \"\\f462\";\n}\n\n.fa-band-aid::before {\n  content: \"\\f462\";\n}\n\n.fa-calendar-minus::before {\n  content: \"\\f272\";\n}\n\n.fa-circle-xmark::before {\n  content: \"\\f057\";\n}\n\n.fa-times-circle::before {\n  content: \"\\f057\";\n}\n\n.fa-xmark-circle::before {\n  content: \"\\f057\";\n}\n\n.fa-gifts::before {\n  content: \"\\f79c\";\n}\n\n.fa-hotel::before {\n  content: \"\\f594\";\n}\n\n.fa-earth-asia::before {\n  content: \"\\f57e\";\n}\n\n.fa-globe-asia::before {\n  content: \"\\f57e\";\n}\n\n.fa-id-card-clip::before {\n  content: \"\\f47f\";\n}\n\n.fa-id-card-alt::before {\n  content: \"\\f47f\";\n}\n\n.fa-magnifying-glass-plus::before {\n  content: \"\\f00e\";\n}\n\n.fa-search-plus::before {\n  content: \"\\f00e\";\n}\n\n.fa-thumbs-up::before {\n  content: \"\\f164\";\n}\n\n.fa-user-clock::before {\n  content: \"\\f4fd\";\n}\n\n.fa-hand-dots::before {\n  content: \"\\f461\";\n}\n\n.fa-allergies::before {\n  content: \"\\f461\";\n}\n\n.fa-file-invoice::before {\n  content: \"\\f570\";\n}\n\n.fa-window-minimize::before {\n  content: \"\\f2d1\";\n}\n\n.fa-mug-saucer::before {\n  content: \"\\f0f4\";\n}\n\n.fa-coffee::before {\n  content: \"\\f0f4\";\n}\n\n.fa-brush::before {\n  content: \"\\f55d\";\n}\n\n.fa-mask::before {\n  content: \"\\f6fa\";\n}\n\n.fa-magnifying-glass-minus::before {\n  content: \"\\f010\";\n}\n\n.fa-search-minus::before {\n  content: \"\\f010\";\n}\n\n.fa-ruler-vertical::before {\n  content: \"\\f548\";\n}\n\n.fa-user-large::before {\n  content: \"\\f406\";\n}\n\n.fa-user-alt::before {\n  content: \"\\f406\";\n}\n\n.fa-train-tram::before {\n  content: \"\\e5b4\";\n}\n\n.fa-user-nurse::before {\n  content: \"\\f82f\";\n}\n\n.fa-syringe::before {\n  content: \"\\f48e\";\n}\n\n.fa-cloud-sun::before {\n  content: \"\\f6c4\";\n}\n\n.fa-stopwatch-20::before {\n  content: \"\\e06f\";\n}\n\n.fa-square-full::before {\n  content: \"\\f45c\";\n}\n\n.fa-magnet::before {\n  content: \"\\f076\";\n}\n\n.fa-jar::before {\n  content: \"\\e516\";\n}\n\n.fa-note-sticky::before {\n  content: \"\\f249\";\n}\n\n.fa-sticky-note::before {\n  content: \"\\f249\";\n}\n\n.fa-bug-slash::before {\n  content: \"\\e490\";\n}\n\n.fa-arrow-up-from-water-pump::before {\n  content: \"\\e4b6\";\n}\n\n.fa-bone::before {\n  content: \"\\f5d7\";\n}\n\n.fa-user-injured::before {\n  content: \"\\f728\";\n}\n\n.fa-face-sad-tear::before {\n  content: \"\\f5b4\";\n}\n\n.fa-sad-tear::before {\n  content: \"\\f5b4\";\n}\n\n.fa-plane::before {\n  content: \"\\f072\";\n}\n\n.fa-tent-arrows-down::before {\n  content: \"\\e581\";\n}\n\n.fa-exclamation::before {\n  content: \"\\!\";\n}\n\n.fa-arrows-spin::before {\n  content: \"\\e4bb\";\n}\n\n.fa-print::before {\n  content: \"\\f02f\";\n}\n\n.fa-turkish-lira-sign::before {\n  content: \"\\e2bb\";\n}\n\n.fa-try::before {\n  content: \"\\e2bb\";\n}\n\n.fa-turkish-lira::before {\n  content: \"\\e2bb\";\n}\n\n.fa-dollar-sign::before {\n  content: \"\\$\";\n}\n\n.fa-dollar::before {\n  content: \"\\$\";\n}\n\n.fa-usd::before {\n  content: \"\\$\";\n}\n\n.fa-x::before {\n  content: \"X\";\n}\n\n.fa-magnifying-glass-dollar::before {\n  content: \"\\f688\";\n}\n\n.fa-search-dollar::before {\n  content: \"\\f688\";\n}\n\n.fa-users-gear::before {\n  content: \"\\f509\";\n}\n\n.fa-users-cog::before {\n  content: \"\\f509\";\n}\n\n.fa-person-military-pointing::before {\n  content: \"\\e54a\";\n}\n\n.fa-building-columns::before {\n  content: \"\\f19c\";\n}\n\n.fa-bank::before {\n  content: \"\\f19c\";\n}\n\n.fa-institution::before {\n  content: \"\\f19c\";\n}\n\n.fa-museum::before {\n  content: \"\\f19c\";\n}\n\n.fa-university::before {\n  content: \"\\f19c\";\n}\n\n.fa-umbrella::before {\n  content: \"\\f0e9\";\n}\n\n.fa-trowel::before {\n  content: \"\\e589\";\n}\n\n.fa-d::before {\n  content: \"D\";\n}\n\n.fa-stapler::before {\n  content: \"\\e5af\";\n}\n\n.fa-masks-theater::before {\n  content: \"\\f630\";\n}\n\n.fa-theater-masks::before {\n  content: \"\\f630\";\n}\n\n.fa-kip-sign::before {\n  content: \"\\e1c4\";\n}\n\n.fa-hand-point-left::before {\n  content: \"\\f0a5\";\n}\n\n.fa-handshake-simple::before {\n  content: \"\\f4c6\";\n}\n\n.fa-handshake-alt::before {\n  content: \"\\f4c6\";\n}\n\n.fa-jet-fighter::before {\n  content: \"\\f0fb\";\n}\n\n.fa-fighter-jet::before {\n  content: \"\\f0fb\";\n}\n\n.fa-square-share-nodes::before {\n  content: \"\\f1e1\";\n}\n\n.fa-share-alt-square::before {\n  content: \"\\f1e1\";\n}\n\n.fa-barcode::before {\n  content: \"\\f02a\";\n}\n\n.fa-plus-minus::before {\n  content: \"\\e43c\";\n}\n\n.fa-video::before {\n  content: \"\\f03d\";\n}\n\n.fa-video-camera::before {\n  content: \"\\f03d\";\n}\n\n.fa-graduation-cap::before {\n  content: \"\\f19d\";\n}\n\n.fa-mortar-board::before {\n  content: \"\\f19d\";\n}\n\n.fa-hand-holding-medical::before {\n  content: \"\\e05c\";\n}\n\n.fa-person-circle-check::before {\n  content: \"\\e53e\";\n}\n\n.fa-turn-up::before {\n  content: \"\\f3bf\";\n}\n\n.fa-level-up-alt::before {\n  content: \"\\f3bf\";\n}\n\n.sr-only,\n.fa-sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  white-space: nowrap;\n  border-width: 0;\n}\n\n.sr-only-focusable:not(:focus),\n.fa-sr-only-focusable:not(:focus) {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  white-space: nowrap;\n  border-width: 0;\n}\n\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n:root, :host {\n  --fa-style-family-classic: \"Font Awesome 6 Free\";\n  --fa-font-solid: normal 900 1em/1 \"Font Awesome 6 Free\";\n}\n\n@font-face {\n  font-family: \"Font Awesome 6 Free\";\n  font-style: normal;\n  font-weight: 900;\n  font-display: block;\n  src: url(\"../webfonts/fa-solid-900.woff2\") format(\"woff2\"), url(\"../webfonts/fa-solid-900.ttf\") format(\"truetype\");\n}\n.fas,\n.fa-solid {\n  font-weight: 900;\n}\n\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n:root, :host {\n  --fa-style-family-classic: \"Font Awesome 6 Free\";\n  --fa-font-regular: normal 400 1em/1 \"Font Awesome 6 Free\";\n}\n\n@font-face {\n  font-family: \"Font Awesome 6 Free\";\n  font-style: normal;\n  font-weight: 400;\n  font-display: block;\n  src: url(\"../webfonts/fa-regular-400.woff2\") format(\"woff2\"), url(\"../webfonts/fa-regular-400.ttf\") format(\"truetype\");\n}\n.far,\n.fa-regular {\n  font-weight: 400;\n}\n\n/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n:root, :host {\n  --fa-style-family-brands: \"Font Awesome 6 Brands\";\n  --fa-font-brands: normal 400 1em/1 \"Font Awesome 6 Brands\";\n}\n\n@font-face {\n  font-family: \"Font Awesome 6 Brands\";\n  font-style: normal;\n  font-weight: 400;\n  font-display: block;\n  src: url(\"../webfonts/fa-brands-400.woff2\") format(\"woff2\"), url(\"../webfonts/fa-brands-400.ttf\") format(\"truetype\");\n}\n.fab,\n.fa-brands {\n  font-weight: 400;\n}\n\n.fa-monero:before {\n  content: \"\\f3d0\";\n}\n\n.fa-hooli:before {\n  content: \"\\f427\";\n}\n\n.fa-yelp:before {\n  content: \"\\f1e9\";\n}\n\n.fa-cc-visa:before {\n  content: \"\\f1f0\";\n}\n\n.fa-lastfm:before {\n  content: \"\\f202\";\n}\n\n.fa-shopware:before {\n  content: \"\\f5b5\";\n}\n\n.fa-creative-commons-nc:before {\n  content: \"\\f4e8\";\n}\n\n.fa-aws:before {\n  content: \"\\f375\";\n}\n\n.fa-redhat:before {\n  content: \"\\f7bc\";\n}\n\n.fa-yoast:before {\n  content: \"\\f2b1\";\n}\n\n.fa-cloudflare:before {\n  content: \"\\e07d\";\n}\n\n.fa-ups:before {\n  content: \"\\f7e0\";\n}\n\n.fa-pixiv:before {\n  content: \"\\e640\";\n}\n\n.fa-wpexplorer:before {\n  content: \"\\f2de\";\n}\n\n.fa-dyalog:before {\n  content: \"\\f399\";\n}\n\n.fa-bity:before {\n  content: \"\\f37a\";\n}\n\n.fa-stackpath:before {\n  content: \"\\f842\";\n}\n\n.fa-buysellads:before {\n  content: \"\\f20d\";\n}\n\n.fa-first-order:before {\n  content: \"\\f2b0\";\n}\n\n.fa-modx:before {\n  content: \"\\f285\";\n}\n\n.fa-guilded:before {\n  content: \"\\e07e\";\n}\n\n.fa-vnv:before {\n  content: \"\\f40b\";\n}\n\n.fa-square-js:before {\n  content: \"\\f3b9\";\n}\n\n.fa-js-square:before {\n  content: \"\\f3b9\";\n}\n\n.fa-microsoft:before {\n  content: \"\\f3ca\";\n}\n\n.fa-qq:before {\n  content: \"\\f1d6\";\n}\n\n.fa-orcid:before {\n  content: \"\\f8d2\";\n}\n\n.fa-java:before {\n  content: \"\\f4e4\";\n}\n\n.fa-invision:before {\n  content: \"\\f7b0\";\n}\n\n.fa-creative-commons-pd-alt:before {\n  content: \"\\f4ed\";\n}\n\n.fa-centercode:before {\n  content: \"\\f380\";\n}\n\n.fa-glide-g:before {\n  content: \"\\f2a6\";\n}\n\n.fa-drupal:before {\n  content: \"\\f1a9\";\n}\n\n.fa-jxl:before {\n  content: \"\\e67b\";\n}\n\n.fa-hire-a-helper:before {\n  content: \"\\f3b0\";\n}\n\n.fa-creative-commons-by:before {\n  content: \"\\f4e7\";\n}\n\n.fa-unity:before {\n  content: \"\\e049\";\n}\n\n.fa-whmcs:before {\n  content: \"\\f40d\";\n}\n\n.fa-rocketchat:before {\n  content: \"\\f3e8\";\n}\n\n.fa-vk:before {\n  content: \"\\f189\";\n}\n\n.fa-untappd:before {\n  content: \"\\f405\";\n}\n\n.fa-mailchimp:before {\n  content: \"\\f59e\";\n}\n\n.fa-css3-alt:before {\n  content: \"\\f38b\";\n}\n\n.fa-square-reddit:before {\n  content: \"\\f1a2\";\n}\n\n.fa-reddit-square:before {\n  content: \"\\f1a2\";\n}\n\n.fa-vimeo-v:before {\n  content: \"\\f27d\";\n}\n\n.fa-contao:before {\n  content: \"\\f26d\";\n}\n\n.fa-square-font-awesome:before {\n  content: \"\\e5ad\";\n}\n\n.fa-deskpro:before {\n  content: \"\\f38f\";\n}\n\n.fa-brave:before {\n  content: \"\\e63c\";\n}\n\n.fa-sistrix:before {\n  content: \"\\f3ee\";\n}\n\n.fa-square-instagram:before {\n  content: \"\\e055\";\n}\n\n.fa-instagram-square:before {\n  content: \"\\e055\";\n}\n\n.fa-battle-net:before {\n  content: \"\\f835\";\n}\n\n.fa-the-red-yeti:before {\n  content: \"\\f69d\";\n}\n\n.fa-square-hacker-news:before {\n  content: \"\\f3af\";\n}\n\n.fa-hacker-news-square:before {\n  content: \"\\f3af\";\n}\n\n.fa-edge:before {\n  content: \"\\f282\";\n}\n\n.fa-threads:before {\n  content: \"\\e618\";\n}\n\n.fa-napster:before {\n  content: \"\\f3d2\";\n}\n\n.fa-square-snapchat:before {\n  content: \"\\f2ad\";\n}\n\n.fa-snapchat-square:before {\n  content: \"\\f2ad\";\n}\n\n.fa-google-plus-g:before {\n  content: \"\\f0d5\";\n}\n\n.fa-artstation:before {\n  content: \"\\f77a\";\n}\n\n.fa-markdown:before {\n  content: \"\\f60f\";\n}\n\n.fa-sourcetree:before {\n  content: \"\\f7d3\";\n}\n\n.fa-google-plus:before {\n  content: \"\\f2b3\";\n}\n\n.fa-diaspora:before {\n  content: \"\\f791\";\n}\n\n.fa-foursquare:before {\n  content: \"\\f180\";\n}\n\n.fa-stack-overflow:before {\n  content: \"\\f16c\";\n}\n\n.fa-github-alt:before {\n  content: \"\\f113\";\n}\n\n.fa-phoenix-squadron:before {\n  content: \"\\f511\";\n}\n\n.fa-pagelines:before {\n  content: \"\\f18c\";\n}\n\n.fa-algolia:before {\n  content: \"\\f36c\";\n}\n\n.fa-red-river:before {\n  content: \"\\f3e3\";\n}\n\n.fa-creative-commons-sa:before {\n  content: \"\\f4ef\";\n}\n\n.fa-safari:before {\n  content: \"\\f267\";\n}\n\n.fa-google:before {\n  content: \"\\f1a0\";\n}\n\n.fa-square-font-awesome-stroke:before {\n  content: \"\\f35c\";\n}\n\n.fa-font-awesome-alt:before {\n  content: \"\\f35c\";\n}\n\n.fa-atlassian:before {\n  content: \"\\f77b\";\n}\n\n.fa-linkedin-in:before {\n  content: \"\\f0e1\";\n}\n\n.fa-digital-ocean:before {\n  content: \"\\f391\";\n}\n\n.fa-nimblr:before {\n  content: \"\\f5a8\";\n}\n\n.fa-chromecast:before {\n  content: \"\\f838\";\n}\n\n.fa-evernote:before {\n  content: \"\\f839\";\n}\n\n.fa-hacker-news:before {\n  content: \"\\f1d4\";\n}\n\n.fa-creative-commons-sampling:before {\n  content: \"\\f4f0\";\n}\n\n.fa-adversal:before {\n  content: \"\\f36a\";\n}\n\n.fa-creative-commons:before {\n  content: \"\\f25e\";\n}\n\n.fa-watchman-monitoring:before {\n  content: \"\\e087\";\n}\n\n.fa-fonticons:before {\n  content: \"\\f280\";\n}\n\n.fa-weixin:before {\n  content: \"\\f1d7\";\n}\n\n.fa-shirtsinbulk:before {\n  content: \"\\f214\";\n}\n\n.fa-codepen:before {\n  content: \"\\f1cb\";\n}\n\n.fa-git-alt:before {\n  content: \"\\f841\";\n}\n\n.fa-lyft:before {\n  content: \"\\f3c3\";\n}\n\n.fa-rev:before {\n  content: \"\\f5b2\";\n}\n\n.fa-windows:before {\n  content: \"\\f17a\";\n}\n\n.fa-wizards-of-the-coast:before {\n  content: \"\\f730\";\n}\n\n.fa-square-viadeo:before {\n  content: \"\\f2aa\";\n}\n\n.fa-viadeo-square:before {\n  content: \"\\f2aa\";\n}\n\n.fa-meetup:before {\n  content: \"\\f2e0\";\n}\n\n.fa-centos:before {\n  content: \"\\f789\";\n}\n\n.fa-adn:before {\n  content: \"\\f170\";\n}\n\n.fa-cloudsmith:before {\n  content: \"\\f384\";\n}\n\n.fa-opensuse:before {\n  content: \"\\e62b\";\n}\n\n.fa-pied-piper-alt:before {\n  content: \"\\f1a8\";\n}\n\n.fa-square-dribbble:before {\n  content: \"\\f397\";\n}\n\n.fa-dribbble-square:before {\n  content: \"\\f397\";\n}\n\n.fa-codiepie:before {\n  content: \"\\f284\";\n}\n\n.fa-node:before {\n  content: \"\\f419\";\n}\n\n.fa-mix:before {\n  content: \"\\f3cb\";\n}\n\n.fa-steam:before {\n  content: \"\\f1b6\";\n}\n\n.fa-cc-apple-pay:before {\n  content: \"\\f416\";\n}\n\n.fa-scribd:before {\n  content: \"\\f28a\";\n}\n\n.fa-debian:before {\n  content: \"\\e60b\";\n}\n\n.fa-openid:before {\n  content: \"\\f19b\";\n}\n\n.fa-instalod:before {\n  content: \"\\e081\";\n}\n\n.fa-expeditedssl:before {\n  content: \"\\f23e\";\n}\n\n.fa-sellcast:before {\n  content: \"\\f2da\";\n}\n\n.fa-square-twitter:before {\n  content: \"\\f081\";\n}\n\n.fa-twitter-square:before {\n  content: \"\\f081\";\n}\n\n.fa-r-project:before {\n  content: \"\\f4f7\";\n}\n\n.fa-delicious:before {\n  content: \"\\f1a5\";\n}\n\n.fa-freebsd:before {\n  content: \"\\f3a4\";\n}\n\n.fa-vuejs:before {\n  content: \"\\f41f\";\n}\n\n.fa-accusoft:before {\n  content: \"\\f369\";\n}\n\n.fa-ioxhost:before {\n  content: \"\\f208\";\n}\n\n.fa-fonticons-fi:before {\n  content: \"\\f3a2\";\n}\n\n.fa-app-store:before {\n  content: \"\\f36f\";\n}\n\n.fa-cc-mastercard:before {\n  content: \"\\f1f1\";\n}\n\n.fa-itunes-note:before {\n  content: \"\\f3b5\";\n}\n\n.fa-golang:before {\n  content: \"\\e40f\";\n}\n\n.fa-kickstarter:before {\n  content: \"\\f3bb\";\n}\n\n.fa-square-kickstarter:before {\n  content: \"\\f3bb\";\n}\n\n.fa-grav:before {\n  content: \"\\f2d6\";\n}\n\n.fa-weibo:before {\n  content: \"\\f18a\";\n}\n\n.fa-uncharted:before {\n  content: \"\\e084\";\n}\n\n.fa-firstdraft:before {\n  content: \"\\f3a1\";\n}\n\n.fa-square-youtube:before {\n  content: \"\\f431\";\n}\n\n.fa-youtube-square:before {\n  content: \"\\f431\";\n}\n\n.fa-wikipedia-w:before {\n  content: \"\\f266\";\n}\n\n.fa-wpressr:before {\n  content: \"\\f3e4\";\n}\n\n.fa-rendact:before {\n  content: \"\\f3e4\";\n}\n\n.fa-angellist:before {\n  content: \"\\f209\";\n}\n\n.fa-galactic-republic:before {\n  content: \"\\f50c\";\n}\n\n.fa-nfc-directional:before {\n  content: \"\\e530\";\n}\n\n.fa-skype:before {\n  content: \"\\f17e\";\n}\n\n.fa-joget:before {\n  content: \"\\f3b7\";\n}\n\n.fa-fedora:before {\n  content: \"\\f798\";\n}\n\n.fa-stripe-s:before {\n  content: \"\\f42a\";\n}\n\n.fa-meta:before {\n  content: \"\\e49b\";\n}\n\n.fa-laravel:before {\n  content: \"\\f3bd\";\n}\n\n.fa-hotjar:before {\n  content: \"\\f3b1\";\n}\n\n.fa-bluetooth-b:before {\n  content: \"\\f294\";\n}\n\n.fa-square-letterboxd:before {\n  content: \"\\e62e\";\n}\n\n.fa-sticker-mule:before {\n  content: \"\\f3f7\";\n}\n\n.fa-creative-commons-zero:before {\n  content: \"\\f4f3\";\n}\n\n.fa-hips:before {\n  content: \"\\f452\";\n}\n\n.fa-behance:before {\n  content: \"\\f1b4\";\n}\n\n.fa-reddit:before {\n  content: \"\\f1a1\";\n}\n\n.fa-discord:before {\n  content: \"\\f392\";\n}\n\n.fa-chrome:before {\n  content: \"\\f268\";\n}\n\n.fa-app-store-ios:before {\n  content: \"\\f370\";\n}\n\n.fa-cc-discover:before {\n  content: \"\\f1f2\";\n}\n\n.fa-wpbeginner:before {\n  content: \"\\f297\";\n}\n\n.fa-confluence:before {\n  content: \"\\f78d\";\n}\n\n.fa-shoelace:before {\n  content: \"\\e60c\";\n}\n\n.fa-mdb:before {\n  content: \"\\f8ca\";\n}\n\n.fa-dochub:before {\n  content: \"\\f394\";\n}\n\n.fa-accessible-icon:before {\n  content: \"\\f368\";\n}\n\n.fa-ebay:before {\n  content: \"\\f4f4\";\n}\n\n.fa-amazon:before {\n  content: \"\\f270\";\n}\n\n.fa-unsplash:before {\n  content: \"\\e07c\";\n}\n\n.fa-yarn:before {\n  content: \"\\f7e3\";\n}\n\n.fa-square-steam:before {\n  content: \"\\f1b7\";\n}\n\n.fa-steam-square:before {\n  content: \"\\f1b7\";\n}\n\n.fa-500px:before {\n  content: \"\\f26e\";\n}\n\n.fa-square-vimeo:before {\n  content: \"\\f194\";\n}\n\n.fa-vimeo-square:before {\n  content: \"\\f194\";\n}\n\n.fa-asymmetrik:before {\n  content: \"\\f372\";\n}\n\n.fa-font-awesome:before {\n  content: \"\\f2b4\";\n}\n\n.fa-font-awesome-flag:before {\n  content: \"\\f2b4\";\n}\n\n.fa-font-awesome-logo-full:before {\n  content: \"\\f2b4\";\n}\n\n.fa-gratipay:before {\n  content: \"\\f184\";\n}\n\n.fa-apple:before {\n  content: \"\\f179\";\n}\n\n.fa-hive:before {\n  content: \"\\e07f\";\n}\n\n.fa-gitkraken:before {\n  content: \"\\f3a6\";\n}\n\n.fa-keybase:before {\n  content: \"\\f4f5\";\n}\n\n.fa-apple-pay:before {\n  content: \"\\f415\";\n}\n\n.fa-padlet:before {\n  content: \"\\e4a0\";\n}\n\n.fa-amazon-pay:before {\n  content: \"\\f42c\";\n}\n\n.fa-square-github:before {\n  content: \"\\f092\";\n}\n\n.fa-github-square:before {\n  content: \"\\f092\";\n}\n\n.fa-stumbleupon:before {\n  content: \"\\f1a4\";\n}\n\n.fa-fedex:before {\n  content: \"\\f797\";\n}\n\n.fa-phoenix-framework:before {\n  content: \"\\f3dc\";\n}\n\n.fa-shopify:before {\n  content: \"\\e057\";\n}\n\n.fa-neos:before {\n  content: \"\\f612\";\n}\n\n.fa-square-threads:before {\n  content: \"\\e619\";\n}\n\n.fa-hackerrank:before {\n  content: \"\\f5f7\";\n}\n\n.fa-researchgate:before {\n  content: \"\\f4f8\";\n}\n\n.fa-swift:before {\n  content: \"\\f8e1\";\n}\n\n.fa-angular:before {\n  content: \"\\f420\";\n}\n\n.fa-speakap:before {\n  content: \"\\f3f3\";\n}\n\n.fa-angrycreative:before {\n  content: \"\\f36e\";\n}\n\n.fa-y-combinator:before {\n  content: \"\\f23b\";\n}\n\n.fa-empire:before {\n  content: \"\\f1d1\";\n}\n\n.fa-envira:before {\n  content: \"\\f299\";\n}\n\n.fa-google-scholar:before {\n  content: \"\\e63b\";\n}\n\n.fa-square-gitlab:before {\n  content: \"\\e5ae\";\n}\n\n.fa-gitlab-square:before {\n  content: \"\\e5ae\";\n}\n\n.fa-studiovinari:before {\n  content: \"\\f3f8\";\n}\n\n.fa-pied-piper:before {\n  content: \"\\f2ae\";\n}\n\n.fa-wordpress:before {\n  content: \"\\f19a\";\n}\n\n.fa-product-hunt:before {\n  content: \"\\f288\";\n}\n\n.fa-firefox:before {\n  content: \"\\f269\";\n}\n\n.fa-linode:before {\n  content: \"\\f2b8\";\n}\n\n.fa-goodreads:before {\n  content: \"\\f3a8\";\n}\n\n.fa-square-odnoklassniki:before {\n  content: \"\\f264\";\n}\n\n.fa-odnoklassniki-square:before {\n  content: \"\\f264\";\n}\n\n.fa-jsfiddle:before {\n  content: \"\\f1cc\";\n}\n\n.fa-sith:before {\n  content: \"\\f512\";\n}\n\n.fa-themeisle:before {\n  content: \"\\f2b2\";\n}\n\n.fa-page4:before {\n  content: \"\\f3d7\";\n}\n\n.fa-hashnode:before {\n  content: \"\\e499\";\n}\n\n.fa-react:before {\n  content: \"\\f41b\";\n}\n\n.fa-cc-paypal:before {\n  content: \"\\f1f4\";\n}\n\n.fa-squarespace:before {\n  content: \"\\f5be\";\n}\n\n.fa-cc-stripe:before {\n  content: \"\\f1f5\";\n}\n\n.fa-creative-commons-share:before {\n  content: \"\\f4f2\";\n}\n\n.fa-bitcoin:before {\n  content: \"\\f379\";\n}\n\n.fa-keycdn:before {\n  content: \"\\f3ba\";\n}\n\n.fa-opera:before {\n  content: \"\\f26a\";\n}\n\n.fa-itch-io:before {\n  content: \"\\f83a\";\n}\n\n.fa-umbraco:before {\n  content: \"\\f8e8\";\n}\n\n.fa-galactic-senate:before {\n  content: \"\\f50d\";\n}\n\n.fa-ubuntu:before {\n  content: \"\\f7df\";\n}\n\n.fa-draft2digital:before {\n  content: \"\\f396\";\n}\n\n.fa-stripe:before {\n  content: \"\\f429\";\n}\n\n.fa-houzz:before {\n  content: \"\\f27c\";\n}\n\n.fa-gg:before {\n  content: \"\\f260\";\n}\n\n.fa-dhl:before {\n  content: \"\\f790\";\n}\n\n.fa-square-pinterest:before {\n  content: \"\\f0d3\";\n}\n\n.fa-pinterest-square:before {\n  content: \"\\f0d3\";\n}\n\n.fa-xing:before {\n  content: \"\\f168\";\n}\n\n.fa-blackberry:before {\n  content: \"\\f37b\";\n}\n\n.fa-creative-commons-pd:before {\n  content: \"\\f4ec\";\n}\n\n.fa-playstation:before {\n  content: \"\\f3df\";\n}\n\n.fa-quinscape:before {\n  content: \"\\f459\";\n}\n\n.fa-less:before {\n  content: \"\\f41d\";\n}\n\n.fa-blogger-b:before {\n  content: \"\\f37d\";\n}\n\n.fa-opencart:before {\n  content: \"\\f23d\";\n}\n\n.fa-vine:before {\n  content: \"\\f1ca\";\n}\n\n.fa-signal-messenger:before {\n  content: \"\\e663\";\n}\n\n.fa-paypal:before {\n  content: \"\\f1ed\";\n}\n\n.fa-gitlab:before {\n  content: \"\\f296\";\n}\n\n.fa-typo3:before {\n  content: \"\\f42b\";\n}\n\n.fa-reddit-alien:before {\n  content: \"\\f281\";\n}\n\n.fa-yahoo:before {\n  content: \"\\f19e\";\n}\n\n.fa-dailymotion:before {\n  content: \"\\e052\";\n}\n\n.fa-affiliatetheme:before {\n  content: \"\\f36b\";\n}\n\n.fa-pied-piper-pp:before {\n  content: \"\\f1a7\";\n}\n\n.fa-bootstrap:before {\n  content: \"\\f836\";\n}\n\n.fa-odnoklassniki:before {\n  content: \"\\f263\";\n}\n\n.fa-nfc-symbol:before {\n  content: \"\\e531\";\n}\n\n.fa-mintbit:before {\n  content: \"\\e62f\";\n}\n\n.fa-ethereum:before {\n  content: \"\\f42e\";\n}\n\n.fa-speaker-deck:before {\n  content: \"\\f83c\";\n}\n\n.fa-creative-commons-nc-eu:before {\n  content: \"\\f4e9\";\n}\n\n.fa-patreon:before {\n  content: \"\\f3d9\";\n}\n\n.fa-avianex:before {\n  content: \"\\f374\";\n}\n\n.fa-ello:before {\n  content: \"\\f5f1\";\n}\n\n.fa-gofore:before {\n  content: \"\\f3a7\";\n}\n\n.fa-bimobject:before {\n  content: \"\\f378\";\n}\n\n.fa-brave-reverse:before {\n  content: \"\\e63d\";\n}\n\n.fa-facebook-f:before {\n  content: \"\\f39e\";\n}\n\n.fa-square-google-plus:before {\n  content: \"\\f0d4\";\n}\n\n.fa-google-plus-square:before {\n  content: \"\\f0d4\";\n}\n\n.fa-web-awesome:before {\n  content: \"\\e682\";\n}\n\n.fa-mandalorian:before {\n  content: \"\\f50f\";\n}\n\n.fa-first-order-alt:before {\n  content: \"\\f50a\";\n}\n\n.fa-osi:before {\n  content: \"\\f41a\";\n}\n\n.fa-google-wallet:before {\n  content: \"\\f1ee\";\n}\n\n.fa-d-and-d-beyond:before {\n  content: \"\\f6ca\";\n}\n\n.fa-periscope:before {\n  content: \"\\f3da\";\n}\n\n.fa-fulcrum:before {\n  content: \"\\f50b\";\n}\n\n.fa-cloudscale:before {\n  content: \"\\f383\";\n}\n\n.fa-forumbee:before {\n  content: \"\\f211\";\n}\n\n.fa-mizuni:before {\n  content: \"\\f3cc\";\n}\n\n.fa-schlix:before {\n  content: \"\\f3ea\";\n}\n\n.fa-square-xing:before {\n  content: \"\\f169\";\n}\n\n.fa-xing-square:before {\n  content: \"\\f169\";\n}\n\n.fa-bandcamp:before {\n  content: \"\\f2d5\";\n}\n\n.fa-wpforms:before {\n  content: \"\\f298\";\n}\n\n.fa-cloudversify:before {\n  content: \"\\f385\";\n}\n\n.fa-usps:before {\n  content: \"\\f7e1\";\n}\n\n.fa-megaport:before {\n  content: \"\\f5a3\";\n}\n\n.fa-magento:before {\n  content: \"\\f3c4\";\n}\n\n.fa-spotify:before {\n  content: \"\\f1bc\";\n}\n\n.fa-optin-monster:before {\n  content: \"\\f23c\";\n}\n\n.fa-fly:before {\n  content: \"\\f417\";\n}\n\n.fa-aviato:before {\n  content: \"\\f421\";\n}\n\n.fa-itunes:before {\n  content: \"\\f3b4\";\n}\n\n.fa-cuttlefish:before {\n  content: \"\\f38c\";\n}\n\n.fa-blogger:before {\n  content: \"\\f37c\";\n}\n\n.fa-flickr:before {\n  content: \"\\f16e\";\n}\n\n.fa-viber:before {\n  content: \"\\f409\";\n}\n\n.fa-soundcloud:before {\n  content: \"\\f1be\";\n}\n\n.fa-digg:before {\n  content: \"\\f1a6\";\n}\n\n.fa-tencent-weibo:before {\n  content: \"\\f1d5\";\n}\n\n.fa-letterboxd:before {\n  content: \"\\e62d\";\n}\n\n.fa-symfony:before {\n  content: \"\\f83d\";\n}\n\n.fa-maxcdn:before {\n  content: \"\\f136\";\n}\n\n.fa-etsy:before {\n  content: \"\\f2d7\";\n}\n\n.fa-facebook-messenger:before {\n  content: \"\\f39f\";\n}\n\n.fa-audible:before {\n  content: \"\\f373\";\n}\n\n.fa-think-peaks:before {\n  content: \"\\f731\";\n}\n\n.fa-bilibili:before {\n  content: \"\\e3d9\";\n}\n\n.fa-erlang:before {\n  content: \"\\f39d\";\n}\n\n.fa-x-twitter:before {\n  content: \"\\e61b\";\n}\n\n.fa-cotton-bureau:before {\n  content: \"\\f89e\";\n}\n\n.fa-dashcube:before {\n  content: \"\\f210\";\n}\n\n.fa-42-group:before {\n  content: \"\\e080\";\n}\n\n.fa-innosoft:before {\n  content: \"\\e080\";\n}\n\n.fa-stack-exchange:before {\n  content: \"\\f18d\";\n}\n\n.fa-elementor:before {\n  content: \"\\f430\";\n}\n\n.fa-square-pied-piper:before {\n  content: \"\\e01e\";\n}\n\n.fa-pied-piper-square:before {\n  content: \"\\e01e\";\n}\n\n.fa-creative-commons-nd:before {\n  content: \"\\f4eb\";\n}\n\n.fa-palfed:before {\n  content: \"\\f3d8\";\n}\n\n.fa-superpowers:before {\n  content: \"\\f2dd\";\n}\n\n.fa-resolving:before {\n  content: \"\\f3e7\";\n}\n\n.fa-xbox:before {\n  content: \"\\f412\";\n}\n\n.fa-square-web-awesome-stroke:before {\n  content: \"\\e684\";\n}\n\n.fa-searchengin:before {\n  content: \"\\f3eb\";\n}\n\n.fa-tiktok:before {\n  content: \"\\e07b\";\n}\n\n.fa-square-facebook:before {\n  content: \"\\f082\";\n}\n\n.fa-facebook-square:before {\n  content: \"\\f082\";\n}\n\n.fa-renren:before {\n  content: \"\\f18b\";\n}\n\n.fa-linux:before {\n  content: \"\\f17c\";\n}\n\n.fa-glide:before {\n  content: \"\\f2a5\";\n}\n\n.fa-linkedin:before {\n  content: \"\\f08c\";\n}\n\n.fa-hubspot:before {\n  content: \"\\f3b2\";\n}\n\n.fa-deploydog:before {\n  content: \"\\f38e\";\n}\n\n.fa-twitch:before {\n  content: \"\\f1e8\";\n}\n\n.fa-ravelry:before {\n  content: \"\\f2d9\";\n}\n\n.fa-mixer:before {\n  content: \"\\e056\";\n}\n\n.fa-square-lastfm:before {\n  content: \"\\f203\";\n}\n\n.fa-lastfm-square:before {\n  content: \"\\f203\";\n}\n\n.fa-vimeo:before {\n  content: \"\\f40a\";\n}\n\n.fa-mendeley:before {\n  content: \"\\f7b3\";\n}\n\n.fa-uniregistry:before {\n  content: \"\\f404\";\n}\n\n.fa-figma:before {\n  content: \"\\f799\";\n}\n\n.fa-creative-commons-remix:before {\n  content: \"\\f4ee\";\n}\n\n.fa-cc-amazon-pay:before {\n  content: \"\\f42d\";\n}\n\n.fa-dropbox:before {\n  content: \"\\f16b\";\n}\n\n.fa-instagram:before {\n  content: \"\\f16d\";\n}\n\n.fa-cmplid:before {\n  content: \"\\e360\";\n}\n\n.fa-upwork:before {\n  content: \"\\e641\";\n}\n\n.fa-facebook:before {\n  content: \"\\f09a\";\n}\n\n.fa-gripfire:before {\n  content: \"\\f3ac\";\n}\n\n.fa-jedi-order:before {\n  content: \"\\f50e\";\n}\n\n.fa-uikit:before {\n  content: \"\\f403\";\n}\n\n.fa-fort-awesome-alt:before {\n  content: \"\\f3a3\";\n}\n\n.fa-phabricator:before {\n  content: \"\\f3db\";\n}\n\n.fa-ussunnah:before {\n  content: \"\\f407\";\n}\n\n.fa-earlybirds:before {\n  content: \"\\f39a\";\n}\n\n.fa-trade-federation:before {\n  content: \"\\f513\";\n}\n\n.fa-autoprefixer:before {\n  content: \"\\f41c\";\n}\n\n.fa-whatsapp:before {\n  content: \"\\f232\";\n}\n\n.fa-square-upwork:before {\n  content: \"\\e67c\";\n}\n\n.fa-slideshare:before {\n  content: \"\\f1e7\";\n}\n\n.fa-google-play:before {\n  content: \"\\f3ab\";\n}\n\n.fa-viadeo:before {\n  content: \"\\f2a9\";\n}\n\n.fa-line:before {\n  content: \"\\f3c0\";\n}\n\n.fa-google-drive:before {\n  content: \"\\f3aa\";\n}\n\n.fa-servicestack:before {\n  content: \"\\f3ec\";\n}\n\n.fa-simplybuilt:before {\n  content: \"\\f215\";\n}\n\n.fa-bitbucket:before {\n  content: \"\\f171\";\n}\n\n.fa-imdb:before {\n  content: \"\\f2d8\";\n}\n\n.fa-deezer:before {\n  content: \"\\e077\";\n}\n\n.fa-raspberry-pi:before {\n  content: \"\\f7bb\";\n}\n\n.fa-jira:before {\n  content: \"\\f7b1\";\n}\n\n.fa-docker:before {\n  content: \"\\f395\";\n}\n\n.fa-screenpal:before {\n  content: \"\\e570\";\n}\n\n.fa-bluetooth:before {\n  content: \"\\f293\";\n}\n\n.fa-gitter:before {\n  content: \"\\f426\";\n}\n\n.fa-d-and-d:before {\n  content: \"\\f38d\";\n}\n\n.fa-microblog:before {\n  content: \"\\e01a\";\n}\n\n.fa-cc-diners-club:before {\n  content: \"\\f24c\";\n}\n\n.fa-gg-circle:before {\n  content: \"\\f261\";\n}\n\n.fa-pied-piper-hat:before {\n  content: \"\\f4e5\";\n}\n\n.fa-kickstarter-k:before {\n  content: \"\\f3bc\";\n}\n\n.fa-yandex:before {\n  content: \"\\f413\";\n}\n\n.fa-readme:before {\n  content: \"\\f4d5\";\n}\n\n.fa-html5:before {\n  content: \"\\f13b\";\n}\n\n.fa-sellsy:before {\n  content: \"\\f213\";\n}\n\n.fa-square-web-awesome:before {\n  content: \"\\e683\";\n}\n\n.fa-sass:before {\n  content: \"\\f41e\";\n}\n\n.fa-wirsindhandwerk:before {\n  content: \"\\e2d0\";\n}\n\n.fa-wsh:before {\n  content: \"\\e2d0\";\n}\n\n.fa-buromobelexperte:before {\n  content: \"\\f37f\";\n}\n\n.fa-salesforce:before {\n  content: \"\\f83b\";\n}\n\n.fa-octopus-deploy:before {\n  content: \"\\e082\";\n}\n\n.fa-medapps:before {\n  content: \"\\f3c6\";\n}\n\n.fa-ns8:before {\n  content: \"\\f3d5\";\n}\n\n.fa-pinterest-p:before {\n  content: \"\\f231\";\n}\n\n.fa-apper:before {\n  content: \"\\f371\";\n}\n\n.fa-fort-awesome:before {\n  content: \"\\f286\";\n}\n\n.fa-waze:before {\n  content: \"\\f83f\";\n}\n\n.fa-bluesky:before {\n  content: \"\\e671\";\n}\n\n.fa-cc-jcb:before {\n  content: \"\\f24b\";\n}\n\n.fa-snapchat:before {\n  content: \"\\f2ab\";\n}\n\n.fa-snapchat-ghost:before {\n  content: \"\\f2ab\";\n}\n\n.fa-fantasy-flight-games:before {\n  content: \"\\f6dc\";\n}\n\n.fa-rust:before {\n  content: \"\\e07a\";\n}\n\n.fa-wix:before {\n  content: \"\\f5cf\";\n}\n\n.fa-square-behance:before {\n  content: \"\\f1b5\";\n}\n\n.fa-behance-square:before {\n  content: \"\\f1b5\";\n}\n\n.fa-supple:before {\n  content: \"\\f3f9\";\n}\n\n.fa-webflow:before {\n  content: \"\\e65c\";\n}\n\n.fa-rebel:before {\n  content: \"\\f1d0\";\n}\n\n.fa-css3:before {\n  content: \"\\f13c\";\n}\n\n.fa-staylinked:before {\n  content: \"\\f3f5\";\n}\n\n.fa-kaggle:before {\n  content: \"\\f5fa\";\n}\n\n.fa-space-awesome:before {\n  content: \"\\e5ac\";\n}\n\n.fa-deviantart:before {\n  content: \"\\f1bd\";\n}\n\n.fa-cpanel:before {\n  content: \"\\f388\";\n}\n\n.fa-goodreads-g:before {\n  content: \"\\f3a9\";\n}\n\n.fa-square-git:before {\n  content: \"\\f1d2\";\n}\n\n.fa-git-square:before {\n  content: \"\\f1d2\";\n}\n\n.fa-square-tumblr:before {\n  content: \"\\f174\";\n}\n\n.fa-tumblr-square:before {\n  content: \"\\f174\";\n}\n\n.fa-trello:before {\n  content: \"\\f181\";\n}\n\n.fa-creative-commons-nc-jp:before {\n  content: \"\\f4ea\";\n}\n\n.fa-get-pocket:before {\n  content: \"\\f265\";\n}\n\n.fa-perbyte:before {\n  content: \"\\e083\";\n}\n\n.fa-grunt:before {\n  content: \"\\f3ad\";\n}\n\n.fa-weebly:before {\n  content: \"\\f5cc\";\n}\n\n.fa-connectdevelop:before {\n  content: \"\\f20e\";\n}\n\n.fa-leanpub:before {\n  content: \"\\f212\";\n}\n\n.fa-black-tie:before {\n  content: \"\\f27e\";\n}\n\n.fa-themeco:before {\n  content: \"\\f5c6\";\n}\n\n.fa-python:before {\n  content: \"\\f3e2\";\n}\n\n.fa-android:before {\n  content: \"\\f17b\";\n}\n\n.fa-bots:before {\n  content: \"\\e340\";\n}\n\n.fa-free-code-camp:before {\n  content: \"\\f2c5\";\n}\n\n.fa-hornbill:before {\n  content: \"\\f592\";\n}\n\n.fa-js:before {\n  content: \"\\f3b8\";\n}\n\n.fa-ideal:before {\n  content: \"\\e013\";\n}\n\n.fa-git:before {\n  content: \"\\f1d3\";\n}\n\n.fa-dev:before {\n  content: \"\\f6cc\";\n}\n\n.fa-sketch:before {\n  content: \"\\f7c6\";\n}\n\n.fa-yandex-international:before {\n  content: \"\\f414\";\n}\n\n.fa-cc-amex:before {\n  content: \"\\f1f3\";\n}\n\n.fa-uber:before {\n  content: \"\\f402\";\n}\n\n.fa-github:before {\n  content: \"\\f09b\";\n}\n\n.fa-php:before {\n  content: \"\\f457\";\n}\n\n.fa-alipay:before {\n  content: \"\\f642\";\n}\n\n.fa-youtube:before {\n  content: \"\\f167\";\n}\n\n.fa-skyatlas:before {\n  content: \"\\f216\";\n}\n\n.fa-firefox-browser:before {\n  content: \"\\e007\";\n}\n\n.fa-replyd:before {\n  content: \"\\f3e6\";\n}\n\n.fa-suse:before {\n  content: \"\\f7d6\";\n}\n\n.fa-jenkins:before {\n  content: \"\\f3b6\";\n}\n\n.fa-twitter:before {\n  content: \"\\f099\";\n}\n\n.fa-rockrms:before {\n  content: \"\\f3e9\";\n}\n\n.fa-pinterest:before {\n  content: \"\\f0d2\";\n}\n\n.fa-buffer:before {\n  content: \"\\f837\";\n}\n\n.fa-npm:before {\n  content: \"\\f3d4\";\n}\n\n.fa-yammer:before {\n  content: \"\\f840\";\n}\n\n.fa-btc:before {\n  content: \"\\f15a\";\n}\n\n.fa-dribbble:before {\n  content: \"\\f17d\";\n}\n\n.fa-stumbleupon-circle:before {\n  content: \"\\f1a3\";\n}\n\n.fa-internet-explorer:before {\n  content: \"\\f26b\";\n}\n\n.fa-stubber:before {\n  content: \"\\e5c7\";\n}\n\n.fa-telegram:before {\n  content: \"\\f2c6\";\n}\n\n.fa-telegram-plane:before {\n  content: \"\\f2c6\";\n}\n\n.fa-old-republic:before {\n  content: \"\\f510\";\n}\n\n.fa-odysee:before {\n  content: \"\\e5c6\";\n}\n\n.fa-square-whatsapp:before {\n  content: \"\\f40c\";\n}\n\n.fa-whatsapp-square:before {\n  content: \"\\f40c\";\n}\n\n.fa-node-js:before {\n  content: \"\\f3d3\";\n}\n\n.fa-edge-legacy:before {\n  content: \"\\e078\";\n}\n\n.fa-slack:before {\n  content: \"\\f198\";\n}\n\n.fa-slack-hash:before {\n  content: \"\\f198\";\n}\n\n.fa-medrt:before {\n  content: \"\\f3c8\";\n}\n\n.fa-usb:before {\n  content: \"\\f287\";\n}\n\n.fa-tumblr:before {\n  content: \"\\f173\";\n}\n\n.fa-vaadin:before {\n  content: \"\\f408\";\n}\n\n.fa-quora:before {\n  content: \"\\f2c4\";\n}\n\n.fa-square-x-twitter:before {\n  content: \"\\e61a\";\n}\n\n.fa-reacteurope:before {\n  content: \"\\f75d\";\n}\n\n.fa-medium:before {\n  content: \"\\f23a\";\n}\n\n.fa-medium-m:before {\n  content: \"\\f23a\";\n}\n\n.fa-amilia:before {\n  content: \"\\f36d\";\n}\n\n.fa-mixcloud:before {\n  content: \"\\f289\";\n}\n\n.fa-flipboard:before {\n  content: \"\\f44d\";\n}\n\n.fa-viacoin:before {\n  content: \"\\f237\";\n}\n\n.fa-critical-role:before {\n  content: \"\\f6c9\";\n}\n\n.fa-sitrox:before {\n  content: \"\\e44a\";\n}\n\n.fa-discourse:before {\n  content: \"\\f393\";\n}\n\n.fa-joomla:before {\n  content: \"\\f1aa\";\n}\n\n.fa-mastodon:before {\n  content: \"\\f4f6\";\n}\n\n.fa-airbnb:before {\n  content: \"\\f834\";\n}\n\n.fa-wolf-pack-battalion:before {\n  content: \"\\f514\";\n}\n\n.fa-buy-n-large:before {\n  content: \"\\f8a6\";\n}\n\n.fa-gulp:before {\n  content: \"\\f3ae\";\n}\n\n.fa-creative-commons-sampling-plus:before {\n  content: \"\\f4f1\";\n}\n\n.fa-strava:before {\n  content: \"\\f428\";\n}\n\n.fa-ember:before {\n  content: \"\\f423\";\n}\n\n.fa-canadian-maple-leaf:before {\n  content: \"\\f785\";\n}\n\n.fa-teamspeak:before {\n  content: \"\\f4f9\";\n}\n\n.fa-pushed:before {\n  content: \"\\f3e1\";\n}\n\n.fa-wordpress-simple:before {\n  content: \"\\f411\";\n}\n\n.fa-nutritionix:before {\n  content: \"\\f3d6\";\n}\n\n.fa-wodu:before {\n  content: \"\\e088\";\n}\n\n.fa-google-pay:before {\n  content: \"\\e079\";\n}\n\n.fa-intercom:before {\n  content: \"\\f7af\";\n}\n\n.fa-zhihu:before {\n  content: \"\\f63f\";\n}\n\n.fa-korvue:before {\n  content: \"\\f42f\";\n}\n\n.fa-pix:before {\n  content: \"\\e43a\";\n}\n\n.fa-steam-symbol:before {\n  content: \"\\f3f6\";\n}\n\nhtml {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n/*******************************************************************************\n* Define the animation behaviour\n*/\n/*******************************************************************************\n* UI shaping and padding\n*/\nhtml {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n  --pst-font-size-base: 1rem;\n  --pst-font-size-h1: 2.625rem;\n  --pst-font-size-h2: 2.125rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1rem;\n  --pst-font-size-milli: 0.9rem;\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n  --pst-admonition-font-weight-heading: 600;\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 600;\n  --pst-font-family-base-system: -apple-system, \"BlinkMacSystemFont\", \"Segoe UI\",\n    \"Helvetica Neue\", \"Arial\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\",\n    \"Segoe UI Symbol\";\n  --pst-font-family-monospace-system: \"SFMono-Regular\", \"Menlo\", \"Consolas\",\n    \"Monaco\", \"Liberation Mono\", \"Lucida Console\", monospace;\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n\nhtml {\n  /*****************************************************************************\n  * Icon\n  */\n  --pst-font-size-icon: 1.5rem;\n  --pst-icon-check-circle: \"\";\n  --pst-icon-info-circle: \"\";\n  --pst-icon-exclamation-triangle: \"\";\n  --pst-icon-exclamation-circle: \"\";\n  --pst-icon-times-circle: \"\";\n  --pst-icon-lightbulb: \"\";\n  --pst-icon-download: \"\";\n  --pst-icon-angle-left: \"\";\n  --pst-icon-angle-right: \"\";\n  --pst-icon-external-link: \"\";\n  --pst-icon-search-minus: \"\";\n  --pst-icon-github: \"\";\n  --pst-icon-gitlab: \"\";\n  --pst-icon-share: \"\";\n  --pst-icon-bell: \"\";\n  --pst-icon-pencil: \"\";\n  --pst-breadcrumb-divider: \"\";\n}\n\nhtml {\n  /*****************************************************************************\n  * Admonitions\n  **/\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n\nhtml {\n  /*****************************************************************************\n  * versionmodified\n  **/\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n\n/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n/**\n* Function to get items from nested maps\n*/\n/* Assign base colors for the PyData theme */\n:root {\n  --pst-teal-50: #f4fbfc;\n  --pst-teal-100: #e9f6f8;\n  --pst-teal-200: #d0ecf1;\n  --pst-teal-300: #abdde6;\n  --pst-teal-400: #3fb1c5;\n  --pst-teal-500: #0a7d91;\n  --pst-teal-600: #085d6c;\n  --pst-teal-700: #064752;\n  --pst-teal-800: #042c33;\n  --pst-teal-900: #021b1f;\n  --pst-violet-50: #f4eefb;\n  --pst-violet-100: #e0c7ff;\n  --pst-violet-200: #d5b4fd;\n  --pst-violet-300: #b780ff;\n  --pst-violet-400: #9c5ffd;\n  --pst-violet-500: #8045e5;\n  --pst-violet-600: #6432bd;\n  --pst-violet-700: #4b258f;\n  --pst-violet-800: #341a61;\n  --pst-violet-900: #1e0e39;\n  --pst-gray-50: #f9f9fa;\n  --pst-gray-100: #f3f4f5;\n  --pst-gray-200: #e5e7ea;\n  --pst-gray-300: #d1d5da;\n  --pst-gray-400: #9ca4af;\n  --pst-gray-500: #677384;\n  --pst-gray-600: #48566b;\n  --pst-gray-700: #29313d;\n  --pst-gray-800: #222832;\n  --pst-gray-900: #14181e;\n  --pst-pink-50: #fcf8fd;\n  --pst-pink-100: #fcf0fa;\n  --pst-pink-200: #f8dff5;\n  --pst-pink-300: #f3c7ee;\n  --pst-pink-400: #e47fd7;\n  --pst-pink-500: #c132af;\n  --pst-pink-600: #912583;\n  --pst-pink-700: #6e1c64;\n  --pst-pink-800: #46123f;\n  --pst-pink-900: #2b0b27;\n  --pst-foundation-white: #ffffff;\n  --pst-foundation-black: #14181e;\n}\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * something inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #d5b4fd;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n}\nhtml:not([data-theme]) {\n  --pst-color-heading: var(--pst-color-text-base);\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\nhtml:not([data-theme]) .only-dark,\nhtml:not([data-theme]) .only-dark ~ figcaption {\n  display: none !important;\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\nhtml[data-theme=light] {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #d5b4fd;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n  color-scheme: light;\n}\nhtml[data-theme=light] {\n  --pst-color-heading: var(--pst-color-text-base);\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\nhtml[data-theme=light] .only-dark,\nhtml[data-theme=light] .only-dark ~ figcaption {\n  display: none !important;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-primary: #3fb1c5;\n  --pst-color-primary-bg: #042c33;\n  --pst-color-secondary: #9c5ffd;\n  --pst-color-secondary-bg: #341a61;\n  --pst-color-accent: #e47fd7;\n  --pst-color-accent-bg: #46123f;\n  --pst-color-info: #79a3f2;\n  --pst-color-info-bg: #06245d;\n  --pst-color-warning: #ff9245;\n  --pst-color-warning-bg: #652a02;\n  --pst-color-success: #5fb488;\n  --pst-color-success-bg: #002f17;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #e78894;\n  --pst-color-danger-bg: #4e111b;\n  --pst-color-text-base: #ced6dd;\n  --pst-color-text-muted: #9ca4af;\n  --pst-color-shadow: rgba(0, 0, 0, 0.2);\n  --pst-color-border: #48566b;\n  --pst-color-border-muted: #29313d;\n  --pst-color-blockquote-notch: #9ca4af;\n  --pst-color-inline-code: #f3c7ee;\n  --pst-color-inline-code-links: #3fb1c5;\n  --pst-color-target: #675c04;\n  --pst-color-table: #ffffff;\n  --pst-color-table-row-hover-bg: #4b258f;\n  --pst-color-table-inner-border: #364150;\n  --pst-color-background: #14181e;\n  --pst-color-on-background: #222832;\n  --pst-color-surface: #29313d;\n  --pst-color-on-surface: #f3f4f5;\n  /* Adjust images in dark mode (unless they have class .only-dark or\n   * .dark-light, in which case assume they're already optimized for dark\n   * mode).\n   */\n  /* Give images a light background in dark mode in case they have\n  *  transparency and black text (unless they have class .only-dark or .dark-light, in\n  *  which case assume they're already optimized for dark mode).\n  */\n  color-scheme: dark;\n}\nhtml[data-theme=dark] {\n  --pst-color-heading: var(--pst-color-text-base);\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\nhtml[data-theme=dark] .only-light,\nhtml[data-theme=dark] .only-light ~ figcaption {\n  display: none !important;\n}\nhtml[data-theme=dark] img:not(.only-dark, .dark-light) {\n  filter: brightness(0.8) contrast(1.2);\n}\nhtml[data-theme=dark] .bd-content img:not(.only-dark, .dark-light) {\n  background-color: rgb(255, 255, 255);\n  border-radius: 0.25rem;\n}\nhtml[data-theme=dark] .MathJax_SVG * {\n  fill: var(--pst-color-text-base);\n}\n\n.pst-color-primary {\n  color: var(--pst-color-primary);\n}\n\n.pst-color-secondary {\n  color: var(--pst-color-secondary);\n}\n\n.pst-color-accent {\n  color: var(--pst-color-accent);\n}\n\n.pst-color-info {\n  color: var(--pst-color-info);\n}\n\n.pst-color-warning {\n  color: var(--pst-color-warning);\n}\n\n.pst-color-success {\n  color: var(--pst-color-success);\n}\n\n.pst-color-attention {\n  color: var(--pst-color-attention);\n}\n\n.pst-color-danger {\n  color: var(--pst-color-danger);\n}\n\n.pst-color-text-base {\n  color: var(--pst-color-text-base);\n}\n\n.pst-color-text-muted {\n  color: var(--pst-color-text-muted);\n}\n\n.pst-color-shadow {\n  color: var(--pst-color-shadow);\n}\n\n.pst-color-border {\n  color: var(--pst-color-border);\n}\n\n.pst-color-border-muted {\n  color: var(--pst-color-border-muted);\n}\n\n.pst-color-blockquote-notch {\n  color: var(--pst-color-blockquote-notch);\n}\n\n.pst-color-inline-code {\n  color: var(--pst-color-inline-code);\n}\n\n.pst-color-inline-code-links {\n  color: var(--pst-color-inline-code-links);\n}\n\n.pst-color-target {\n  color: var(--pst-color-target);\n}\n\n.pst-color-table {\n  color: var(--pst-color-table);\n}\n\n.pst-color-table-row-hover {\n  color: var(--pst-color-table-row-hover);\n}\n\n.pst-color-table-inner-border {\n  color: var(--pst-color-table-inner-border);\n}\n\n.pst-color-background {\n  color: var(--pst-color-background);\n}\n\n.pst-color-on-background {\n  color: var(--pst-color-on-background);\n}\n\n.pst-color-surface {\n  color: var(--pst-color-surface);\n}\n\n.pst-color-on-surface {\n  color: var(--pst-color-on-surface);\n}\n\n/**\n * Miscellaneous color functions and mixins\n**/\n/**\n* Function to get items from nested maps\n*/\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n/*********************************************\n* SASS Mixins\n*********************************************/\n/**\n * A consistent box shadow style we apply across elements.\n */\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\nhtml {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: 1.65;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n}\nbody::-webkit-scrollbar-track {\n  background-color: var(--pst-color-background);\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n  /* section header in docstring pages */\n}\np.rubric {\n  border-bottom: 1px solid var(--pst-color-border);\n}\np.centered {\n  text-align: center;\n}\n\na {\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n}\na:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\na:active {\n  color: var(--pst-color-link);\n}\na:visited {\n  color: var(--pst-color-link);\n}\na:visited:hover {\n  color: var(--pst-color-link-hover);\n}\na.headerlink {\n  color: var(--pst-color-secondary);\n  font-size: 0.8em;\n  padding: 0 4px;\n  margin-left: 0.2em;\n  text-decoration: none;\n  user-select: none;\n}\na.github::before, a.gitlab::before {\n  color: var(--pst-color-text-muted);\n  font: var(--fa-font-brands);\n  margin-right: 0.25rem;\n}\na.github::before {\n  content: var(--pst-icon-github);\n}\na.gitlab::before {\n  content: var(--pst-icon-gitlab);\n}\n\nh6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1, .h1 {\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-color-heading);\n}\n\nh2, .h2 {\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-color-heading);\n}\n\nh3, .h3 {\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-color-heading);\n}\n\nh4, .h4 {\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-color-heading);\n}\n\nh5, .h5 {\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6, .h6 {\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall, .small,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n}\nkbd:not(.compound) {\n  border: 1px solid var(--pst-color-border);\n  margin: 0 0.1rem;\n  padding: 0.1rem 0.4rem;\n  box-shadow: 1px 1px 1px var(--pst-color-shadow);\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\npre .linenos {\n  opacity: 0.8;\n  padding-right: 10px;\n}\n\n#pst-back-to-top {\n  z-index: 1080;\n  position: fixed;\n  display: none;\n  top: 90vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n}\n#pst-back-to-top .fa-arrow-up {\n  margin-inline-end: 0.5em;\n}\n#pst-back-to-top:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: 1px;\n  background-color: var(--pst-color-secondary-highlight);\n  color: var(--pst-color-secondary-highlight-text);\n}\n#pst-back-to-top:focus-visible {\n  box-shadow: none;\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-color: var(--pst-color-secondary);\n  outline-offset: 0.1875rem;\n}\n\n:focus-visible {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  box-shadow: none;\n}\n\n/***\n * Rules for the UX/UI of skip navigation link btn.\n * It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  padding: 0.5rem;\n  z-index: 1055;\n  border-bottom: 1px solid var(--pst-color-border);\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n}\n.skip-link:focus-within {\n  transform: translateY(0%);\n}\n.skip-link a {\n  color: var(--pst-color-warning-text) !important;\n}\n.skip-link a:focus-visible {\n  outline-color: #14181e;\n}\n\n.bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n}\n.bd-container .bd-container__inner {\n  display: flex;\n}\n\n.bd-page-width {\n  width: 100%;\n}\n@media (min-width: 960px) {\n  .bd-page-width {\n    max-width: 88rem;\n  }\n}\n\n.pst-async-banner-revealer {\n  height: 0;\n  overflow: hidden;\n  transition: height 300ms ease-in-out;\n}\n@media (prefers-reduced-motion) {\n  .pst-async-banner-revealer {\n    transition: none;\n  }\n}\n\n#bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%;\n}\n@media (max-width: 959.98px) {\n  #bd-header-version-warning,\n  .bd-header-announcement {\n    padding: 0.5rem 2%;\n  }\n}\n#bd-header-version-warning p,\n.bd-header-announcement p {\n  font-weight: bold;\n  margin: 0;\n}\n#bd-header-version-warning a,\n.bd-header-announcement a {\n  color: var(--pst-color-inline-code-links);\n}\n#bd-header-version-warning .pst-button-link-to-stable-version,\n.bd-header-announcement .pst-button-link-to-stable-version {\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n  background-color: var(--pst-color-danger);\n  border-color: var(--pst-color-danger);\n  color: var(--pst-color-danger-text);\n  border-radius: 0.25rem;\n}\n#bd-header-version-warning .pst-button-link-to-stable-version:hover,\n.bd-header-announcement .pst-button-link-to-stable-version:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: 1px;\n  background-color: var(--pst-color-danger-highlight);\n  border-color: var(--pst-color-danger-highlight);\n  color: var(--pst-color-danger-highlight-text);\n}\n#bd-header-version-warning .pst-button-link-to-stable-version:focus-visible,\n.bd-header-announcement .pst-button-link-to-stable-version:focus-visible {\n  outline: 0.1875rem solid var(--pst-color-danger);\n  outline-offset: 0.1875rem;\n}\n\n.bd-header-announcement {\n  background-color: var(--pst-color-secondary-bg);\n}\n\n#bd-header-version-warning {\n  background-color: var(--pst-color-danger-bg);\n}\n\n/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n}\n.bd-main .bd-content {\n  display: flex;\n  justify-content: center;\n  height: 100%;\n}\n.bd-main .bd-content .bd-article-container {\n  justify-content: start;\n  display: flex;\n  flex-direction: column;\n  width: 100%;\n  max-width: 60em;\n  overflow-x: auto;\n  padding: 1rem;\n}\n@media (min-width: 1200px) {\n  .bd-main .bd-content .bd-article-container .bd-article {\n    padding-top: 1.5rem;\n    padding-left: 2rem;\n  }\n}\n\n.bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n}\n.bd-footer .bd-footer__inner {\n  display: flex;\n  flex-grow: 1;\n  padding: 1rem;\n  margin: auto;\n}\n.bd-footer .footer-items__start,\n.bd-footer .footer-items__center,\n.bd-footer .footer-items__end {\n  display: flex;\n  flex-direction: column;\n  gap: 0.5rem;\n  justify-content: center;\n  flex-grow: 1;\n}\n.bd-footer .footer-items__center {\n  text-align: center;\n}\n.bd-footer .footer-items__end {\n  text-align: end;\n}\n.bd-footer .footer-item p {\n  margin-bottom: 0;\n}\n\n.bd-footer-article {\n  margin-top: auto;\n}\n.bd-footer-article .footer-article-items {\n  display: flex;\n  flex-direction: column;\n}\n\n.bd-footer-content .footer-content-items {\n  display: flex;\n  flex-direction: column;\n  margin-top: auto;\n}\n\n/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: 1030;\n  background-color: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n}\n.bd-header .bd-header__inner {\n  display: flex;\n  align-items: center;\n  height: fit-content;\n  padding-left: 1rem;\n  padding-right: 1rem;\n}\n.bd-header :focus-visible {\n  border-radius: 0.125rem;\n}\n.bd-header .navbar-item {\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  display: flex;\n  align-items: center;\n}\n.bd-header .navbar-header-items {\n  display: none;\n  flex-shrink: 1;\n}\n@media (min-width: 960px) {\n  .bd-header .navbar-header-items {\n    display: inherit;\n    flex-grow: 1;\n    padding: 0 0 0 0.5rem;\n  }\n}\n.bd-header .navbar-header-items__end,\n.bd-header .navbar-header-items__center,\n.bd-header .navbar-header-items__start {\n  display: flex;\n  align-items: center;\n  flex-flow: wrap;\n  row-gap: 0;\n}\n.bd-header .navbar-header-items__end,\n.bd-header .navbar-header-items__center {\n  column-gap: 1.12rem;\n}\n.bd-header .navbar-header-items__start {\n  flex-shrink: 0;\n  margin-right: auto;\n  gap: 0.5rem;\n}\n.bd-header .navbar-header-items__end {\n  justify-content: end;\n}\n.bd-header ul.navbar-nav {\n  display: flex;\n}\n@media (min-width: 960px) {\n  .bd-header ul.navbar-nav {\n    align-items: baseline;\n  }\n}\n.bd-header ul.navbar-nav > li.nav-item {\n  margin-inline: 2px;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link {\n  color: var(--pst-color-text-muted);\n  position: relative;\n  padding-inline: 6px;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link::before {\n  content: \"\";\n  display: block;\n  position: absolute;\n  inset: 0;\n  background-color: transparent;\n  bottom: calc(-1 * max(3px, .1875rem, .12em));\n  margin: max(3px, .1875rem, .12em) 0;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link:hover {\n  color: var(--pst-color-secondary);\n  text-decoration: none;\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link:hover::before {\n  border-bottom: max(3px, .1875rem, .12em) solid var(--pst-color-secondary);\n}\n.bd-header ul.navbar-nav > li.nav-item > .nav-link:focus-visible {\n  box-shadow: none;\n  outline: 3px solid var(--pst-color-accent);\n  outline-offset: 0.1875rem;\n}\n.bd-header ul.navbar-nav > li.nav-item.current > .nav-link {\n  color: var(--pst-color-primary);\n}\n.bd-header ul.navbar-nav > li.nav-item.current > .nav-link::before {\n  border-bottom: 3px solid var(--pst-color-primary);\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown {\n  margin-inline: 4px;\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown button {\n  padding-inline: 8px;\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown > .dropdown-toggle {\n  border-radius: 0.125rem;\n  color: var(--pst-color-text-muted);\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown > .dropdown-toggle:focus-visible {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.bd-header ul.navbar-nav > li.nav-item.dropdown > .dropdown-toggle:hover {\n  text-decoration: none;\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-link-hover);\n  color: var(--pst-color-text-base);\n}\n.bd-header ul.navbar-nav li a.nav-link.dropdown-item {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\n.bd-header ul.navbar-nav li a.nav-link.dropdown-item:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n.bd-header ul.navbar-nav .dropdown button {\n  display: unset;\n  border: none;\n}\n.bd-header ul.navbar-nav .dropdown button:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu {\n  z-index: 1070;\n  border: 1px solid var(--pst-color-border);\n  box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n  background-color: var(--pst-color-on-background);\n  padding: 0.5rem 0;\n  margin: 0.5rem 0;\n  min-width: 20rem;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu .dropdown-item {\n  padding: 0.25rem 1.5rem;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu .dropdown-item:focus:not(:hover, :active) {\n  background-color: inherit;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu .dropdown-item:focus-visible {\n  z-index: 10;\n}\n.bd-header ul.navbar-nav .dropdown .dropdown-menu:not(.show) {\n  display: none;\n}\n.bd-header button.sidebar-toggle {\n  font-size: var(--pst-font-size-icon);\n  color: var(--pst-color-muted);\n  margin-bottom: 0;\n  background-color: inherit;\n  padding: 0.5rem;\n}\n.bd-header button.primary-toggle {\n  margin-right: 1rem;\n}\n@media (min-width: 960px) {\n  .bd-header button.primary-toggle {\n    display: none;\n  }\n}\n.bd-header button.secondary-toggle {\n  margin-left: 1rem;\n}\n@media (min-width: 1200px) {\n  .bd-header button.secondary-toggle {\n    display: none;\n  }\n}\n\n@media (min-width: 960px) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n.nav-link {\n  transition: none;\n}\n.nav-link:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n}\n.nav-link.nav-external::after {\n  font: var(--fa-font-solid);\n  content: var(--pst-icon-external-link);\n  font-size: 0.75em;\n  margin-left: 0.3em;\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n.navbar-persistent--mobile {\n  margin-left: auto;\n}\n@media (min-width: 960px) {\n  .navbar-persistent--mobile {\n    display: none;\n  }\n}\n\n.navbar-persistent--container {\n  display: none;\n}\n@media (min-width: 960px) {\n  .navbar-persistent--container {\n    display: flex;\n  }\n}\n\n.header-article__inner {\n  padding: 0 0.5rem;\n}\n.header-article__inner .header-article-item {\n  min-height: var(--pst-header-article-height);\n}\n.header-article__inner .header-article-items__end {\n  margin-left: auto;\n}\n\n/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n  flex: 0 0 auto;\n  width: 25%;\n  padding: 2rem 1rem 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n.bd-sidebar-primary :focus-visible {\n  border-radius: 0.125rem;\n}\n.bd-sidebar-primary .nav-link {\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\n.bd-sidebar-primary.no-sidebar {\n  border-right: 0;\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary.hide-on-wide {\n    display: none;\n  }\n}\n.bd-sidebar-primary h1, .bd-sidebar-primary .h1,\n.bd-sidebar-primary h2,\n.bd-sidebar-primary .h2,\n.bd-sidebar-primary h3,\n.bd-sidebar-primary .h3,\n.bd-sidebar-primary h4,\n.bd-sidebar-primary .h4 {\n  color: var(--pst-color-text-base);\n}\n.bd-sidebar-primary .sidebar-primary-items__start .sidebar-primary-item,\n.bd-sidebar-primary .sidebar-primary-items__end .sidebar-primary-item {\n  padding: 0.5rem 0;\n}\n.bd-sidebar-primary .sidebar-header-items {\n  display: flex;\n  flex-direction: column;\n}\n.bd-sidebar-primary .sidebar-header-items .sidebar-header-items__title {\n  font-weight: var(--pst-sidebar-header-font-weight);\n  font-size: var(--pst-sidebar-header-font-size);\n  color: var(--pst-color-text-base);\n  margin-bottom: 0.5rem;\n}\n.bd-sidebar-primary .sidebar-header-items .bd-navbar-elements .nav-link:focus-visible {\n  box-shadow: none;\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: 0.1875rem;\n}\n.bd-sidebar-primary .sidebar-header-items .sidebar-header-items__center {\n  display: flex;\n  flex-direction: column;\n}\n.bd-sidebar-primary .sidebar-header-items .sidebar-header-items__end {\n  display: flex;\n  align-items: center;\n  gap: 1rem;\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary .sidebar-header-items {\n    display: none;\n  }\n}\n.bd-sidebar-primary .sidebar-primary-items__start {\n  border-top: 1px solid var(--pst-color-border);\n}\n@media (min-width: 960px) {\n  .bd-sidebar-primary .sidebar-primary-items__start {\n    border-top: none;\n  }\n}\n.bd-sidebar-primary .sidebar-primary-items__end {\n  margin-top: auto;\n  margin-bottom: 1em;\n}\n.bd-sidebar-primary .list-caption {\n  list-style: none;\n  padding-left: 0;\n}\n.bd-sidebar-primary .list-caption li.toctree-l0.has-children > details > summary {\n  position: relative;\n  height: auto;\n  width: auto;\n  display: flex;\n  justify-content: space-between;\n  align-items: baseline;\n}\n.bd-sidebar-primary .list-caption li.toctree-l0.has-children > details > summary .toctree-toggle {\n  flex: 0 0 auto;\n}\n.bd-sidebar-primary .list-caption li.toctree-l0.has-children > details > summary .toctree-toggle .fa-chevron-down {\n  font-size: 1rem;\n}\n.bd-sidebar-primary li.has-children {\n  position: relative;\n}\n.bd-sidebar-primary li.has-children > .reference,\n.bd-sidebar-primary li.has-children .caption {\n  margin-right: calc(30px + 0.1875rem);\n  padding-top: 0.25rem;\n}\n.bd-sidebar-primary li.has-children > details > summary {\n  list-style: none;\n}\n.bd-sidebar-primary li.has-children > details > summary::-webkit-details-marker {\n  display: none;\n}\n.bd-sidebar-primary li.has-children > details > summary:focus-visible {\n  outline: none;\n}\n.bd-sidebar-primary li.has-children > details > summary:focus-visible > .toctree-toggle {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: -0.1875rem;\n}\n.bd-sidebar-primary li.has-children > details > summary .toctree-toggle {\n  cursor: pointer;\n  position: absolute;\n  top: 0;\n  right: 0;\n  width: 30px;\n  height: 30px;\n  display: inline-flex;\n  justify-content: center;\n  align-items: center;\n}\n.bd-sidebar-primary li.has-children > details > summary .toctree-toggle .fa-chevron-down {\n  font-size: 0.75rem;\n}\n.bd-sidebar-primary li.has-children > details[open] > summary .fa-chevron-down {\n  transform: rotate(180deg);\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -1rem;\n}\n@media (min-width: 960px) {\n  nav.bd-links {\n    display: block;\n  }\n}\nnav.bd-links ul {\n  display: block;\n  list-style: none;\n}\nnav.bd-links ul ul {\n  padding: 0 0 0 1rem;\n}\nnav.bd-links li > a {\n  display: block;\n  padding: 0.25rem 0.65rem;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n  box-shadow: none;\n  margin-right: 0.1875rem;\n}\nnav.bd-links li > a:hover {\n  text-decoration: underline;\n  background-color: transparent;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\nnav.bd-links li > a:active {\n  color: var(--pst-color-link-hover);\n}\nnav.bd-links li > a:focus-visible {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n  z-index: 10;\n}\nnav.bd-links li > a.reference.external::after {\n  font: var(--fa-font-solid);\n  content: var(--pst-icon-external-link);\n  font-size: 0.75em;\n  margin-left: 0.3em;\n}\nnav.bd-links .current > a {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary);\n  background-color: transparent;\n}\nnav.bd-links .current > a:focus-visible {\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary), 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n}\nnav.bd-links p.bd-links__title {\n  font-size: var(--pst-sidebar-header-font-size);\n  font-weight: var(--pst-sidebar-header-font-weight);\n  margin-bottom: 0.5rem;\n}\nnav.bd-links p.caption {\n  font-weight: var(--pst-sidebar-header-font-weight);\n  position: relative;\n  margin-top: 1.25rem;\n  margin-bottom: 0.5rem;\n  color: var(--pst-color-text-base);\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\nnav.bd-links p.caption:first-child {\n  margin-top: 0;\n}\n@media (min-width: 960px) {\n  nav.bd-links p.caption {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n\n/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n@media (min-width: 1200px) {\n  .bd-sidebar-secondary {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem;\n}\n@media (min-width: 1200px) {\n  .sidebar-secondary-item {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n}\n.sidebar-secondary-item i {\n  padding-right: 0.5rem;\n}\n\n/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n/**\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n.bd-sidebar::backdrop {\n  background-color: black;\n  opacity: 0.5;\n}\n\n@media (max-width: 959.98px) {\n  .bd-sidebar-primary {\n    position: fixed;\n    top: 0;\n    z-index: 1055;\n    height: 100vh;\n    max-height: 100vh;\n    width: 75%;\n    flex-grow: 0.75;\n    max-width: 350px;\n    transition: visibility 200ms ease-out, margin 200ms ease-out;\n    visibility: hidden;\n    border: 0;\n    margin-left: -75%;\n    left: 0;\n  }\n}\n.bd-sidebar-primary[open] {\n  margin-left: 0;\n  visibility: visible;\n}\n\n@media (max-width: 1199.98px) {\n  .bd-sidebar-secondary {\n    position: fixed;\n    top: 0;\n    z-index: 1055;\n    height: 100vh;\n    max-height: 100vh;\n    width: 75%;\n    flex-grow: 0.75;\n    max-width: 350px;\n    transition: visibility 200ms ease-out, margin 200ms ease-out;\n    visibility: hidden;\n    border: 0;\n    margin-right: -75%;\n    right: 0;\n  }\n}\n.bd-sidebar-secondary[open] {\n  margin-right: 0;\n  visibility: visible;\n}\n\n/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n  font-size: 0.8rem;\n}\nul.bd-breadcrumbs li.breadcrumb-item {\n  display: flex;\n  align-items: center;\n  white-space: nowrap;\n  overflow-x: hidden;\n  font-weight: bold;\n}\nul.bd-breadcrumbs li.breadcrumb-item a,\nul.bd-breadcrumbs li.breadcrumb-item .ellipsis {\n  overflow-x: hidden;\n  text-overflow: ellipsis;\n  margin: 0.1875rem;\n}\nul.bd-breadcrumbs li.breadcrumb-item a {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\nul.bd-breadcrumbs li.breadcrumb-item a:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\nul.bd-breadcrumbs li.breadcrumb-item:not(.breadcrumb-home)::before {\n  font: var(--fa-font-solid);\n  font-size: 0.8rem;\n  content: var(--pst-breadcrumb-divider);\n  color: var(--pst-color-text-muted);\n  padding: 0 0.5rem;\n}\n\n/**\n * Icon links in the navbar\n */\nhtml .pst-navbar-icon {\n  box-sizing: border-box;\n  min-width: 24px;\n  min-height: 24px;\n  color: var(--pst-color-text-muted);\n  position: relative;\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  border-radius: 0;\n  border: none;\n  font-size: 1rem;\n  line-height: 1.65;\n  padding: 0.25rem 0;\n  height: 2.15rem;\n}\nhtml .pst-navbar-icon::before {\n  content: \"\";\n  display: block;\n  position: absolute;\n  inset: 0;\n  background-color: transparent;\n  bottom: calc(-1 * max(3px, .1875rem, .12em));\n  margin: max(3px, .1875rem, .12em) 0;\n}\nhtml .pst-navbar-icon:hover {\n  color: var(--pst-color-secondary);\n  text-decoration: none;\n}\nhtml .pst-navbar-icon:hover::before {\n  border-bottom: max(3px, .1875rem, .12em) solid var(--pst-color-secondary);\n}\nhtml .pst-navbar-icon:focus-visible {\n  box-shadow: none;\n  outline: 3px solid var(--pst-color-accent);\n  outline-offset: 0.1875rem;\n}\n\nul.navbar-icon-links {\n  display: flex;\n  flex-flow: row wrap;\n  column-gap: 1.12rem;\n  justify-content: space-evenly;\n  align-items: center;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\nul.navbar-icon-links i {\n  /* Social media buttons hard-code the brand color */\n}\nul.navbar-icon-links i.fa-brands, ul.navbar-icon-links i.fa-regular, ul.navbar-icon-links i.fa-solid {\n  vertical-align: middle;\n  font-style: normal;\n  font-size: var(--pst-font-size-icon);\n}\nul.navbar-icon-links i.fa-square-twitter::before {\n  color: #55acee;\n}\nul.navbar-icon-links i.fa-square-gitlab::before {\n  color: #548;\n}\nul.navbar-icon-links i.fa-bitbucket::before {\n  color: #0052cc;\n}\nul.navbar-icon-links img.icon-link-image {\n  height: 1.5em;\n  border-radius: 0.2rem;\n}\nul.navbar-icon-links .fa-pydata {\n  stroke: var(--pst-color-background);\n  stroke-linejoin: round;\n  stroke-width: 0.35;\n}\n\n/**\n * Logo in the navbar\n */\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n}\n.navbar-brand p {\n  color: var(--pst-color-text-base);\n  margin-bottom: 0;\n}\n.navbar-brand img {\n  max-width: 100%;\n  height: 100%;\n  width: auto;\n}\n.navbar-brand:hover, .navbar-brand:visited:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  color: var(--pst-color-text-base);\n}\n\n/**\n * Navigation links in the navbar and icon links\n */\nul.navbar-nav ul {\n  padding: 0 0 0 1rem;\n}\nul.navbar-nav li {\n  display: flex;\n  flex-direction: column;\n}\nul.navbar-nav li a {\n  display: flex;\n  align-items: center;\n  height: 100%;\n  padding-top: 0.25rem;\n  padding-bottom: 0.25rem;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\nul.navbar-nav li a:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n\n/**\n * The list of in-page TOC links\n */\n.page-toc .section-nav {\n  padding-left: 0;\n  border-bottom: none;\n}\n.page-toc .section-nav ul {\n  padding-left: 1rem;\n}\n.page-toc .nav-link {\n  font-size: var(--pst-sidebar-font-size-mobile);\n}\n@media (min-width: 1200px) {\n  .page-toc .nav-link {\n    font-size: var(--pst-sidebar-font-size);\n  }\n}\n.page-toc .onthispage {\n  color: var(--pst-color-text-base);\n  font-weight: var(--pst-sidebar-header-font-weight);\n  margin-bottom: 0.5rem;\n}\n\n/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n}\n.prev-next-area p {\n  color: var(--pst-color-text-muted);\n  margin: 0 0.3em;\n  line-height: 1.3em;\n}\n.prev-next-area i {\n  font-size: 1.2em;\n}\n.prev-next-area a {\n  display: flex;\n  align-items: center;\n  border: none;\n  padding: 10px;\n  max-width: 45%;\n  overflow-x: hidden;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\n.prev-next-area a p.prev-next-title {\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  font-weight: var(--pst-admonition-font-weight-heading);\n  font-size: 1.1em;\n}\n.prev-next-area a p.prev-next-title:hover {\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n.prev-next-area a p.prev-next-title:active {\n  color: var(--pst-color-link);\n}\n.prev-next-area a p.prev-next-title:visited {\n  color: var(--pst-color-link);\n}\n.prev-next-area a p.prev-next-title:visited:hover {\n  color: var(--pst-color-link-hover);\n}\n.prev-next-area a:hover p.prev-next-title, .prev-next-area a:visited:hover p.prev-next-title {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n}\n.prev-next-area a .prev-next-info {\n  flex-direction: column;\n  margin: 0 0.5em;\n}\n.prev-next-area a .prev-next-info .prev-next-subtitle {\n  text-transform: capitalize;\n}\n.prev-next-area a.left-prev {\n  float: left;\n}\n.prev-next-area a.right-next {\n  float: right;\n}\n.prev-next-area a.right-next div.prev-next-info {\n  text-align: right;\n}\n\n/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: 0.25rem;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n}\n.bd-search:active {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-muted);\n}\n.bd-search:focus-within {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n.bd-search:focus-within input:focus {\n  box-shadow: none;\n}\n.bd-search .icon {\n  position: absolute;\n  color: var(--pst-color-border);\n  left: 25px;\n}\n.bd-search .fa-solid.fa-magnifying-glass {\n  position: absolute;\n  left: calc((2.5rem - 0.7em) / 2);\n  color: var(--pst-color-text-muted);\n}\n.bd-search input.form-control {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-base);\n  border: none;\n}\n.bd-search input.form-control::placeholder {\n  color: var(--pst-color-text-muted);\n}\n.bd-search input.form-control::-webkit-search-cancel-button, .bd-search input.form-control::-webkit-search-decoration {\n  appearance: none;\n}\n.bd-search input.form-control:focus, .bd-search input.form-control:focus-visible {\n  color: var(--pst-color-text-muted);\n}\n.bd-search .search-button__kbd-shortcut {\n  display: none;\n  margin-inline-end: 0.5rem;\n  color: var(--pst-color-border);\n}\n@media (min-width: 720px) {\n  .bd-search .search-button__kbd-shortcut {\n    display: flex;\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n.search-button i {\n  font-size: 1.3rem;\n}\n\n/**\n * The search modal <dialog>\n */\n#pst-search-dialog {\n  display: none;\n}\n#pst-search-dialog[open] {\n  display: flex;\n  position: fixed;\n  z-index: 1055;\n  top: 30%;\n  left: 50%;\n  transform: translate(-50%, -50%);\n  right: 1rem;\n  margin-top: 0.5rem;\n  width: 90%;\n  max-width: 800px;\n  background-color: transparent;\n  padding: 0.1875rem;\n  border: none;\n}\n#pst-search-dialog[open]::backdrop {\n  background-color: black;\n  opacity: 0.5;\n}\n#pst-search-dialog[open] form.bd-search {\n  flex-grow: 1;\n}\n#pst-search-dialog[open] form.bd-search svg,\n#pst-search-dialog[open] form.bd-search input {\n  font-size: var(--pst-font-size-icon);\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: 1.5em;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n}\n.search-button-field:hover {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-link-hover);\n}\n.search-button-field:focus-visible {\n  border-radius: 1.5em;\n}\n.search-button-field .search-button__default-text {\n  font-size: var(--bs-nav-link-font-size);\n  font-weight: var(--bs-nav-link-font-weight);\n  margin-right: 0.5em;\n  margin-left: 0.5em;\n}\n.search-button-field .kbd-shortcut__modifier {\n  font-size: 0.75em;\n}\n.search-button-field > * {\n  align-items: center;\n}\n.search-button-field > :not(svg) {\n  display: none;\n}\n@media (min-width: 960px) {\n  .search-button-field > :not(svg) {\n    display: flex;\n  }\n}\n\n/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox p.highlight-link {\n  margin: 1rem 0;\n  width: fit-content;\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n@media (min-width: 1200px) {\n  div#searchbox p.highlight-link {\n    margin-left: 2rem;\n  }\n}\ndiv#searchbox p.highlight-link a {\n  border-radius: 0.25rem;\n  font-size: 1.25rem;\n  padding: 0.75rem;\n  background-color: var(--pst-color-primary);\n  color: var(--pst-color-primary-text);\n  text-decoration: none;\n  transition: box-shadow 0.25s ease-out;\n}\ndiv#searchbox p.highlight-link a:hover {\n  box-shadow: inset 0 0 50px 50px rgba(0, 0, 0, 0.25);\n}\ndiv#searchbox p.highlight-link a::before {\n  content: var(--pst-icon-search-minus);\n  color: unset;\n  font: var(--fa-font-solid);\n  margin-right: 0.5rem;\n}\n\n/**\n * Light/dark theme switcher\n */\n.theme-switch-button .theme-switch {\n  display: none;\n}\n.theme-switch-button .theme-switch:active {\n  text-decoration: none;\n  color: var(--pst-color-link-hover);\n}\n.theme-switch-button .theme-switch .fa-lg {\n  aspect-ratio: 1/1;\n}\n\nhtml[data-mode=auto] .theme-switch-button .theme-switch[data-mode=auto] {\n  display: inline;\n}\n\nhtml[data-mode=light] .theme-switch-button .theme-switch[data-mode=light] {\n  display: inline;\n}\n\nhtml[data-mode=dark] .theme-switch-button .theme-switch[data-mode=dark] {\n  display: inline;\n}\n\nbutton.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n  margin-bottom: 1em;\n}\n@media (min-width: 960px) {\n  button.btn.version-switcher__button {\n    margin-bottom: unset;\n  }\n}\nbutton.btn.version-switcher__button:hover {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-secondary);\n  border-color: transparent;\n}\nbutton.btn.version-switcher__button:active {\n  color: var(--pst-color-text-base);\n  border-color: var(--pst-color-border);\n}\nbutton.btn.version-switcher__button:focus-visible {\n  border-color: transparent;\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n}\n.version-switcher__menu a.list-group-item {\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-base);\n  padding: 0.75rem 1.25rem;\n}\n.version-switcher__menu a.list-group-item:not(:last-child) {\n  border-bottom: 1px solid var(--pst-color-border);\n}\n.version-switcher__menu a.list-group-item:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  background-color: var(--pst-color-surface);\n}\n.version-switcher__menu a.list-group-item.active {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary);\n  position: relative;\n  z-index: 1;\n}\n.version-switcher__menu a.list-group-item.active:focus-visible {\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary), 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n}\n.version-switcher__menu a.list-group-item.active span::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  z-index: -1;\n  left: 0;\n  top: 0;\n}\n.version-switcher__menu a.list-group-item:focus-visible {\n  z-index: 10;\n}\n\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em;\n  z-index: 1055;\n  box-sizing: border-box;\n  min-width: 24px;\n  min-height: 24px;\n}\n@media (min-width: 960px) {\n  button.version-switcher__button,\n  .version-switcher__menu {\n    font-size: unset;\n  }\n}\n\n/* Collapsing of the TOC sidebar while scrolling */\n/* Nav: hide second level (shown on .active) */\nnav.page-toc {\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav .nav {\n  display: none;\n}\n.bd-toc .nav .nav.visible {\n  display: block;\n}\n.bd-toc .nav > .active > ul {\n  display: block;\n}\n\n.toc-entry {\n  display: block;\n}\n.toc-entry a > code {\n  color: var(--pst-color-text-muted);\n}\n.toc-entry a.nav-link {\n  display: block;\n  padding: 0.25rem 0;\n  padding-left: 1rem;\n  margin-left: -1rem;\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n}\n.toc-entry a.nav-link:hover {\n  text-decoration: underline;\n  background-color: transparent;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n}\n.toc-entry a.nav-link:active {\n  color: var(--pst-color-link-hover);\n}\n.toc-entry a.nav-link:focus-visible {\n  box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n  z-index: 10;\n}\n.toc-entry a.nav-link.active {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary);\n  background-color: transparent;\n}\n.toc-entry a.nav-link.active:focus-visible {\n  box-shadow: inset max(3px, .1875rem, .12em) 0 0 var(--pst-color-primary), 0 0 0 0.1875rem var(--pst-color-accent);\n  outline: none;\n}\n.toc-entry a.nav-link.active:hover {\n  color: var(--pst-color-link-hover);\n}\n.toc-entry a.nav-link:focus-visible {\n  border-radius: 0.125rem;\n}\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem;\n  overflow: hidden;\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: 0.25rem;\n  background-color: var(--pst-color-on-background);\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n  position: relative;\n}\ndiv.versionadded > p,\ndiv.versionchanged > p,\ndiv.deprecated > p {\n  margin-bottom: 0.6rem;\n  margin-top: 0.6rem;\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n}\nspan.versionmodified::before {\n  margin-right: 0.6rem;\n  color: var(--pst-color-info);\n  font: var(--fa-font-solid);\n  content: var(--pst-icon-versionmodified-default);\n}\n\nspan.versionmodified.added::before {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-versionmodified-added);\n}\n\nspan.versionmodified.changed::before {\n  color: var(--pst-color-warning);\n  content: var(--pst-icon-versionmodified-changed);\n}\n\nspan.versionmodified.deprecated::before {\n  color: var(--pst-color-danger);\n  content: var(--pst-icon-versionmodified-deprecated);\n}\n\n.sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n}\n@media (min-width: 960px) {\n  .sidebar-indices-items {\n    border-top: none;\n  }\n}\n.sidebar-indices-items .sidebar-indices-items__title {\n  font-weight: var(--pst-sidebar-header-font-weight);\n  font-size: var(--pst-sidebar-header-font-size);\n  color: var(--pst-color-text-base);\n  margin-bottom: 0.5rem;\n}\n.sidebar-indices-items ul.indices-link {\n  margin-right: -1rem;\n  list-style: none;\n  padding: 0;\n}\n.sidebar-indices-items ul.indices-link li > a {\n  display: block;\n  padding: 0.25rem 0;\n  color: var(--pst-color-text-muted);\n}\n.sidebar-indices-items ul.indices-link li > a:hover {\n  color: var(--pst-color-primary);\n  text-decoration: none;\n  background-color: transparent;\n}\n\n/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem;\n  overflow: hidden;\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: 0.25rem;\n  background-color: var(--pst-color-on-background);\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n}\ndiv.admonition *:last-child,\n.admonition *:last-child {\n  margin-bottom: 0;\n}\ndiv.admonition p.admonition-title ~ *,\n.admonition p.admonition-title ~ * {\n  margin-left: 1.4rem;\n  margin-right: 1.4rem;\n}\ndiv.admonition > ol,\ndiv.admonition > ul,\n.admonition > ol,\n.admonition > ul {\n  margin-left: 1em;\n}\ndiv.admonition > .admonition-title,\n.admonition > .admonition-title {\n  margin: 0 -0.6rem;\n  padding: 0.4rem 0.6rem 0.4rem 2rem;\n  font-weight: var(--pst-admonition-font-weight-heading);\n  position: relative;\n  background-color: var(--pst-color-info-bg);\n  z-index: 1;\n}\ndiv.admonition > .admonition-title::before,\n.admonition > .admonition-title::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\ndiv.admonition > .admonition-title::after,\n.admonition > .admonition-title::after {\n  position: absolute;\n  left: 0.5rem;\n  width: 1rem;\n  height: 1rem;\n  color: var(--pst-color-info);\n  font: var(--fa-font-solid);\n  line-height: inherit;\n  content: var(--pst-icon-admonition-default);\n  opacity: 1;\n}\ndiv.admonition > .admonition-title + *,\n.admonition > .admonition-title + * {\n  margin-top: 0.4em;\n}\ndiv.admonition.attention,\n.admonition.attention {\n  border-color: var(--pst-color-attention);\n}\ndiv.admonition.attention > .admonition-title,\n.admonition.attention > .admonition-title {\n  background-color: var(--pst-color-attention-bg);\n}\ndiv.admonition.attention > .admonition-title::after,\n.admonition.attention > .admonition-title::after {\n  color: var(--pst-color-attention);\n  content: var(--pst-icon-admonition-attention);\n}\ndiv.admonition.caution,\n.admonition.caution {\n  border-color: var(--pst-color-warning);\n}\ndiv.admonition.caution > .admonition-title,\n.admonition.caution > .admonition-title {\n  background-color: var(--pst-color-warning-bg);\n}\ndiv.admonition.caution > .admonition-title::after,\n.admonition.caution > .admonition-title::after {\n  color: var(--pst-color-warning);\n  content: var(--pst-icon-admonition-caution);\n}\ndiv.admonition.warning,\n.admonition.warning {\n  border-color: var(--pst-color-warning);\n}\ndiv.admonition.warning > .admonition-title,\n.admonition.warning > .admonition-title {\n  background-color: var(--pst-color-warning-bg);\n}\ndiv.admonition.warning > .admonition-title::after,\n.admonition.warning > .admonition-title::after {\n  color: var(--pst-color-warning);\n  content: var(--pst-icon-admonition-warning);\n}\ndiv.admonition.danger,\n.admonition.danger {\n  border-color: var(--pst-color-danger);\n}\ndiv.admonition.danger > .admonition-title,\n.admonition.danger > .admonition-title {\n  background-color: var(--pst-color-danger-bg);\n}\ndiv.admonition.danger > .admonition-title::after,\n.admonition.danger > .admonition-title::after {\n  color: var(--pst-color-danger);\n  content: var(--pst-icon-admonition-danger);\n}\ndiv.admonition.error,\n.admonition.error {\n  border-color: var(--pst-color-danger);\n}\ndiv.admonition.error > .admonition-title,\n.admonition.error > .admonition-title {\n  background-color: var(--pst-color-danger-bg);\n}\ndiv.admonition.error > .admonition-title::after,\n.admonition.error > .admonition-title::after {\n  color: var(--pst-color-danger);\n  content: var(--pst-icon-admonition-error);\n}\ndiv.admonition.hint,\n.admonition.hint {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.hint > .admonition-title,\n.admonition.hint > .admonition-title {\n  background-color: var(--pst-color-success-bg);\n}\ndiv.admonition.hint > .admonition-title::after,\n.admonition.hint > .admonition-title::after {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-admonition-hint);\n}\ndiv.admonition.tip,\n.admonition.tip {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.tip > .admonition-title,\n.admonition.tip > .admonition-title {\n  background-color: var(--pst-color-success-bg);\n}\ndiv.admonition.tip > .admonition-title::after,\n.admonition.tip > .admonition-title::after {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-admonition-tip);\n}\ndiv.admonition.important,\n.admonition.important {\n  border-color: var(--pst-color-attention);\n}\ndiv.admonition.important > .admonition-title,\n.admonition.important > .admonition-title {\n  background-color: var(--pst-color-attention-bg);\n}\ndiv.admonition.important > .admonition-title::after,\n.admonition.important > .admonition-title::after {\n  color: var(--pst-color-attention);\n  content: var(--pst-icon-admonition-important);\n}\ndiv.admonition.note,\n.admonition.note {\n  border-color: var(--pst-color-info);\n}\ndiv.admonition.note > .admonition-title,\n.admonition.note > .admonition-title {\n  background-color: var(--pst-color-info-bg);\n}\ndiv.admonition.note > .admonition-title::after,\n.admonition.note > .admonition-title::after {\n  color: var(--pst-color-info);\n  content: var(--pst-icon-admonition-note);\n}\ndiv.admonition.seealso,\n.admonition.seealso {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.seealso > .admonition-title,\n.admonition.seealso > .admonition-title {\n  background-color: var(--pst-color-success-bg);\n}\ndiv.admonition.seealso > .admonition-title::after,\n.admonition.seealso > .admonition-title::after {\n  color: var(--pst-color-success);\n  content: var(--pst-icon-admonition-seealso);\n}\ndiv.admonition.admonition-todo,\n.admonition.admonition-todo {\n  border-color: var(--pst-color-secondary);\n}\ndiv.admonition.admonition-todo > .admonition-title,\n.admonition.admonition-todo > .admonition-title {\n  background-color: var(--pst-color-secondary-bg);\n}\ndiv.admonition.admonition-todo > .admonition-title::after,\n.admonition.admonition-todo > .admonition-title::after {\n  color: var(--pst-color-secondary);\n  content: var(--pst-icon-admonition-todo);\n}\ndiv.admonition.sidebar,\n.admonition.sidebar {\n  max-width: 40%;\n  float: right;\n  clear: both;\n  margin-left: 0.5rem;\n  margin-top: 0;\n  border-width: 0 0 0 0.2rem;\n}\ndiv.admonition.sidebar.attention, div.admonition.sidebar.important,\n.admonition.sidebar.attention,\n.admonition.sidebar.important {\n  border-color: var(--pst-color-attention);\n}\ndiv.admonition.sidebar.caution, div.admonition.sidebar.warning,\n.admonition.sidebar.caution,\n.admonition.sidebar.warning {\n  border-color: var(--pst-color-warning);\n}\ndiv.admonition.sidebar.danger, div.admonition.sidebar.error,\n.admonition.sidebar.danger,\n.admonition.sidebar.error {\n  border-color: var(--pst-color-danger);\n}\ndiv.admonition.sidebar.hint, div.admonition.sidebar.tip, div.admonition.sidebar.seealso,\n.admonition.sidebar.hint,\n.admonition.sidebar.tip,\n.admonition.sidebar.seealso {\n  border-color: var(--pst-color-success);\n}\ndiv.admonition.sidebar.note, div.admonition.sidebar.todo,\n.admonition.sidebar.note,\n.admonition.sidebar.todo {\n  border-color: var(--pst-color-info);\n}\ndiv.admonition.sidebar p.admonition-title ~ *,\n.admonition.sidebar p.admonition-title ~ * {\n  margin-left: 0;\n  margin-right: 0;\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n/**\n * Topics and the {contents} directive\n */\ndiv.topic,\ndiv.topic.contents,\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: 0.25rem;\n  padding: 1rem 1.25rem;\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\ndiv.topic .topic-title,\ndiv.topic.contents .topic-title,\nnav.contents .topic-title,\naside.topic .topic-title {\n  margin: 0 0 0.5rem;\n}\ndiv.topic p,\ndiv.topic.contents p,\nnav.contents p,\naside.topic p {\n  color: var(--pst-color-on-surface) !important;\n}\ndiv.topic ul.simple,\ndiv.topic.contents ul.simple,\nnav.contents ul.simple,\naside.topic ul.simple {\n  padding-left: 1rem;\n}\ndiv.topic ul.simple ul,\ndiv.topic.contents ul.simple ul,\nnav.contents ul.simple ul,\naside.topic ul.simple ul {\n  padding-left: 2em;\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: 0.25rem;\n  margin-left: 0.5rem;\n  padding: 0;\n}\naside.sidebar > *:last-child {\n  padding-bottom: 1rem;\n}\naside.sidebar p.sidebar-title {\n  position: relative;\n  margin-bottom: 0;\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  border-bottom: 1px solid var(--pst-color-border);\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-admonition-font-weight-heading);\n}\naside.sidebar > *:not(.sidebar-title):first-child,\naside.sidebar > p.sidebar-title + * {\n  margin-top: 1rem;\n}\naside.sidebar > * {\n  padding-left: 1rem;\n  padding-right: 1rem;\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n\n/*******************************************************************************\n* Styling for field lists\n*/\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n  /* italic font for parameter types */\n}\ntable.field-list th.field-name {\n  padding: 1px 8px 1px 5px;\n  white-space: nowrap;\n  background-color: var(--pst-color-surface);\n}\ntable.field-list td.field-body {\n  /* reduced space around parameter description */\n}\ntable.field-list td.field-body p {\n  font-style: italic;\n}\ntable.field-list td.field-body p > strong {\n  font-style: normal;\n}\ntable.field-list td.field-body blockquote {\n  border-left: none;\n  margin: 0 0 0.3em;\n  padding-left: 30px;\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n.table.autosummary td:first-child {\n  white-space: nowrap;\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors */\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\ndl > dt > a:has(.viewcode-link) {\n  float: right;\n}\ndl > dt > a:has(.viewcode-link) .viewcode-link {\n  float: none;\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) {\n  margin-bottom: 3rem;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dd {\n  margin-left: 2rem;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dd > dl.simple > dt {\n  display: flex;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dl.field-list {\n  display: grid;\n  grid-template-columns: unset;\n}\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dt.field-odd,\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) dt.field-even {\n  margin-top: 0.2rem;\n  margin-bottom: 0.1rem;\n  background-color: var(--pst-color-surface);\n}\n\n/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\ndiv[class*=highlight-],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: 0.25rem;\n  break-inside: avoid;\n}\n\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\ndiv.literal-block-wrapper div.code-block-caption {\n  margin: 0;\n  border-bottom: 1px solid var(--pst-color-border);\n  padding: 0.5rem;\n  font-size: 1rem;\n  font-weight: var(--pst-font-weight-caption);\n}\ndiv.literal-block-wrapper div.code-block-caption a.headerlink {\n  font-size: inherit;\n}\ndiv.literal-block-wrapper div[class*=highlight-] {\n  margin: 0;\n  border-radius: 0;\n}\ndiv.literal-block-wrapper div[class*=highlight-] pre {\n  border: none;\n  box-shadow: none;\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\na > code:hover {\n  color: var(--pst-color-link-hover);\n}\n\nspan.linenos {\n  opacity: 0.8 !important;\n}\n\nfigure > a,\nfigure > a > img,\nfigure > img,\nfigure > video {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\nfigure a.headerlink {\n  position: absolute;\n  font-size: inherit;\n}\nfigure:hover a.headerlink {\n  visibility: visible;\n}\nfigure figcaption {\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-caption);\n  color: var(--pst-color-text-muted);\n  margin-left: auto;\n  margin-right: auto;\n  margin-top: 0.3rem;\n  text-align: center;\n}\nfigure figcaption > p:last-child {\n  margin-bottom: 0;\n}\nfigure figcaption p {\n  text-align: start;\n  display: inline-block;\n}\nfigure figcaption table.table {\n  width: fit-content;\n  margin-left: auto;\n  margin-right: auto;\n}\n\ndt.label > span.brackets:not(:only-child)::before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child)::after {\n  content: \"]\";\n}\n\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\naside.footnote {\n  margin-bottom: 0.5rem;\n}\naside.footnote:last-child {\n  margin-bottom: 1rem;\n}\naside.footnote span.label,\naside.footnote span.backrefs {\n  font-weight: bold;\n}\naside.footnote:target {\n  background-color: var(--pst-color-target);\n}\n\n/**\n * Hacky fixes that don't fit cleanly into other sections\n */\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n}\n\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n}\nol li > p:first-child,\nul li > p:first-child {\n  margin-bottom: 0.25rem;\n  margin-top: 0.25rem;\n}\n\nblockquote {\n  padding: 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-blockquote-notch);\n  border-radius: 0.25rem;\n  position: relative;\n  background-color: var(--pst-color-surface);\n}\nblockquote p {\n  color: var(--pst-color-text-base);\n}\nblockquote .line-block {\n  margin: 0;\n}\nblockquote p:last-child {\n  margin-bottom: 0;\n}\nblockquote::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\nblockquote a {\n  color: var(--pst-color-inline-code-links);\n}\nblockquote::before {\n  z-index: -1;\n}\n\n/**\n * Span-level styling within content\n */\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n  background-color: var(--pst-color-info-bg);\n}\nspan.guilabel::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\n\na.reference.download::before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n\n/**\n * Tables\n */\ntable {\n  margin-left: auto;\n  margin-right: auto;\n}\ntable.table-right {\n  margin-right: 0;\n}\ntable.table-left {\n  margin-left: 0;\n}\n\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\nth.text-left,\ntd.text-left {\n  text-align: left;\n}\nth.text-right,\ntd.text-right {\n  text-align: right;\n}\nth.text-center,\ntd.text-center {\n  text-align: center;\n}\n\n.table {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n  --bs-table-bg: transparent;\n  --bs-table-color: var(\n    --pst-color-text-base\n  );\n}\n.table th ~ th,\n.table th ~ td,\n.table td ~ th,\n.table td ~ td {\n  border-left: 1px solid var(--pst-color-table-inner-border);\n}\n.table thead tr {\n  background-color: var(--pst-color-table-heading-bg);\n  border-bottom: 2px solid var(--pst-color-primary);\n}\n.table tbody tr:nth-child(odd) {\n  background-color: var(--pst-color-table-row-zebra-low-bg);\n}\n.table tbody tr:nth-child(even) {\n  background-color: var(--pst-color-table-row-zebra-high-bg);\n}\n\n.pst-scrollable-table-container {\n  overflow-x: auto;\n}\n\n/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper p.caption {\n  font-size: 1.5em;\n  margin-bottom: 0;\n}\n.toctree-wrapper > ul {\n  padding-left: 0;\n}\n.toctree-wrapper li[class^=toctree-l] {\n  list-style: none;\n  margin-bottom: 0.2em;\n}\n.toctree-wrapper li[class^=toctree-l] > a {\n  list-style: none;\n  font-size: 1.1em;\n}\n.toctree-wrapper li[class^=toctree-l] > ul {\n  list-style: none;\n  padding-inline-start: 1.5em;\n}\n.toctree-wrapper .toctree-l1 > a {\n  font-size: 1.3em;\n}\n\ndiv.topic.contents ul.simple,\nnav.contents ul.simple {\n  list-style: none;\n  padding-left: 0;\n}\n\n/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n  overflow: hidden;\n}\n\nspan.math {\n  display: inline-flex;\n}\n\ndiv.math {\n  gap: 0.5em;\n  flex-direction: row-reverse;\n}\ndiv.math span.eqno a.headerlink {\n  position: relative;\n  font-size: 1em;\n}\ndiv.math mjx-container {\n  flex-grow: 1;\n  padding-bottom: 0.2rem;\n  overflow: auto;\n}\ndiv.math mjx-container mjx-assistive-mml {\n  height: 0;\n}\n\n/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item h2, .ablog-sidebar-item .h2,\n.ablog-sidebar-item h3,\n.ablog-sidebar-item .h3 {\n  font-size: var(--pst-sidebar-header-font-size);\n  margin-top: 0.5rem;\n}\n.ablog-sidebar-item h2 a, .ablog-sidebar-item .h2 a,\n.ablog-sidebar-item h3 a,\n.ablog-sidebar-item .h3 a {\n  color: var(--pst-color-text-base);\n}\n.ablog-sidebar-item ul {\n  list-style: none;\n  padding-left: 0;\n  overflow-y: hidden;\n  display: flex;\n  flex-direction: column;\n  gap: 0.5em;\n  margin-bottom: 0;\n}\n.ablog-sidebar-item ul.ablog-cloud {\n  flex-flow: row wrap;\n  gap: 0.5rem;\n}\n.ablog-sidebar-item ul.ablog-cloud li {\n  display: flex;\n  align-items: center;\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n}\n.ablog__prev-next > span {\n  display: flex;\n  max-width: 45%;\n}\n.ablog__prev-next > span a {\n  display: flex;\n  align-items: center;\n  margin-left: auto;\n  gap: 1rem;\n  line-height: 1.5rem;\n}\n.ablog__prev-next > span a i::before {\n  color: var(--pst-color-text-base);\n}\n.ablog__prev-next span.ablog__prev i.fa-arrow-circle-left::before {\n  content: var(--pst-icon-angle-left);\n}\n.ablog__prev-next span.ablog__spacer {\n  display: none;\n}\n.ablog__prev-next span.ablog__next {\n  margin-left: auto;\n  text-align: right;\n}\n.ablog__prev-next span.ablog__next i.fa-arrow-circle-right::before {\n  content: var(--pst-icon-angle-right);\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n}\n.ablog__collection .ablog-post,\n.postlist .ablog-post {\n  list-style: none;\n}\n.ablog__collection .ablog-post .ablog-archive,\n.postlist .ablog-post .ablog-archive {\n  display: flex;\n  flex-flow: row wrap;\n  gap: 1rem;\n  list-style: none;\n  font-size: 0.75rem;\n  padding-left: 0;\n}\n.ablog__collection .ablog-post .ablog-post-title,\n.postlist .ablog-post .ablog-post-title {\n  margin-top: 0;\n  font-size: 1.25rem;\n}\n.ablog__collection .ablog-post .ablog-post-title a,\n.postlist .ablog-post .ablog-post-title a {\n  font-weight: bold;\n}\n.ablog__collection .ablog-post .ablog-post-expand,\n.postlist .ablog-post .ablog-post-expand {\n  margin-bottom: 0.5rem;\n}\n\n/**\n * Special cases for Bootstrap functionality\n */\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n\n.btn {\n  --bs-btn-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);\n}\n\n/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\ndiv.highlight button.copybtn {\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  border: none;\n  background-color: var(--pst-color-surface);\n}\ndiv.highlight button.copybtn:not(.success) {\n  color: var(--pst-color-muted);\n}\ndiv.highlight button.copybtn:hover:not(.success) {\n  color: var(--pst-color-text);\n  background-color: var(--pst-color-shadow);\n}\ndiv.highlight button.copybtn.o-tooltip--left::after {\n  color: var(--pst-color-text);\n  background-color: var(--pst-color-surface);\n}\ndiv.highlight button.copybtn:focus {\n  opacity: 1;\n}\ndiv.highlight button.copybtn:focus-visible {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n}\n\ndiv.highlight:has(button.copybtn) {\n  min-height: 2em;\n}\n\n#ethical-ad-placement .ethical-sidebar a,\n#ethical-ad-placement .ethical-sidebar a:visited,\n#ethical-ad-placement .ethical-sidebar a:hover,\n#ethical-ad-placement .ethical-sidebar a:active,\n#ethical-ad-placement .ethical-footer a,\n#ethical-ad-placement .ethical-footer a:visited,\n#ethical-ad-placement .ethical-footer a:hover,\n#ethical-ad-placement .ethical-footer a:active {\n  color: var(--pst-color-text-base);\n}\n#ethical-ad-placement .ethical-sidebar,\n#ethical-ad-placement .ethical-footer {\n  background-color: var(--pst-color-background);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 5px;\n  color: var(--pst-color-text-base);\n  font-size: 14px;\n  line-height: 20px;\n}\n\n/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n/******************************************************************************\n * Jupyter Sphinx\n */\n.bd-content div.jupyter_container {\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n}\n.bd-content div.jupyter_container div.output,\n.bd-content div.jupyter_container div.highlight {\n  border-radius: 0.25rem;\n}\n.bd-content div.jupyter_container div.highlight {\n  background-color: var(--pst-color-surface);\n}\n.bd-content div.jupyter_container .cell_input,\n.bd-content div.jupyter_container .cell_output {\n  border-radius: 0.25rem;\n}\n.bd-content div.jupyter_container .cell_input pre,\n.bd-content div.jupyter_container .cell_output pre {\n  padding: 1rem;\n}\n\n/* Styles for graphviz generated output from Sphinx */\n/* Style the inheritance diagram such that it has a dark mode */\nhtml[data-theme=dark] div.graphviz > object.inheritance {\n  filter: brightness(0.8) invert(0.82) contrast(1.2);\n  color-scheme: normal;\n}\n\n/**\n * Special-cases for packages in the PyData ecosystem\n */\n.xr-wrap[hidden] {\n  display: block !important;\n}\n\n.jp-OutputArea-output.lm-Widget {\n  overflow: auto;\n}\n\n/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n/**\n* Function to get items from nested maps\n*/\n/* Assign base colors for the PyData theme */\n:root {\n  --pst-teal-50: #f4fbfc;\n  --pst-teal-100: #e9f6f8;\n  --pst-teal-200: #d0ecf1;\n  --pst-teal-300: #abdde6;\n  --pst-teal-400: #3fb1c5;\n  --pst-teal-500: #0a7d91;\n  --pst-teal-600: #085d6c;\n  --pst-teal-700: #064752;\n  --pst-teal-800: #042c33;\n  --pst-teal-900: #021b1f;\n  --pst-violet-50: #f4eefb;\n  --pst-violet-100: #e0c7ff;\n  --pst-violet-200: #d5b4fd;\n  --pst-violet-300: #b780ff;\n  --pst-violet-400: #9c5ffd;\n  --pst-violet-500: #8045e5;\n  --pst-violet-600: #6432bd;\n  --pst-violet-700: #4b258f;\n  --pst-violet-800: #341a61;\n  --pst-violet-900: #1e0e39;\n  --pst-gray-50: #f9f9fa;\n  --pst-gray-100: #f3f4f5;\n  --pst-gray-200: #e5e7ea;\n  --pst-gray-300: #d1d5da;\n  --pst-gray-400: #9ca4af;\n  --pst-gray-500: #677384;\n  --pst-gray-600: #48566b;\n  --pst-gray-700: #29313d;\n  --pst-gray-800: #222832;\n  --pst-gray-900: #14181e;\n  --pst-pink-50: #fcf8fd;\n  --pst-pink-100: #fcf0fa;\n  --pst-pink-200: #f8dff5;\n  --pst-pink-300: #f3c7ee;\n  --pst-pink-400: #e47fd7;\n  --pst-pink-500: #c132af;\n  --pst-pink-600: #912583;\n  --pst-pink-700: #6e1c64;\n  --pst-pink-800: #46123f;\n  --pst-pink-900: #2b0b27;\n  --pst-foundation-white: #ffffff;\n  --pst-foundation-black: #14181e;\n}\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * something inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #d5b4fd;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n}\n\nhtml:not([data-theme]) {\n  --pst-color-heading: var(--pst-color-text-base);\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\n\nhtml:not([data-theme]) .only-dark,\nhtml:not([data-theme]) .only-dark ~ figcaption {\n  display: none !important;\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\nhtml[data-theme=light] {\n  --pst-color-primary: #0a7d91;\n  --pst-color-primary-bg: #d0ecf1;\n  --pst-color-secondary: #8045e5;\n  --pst-color-secondary-bg: #e0c7ff;\n  --pst-color-accent: #c132af;\n  --pst-color-accent-bg: #f8dff5;\n  --pst-color-info: #276be9;\n  --pst-color-info-bg: #dce7fc;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-bg: #f8e3d0;\n  --pst-color-success: #00843f;\n  --pst-color-success-bg: #d6ece1;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-bg: #f9e1e4;\n  --pst-color-text-base: #222832;\n  --pst-color-text-muted: #48566b;\n  --pst-color-shadow: rgba(0, 0, 0, 0.1);\n  --pst-color-border: #d1d5da;\n  --pst-color-border-muted: rgba(23, 23, 26, 0.2);\n  --pst-color-blockquote-notch: #677384;\n  --pst-color-inline-code: #912583;\n  --pst-color-inline-code-links: #085d6c;\n  --pst-color-target: #f3cf95;\n  --pst-color-table: #14181e;\n  --pst-color-table-row-hover-bg: #d5b4fd;\n  --pst-color-table-inner-border: #e5e7ea;\n  --pst-color-background: #ffffff;\n  --pst-color-on-background: #ffffff;\n  --pst-color-surface: #f3f4f5;\n  --pst-color-on-surface: #222832;\n  color-scheme: light;\n}\n\nhtml[data-theme=light] {\n  --pst-color-heading: var(--pst-color-text-base);\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\n\nhtml[data-theme=light] .only-dark,\nhtml[data-theme=light] .only-dark ~ figcaption {\n  display: none !important;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-primary: #3fb1c5;\n  --pst-color-primary-bg: #042c33;\n  --pst-color-secondary: #9c5ffd;\n  --pst-color-secondary-bg: #341a61;\n  --pst-color-accent: #e47fd7;\n  --pst-color-accent-bg: #46123f;\n  --pst-color-info: #79a3f2;\n  --pst-color-info-bg: #06245d;\n  --pst-color-warning: #ff9245;\n  --pst-color-warning-bg: #652a02;\n  --pst-color-success: #5fb488;\n  --pst-color-success-bg: #002f17;\n  --pst-color-attention: var(--pst-color-warning);\n  --pst-color-attention-bg: var(--pst-color-warning-bg);\n  --pst-color-danger: #e78894;\n  --pst-color-danger-bg: #4e111b;\n  --pst-color-text-base: #ced6dd;\n  --pst-color-text-muted: #9ca4af;\n  --pst-color-shadow: rgba(0, 0, 0, 0.2);\n  --pst-color-border: #48566b;\n  --pst-color-border-muted: #29313d;\n  --pst-color-blockquote-notch: #9ca4af;\n  --pst-color-inline-code: #f3c7ee;\n  --pst-color-inline-code-links: #3fb1c5;\n  --pst-color-target: #675c04;\n  --pst-color-table: #ffffff;\n  --pst-color-table-row-hover-bg: #4b258f;\n  --pst-color-table-inner-border: #364150;\n  --pst-color-background: #14181e;\n  --pst-color-on-background: #222832;\n  --pst-color-surface: #29313d;\n  --pst-color-on-surface: #f3f4f5;\n  /* Adjust images in dark mode (unless they have class .only-dark or\n   * .dark-light, in which case assume they're already optimized for dark\n   * mode).\n   */\n  /* Give images a light background in dark mode in case they have\n  *  transparency and black text (unless they have class .only-dark or .dark-light, in\n  *  which case assume they're already optimized for dark mode).\n  */\n  color-scheme: dark;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-heading: var(--pst-color-text-base);\n  --pst-color-link: var(--pst-color-primary);\n  --pst-color-link-hover: var(--pst-color-secondary);\n  --pst-color-table-outer-border: var(--pst-color-surface);\n  --pst-color-table-heading-bg: var(--pst-color-surface);\n  --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n  --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n}\n\nhtml[data-theme=dark] .only-light,\nhtml[data-theme=dark] .only-light ~ figcaption {\n  display: none !important;\n}\n\nhtml[data-theme=dark] img:not(.only-dark, .dark-light) {\n  filter: brightness(0.8) contrast(1.2);\n}\n\nhtml[data-theme=dark] .bd-content img:not(.only-dark, .dark-light) {\n  background-color: rgb(255, 255, 255);\n  border-radius: 0.25rem;\n}\n\nhtml[data-theme=dark] .MathJax_SVG * {\n  fill: var(--pst-color-text-base);\n}\n\n.pst-color-primary {\n  color: var(--pst-color-primary);\n}\n\n.pst-color-secondary {\n  color: var(--pst-color-secondary);\n}\n\n.pst-color-accent {\n  color: var(--pst-color-accent);\n}\n\n.pst-color-info {\n  color: var(--pst-color-info);\n}\n\n.pst-color-warning {\n  color: var(--pst-color-warning);\n}\n\n.pst-color-success {\n  color: var(--pst-color-success);\n}\n\n.pst-color-attention {\n  color: var(--pst-color-attention);\n}\n\n.pst-color-danger {\n  color: var(--pst-color-danger);\n}\n\n.pst-color-text-base {\n  color: var(--pst-color-text-base);\n}\n\n.pst-color-text-muted {\n  color: var(--pst-color-text-muted);\n}\n\n.pst-color-shadow {\n  color: var(--pst-color-shadow);\n}\n\n.pst-color-border {\n  color: var(--pst-color-border);\n}\n\n.pst-color-border-muted {\n  color: var(--pst-color-border-muted);\n}\n\n.pst-color-blockquote-notch {\n  color: var(--pst-color-blockquote-notch);\n}\n\n.pst-color-inline-code {\n  color: var(--pst-color-inline-code);\n}\n\n.pst-color-inline-code-links {\n  color: var(--pst-color-inline-code-links);\n}\n\n.pst-color-target {\n  color: var(--pst-color-target);\n}\n\n.pst-color-table {\n  color: var(--pst-color-table);\n}\n\n.pst-color-table-row-hover {\n  color: var(--pst-color-table-row-hover);\n}\n\n.pst-color-table-inner-border {\n  color: var(--pst-color-table-inner-border);\n}\n\n.pst-color-background {\n  color: var(--pst-color-background);\n}\n\n.pst-color-on-background {\n  color: var(--pst-color-on-background);\n}\n\n.pst-color-surface {\n  color: var(--pst-color-surface);\n}\n\n.pst-color-on-surface {\n  color: var(--pst-color-on-surface);\n}\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\nhtml[data-theme=light] {\n  --pst-color-primary: rgb(10, 125, 145);\n  --pst-color-primary-text: #fff;\n  --pst-color-primary-highlight: #086b7c;\n  --sd-color-primary: var(--pst-color-primary);\n  --sd-color-primary-text: var(--pst-color-primary-text);\n  --sd-color-primary-highlight: var(--pst-color-primary-highlight);\n  --pst-color-primary-highlight-text: #fff;\n  --sd-color-primary-bg: #d0ecf1;\n  --sd-color-primary-bg-text: #14181e;\n  --pst-color-secondary: rgb(128, 69, 229);\n  --pst-color-secondary-text: #fff;\n  --pst-color-secondary-highlight: #651ee0;\n  --sd-color-secondary: var(--pst-color-secondary);\n  --sd-color-secondary-text: var(--pst-color-secondary-text);\n  --sd-color-secondary-highlight: var(--pst-color-secondary-highlight);\n  --pst-color-secondary-highlight-text: #fff;\n  --sd-color-secondary-bg: #e0c7ff;\n  --sd-color-secondary-bg-text: #14181e;\n  --pst-color-success: #00843f;\n  --pst-color-success-text: #fff;\n  --pst-color-success-highlight: #007036;\n  --sd-color-success: var(--pst-color-success);\n  --sd-color-success-text: var(--pst-color-success-text);\n  --sd-color-success-highlight: var(--pst-color-success-highlight);\n  --pst-color-success-highlight-text: #fff;\n  --sd-color-success-bg: #d6ece1;\n  --sd-color-success-bg-text: #14181e;\n  --pst-color-info: #276be9;\n  --pst-color-info-text: #fff;\n  --pst-color-info-highlight: #1457d3;\n  --sd-color-info: var(--pst-color-info);\n  --sd-color-info-text: var(--pst-color-info-text);\n  --sd-color-info-highlight: var(--pst-color-info-highlight);\n  --pst-color-info-highlight-text: #fff;\n  --sd-color-info-bg: #dce7fc;\n  --sd-color-info-bg-text: #14181e;\n  --pst-color-warning: #f66a0a;\n  --pst-color-warning-text: #14181e;\n  --pst-color-warning-highlight: #d25a07;\n  --sd-color-warning: var(--pst-color-warning);\n  --sd-color-warning-text: var(--pst-color-warning-text);\n  --sd-color-warning-highlight: var(--pst-color-warning-highlight);\n  --pst-color-warning-highlight-text: #14181e;\n  --sd-color-warning-bg: #f8e3d0;\n  --sd-color-warning-bg-text: #14181e;\n  --pst-color-danger: #d72d47;\n  --pst-color-danger-text: #fff;\n  --pst-color-danger-highlight: #bb2239;\n  --sd-color-danger: var(--pst-color-danger);\n  --sd-color-danger-text: var(--pst-color-danger-text);\n  --sd-color-danger-highlight: var(--pst-color-danger-highlight);\n  --pst-color-danger-highlight-text: #fff;\n  --sd-color-danger-bg: #f9e1e4;\n  --sd-color-danger-bg-text: #14181e;\n  --pst-color-light: #f3f4f5;\n  --pst-color-light-text: #14181e;\n  --pst-color-light-highlight: #c9cfd6;\n  --sd-color-light: var(--pst-color-light);\n  --sd-color-light-text: var(--pst-color-light-text);\n  --sd-color-light-highlight: var(--pst-color-light-highlight);\n  --pst-color-light-highlight-text: #14181e;\n  --sd-color-light-bg: #f7f7f8;\n  --sd-color-light-bg-text: #14181e;\n  --pst-color-muted: #29313d;\n  --pst-color-muted-text: #fff;\n  --pst-color-muted-highlight: #212936;\n  --sd-color-muted: var(--pst-color-muted);\n  --sd-color-muted-text: var(--pst-color-muted-text);\n  --sd-color-muted-highlight: var(--pst-color-muted-highlight);\n  --pst-color-muted-highlight-text: #fff;\n  --sd-color-muted-bg: #5a6c86;\n  --sd-color-muted-bg-text: #fff;\n  --pst-color-dark: #222832;\n  --pst-color-dark-text: #fff;\n  --pst-color-dark-highlight: #1b222c;\n  --sd-color-dark: var(--pst-color-dark);\n  --sd-color-dark-text: var(--pst-color-dark-text);\n  --sd-color-dark-highlight: var(--pst-color-dark-highlight);\n  --pst-color-dark-highlight-text: #fff;\n  --pst-color-black: #14181e;\n  --pst-color-black-text: #fff;\n  --pst-color-black-highlight: #10141a;\n  --sd-color-black: var(--pst-color-black);\n  --sd-color-black-text: var(--pst-color-black-text);\n  --sd-color-black-highlight: var(--pst-color-black-highlight);\n  --pst-color-black-highlight-text: #fff;\n  --pst-color-white: #fff;\n  --pst-color-white-text: #14181e;\n  --pst-color-white-highlight: #dbd7d7;\n  --sd-color-white: var(--pst-color-white);\n  --sd-color-white-text: var(--pst-color-white-text);\n  --sd-color-white-highlight: var(--pst-color-white-highlight);\n  --pst-color-white-highlight-text: #14181e;\n}\n\nhtml[data-theme=dark] {\n  --pst-color-primary: rgb(63, 177, 197);\n  --pst-color-primary-text: #14181e;\n  --pst-color-primary-highlight: #319aac;\n  --sd-color-primary: var(--pst-color-primary);\n  --sd-color-primary-text: var(--pst-color-primary-text);\n  --sd-color-primary-highlight: var(--pst-color-primary-highlight);\n  --pst-color-primary-highlight-text: #14181e;\n  --sd-color-primary-bg: #042c33;\n  --sd-color-primary-bg-text: #fff;\n  --pst-color-secondary: rgb(156, 95, 253);\n  --pst-color-secondary-text: #14181e;\n  --pst-color-secondary-highlight: #7c2bfc;\n  --sd-color-secondary: var(--pst-color-secondary);\n  --sd-color-secondary-text: var(--pst-color-secondary-text);\n  --sd-color-secondary-highlight: var(--pst-color-secondary-highlight);\n  --pst-color-secondary-highlight-text: #fff;\n  --sd-color-secondary-bg: #341a61;\n  --sd-color-secondary-bg-text: #fff;\n  --pst-color-success: #5fb488;\n  --pst-color-success-text: #14181e;\n  --pst-color-success-highlight: #47a373;\n  --sd-color-success: var(--pst-color-success);\n  --sd-color-success-text: var(--pst-color-success-text);\n  --sd-color-success-highlight: var(--pst-color-success-highlight);\n  --pst-color-success-highlight-text: #14181e;\n  --sd-color-success-bg: #002f17;\n  --sd-color-success-bg-text: #fff;\n  --pst-color-info: #79a3f2;\n  --pst-color-info-text: #14181e;\n  --pst-color-info-highlight: #4681ee;\n  --sd-color-info: var(--pst-color-info);\n  --sd-color-info-text: var(--pst-color-info-text);\n  --sd-color-info-highlight: var(--pst-color-info-highlight);\n  --pst-color-info-highlight-text: #14181e;\n  --sd-color-info-bg: #06245d;\n  --sd-color-info-bg-text: #fff;\n  --pst-color-warning: #ff9245;\n  --pst-color-warning-text: #14181e;\n  --pst-color-warning-highlight: #ff7614;\n  --sd-color-warning: var(--pst-color-warning);\n  --sd-color-warning-text: var(--pst-color-warning-text);\n  --sd-color-warning-highlight: var(--pst-color-warning-highlight);\n  --pst-color-warning-highlight-text: #14181e;\n  --sd-color-warning-bg: #652a02;\n  --sd-color-warning-bg-text: #fff;\n  --pst-color-danger: #e78894;\n  --pst-color-danger-text: #14181e;\n  --pst-color-danger-highlight: #df596a;\n  --sd-color-danger: var(--pst-color-danger);\n  --sd-color-danger-text: var(--pst-color-danger-text);\n  --sd-color-danger-highlight: var(--pst-color-danger-highlight);\n  --pst-color-danger-highlight-text: #14181e;\n  --sd-color-danger-bg: #4e111b;\n  --sd-color-danger-bg-text: #fff;\n  --pst-color-light: #f3f4f5;\n  --pst-color-light-text: #14181e;\n  --pst-color-light-highlight: #c9cfd6;\n  --sd-color-light: var(--pst-color-light);\n  --sd-color-light-text: var(--pst-color-light-text);\n  --sd-color-light-highlight: var(--pst-color-light-highlight);\n  --pst-color-light-highlight-text: #14181e;\n  --sd-color-light-bg: #a3abb2;\n  --sd-color-light-bg-text: #14181e;\n  --pst-color-muted: #f3f4f5;\n  --pst-color-muted-text: #14181e;\n  --pst-color-muted-highlight: #c9cfd6;\n  --sd-color-muted: var(--pst-color-muted);\n  --sd-color-muted-text: var(--pst-color-muted-text);\n  --sd-color-muted-highlight: var(--pst-color-muted-highlight);\n  --pst-color-muted-highlight-text: #14181e;\n  --sd-color-muted-bg: #1d222b;\n  --sd-color-muted-bg-text: #fff;\n  --pst-color-dark: #222832;\n  --pst-color-dark-text: #fff;\n  --pst-color-dark-highlight: #1b222c;\n  --sd-color-dark: var(--pst-color-dark);\n  --sd-color-dark-text: var(--pst-color-dark-text);\n  --sd-color-dark-highlight: var(--pst-color-dark-highlight);\n  --pst-color-dark-highlight-text: #fff;\n  --pst-color-black: #14181e;\n  --pst-color-black-text: #fff;\n  --pst-color-black-highlight: #10141a;\n  --sd-color-black: var(--pst-color-black);\n  --sd-color-black-text: var(--pst-color-black-text);\n  --sd-color-black-highlight: var(--pst-color-black-highlight);\n  --pst-color-black-highlight-text: #fff;\n  --pst-color-white: #fff;\n  --pst-color-white-text: #14181e;\n  --pst-color-white-highlight: #dbd7d7;\n  --sd-color-white: var(--pst-color-white);\n  --sd-color-white-text: var(--pst-color-white-text);\n  --sd-color-white-highlight: var(--pst-color-white-highlight);\n  --pst-color-white-highlight-text: #14181e;\n}\n\nhtml[data-theme=light] {\n  --sd-color-card-border: var(--pst-color-border);\n}\n\nhtml[data-theme=dark] {\n  --sd-color-card-border: var(--pst-color-border);\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=light] .sd-shadow-xs,\nhtml[data-theme=light] .sd-shadow-sm,\nhtml[data-theme=light] .sd-shadow-md,\nhtml[data-theme=light] .sd-shadow-lg {\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/*******************************************************************************\n  * cards\n  */\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n}\n.bd-content .sd-card .sd-card-header {\n  background-color: var(--pst-color-on-background);\n  border-bottom: 1px solid var(--pst-color-border);\n}\n.bd-content .sd-card .sd-card-footer {\n  background-color: var(--pst-color-on-background);\n  border-top: 1px solid var(--pst-color-border);\n}\n.bd-content .sd-card .sd-card-body {\n  background-color: var(--pst-color-on-background);\n}\n.bd-content .sd-card .sd-stretched-link:focus-visible {\n  outline: none;\n}\n.bd-content .sd-card .sd-stretched-link:focus-visible::after {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  border-radius: 0.25rem;\n}\n.bd-content .sd-card.sd-card-hover:hover {\n  border-color: var(--pst-color-link-hover);\n}\n\n/*******************************************************************************\n  * tabs\n  */\n.bd-content .sd-tab-set > input:checked + label {\n  border-style: solid solid none;\n  border-color: var(--pst-color-primary) var(--pst-color-primary) transparent;\n  border-width: 0.125rem 0.125rem 0;\n  border-radius: 0.125rem 0.125rem 0 0;\n  background-color: var(--pst-color-on-background);\n  transform: translateY(0.125rem);\n  color: var(--pst-color-primary);\n}\n.bd-content .sd-tab-set > input:focus-visible + label {\n  border: 0.125rem solid var(--pst-color-accent);\n  border-radius: 0.125rem;\n  background-color: var(--pst-color-accent-bg);\n  color: var(--pst-color-on-surface);\n}\n.bd-content .sd-tab-set > input:not(:checked, :focus-visible) + label:hover {\n  border-color: transparent;\n  color: var(--pst-color-secondary);\n  text-decoration-line: underline;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n}\n.bd-content .sd-tab-set > label {\n  color: var(--pst-color-on-surface);\n  border: 0.125rem solid transparent;\n  border-radius: 0.125rem 0.125rem 0 0;\n  background-color: var(--pst-color-surface);\n  padding: 0 0.75em;\n  margin-inline-end: 0.25rem;\n  line-height: 1.95;\n}\nhtml[data-theme=dark] .bd-content .sd-tab-set > label {\n  background-color: var(--pst-color-on-background);\n}\n.bd-content .sd-tab-set .sd-tab-content {\n  border: 0.125rem solid var(--pst-color-primary);\n  border-radius: 0.1875rem;\n  box-shadow: unset;\n  padding: 0.625rem;\n  background-color: var(--pst-color-on-background);\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\ndetails.sd-dropdown {\n  border: 0 !important;\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow), 0 0 0.0625rem var(--pst-color-shadow) !important;\n}\ndetails.sd-dropdown summary.sd-card-header {\n  border: 0 !important;\n  display: flex;\n  align-items: center;\n  position: relative;\n  font-weight: 600;\n  padding-top: 0.5rem;\n  padding-bottom: 0.5rem;\n  --pst-sd-dropdown-color: var(--pst-gray-500);\n  --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n  background-color: var(--pst-sd-dropdown-bg-color) !important;\n  border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n}\ndetails.sd-dropdown summary.sd-card-header + div.sd-summary-content {\n  border: 0;\n  --pst-sd-dropdown-color: var(--sd-color-card-border);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-primary {\n  --pst-sd-dropdown-color: var(--sd-color-primary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-primary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-primary + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-primary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-primary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-primary {\n  color: var(--sd-color-primary-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-secondary {\n  --pst-sd-dropdown-color: var(--sd-color-secondary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-secondary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-secondary + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-secondary);\n  --pst-sd-dropdown-bg-color: var(--sd-color-secondary-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-secondary {\n  color: var(--sd-color-secondary-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-success {\n  --pst-sd-dropdown-color: var(--sd-color-success);\n  --pst-sd-dropdown-bg-color: var(--sd-color-success-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-success + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-success);\n  --pst-sd-dropdown-bg-color: var(--sd-color-success-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-success {\n  color: var(--sd-color-success-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-info {\n  --pst-sd-dropdown-color: var(--sd-color-info);\n  --pst-sd-dropdown-bg-color: var(--sd-color-info-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-info + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-info);\n  --pst-sd-dropdown-bg-color: var(--sd-color-info-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-info {\n  color: var(--sd-color-info-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-warning {\n  --pst-sd-dropdown-color: var(--sd-color-warning);\n  --pst-sd-dropdown-bg-color: var(--sd-color-warning-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-warning + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-warning);\n  --pst-sd-dropdown-bg-color: var(--sd-color-warning-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-warning {\n  color: var(--sd-color-warning-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-danger {\n  --pst-sd-dropdown-color: var(--sd-color-danger);\n  --pst-sd-dropdown-bg-color: var(--sd-color-danger-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-danger + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-danger);\n  --pst-sd-dropdown-bg-color: var(--sd-color-danger-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-danger {\n  color: var(--sd-color-danger-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-light {\n  --pst-sd-dropdown-color: var(--sd-color-light);\n  --pst-sd-dropdown-bg-color: var(--sd-color-light-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-light + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-light);\n  --pst-sd-dropdown-bg-color: var(--sd-color-light-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-light {\n  color: var(--sd-color-light-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-muted {\n  --pst-sd-dropdown-color: var(--sd-color-muted);\n  --pst-sd-dropdown-bg-color: var(--sd-color-muted-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-muted + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-muted);\n  --pst-sd-dropdown-bg-color: var(--sd-color-muted-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-muted {\n  color: var(--sd-color-muted-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-dark {\n  --pst-sd-dropdown-color: var(--sd-color-dark);\n  --pst-sd-dropdown-bg-color: var(--sd-color-dark-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-dark + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-dark);\n  --pst-sd-dropdown-bg-color: var(--sd-color-dark-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-dark {\n  color: var(--sd-color-dark-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-black {\n  --pst-sd-dropdown-color: var(--sd-color-black);\n  --pst-sd-dropdown-bg-color: var(--sd-color-black-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-black + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-black);\n  --pst-sd-dropdown-bg-color: var(--sd-color-black-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-black {\n  color: var(--sd-color-black-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-white {\n  --pst-sd-dropdown-color: var(--sd-color-white);\n  --pst-sd-dropdown-bg-color: var(--sd-color-white-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-white + div.sd-summary-content {\n  --pst-sd-dropdown-color: var(--sd-color-white);\n  --pst-sd-dropdown-bg-color: var(--sd-color-white-bg);\n}\ndetails.sd-dropdown summary.sd-card-header.sd-bg-text-white {\n  color: var(--sd-color-white-bg-text) !important;\n}\ndetails.sd-dropdown summary.sd-card-header::before {\n  content: \"\";\n  width: 100%;\n  height: 100%;\n  position: absolute;\n  left: 0;\n  top: 0;\n  z-index: -1;\n  pointer-events: none;\n}\ndetails.sd-dropdown summary.sd-card-header + div.sd-summary-content {\n  border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n  border-bottom-left-radius: calc(0.25rem - 1px);\n  background-color: var(--pst-color-on-background);\n}\ndetails.sd-dropdown summary.sd-card-header span.sd-summary-icon {\n  display: inline-flex;\n  align-items: center;\n  color: var(--pst-sd-dropdown-color) !important;\n}\ndetails.sd-dropdown summary.sd-card-header span.sd-summary-icon svg {\n  opacity: 1;\n}\ndetails.sd-dropdown summary.sd-card-header .sd-summary-up,\ndetails.sd-dropdown summary.sd-card-header .sd-summary-down {\n  top: 0.7rem;\n}\ndetails.sd-dropdown summary.sd-card-header:hover {\n  filter: brightness(0.9);\n}\nhtml[data-theme=dark] details.sd-dropdown summary.sd-card-header:hover {\n  filter: brightness(1.1);\n}\ndetails.sd-dropdown summary.sd-card-header:focus:focus-visible {\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n}\ndetails.sd-dropdown[open] summary.sd-card-header:focus:focus-visible {\n  border-bottom-left-radius: 0;\n  border-bottom-right-radius: 0;\n}\n\n/*******************************************************************************\n* Buttons (which in Sphinx Design are actually links that look like buttons)\n* ref: https://sphinx-design.readthedocs.io/en/pydata-theme/badges_buttons.html#buttons\n*/\nhtml .sd-btn {\n  min-width: 2.25rem;\n  padding: 0.3125rem 0.75rem 0.4375rem;\n}\nhtml .sd-btn:hover {\n  text-decoration: underline;\n  text-decoration-thickness: max(1px, .0625rem);\n  text-underline-offset: 0.1578em;\n  text-decoration-thickness: max(3px, .1875rem, .12em);\n  text-decoration-skip-ink: none;\n  color: var(--pst-color-link-hover);\n  text-decoration-thickness: 1px;\n}\nhtml .sd-btn-primary:hover {\n  color: var(--pst-color-primary-highlight-text) !important;\n}\nhtml .sd-btn-secondary:hover {\n  color: var(--pst-color-secondary-highlight-text) !important;\n}\nhtml .sd-btn-success:hover {\n  color: var(--pst-color-success-highlight-text) !important;\n}\nhtml .sd-btn-info:hover {\n  color: var(--pst-color-info-highlight-text) !important;\n}\nhtml .sd-btn-warning:hover {\n  color: var(--pst-color-warning-highlight-text) !important;\n}\nhtml .sd-btn-danger:hover {\n  color: var(--pst-color-danger-highlight-text) !important;\n}\nhtml .sd-btn-light:hover {\n  color: var(--pst-color-light-highlight-text) !important;\n}\nhtml .sd-btn-muted:hover {\n  color: var(--pst-color-muted-highlight-text) !important;\n}\nhtml .sd-btn-dark:hover {\n  color: var(--pst-color-dark-highlight-text) !important;\n}\nhtml .sd-btn-black:hover {\n  color: var(--pst-color-black-highlight-text) !important;\n}\nhtml .sd-btn-white:hover {\n  color: var(--pst-color-white-highlight-text) !important;\n}\nhtml .sd-btn-primary:focus-visible,\nhtml .sd-btn-outline-primary:focus-visible {\n  outline: var(--sd-color-primary) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-primary) !important;\n  border-color: var(--sd-color-primary) !important;\n}\nhtml .sd-btn-secondary:focus-visible,\nhtml .sd-btn-outline-secondary:focus-visible {\n  outline: var(--sd-color-secondary) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-secondary) !important;\n  border-color: var(--sd-color-secondary) !important;\n}\nhtml .sd-btn-success:focus-visible,\nhtml .sd-btn-outline-success:focus-visible {\n  outline: var(--sd-color-success) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-success) !important;\n  border-color: var(--sd-color-success) !important;\n}\nhtml .sd-btn-info:focus-visible,\nhtml .sd-btn-outline-info:focus-visible {\n  outline: var(--sd-color-info) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-info) !important;\n  border-color: var(--sd-color-info) !important;\n}\nhtml .sd-btn-warning:focus-visible,\nhtml .sd-btn-outline-warning:focus-visible {\n  outline: var(--sd-color-warning) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-warning) !important;\n  border-color: var(--sd-color-warning) !important;\n}\nhtml .sd-btn-danger:focus-visible,\nhtml .sd-btn-outline-danger:focus-visible {\n  outline: var(--sd-color-danger) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-danger) !important;\n  border-color: var(--sd-color-danger) !important;\n}\nhtml .sd-btn-light:focus-visible,\nhtml .sd-btn-outline-light:focus-visible {\n  outline: var(--sd-color-light) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-light) !important;\n  border-color: var(--sd-color-light) !important;\n}\nhtml .sd-btn-muted:focus-visible,\nhtml .sd-btn-outline-muted:focus-visible {\n  outline: var(--sd-color-muted) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-muted) !important;\n  border-color: var(--sd-color-muted) !important;\n}\nhtml .sd-btn-dark:focus-visible,\nhtml .sd-btn-outline-dark:focus-visible {\n  outline: var(--sd-color-dark) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-dark) !important;\n  border-color: var(--sd-color-dark) !important;\n}\nhtml .sd-btn-black:focus-visible,\nhtml .sd-btn-outline-black:focus-visible {\n  outline: var(--sd-color-black) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-black) !important;\n  border-color: var(--sd-color-black) !important;\n}\nhtml .sd-btn-white:focus-visible,\nhtml .sd-btn-outline-white:focus-visible {\n  outline: var(--sd-color-white) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n  background-color: var(--sd-color-white) !important;\n  border-color: var(--sd-color-white) !important;\n}\n\n/**\n * Sphinx togglebutton\n *\n * The rules in this style sheet are meant to tweak the\n * [sphinx-togglebutton](https://sphinx-togglebutton.readthedocs.io/en/latest/index.html)\n * extension so that it matches the look and feel of this theme.\n */\n.bd-content .dropdown.admonition.toggle .admonition-title:hover .tb-icon {\n  opacity: 1;\n  scale: 1.1;\n}\n.bd-content .dropdown.admonition.toggle .admonition-title .tb-icon {\n  opacity: 0.6;\n}\n.bd-content .dropdown.admonition.toggle .admonition-title:hover {\n  filter: brightness(0.9);\n}\nhtml[data-theme=dark] .bd-content .dropdown.admonition.toggle .admonition-title:hover {\n  filter: brightness(1.1);\n}\n.bd-content .dropdown.admonition.toggle .admonition-title:hover {\n  box-shadow: none;\n}\n.bd-content .dropdown.admonition.toggle button.toggle-button {\n  color: inherit;\n}\n.bd-content .dropdown.admonition.toggle button.toggle-button.toggle-button-hidden .toggle-chevron-right {\n  transform: rotate(90deg);\n  transition: none;\n}\n.bd-content .dropdown.admonition.toggle button.toggle-button .toggle-chevron-right {\n  transform: rotate(-90deg);\n  transition: none;\n}\n.bd-content .dropdown.admonition.toggle:has(:focus-visible) {\n  /* Override Sphinx Toggle Button. Make the overflow visible, otherwise the\n  focus ring is hidden. */\n  overflow: visible;\n  /*\n  Why not just do the following?\n\n  ```\n  .admonition-title {\n    outline: $focus-ring-outline;\n  }\n  ```\n\n  Why use ::before? If we put the focus ring on the ::before pseudo-element,\n  we can reposition the focus ring by repositioning the pseudo-element.\n\n  Why reposition? The left edge of the admonition title box does not align\n  with the left edge of the overall admonition box. There is a left border\n  that belongs to the overall box. The border is outside of the admonition\n  title, which means it is also outside of a focus ring around the title. We\n  can make the focus ring bigger, with `outline-offset`, but this will\n  result in a ring that looks off-centered. So we have to pull the ring left\n  and stretch it right. That's what the pseudo-element allows us to do.\n\n  Note: we do not have to do this for collapsible admonitions made with\n  Sphinx Design (as opposed to sphinx-togglebutton).\n  */\n}\n.bd-content .dropdown.admonition.toggle:has(:focus-visible) .admonition-title::before {\n  content: \"\";\n  left: -0.2rem;\n  width: calc(100% + 0.2rem);\n  height: 100%;\n  outline: var(--pst-color-accent) solid 0.1875rem;\n  outline-offset: 0.125rem;\n  border-radius: 0.1875rem;\n}\n.bd-content .dropdown.admonition.toggle:has(:focus-visible):not(.toggle-hidden) .admonition-title::before {\n  border-bottom-left-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.bd-content details.toggle-details summary {\n  border-left: 3px solid var(--pst-color-primary);\n}\n.bd-content details.toggle-details summary .toggle-chevron-right {\n  transform: rotate(90deg);\n  transition: none;\n}\n.bd-content details.toggle-details summary:hover .tb-icon {\n  opacity: 1;\n  scale: 1.1;\n}\n.bd-content details.toggle-details summary .tb-icon {\n  opacity: 0.6;\n}\n.bd-content details.toggle-details summary:hover {\n  filter: brightness(0.9);\n}\nhtml[data-theme=dark] .bd-content details.toggle-details summary:hover {\n  filter: brightness(1.1);\n}\n.bd-content details.toggle-details summary:hover {\n  box-shadow: none;\n}\n.bd-content details.toggle-details summary:focus-visible {\n  outline-offset: 0.125rem;\n}\n.bd-content details.toggle-details[open] .toggle-chevron-right {\n  transform: rotate(-90deg);\n  transition: none;\n}\n.bd-content details.toggle-details[open] :focus-visible {\n  border-bottom-left-radius: 0;\n  border-bottom-right-radius: 0;\n}\n\n/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html,\nhtml .jp-RenderedHTMLCommon {\n  margin: 0.1875rem;\n}\nhtml div.rendered_html table,\nhtml .jp-RenderedHTMLCommon table {\n  table-layout: auto;\n}\n\n.bd-content .nboutput .output_area.rendered_html table.dataframe,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe th ~ th,\n.bd-content .nboutput .output_area.rendered_html table.dataframe th ~ td,\n.bd-content .nboutput .output_area.rendered_html table.dataframe td ~ th,\n.bd-content .nboutput .output_area.rendered_html table.dataframe td ~ td,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe th ~ th,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe th ~ td,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe td ~ th,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe td ~ td {\n  border-left: 1px solid var(--pst-color-table-inner-border);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe thead tr,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe thead tr {\n  background-color: var(--pst-color-table-heading-bg);\n  border-bottom: 2px solid var(--pst-color-primary);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe tbody tr:nth-child(odd),\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe tbody tr:nth-child(odd) {\n  background-color: var(--pst-color-table-row-zebra-low-bg);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe tbody tr:nth-child(even),\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe tbody tr:nth-child(even) {\n  background-color: var(--pst-color-table-row-zebra-high-bg);\n}\n.bd-content .nboutput .output_area.rendered_html table.dataframe tbody tr:hover,\n.bd-content .nboutput .output_area .jp-RenderedHTMLCommon table.dataframe tbody tr:hover {\n  background-color: var(--pst-color-table-row-hover-bg);\n}\nhtml[data-theme=dark] .bd-content .nboutput .output_area.rendered_html:not(:has(table.dataframe)),\nhtml[data-theme=dark] .bd-content .nboutput .output_area .widget-subarea {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\nhtml[data-theme=dark] .bd-content .nboutput .output_area.stderr {\n  background-color: var(--pst-color-danger);\n}\n\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\nhtml[data-theme=light] .nbinput.container .prompt pre {\n  color: #005b82;\n}\nhtml[data-theme=dark] .nbinput.container .prompt pre {\n  color: #00e0e0;\n}\n\nhtml[data-theme=light] .nboutput.container .prompt pre {\n  color: #a12236;\n}\nhtml[data-theme=dark] .nboutput.container .prompt pre {\n  color: #ffa07a;\n}\n\n/*******************************************************************************\n * myst NB\n */\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n.bd-content div.cell_output table.dataframe {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n}\n.bd-content div.cell_output table.dataframe th ~ th,\n.bd-content div.cell_output table.dataframe th ~ td,\n.bd-content div.cell_output table.dataframe td ~ th,\n.bd-content div.cell_output table.dataframe td ~ td {\n  border-left: 1px solid var(--pst-color-table-inner-border);\n}\n.bd-content div.cell_output table.dataframe thead tr {\n  background-color: var(--pst-color-table-heading-bg);\n  border-bottom: 2px solid var(--pst-color-primary);\n}\n.bd-content div.cell_output table.dataframe tbody tr:nth-child(odd) {\n  background-color: var(--pst-color-table-row-zebra-low-bg);\n}\n.bd-content div.cell_output table.dataframe tbody tr:nth-child(even) {\n  background-color: var(--pst-color-table-row-zebra-high-bg);\n}\n.bd-content div.cell_output table.dataframe tbody tr:hover {\n  background-color: var(--pst-color-table-row-hover-bg);\n}\nhtml[data-theme=dark] .bd-content div.cell_output img,\nhtml[data-theme=dark] .bd-content div.cell_output .text_html:not(:has(table.dataframe)),\nhtml[data-theme=dark] .bd-content div.cell_output .widget-subarea {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n\n.bd-content div.cell_input {\n  display: flex;\n  flex-direction: column;\n  justify-content: stretch;\n}\n.bd-content div.cell_input,\n.bd-content div.output {\n  border-radius: 0.25rem;\n}\n.bd-content div.output table {\n  table-layout: auto;\n}\n\n/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n\n/**\n * /search.html page special-cases\n */\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n}\n.bd-search-container div#search-results > h2, .bd-search-container div#search-results > .h2 {\n  font-size: var(--pst-font-size-icon);\n  margin-top: 1rem;\n}\n.bd-search-container div#search-results p.search-summary {\n  color: var(--pst-color-text-muted);\n}\n.bd-search-container ul.search {\n  margin: 0;\n  list-style: none;\n}\n.bd-search-container ul.search li {\n  background-image: none;\n  padding: 1rem 0;\n  margin: 1rem 0;\n  border-top: 1px solid var(--pst-color-text-muted);\n}\n.bd-search-container ul.search li > a {\n  font-size: 1.2em;\n}\n.bd-search-container ul.search li div.context,\n.bd-search-container ul.search li p.context {\n  color: var(--pst-color-text-base);\n  margin: 0.5em 0 0;\n}\n.bd-search-container ul.search li div.context a::before,\n.bd-search-container ul.search li p.context a::before {\n  content: \"#\";\n  padding-right: 0.2em;\n  color: var(--pst-color-text-muted);\n}","@mixin bsBanner($file) {\n  /*!\n   * Bootstrap #{$file} v5.3.3 (https://getbootstrap.com/)\n   * Copyright 2011-2024 The Bootstrap Authors\n   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n   */\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb {\n    --#{$prefix}#{$color}-rgb: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-text {\n    --#{$prefix}#{$color}-text-emphasis: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-bg-subtle {\n    --#{$prefix}#{$color}-bg-subtle: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-border-subtle {\n    --#{$prefix}#{$color}-border-subtle: #{$value};\n  }\n\n  --#{$prefix}white-rgb: #{to-rgb($white)};\n  --#{$prefix}black-rgb: #{to-rgb($black)};\n\n  // Fonts\n\n  // Note: Use `inspect` for lists so that quoted items keep the quotes.\n  // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n  --#{$prefix}font-sans-serif: #{inspect($font-family-sans-serif)};\n  --#{$prefix}font-monospace: #{inspect($font-family-monospace)};\n  --#{$prefix}gradient: #{$gradient};\n\n  // Root and body\n  // scss-docs-start root-body-variables\n  @if $font-size-root != null {\n    --#{$prefix}root-font-size: #{$font-size-root};\n  }\n  --#{$prefix}body-font-family: #{inspect($font-family-base)};\n  @include rfs($font-size-base, --#{$prefix}body-font-size);\n  --#{$prefix}body-font-weight: #{$font-weight-base};\n  --#{$prefix}body-line-height: #{$line-height-base};\n  @if $body-text-align != null {\n    --#{$prefix}body-text-align: #{$body-text-align};\n  }\n\n  --#{$prefix}body-color: #{$body-color};\n  --#{$prefix}body-color-rgb: #{to-rgb($body-color)};\n  --#{$prefix}body-bg: #{$body-bg};\n  --#{$prefix}body-bg-rgb: #{to-rgb($body-bg)};\n\n  --#{$prefix}emphasis-color: #{$body-emphasis-color};\n  --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color)};\n\n  --#{$prefix}secondary-color: #{$body-secondary-color};\n  --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color)};\n  --#{$prefix}secondary-bg: #{$body-secondary-bg};\n  --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg)};\n\n  --#{$prefix}tertiary-color: #{$body-tertiary-color};\n  --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color)};\n  --#{$prefix}tertiary-bg: #{$body-tertiary-bg};\n  --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg)};\n  // scss-docs-end root-body-variables\n\n  --#{$prefix}heading-color: #{$headings-color};\n\n  --#{$prefix}link-color: #{$link-color};\n  --#{$prefix}link-color-rgb: #{to-rgb($link-color)};\n  --#{$prefix}link-decoration: #{$link-decoration};\n\n  --#{$prefix}link-hover-color: #{$link-hover-color};\n  --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color)};\n\n  @if $link-hover-decoration != null {\n    --#{$prefix}link-hover-decoration: #{$link-hover-decoration};\n  }\n\n  --#{$prefix}code-color: #{$code-color};\n  --#{$prefix}highlight-color: #{$mark-color};\n  --#{$prefix}highlight-bg: #{$mark-bg};\n\n  // scss-docs-start root-border-var\n  --#{$prefix}border-width: #{$border-width};\n  --#{$prefix}border-style: #{$border-style};\n  --#{$prefix}border-color: #{$border-color};\n  --#{$prefix}border-color-translucent: #{$border-color-translucent};\n\n  --#{$prefix}border-radius: #{$border-radius};\n  --#{$prefix}border-radius-sm: #{$border-radius-sm};\n  --#{$prefix}border-radius-lg: #{$border-radius-lg};\n  --#{$prefix}border-radius-xl: #{$border-radius-xl};\n  --#{$prefix}border-radius-xxl: #{$border-radius-xxl};\n  --#{$prefix}border-radius-2xl: var(--#{$prefix}border-radius-xxl); // Deprecated in v5.3.0 for consistency\n  --#{$prefix}border-radius-pill: #{$border-radius-pill};\n  // scss-docs-end root-border-var\n\n  --#{$prefix}box-shadow: #{$box-shadow};\n  --#{$prefix}box-shadow-sm: #{$box-shadow-sm};\n  --#{$prefix}box-shadow-lg: #{$box-shadow-lg};\n  --#{$prefix}box-shadow-inset: #{$box-shadow-inset};\n\n  // Focus styles\n  // scss-docs-start root-focus-variables\n  --#{$prefix}focus-ring-width: #{$focus-ring-width};\n  --#{$prefix}focus-ring-opacity: #{$focus-ring-opacity};\n  --#{$prefix}focus-ring-color: #{$focus-ring-color};\n  // scss-docs-end root-focus-variables\n\n  // scss-docs-start root-form-validation-variables\n  --#{$prefix}form-valid-color: #{$form-valid-color};\n  --#{$prefix}form-valid-border-color: #{$form-valid-border-color};\n  --#{$prefix}form-invalid-color: #{$form-invalid-color};\n  --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color};\n  // scss-docs-end root-form-validation-variables\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark, true) {\n    color-scheme: dark;\n\n    // scss-docs-start root-dark-mode-vars\n    --#{$prefix}body-color: #{$body-color-dark};\n    --#{$prefix}body-color-rgb: #{to-rgb($body-color-dark)};\n    --#{$prefix}body-bg: #{$body-bg-dark};\n    --#{$prefix}body-bg-rgb: #{to-rgb($body-bg-dark)};\n\n    --#{$prefix}emphasis-color: #{$body-emphasis-color-dark};\n    --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color-dark)};\n\n    --#{$prefix}secondary-color: #{$body-secondary-color-dark};\n    --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color-dark)};\n    --#{$prefix}secondary-bg: #{$body-secondary-bg-dark};\n    --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg-dark)};\n\n    --#{$prefix}tertiary-color: #{$body-tertiary-color-dark};\n    --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color-dark)};\n    --#{$prefix}tertiary-bg: #{$body-tertiary-bg-dark};\n    --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg-dark)};\n\n    @each $color, $value in $theme-colors-text-dark {\n      --#{$prefix}#{$color}-text-emphasis: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-bg-subtle-dark {\n      --#{$prefix}#{$color}-bg-subtle: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-border-subtle-dark {\n      --#{$prefix}#{$color}-border-subtle: #{$value};\n    }\n\n    --#{$prefix}heading-color: #{$headings-color-dark};\n\n    --#{$prefix}link-color: #{$link-color-dark};\n    --#{$prefix}link-hover-color: #{$link-hover-color-dark};\n    --#{$prefix}link-color-rgb: #{to-rgb($link-color-dark)};\n    --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color-dark)};\n\n    --#{$prefix}code-color: #{$code-color-dark};\n    --#{$prefix}highlight-color: #{$mark-color-dark};\n    --#{$prefix}highlight-bg: #{$mark-bg-dark};\n\n    --#{$prefix}border-color: #{$border-color-dark};\n    --#{$prefix}border-color-translucent: #{$border-color-translucent-dark};\n\n    --#{$prefix}form-valid-color: #{$form-valid-color-dark};\n    --#{$prefix}form-valid-border-color: #{$form-valid-border-color-dark};\n    --#{$prefix}form-invalid-color: #{$form-invalid-color-dark};\n    --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color-dark};\n    // scss-docs-end root-dark-mode-vars\n  }\n}\n","// stylelint-disable scss/dimension-no-non-numeric-values\n\n// SCSS RFS mixin\n//\n// Automated responsive values for font sizes, paddings, margins and much more\n//\n// Licensed under MIT (https://github.com/twbs/rfs/blob/main/LICENSE)\n\n// Configuration\n\n// Base value\n$rfs-base-value: 1.25rem !default;\n$rfs-unit: rem !default;\n\n@if $rfs-unit != rem and $rfs-unit != px {\n  @error \"`#{$rfs-unit}` is not a valid unit for $rfs-unit. Use `px` or `rem`.\";\n}\n\n// Breakpoint at where values start decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n@if $rfs-breakpoint-unit != px and $rfs-breakpoint-unit != em and $rfs-breakpoint-unit != rem {\n  @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n}\n\n// Resize values based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != number or $rfs-factor <= 1 {\n  @error \"`#{$rfs-factor}` is not a valid  $rfs-factor, it must be greater than 1.\";\n}\n\n// Mode. Possibilities: \"min-media-query\", \"max-media-query\"\n$rfs-mode: min-media-query !default;\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-rfs to false\n$enable-rfs: true !default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n  $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n  $dividend: abs($dividend);\n  $divisor: abs($divisor);\n  @if $dividend == 0 {\n    @return 0;\n  }\n  @if $divisor == 0 {\n    @error \"Cannot divide by 0\";\n  }\n  $remainder: $dividend;\n  $result: 0;\n  $factor: 10;\n  @while ($remainder > 0 and $precision >= 0) {\n    $quotient: 0;\n    @while ($remainder >= $divisor) {\n      $remainder: $remainder - $divisor;\n      $quotient: $quotient + 1;\n    }\n    $result: $result * 10 + $quotient;\n    $factor: $factor * .1;\n    $remainder: $remainder * 10;\n    $precision: $precision - 1;\n    @if ($precision < 0 and $remainder >= $divisor * 5) {\n      $result: $result + 1;\n    }\n  }\n  $result: $result * $factor * $sign;\n  $dividend-unit: unit($dividend);\n  $divisor-unit: unit($divisor);\n  $unit-map: (\n    \"px\": 1px,\n    \"rem\": 1rem,\n    \"em\": 1em,\n    \"%\": 1%\n  );\n  @if ($dividend-unit != $divisor-unit and map-has-key($unit-map, $dividend-unit)) {\n    $result: $result * map-get($unit-map, $dividend-unit);\n  }\n  @return $result;\n}\n\n// Remove px-unit from $rfs-base-value for calculations\n@if $rfs-base-value-unit == px {\n  $rfs-base-value: divide($rfs-base-value, $rfs-base-value * 0 + 1);\n}\n@else if $rfs-base-value-unit == rem {\n  $rfs-base-value: divide($rfs-base-value, divide($rfs-base-value * 0 + 1, $rfs-rem-value));\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == px {\n  $rfs-breakpoint: divide($rfs-breakpoint, $rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == rem or $rfs-breakpoint-unit-cache == \"em\" {\n  $rfs-breakpoint: divide($rfs-breakpoint, divide($rfs-breakpoint * 0 + 1, $rfs-rem-value));\n}\n\n// Calculate the media query value\n$rfs-mq-value: if($rfs-breakpoint-unit == px, #{$rfs-breakpoint}px, #{divide($rfs-breakpoint, $rfs-rem-value)}#{$rfs-breakpoint-unit});\n$rfs-mq-property-width: if($rfs-mode == max-media-query, max-width, min-width);\n$rfs-mq-property-height: if($rfs-mode == max-media-query, max-height, min-height);\n\n// Internal mixin used to determine which media query needs to be used\n@mixin _rfs-media-query {\n  @if $rfs-two-dimensional {\n    @if $rfs-mode == max-media-query {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}), (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n    @else {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) and (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n  }\n  @else {\n    @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) {\n      @content;\n    }\n  }\n}\n\n// Internal mixin that adds disable classes to the selector if needed.\n@mixin _rfs-rule {\n  @if $rfs-class == disable and $rfs-mode == max-media-query {\n    // Adding an extra class increases specificity, which prevents the media query to override the property\n    &,\n    .disable-rfs &,\n    &.disable-rfs {\n      @content;\n    }\n  }\n  @else if $rfs-class == enable and $rfs-mode == min-media-query {\n    .enable-rfs &,\n    &.enable-rfs {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Internal mixin that adds enable classes to the selector if needed.\n@mixin _rfs-media-query-rule {\n\n  @if $rfs-class == enable {\n    @if $rfs-mode == min-media-query {\n      @content;\n    }\n\n    @include _rfs-media-query () {\n      .enable-rfs &,\n      &.enable-rfs {\n        @content;\n      }\n    }\n  }\n  @else {\n    @if $rfs-class == disable and $rfs-mode == min-media-query {\n      .disable-rfs &,\n      &.disable-rfs {\n        @content;\n      }\n    }\n    @include _rfs-media-query () {\n      @content;\n    }\n  }\n}\n\n// Helper function to get the formatted non-responsive value\n@function rfs-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    }\n    @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      @if $unit == px {\n        // Convert to rem if needed\n        $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $value * 0 + $rfs-rem-value)}rem, $value);\n      }\n      @else if $unit == rem {\n        // Convert to px if needed\n        $val: $val + \" \" + if($rfs-unit == px, #{divide($value, $value * 0 + 1) * $rfs-rem-value}px, $value);\n      } @else {\n        // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n        $val: $val + \" \" + $value;\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// Helper function to get the responsive value calculated by RFS\n@function rfs-fluid-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    } @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n      @if not $unit or $unit != px and $unit != rem {\n        $val: $val + \" \" + $value;\n      } @else {\n        // Remove unit from $value for calculations\n        $value: divide($value, $value * 0 + if($unit == px, 1, divide(1, $rfs-rem-value)));\n\n        // Only add the media query if the value is greater than the minimum value\n        @if abs($value) <= $rfs-base-value or not $enable-rfs {\n          $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $rfs-rem-value)}rem, #{$value}px);\n        }\n        @else {\n          // Calculate the minimum value\n          $value-min: $rfs-base-value + divide(abs($value) - $rfs-base-value, $rfs-factor);\n\n          // Calculate difference between $value and the minimum value\n          $value-diff: abs($value) - $value-min;\n\n          // Base value formatting\n          $min-width: if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated value\n          $val: $val + \" calc(\" + $min-width + if($value < 0, \" - \", \" + \") + $variable-width + \")\";\n        }\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// RFS mixin\n@mixin rfs($values, $property: font-size) {\n  @if $values != null {\n    $val: rfs-value($values);\n    $fluid-val: rfs-fluid-value($values);\n\n    // Do not print the media query if responsive & non-responsive values are the same\n    @if $val == $fluid-val {\n      #{$property}: $val;\n    }\n    @else {\n      @include _rfs-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $val, $fluid-val);\n\n        // Include safari iframe resize fix if needed\n        min-width: if($rfs-safari-iframe-resize-bug-fix, (0 * 1vw), null);\n      }\n\n      @include _rfs-media-query-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $fluid-val, $val);\n      }\n    }\n  }\n}\n\n// Shorthand helper mixins\n@mixin font-size($value) {\n  @include rfs($value);\n}\n\n@mixin padding($value) {\n  @include rfs($value, padding);\n}\n\n@mixin padding-top($value) {\n  @include rfs($value, padding-top);\n}\n\n@mixin padding-right($value) {\n  @include rfs($value, padding-right);\n}\n\n@mixin padding-bottom($value) {\n  @include rfs($value, padding-bottom);\n}\n\n@mixin padding-left($value) {\n  @include rfs($value, padding-left);\n}\n\n@mixin margin($value) {\n  @include rfs($value, margin);\n}\n\n@mixin margin-top($value) {\n  @include rfs($value, margin-top);\n}\n\n@mixin margin-right($value) {\n  @include rfs($value, margin-right);\n}\n\n@mixin margin-bottom($value) {\n  @include rfs($value, margin-bottom);\n}\n\n@mixin margin-left($value) {\n  @include rfs($value, margin-left);\n}\n","// scss-docs-start color-mode-mixin\n@mixin color-mode($mode: light, $root: false) {\n  @if $color-mode-type == \"media-query\" {\n    @if $root == true {\n      @media (prefers-color-scheme: $mode) {\n        :root {\n          @content;\n        }\n      }\n    } @else {\n      @media (prefers-color-scheme: $mode) {\n        @content;\n      }\n    }\n  } @else {\n    [data-bs-theme=\"#{$mode}\"] {\n      @content;\n    }\n  }\n}\n// scss-docs-end color-mode-mixin\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n\n// Root\n//\n// Ability to the value of the root font sizes, affecting the value of `rem`.\n// null by default, thus nothing is generated.\n\n:root {\n  @if $font-size-root != null {\n    @include font-size(var(--#{$prefix}root-font-size));\n  }\n\n  @if $enable-smooth-scroll {\n    @media (prefers-reduced-motion: no-preference) {\n      scroll-behavior: smooth;\n    }\n  }\n}\n\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Prevent adjustments of font size after orientation changes in iOS.\n// 4. Change the default tap highlight to be completely transparent in iOS.\n\n// scss-docs-start reboot-body-rules\nbody {\n  margin: 0; // 1\n  font-family: var(--#{$prefix}body-font-family);\n  @include font-size(var(--#{$prefix}body-font-size));\n  font-weight: var(--#{$prefix}body-font-weight);\n  line-height: var(--#{$prefix}body-line-height);\n  color: var(--#{$prefix}body-color);\n  text-align: var(--#{$prefix}body-text-align);\n  background-color: var(--#{$prefix}body-bg); // 2\n  -webkit-text-size-adjust: 100%; // 3\n  -webkit-tap-highlight-color: rgba($black, 0); // 4\n}\n// scss-docs-end reboot-body-rules\n\n\n// Content grouping\n//\n// 1. Reset Firefox's gray color\n\nhr {\n  margin: $hr-margin-y 0;\n  color: $hr-color; // 1\n  border: 0;\n  border-top: $hr-border-width solid $hr-border-color;\n  opacity: $hr-opacity;\n}\n\n\n// Typography\n//\n// 1. Remove top margins from headings\n//    By default, `<h1>`-`<h6>` all receive top and bottom margins. We nuke the top\n//    margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n  margin-top: 0; // 1\n  margin-bottom: $headings-margin-bottom;\n  font-family: $headings-font-family;\n  font-style: $headings-font-style;\n  font-weight: $headings-font-weight;\n  line-height: $headings-line-height;\n  color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n  @extend %heading;\n  @include font-size($h1-font-size);\n}\n\nh2 {\n  @extend %heading;\n  @include font-size($h2-font-size);\n}\n\nh3 {\n  @extend %heading;\n  @include font-size($h3-font-size);\n}\n\nh4 {\n  @extend %heading;\n  @include font-size($h4-font-size);\n}\n\nh5 {\n  @extend %heading;\n  @include font-size($h5-font-size);\n}\n\nh6 {\n  @extend %heading;\n  @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `<p>`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n  text-decoration: underline dotted; // 1\n  cursor: help; // 2\n  text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n  margin-bottom: .5rem;\n  margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n  font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n  @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n  padding: $mark-padding;\n  color: var(--#{$prefix}highlight-color);\n  background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n  position: relative;\n  @include font-size($sub-sup-font-size);\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n  color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n  text-decoration: $link-decoration;\n\n  &:hover {\n    --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n    text-decoration: $link-hover-decoration;\n  }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n  &,\n  &:hover {\n    color: inherit;\n    text-decoration: none;\n  }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: $font-family-code;\n  @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n  display: block;\n  margin-top: 0; // 1\n  margin-bottom: 1rem; // 2\n  overflow: auto; // 3\n  @include font-size($code-font-size);\n  color: $pre-color;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    @include font-size(inherit);\n    color: inherit;\n    word-break: normal;\n  }\n}\n\ncode {\n  @include font-size($code-font-size);\n  color: var(--#{$prefix}code-color);\n  word-wrap: break-word;\n\n  // Streamline the style when inside anchors to avoid broken underline and more\n  a > & {\n    color: inherit;\n  }\n}\n\nkbd {\n  padding: $kbd-padding-y $kbd-padding-x;\n  @include font-size($kbd-font-size);\n  color: $kbd-color;\n  background-color: $kbd-bg;\n  @include border-radius($border-radius-sm);\n\n  kbd {\n    padding: 0;\n    @include font-size(1em);\n    font-weight: $nested-kbd-font-weight;\n  }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n  margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: $table-cell-padding-y;\n  padding-bottom: $table-cell-padding-y;\n  color: $table-caption-color;\n  text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `<td>` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n  font-weight: $table-th-font-weight; // 1\n  text-align: inherit; // 2\n  text-align: -webkit-match-parent; // 3\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\n\n// Forms\n//\n// 1. Allow labels to use `margin` for spacing.\n\nlabel {\n  display: inline-block; // 1\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n// See https://github.com/twbs/bootstrap/issues/24093\n\nbutton {\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 0;\n}\n\n// Explicitly remove focus outline in Chromium when it shouldn't be\n// visible (e.g. as result of mouse click or touch tap). It already\n// should be doing this automatically, but seems to currently be\n// confused and applies its very visible two-tone outline anyway.\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\n// 1. Remove the margin in Firefox and Safari\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0; // 1\n  font-family: inherit;\n  @include font-size(inherit);\n  line-height: inherit;\n}\n\n// Remove the inheritance of text transform in Firefox\nbutton,\nselect {\n  text-transform: none;\n}\n// Set the cursor for non-`<button>` buttons\n//\n// Details at https://github.com/twbs/bootstrap/pull/30562\n[role=\"button\"] {\n  cursor: pointer;\n}\n\nselect {\n  // Remove the inheritance of word-wrap in Safari.\n  // See https://github.com/twbs/bootstrap/issues/24990\n  word-wrap: normal;\n\n  // Undo the opacity change from Chrome\n  &:disabled {\n    opacity: 1;\n  }\n}\n\n// Remove the dropdown arrow only from text type inputs built with datalists in Chrome.\n// See https://stackoverflow.com/a/54997118\n\n[list]:not([type=\"date\"]):not([type=\"datetime-local\"]):not([type=\"month\"]):not([type=\"week\"]):not([type=\"time\"])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n//    controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\n// 3. Opinionated: add \"hand\" cursor to non-disabled button elements.\n\nbutton,\n[type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n\n  @if $enable-button-pointers {\n    &:not(:disabled) {\n      cursor: pointer; // 3\n    }\n  }\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\n// 1. Textareas should really only resize vertically so they don't break their (horizontal) containers.\n\ntextarea {\n  resize: vertical; // 1\n}\n\n// 1. Browsers set a default `min-width: min-content;` on fieldsets,\n//    unlike e.g. `<div>`s, which have `min-width: 0;` by default.\n//    So we reset that to ensure fieldsets behave more like a standard block element.\n//    See https://github.com/twbs/bootstrap/issues/12359\n//    and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n// 2. Reset the default outline behavior of fieldsets so they don't affect page layout.\n\nfieldset {\n  min-width: 0; // 1\n  padding: 0; // 2\n  margin: 0; // 2\n  border: 0; // 2\n}\n\n// 1. By using `float: left`, the legend will behave like a block element.\n//    This way the border of a fieldset wraps around the legend if present.\n// 2. Fix wrapping bug.\n//    See https://github.com/twbs/bootstrap/issues/29712\n\nlegend {\n  float: left; // 1\n  width: 100%;\n  padding: 0;\n  margin-bottom: $legend-margin-bottom;\n  @include font-size($legend-font-size);\n  font-weight: $legend-font-weight;\n  line-height: inherit;\n\n  + * {\n    clear: left; // 2\n  }\n}\n\n// Fix height of inputs with a type of datetime-local, date, month, week, or time\n// See https://github.com/twbs/bootstrap/issues/18842\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n// 1. This overrides the extra rounded corners on search inputs in iOS so that our\n//    `.form-control` class can properly style them. Note that this cannot simply\n//    be added to `.form-control` as it's not specific enough. For details, see\n//    https://github.com/twbs/bootstrap/issues/11586.\n// 2. Correct the outline style in Safari.\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  outline-offset: -2px; // 2\n}\n\n// 1. A few input types should stay LTR\n// See https://rtlstyling.com/posts/rtl-styling#form-inputs\n// 2. RTL only output\n// See https://rtlcss.com/learn/usage-guide/control-directives/#raw\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n\n// Remove the inner padding in Chrome and Safari on macOS.\n\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n// Remove padding around color pickers in webkit browsers\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n\n// 1. Inherit font family and line height for file input buttons\n// 2. Correct the inability to style clickable types in iOS and Safari.\n\n::file-selector-button {\n  font: inherit; // 1\n  -webkit-appearance: button; // 2\n}\n\n// Correct element displays\n\noutput {\n  display: inline-block;\n}\n\n// Remove border from iframe\n\niframe {\n  border: 0;\n}\n\n// Summary\n//\n// 1. Add the correct display in all browsers\n\nsummary {\n  display: list-item; // 1\n  cursor: pointer;\n}\n\n\n// Progress\n//\n// Add the correct vertical alignment in Chrome, Firefox, and Opera.\n\nprogress {\n  vertical-align: baseline;\n}\n\n\n// Hidden attribute\n//\n// Always hide an element with the `hidden` HTML attribute.\n\n[hidden] {\n  display: none !important;\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n// fusv-disable\n$accordion-button-focus-border-color:     $input-focus-border-color !default; // Deprecated in v5.3.3\n// fusv-enable\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-active-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n\n@import \"variables-dark\"; // TODO: can be removed safely in v6, only here to avoid breaking changes in v5.3\n","// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n  $return: ();\n  @each $value in $radius {\n    @if type-of($value) == number {\n      $return: append($return, max($value, 0));\n    } @else {\n      $return: append($return, $value);\n    }\n  }\n  @return $return;\n}\n\n// scss-docs-start border-radius-mixins\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n  @if $enable-rounded {\n    border-radius: valid-radius($radius);\n  }\n  @else if $fallback-border-radius != false {\n    border-radius: $fallback-border-radius;\n  }\n}\n\n@mixin border-top-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n// scss-docs-end border-radius-mixins\n","// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n","// Override bootstrap variables\n$spacer: 1rem;\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n$dropdown-link-hover-bg: var(--pst-color-surface);\n\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n$focus-ring-width: 0.1875rem; // 3px at 100% zoom (0.1875 * 16px base font = 3px)\n$focus-ring-opacity: 1;\n$focus-ring-color: var(--pst-color-accent);\n$focus-ring-blur: 0;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color;\n\n// For many elements, we do not use `outline-offset`. For some we set the offset\n// equal to the focus ring width (either outwards or inwards). But for some\n// other elements (e.g., collapsible admonitions) we set it to this value.\n$focus-ring-offset: 0.125rem; // 2px at 100% zoom (0.125 * 16px base font = 2px)\n\n// outline creates the same style of focus ring, it just uses CSS outline instead of box shadow\n$focus-ring-outline: $focus-ring-color solid $focus-ring-width;\n$btn-focus-box-shadow: $focus-ring-box-shadow;\n","// Responsive images (ensure images don't scale beyond their parents)\n//\n// This is purposefully opt-in via an explicit class rather than being the default for all `<img>`s.\n// We previously tried the \"images are responsive by default\" approach in Bootstrap v2,\n// and abandoned it in Bootstrap v3 because it breaks lots of third-party widgets (including Google Maps)\n// which weren't expecting the images within themselves to be involuntarily resized.\n// See also https://github.com/twbs/bootstrap/issues/18178\n.img-fluid {\n  @include img-fluid();\n}\n\n\n// Image thumbnails\n.img-thumbnail {\n  padding: $thumbnail-padding;\n  background-color: $thumbnail-bg;\n  border: $thumbnail-border-width solid $thumbnail-border-color;\n  @include border-radius($thumbnail-border-radius);\n  @include box-shadow($thumbnail-box-shadow);\n\n  // Keep them at most 100% wide\n  @include img-fluid();\n}\n\n//\n// Figures\n//\n\n.figure {\n  // Ensures the caption's text aligns with the image.\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: $spacer * .5;\n  line-height: 1;\n}\n\n.figure-caption {\n  @include font-size($figure-caption-font-size);\n  color: $figure-caption-color;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid {\n  // Part 1: Set a maximum relative to the parent\n  max-width: 100%;\n  // Part 2: Override the height to auto, otherwise images will be stretched\n  // when setting a width and height attribute on the img element.\n  height: auto;\n}\n","// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n@if $enable-container-classes {\n  // Single container class with breakpoint max-widths\n  .container,\n  // 100% wide container at all breakpoints\n  .container-fluid {\n    @include make-container();\n  }\n\n  // Responsive containers that are 100% wide until a breakpoint\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    .container-#{$breakpoint} {\n      @extend .container-fluid;\n    }\n\n    @include media-breakpoint-up($breakpoint, $grid-breakpoints) {\n      %responsive-container-#{$breakpoint} {\n        max-width: $container-max-width;\n      }\n\n      // Extend each breakpoint which is smaller or equal to the current breakpoint\n      $extend-breakpoint: true;\n\n      @each $name, $width in $grid-breakpoints {\n        @if ($extend-breakpoint) {\n          .container#{breakpoint-infix($name, $grid-breakpoints)} {\n            @extend %responsive-container-#{$breakpoint};\n          }\n\n          // Once the current breakpoint is reached, stop extending\n          @if ($breakpoint == $name) {\n            $extend-breakpoint: false;\n          }\n        }\n      }\n    }\n  }\n}\n","// Container mixins\n\n@mixin make-container($gutter: $container-padding-x) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-right: auto;\n  margin-left: auto;\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n","// Row\n//\n// Rows contain your columns.\n\n:root {\n  @each $name, $value in $grid-breakpoints {\n    --#{$prefix}breakpoint-#{$name}: #{$value};\n  }\n}\n\n@if $enable-grid-classes {\n  .row {\n    @include make-row();\n\n    > * {\n      @include make-col-ready();\n    }\n  }\n}\n\n@if $enable-cssgrid {\n  .grid {\n    display: grid;\n    grid-template-rows: repeat(var(--#{$prefix}rows, 1), 1fr);\n    grid-template-columns: repeat(var(--#{$prefix}columns, #{$grid-columns}), 1fr);\n    gap: var(--#{$prefix}gap, #{$grid-gutter-width});\n\n    @include make-cssgrid();\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n@if $enable-grid-classes {\n  @include make-grid-columns();\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is an invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Basic Bootstrap table\n//\n\n.table {\n  // Reset needed for nesting tables\n  --#{$prefix}table-color-type: initial;\n  --#{$prefix}table-bg-type: initial;\n  --#{$prefix}table-color-state: initial;\n  --#{$prefix}table-bg-state: initial;\n  // End of reset\n  --#{$prefix}table-color: #{$table-color};\n  --#{$prefix}table-bg: #{$table-bg};\n  --#{$prefix}table-border-color: #{$table-border-color};\n  --#{$prefix}table-accent-bg: #{$table-accent-bg};\n  --#{$prefix}table-striped-color: #{$table-striped-color};\n  --#{$prefix}table-striped-bg: #{$table-striped-bg};\n  --#{$prefix}table-active-color: #{$table-active-color};\n  --#{$prefix}table-active-bg: #{$table-active-bg};\n  --#{$prefix}table-hover-color: #{$table-hover-color};\n  --#{$prefix}table-hover-bg: #{$table-hover-bg};\n\n  width: 100%;\n  margin-bottom: $spacer;\n  vertical-align: $table-cell-vertical-align;\n  border-color: var(--#{$prefix}table-border-color);\n\n  // Target th & td\n  // We need the child combinator to prevent styles leaking to nested tables which doesn't have a `.table` class.\n  // We use the universal selectors here to simplify the selector (else we would need 6 different selectors).\n  // Another advantage is that this generates less code and makes the selector less specific making it easier to override.\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y $table-cell-padding-x;\n    // Following the precept of cascades: https://codepen.io/miriamsuzanne/full/vYNgodb\n    color: var(--#{$prefix}table-color-state, var(--#{$prefix}table-color-type, var(--#{$prefix}table-color)));\n    background-color: var(--#{$prefix}table-bg);\n    border-bottom-width: $table-border-width;\n    box-shadow: inset 0 0 0 9999px var(--#{$prefix}table-bg-state, var(--#{$prefix}table-bg-type, var(--#{$prefix}table-accent-bg)));\n  }\n\n  > tbody {\n    vertical-align: inherit;\n  }\n\n  > thead {\n    vertical-align: bottom;\n  }\n}\n\n.table-group-divider {\n  border-top: calc(#{$table-border-width} * 2) solid $table-group-separator-color; // stylelint-disable-line function-disallowed-list\n}\n\n//\n// Change placement of captions with a class\n//\n\n.caption-top {\n  caption-side: top;\n}\n\n\n//\n// Condensed table w/ half padding\n//\n\n.table-sm {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y-sm $table-cell-padding-x-sm;\n  }\n}\n\n\n// Border versions\n//\n// Add or remove borders all around the table and between all the columns.\n//\n// When borders are added on all sides of the cells, the corners can render odd when\n// these borders do not have the same color or if they are semi-transparent.\n// Therefore we add top and border bottoms to the `tr`s and left and right borders\n// to the `td`s or `th`s\n\n.table-bordered {\n  > :not(caption) > * {\n    border-width: $table-border-width 0;\n\n    // stylelint-disable-next-line selector-max-universal\n    > * {\n      border-width: 0 $table-border-width;\n    }\n  }\n}\n\n.table-borderless {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    border-bottom-width: 0;\n  }\n\n  > :not(:first-child) {\n    border-top-width: 0;\n  }\n}\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n// For rows\n.table-striped {\n  > tbody > tr:nth-of-type(#{$table-striped-order}) > * {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// For columns\n.table-striped-columns {\n  > :not(caption) > tr > :nth-child(#{$table-striped-columns-order}) {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// Active table\n//\n// The `.table-active` class can be added to highlight rows or cells\n\n.table-active {\n  --#{$prefix}table-color-state: var(--#{$prefix}table-active-color);\n  --#{$prefix}table-bg-state: var(--#{$prefix}table-active-bg);\n}\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover > * {\n    --#{$prefix}table-color-state: var(--#{$prefix}table-hover-color);\n    --#{$prefix}table-bg-state: var(--#{$prefix}table-hover-bg);\n  }\n}\n\n\n// Table variants\n//\n// Table variants set the table cell backgrounds, border colors\n// and the colors of the striped, hovered & active tables\n\n@each $color, $value in $table-variants {\n  @include table-variant($color, $value);\n}\n\n// Responsive tables\n//\n// Generate series of `.table-responsive-*` classes for configuring the screen\n// size of where your table will overflow.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n  @include media-breakpoint-down($breakpoint) {\n    .table-responsive#{$infix} {\n      overflow-x: auto;\n      -webkit-overflow-scrolling: touch;\n    }\n  }\n}\n","// scss-docs-start table-variant\n@mixin table-variant($state, $background) {\n  .table-#{$state} {\n    $color: color-contrast(opaque($body-bg, $background));\n    $hover-bg: mix($color, $background, percentage($table-hover-bg-factor));\n    $striped-bg: mix($color, $background, percentage($table-striped-bg-factor));\n    $active-bg: mix($color, $background, percentage($table-active-bg-factor));\n    $table-border-color: mix($color, $background, percentage($table-border-factor));\n\n    --#{$prefix}table-color: #{$color};\n    --#{$prefix}table-bg: #{$background};\n    --#{$prefix}table-border-color: #{$table-border-color};\n    --#{$prefix}table-striped-bg: #{$striped-bg};\n    --#{$prefix}table-striped-color: #{color-contrast($striped-bg)};\n    --#{$prefix}table-active-bg: #{$active-bg};\n    --#{$prefix}table-active-color: #{color-contrast($active-bg)};\n    --#{$prefix}table-hover-bg: #{$hover-bg};\n    --#{$prefix}table-hover-color: #{color-contrast($hover-bg)};\n\n    color: var(--#{$prefix}table-color);\n    border-color: var(--#{$prefix}table-border-color);\n  }\n}\n// scss-docs-end table-variant\n","//\n// Labels\n//\n\n.form-label {\n  margin-bottom: $form-label-margin-bottom;\n  @include font-size($form-label-font-size);\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  color: $form-label-color;\n}\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align with the form controls.\n.col-form-label {\n  padding-top: add($input-padding-y, $input-border-width);\n  padding-bottom: add($input-padding-y, $input-border-width);\n  margin-bottom: 0; // Override the `<legend>` default\n  @include font-size(inherit); // Override the `<legend>` default\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  line-height: $input-line-height;\n  color: $form-label-color;\n}\n\n.col-form-label-lg {\n  padding-top: add($input-padding-y-lg, $input-border-width);\n  padding-bottom: add($input-padding-y-lg, $input-border-width);\n  @include font-size($input-font-size-lg);\n}\n\n.col-form-label-sm {\n  padding-top: add($input-padding-y-sm, $input-border-width);\n  padding-bottom: add($input-padding-y-sm, $input-border-width);\n  @include font-size($input-font-size-sm);\n}\n","//\n// Form text\n//\n\n.form-text {\n  margin-top: $form-text-margin-top;\n  @include font-size($form-text-font-size);\n  font-style: $form-text-font-style;\n  font-weight: $form-text-font-weight;\n  color: $form-text-color;\n}\n","//\n// General form controls (plus a few specific high-level interventions)\n//\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y $input-padding-x;\n  font-family: $input-font-family;\n  @include font-size($input-font-size);\n  font-weight: $input-font-weight;\n  line-height: $input-line-height;\n  color: $input-color;\n  appearance: none; // Fix appearance for date inputs in Safari\n  background-color: $input-bg;\n  background-clip: padding-box;\n  border: $input-border-width solid $input-border-color;\n\n  // Note: This has no effect on <select>s in some browsers, due to the limited stylability of `<select>`s in CSS.\n  @include border-radius($input-border-radius, 0);\n\n  @include box-shadow($input-box-shadow);\n  @include transition($input-transition);\n\n  &[type=\"file\"] {\n    overflow: hidden; // prevent pseudo element button overlap\n\n    &:not(:disabled):not([readonly]) {\n      cursor: pointer;\n    }\n  }\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  &:focus {\n    color: $input-focus-color;\n    background-color: $input-focus-bg;\n    border-color: $input-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $input-focus-box-shadow;\n    }\n  }\n\n  &::-webkit-date-and-time-value {\n    // On Android Chrome, form-control's \"width: 100%\" makes the input width too small\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    //\n    // On iOS Safari, form-control's \"appearance: none\" + \"width: 100%\" makes the input width too small\n    // Tested under iOS 16.2 / Safari 16.2\n    min-width: 85px; // Seems to be a good minimum safe width\n\n    // Add some height to date inputs on iOS\n    // https://github.com/twbs/bootstrap/issues/23307\n    // TODO: we can remove this workaround once https://bugs.webkit.org/show_bug.cgi?id=198959 is resolved\n    // Multiply line-height by 1em if it has no unit\n    height: if(unit($input-line-height) == \"\", $input-line-height * 1em, $input-line-height);\n\n    // Android Chrome type=\"date\" is taller than the other inputs\n    // because of \"margin: 1px 24px 1px 4px\" inside the shadow DOM\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    margin: 0;\n  }\n\n  // Prevent excessive date input height in Webkit\n  // https://github.com/twbs/bootstrap/issues/34433\n  &::-webkit-datetime-edit {\n    display: block;\n    padding: 0;\n  }\n\n  // Placeholder\n  &::placeholder {\n    color: $input-placeholder-color;\n    // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526.\n    opacity: 1;\n  }\n\n  // Disabled inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &:disabled {\n    color: $input-disabled-color;\n    background-color: $input-disabled-bg;\n    border-color: $input-disabled-border-color;\n    // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655.\n    opacity: 1;\n  }\n\n  // File input buttons theming\n  &::file-selector-button {\n    padding: $input-padding-y $input-padding-x;\n    margin: (-$input-padding-y) (-$input-padding-x);\n    margin-inline-end: $input-padding-x;\n    color: $form-file-button-color;\n    @include gradient-bg($form-file-button-bg);\n    pointer-events: none;\n    border-color: inherit;\n    border-style: solid;\n    border-width: 0;\n    border-inline-end-width: $input-border-width;\n    border-radius: 0; // stylelint-disable-line property-disallowed-list\n    @include transition($btn-transition);\n  }\n\n  &:hover:not(:disabled):not([readonly])::file-selector-button {\n    background-color: $form-file-button-hover-bg;\n  }\n}\n\n// Readonly controls as plain text\n//\n// Apply class to a readonly input to make it appear like regular plain\n// text (without any border, background color, focus indicator)\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y 0;\n  margin-bottom: 0; // match inputs if this class comes on inputs with default margins\n  line-height: $input-line-height;\n  color: $input-plaintext-color;\n  background-color: transparent;\n  border: solid transparent;\n  border-width: $input-border-width 0;\n\n  &:focus {\n    outline: 0;\n  }\n\n  &.form-control-sm,\n  &.form-control-lg {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// Repeated in `_input_group.scss` to avoid Sass extend issues.\n\n.form-control-sm {\n  min-height: $input-height-sm;\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n\n  &::file-selector-button {\n    padding: $input-padding-y-sm $input-padding-x-sm;\n    margin: (-$input-padding-y-sm) (-$input-padding-x-sm);\n    margin-inline-end: $input-padding-x-sm;\n  }\n}\n\n.form-control-lg {\n  min-height: $input-height-lg;\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n\n  &::file-selector-button {\n    padding: $input-padding-y-lg $input-padding-x-lg;\n    margin: (-$input-padding-y-lg) (-$input-padding-x-lg);\n    margin-inline-end: $input-padding-x-lg;\n  }\n}\n\n// Make sure textareas don't shrink too much when resized\n// https://github.com/twbs/bootstrap/pull/29124\n// stylelint-disable selector-no-qualifying-type\ntextarea {\n  &.form-control {\n    min-height: $input-height;\n  }\n\n  &.form-control-sm {\n    min-height: $input-height-sm;\n  }\n\n  &.form-control-lg {\n    min-height: $input-height-lg;\n  }\n}\n// stylelint-enable selector-no-qualifying-type\n\n.form-control-color {\n  width: $form-color-width;\n  height: $input-height;\n  padding: $input-padding-y;\n\n  &:not(:disabled):not([readonly]) {\n    cursor: pointer;\n  }\n\n  &::-moz-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &::-webkit-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &.form-control-sm { height: $input-height-sm; }\n  &.form-control-lg { height: $input-height-lg; }\n}\n","// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n  @if length($transition) == 0 {\n    $transition: $transition-base;\n  }\n\n  @if length($transition) > 1 {\n    @each $value in $transition {\n      @if $value == null or $value == none {\n        @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n      }\n    }\n  }\n\n  @if $enable-transitions {\n    @if nth($transition, 1) != null {\n      transition: $transition;\n    }\n\n    @if $enable-reduced-motion and nth($transition, 1) != null and nth($transition, 1) != none {\n      @media (prefers-reduced-motion: reduce) {\n        transition: none;\n      }\n    }\n  }\n}\n","// Gradients\n\n// scss-docs-start gradient-bg-mixin\n@mixin gradient-bg($color: null) {\n  background-color: $color;\n\n  @if $enable-gradients {\n    background-image: var(--#{$prefix}gradient);\n  }\n}\n// scss-docs-end gradient-bg-mixin\n\n// scss-docs-start gradient-mixins\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n  background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: null, $end-percent: null) {\n  background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n  background-image: linear-gradient($deg, $start-color, $end-color);\n}\n\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n  background-image: radial-gradient(circle, $inner-color, $outer-color);\n}\n\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n  background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n// scss-docs-end gradient-mixins\n","// Select\n//\n// Replaces the browser default select with a custom one, mostly pulled from\n// https://primer.github.io/.\n\n.form-select {\n  --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator)};\n\n  display: block;\n  width: 100%;\n  padding: $form-select-padding-y $form-select-indicator-padding $form-select-padding-y $form-select-padding-x;\n  font-family: $form-select-font-family;\n  @include font-size($form-select-font-size);\n  font-weight: $form-select-font-weight;\n  line-height: $form-select-line-height;\n  color: $form-select-color;\n  appearance: none;\n  background-color: $form-select-bg;\n  background-image: var(--#{$prefix}form-select-bg-img), var(--#{$prefix}form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: $form-select-bg-position;\n  background-size: $form-select-bg-size;\n  border: $form-select-border-width solid $form-select-border-color;\n  @include border-radius($form-select-border-radius, 0);\n  @include box-shadow($form-select-box-shadow);\n  @include transition($form-select-transition);\n\n  &:focus {\n    border-color: $form-select-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($form-select-box-shadow, $form-select-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $form-select-focus-box-shadow;\n    }\n  }\n\n  &[multiple],\n  &[size]:not([size=\"1\"]) {\n    padding-right: $form-select-padding-x;\n    background-image: none;\n  }\n\n  &:disabled {\n    color: $form-select-disabled-color;\n    background-color: $form-select-disabled-bg;\n    border-color: $form-select-disabled-border-color;\n  }\n\n  // Remove outline from select box in FF\n  &:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 $form-select-color;\n  }\n}\n\n.form-select-sm {\n  padding-top: $form-select-padding-y-sm;\n  padding-bottom: $form-select-padding-y-sm;\n  padding-left: $form-select-padding-x-sm;\n  @include font-size($form-select-font-size-sm);\n  @include border-radius($form-select-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: $form-select-padding-y-lg;\n  padding-bottom: $form-select-padding-y-lg;\n  padding-left: $form-select-padding-x-lg;\n  @include font-size($form-select-font-size-lg);\n  @include border-radius($form-select-border-radius-lg);\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-select {\n      --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator-dark)};\n    }\n  }\n}\n","//\n// Check/radio\n//\n\n.form-check {\n  display: block;\n  min-height: $form-check-min-height;\n  padding-left: $form-check-padding-start;\n  margin-bottom: $form-check-margin-bottom;\n\n  .form-check-input {\n    float: left;\n    margin-left: $form-check-padding-start * -1;\n  }\n}\n\n.form-check-reverse {\n  padding-right: $form-check-padding-start;\n  padding-left: 0;\n  text-align: right;\n\n  .form-check-input {\n    float: right;\n    margin-right: $form-check-padding-start * -1;\n    margin-left: 0;\n  }\n}\n\n.form-check-input {\n  --#{$prefix}form-check-bg: #{$form-check-input-bg};\n\n  flex-shrink: 0;\n  width: $form-check-input-width;\n  height: $form-check-input-width;\n  margin-top: ($line-height-base - $form-check-input-width) * .5; // line-height minus check height\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--#{$prefix}form-check-bg);\n  background-image: var(--#{$prefix}form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: $form-check-input-border;\n  print-color-adjust: exact; // Keep themed appearance for print\n  @include transition($form-check-transition);\n\n  &[type=\"checkbox\"] {\n    @include border-radius($form-check-input-border-radius);\n  }\n\n  &[type=\"radio\"] {\n    // stylelint-disable-next-line property-disallowed-list\n    border-radius: $form-check-radio-border-radius;\n  }\n\n  &:active {\n    filter: $form-check-input-active-filter;\n  }\n\n  &:focus {\n    border-color: $form-check-input-focus-border;\n    outline: 0;\n    box-shadow: $form-check-input-focus-box-shadow;\n  }\n\n  &:checked {\n    background-color: $form-check-input-checked-bg-color;\n    border-color: $form-check-input-checked-border-color;\n\n    &[type=\"checkbox\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)};\n      }\n    }\n\n    &[type=\"radio\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)};\n      }\n    }\n  }\n\n  &[type=\"checkbox\"]:indeterminate {\n    background-color: $form-check-input-indeterminate-bg-color;\n    border-color: $form-check-input-indeterminate-border-color;\n\n    @if $enable-gradients {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)}, var(--#{$prefix}gradient);\n    } @else {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)};\n    }\n  }\n\n  &:disabled {\n    pointer-events: none;\n    filter: none;\n    opacity: $form-check-input-disabled-opacity;\n  }\n\n  // Use disabled attribute in addition of :disabled pseudo-class\n  // See: https://github.com/twbs/bootstrap/issues/28247\n  &[disabled],\n  &:disabled {\n    ~ .form-check-label {\n      cursor: default;\n      opacity: $form-check-label-disabled-opacity;\n    }\n  }\n}\n\n.form-check-label {\n  color: $form-check-label-color;\n  cursor: $form-check-label-cursor;\n}\n\n//\n// Switch\n//\n\n.form-switch {\n  padding-left: $form-switch-padding-start;\n\n  .form-check-input {\n    --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image)};\n\n    width: $form-switch-width;\n    margin-left: $form-switch-padding-start * -1;\n    background-image: var(--#{$prefix}form-switch-bg);\n    background-position: left center;\n    @include border-radius($form-switch-border-radius, 0);\n    @include transition($form-switch-transition);\n\n    &:focus {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-focus-bg-image)};\n    }\n\n    &:checked {\n      background-position: $form-switch-checked-bg-position;\n\n      @if $enable-gradients {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)};\n      }\n    }\n  }\n\n  &.form-check-reverse {\n    padding-right: $form-switch-padding-start;\n    padding-left: 0;\n\n    .form-check-input {\n      margin-right: $form-switch-padding-start * -1;\n      margin-left: 0;\n    }\n  }\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: $form-check-inline-margin-end;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n\n  &[disabled],\n  &:disabled {\n    + .btn {\n      pointer-events: none;\n      filter: none;\n      opacity: $form-check-btn-check-disabled-opacity;\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-switch .form-check-input:not(:checked):not(:focus) {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image-dark)};\n    }\n  }\n}\n","// Range\n//\n// Style range inputs the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.form-range {\n  width: 100%;\n  height: add($form-range-thumb-height, $form-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  appearance: none;\n  background-color: transparent;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have an effect.\n    // No box-shadow() mixin for focus accessibility.\n    &::-webkit-slider-thumb { box-shadow: $form-range-thumb-focus-box-shadow; }\n    &::-moz-range-thumb     { box-shadow: $form-range-thumb-focus-box-shadow; }\n  }\n\n  &::-moz-focus-outer {\n    border: 0;\n  }\n\n  &::-webkit-slider-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    margin-top: ($form-range-track-height - $form-range-thumb-height) * .5; // Webkit specific\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-webkit-slider-runnable-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent; // Why?\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent;\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &::-moz-range-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-moz-range-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent;\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent; // Firefox specific?\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &:disabled {\n    pointer-events: none;\n\n    &::-webkit-slider-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n\n    &::-moz-range-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n  }\n}\n",".form-floating {\n  position: relative;\n\n  > .form-control,\n  > .form-control-plaintext,\n  > .form-select {\n    height: $form-floating-height;\n    min-height: $form-floating-height;\n    line-height: $form-floating-line-height;\n  }\n\n  > label {\n    position: absolute;\n    top: 0;\n    left: 0;\n    z-index: 2;\n    height: 100%; // allow textareas\n    padding: $form-floating-padding-y $form-floating-padding-x;\n    overflow: hidden;\n    text-align: start;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    pointer-events: none;\n    border: $input-border-width solid transparent; // Required for aligning label's text with the input as it affects inner box model\n    transform-origin: 0 0;\n    @include transition($form-floating-transition);\n  }\n\n  > .form-control,\n  > .form-control-plaintext {\n    padding: $form-floating-padding-y $form-floating-padding-x;\n\n    &::placeholder {\n      color: transparent;\n    }\n\n    &:focus,\n    &:not(:placeholder-shown) {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n    // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n    &:-webkit-autofill {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n  }\n\n  > .form-select {\n    padding-top: $form-floating-input-padding-t;\n    padding-bottom: $form-floating-input-padding-b;\n  }\n\n  > .form-control:focus,\n  > .form-control:not(:placeholder-shown),\n  > .form-control-plaintext,\n  > .form-select {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n\n      &::after {\n        position: absolute;\n        inset: $form-floating-padding-y ($form-floating-padding-x * .5);\n        z-index: -1;\n        height: $form-floating-label-height;\n        content: \"\";\n        background-color: $input-bg;\n        @include border-radius($input-border-radius);\n      }\n    }\n  }\n  // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n  > .form-control:-webkit-autofill {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n    }\n  }\n\n  > .form-control-plaintext {\n    ~ label {\n      border-width: $input-border-width 0; // Required to properly position label text - as explained above\n    }\n  }\n\n  > :disabled ~ label,\n  > .form-control:disabled ~ label { // Required for `.form-control`s because of specificity\n    color: $form-floating-label-disabled-color;\n\n    &::after {\n      background-color: $input-disabled-bg;\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // For form validation feedback\n  align-items: stretch;\n  width: 100%;\n\n  > .form-control,\n  > .form-select,\n  > .form-floating {\n    position: relative; // For focus state's z-index\n    flex: 1 1 auto;\n    width: 1%;\n    min-width: 0; // https://stackoverflow.com/questions/36247140/why-dont-flex-items-shrink-past-content-size\n  }\n\n  // Bring the \"active\" form control to the top of surrounding elements\n  > .form-control:focus,\n  > .form-select:focus,\n  > .form-floating:focus-within {\n    z-index: 5;\n  }\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text` since it shares the same border-color\n  // as our inputs.\n  .btn {\n    position: relative;\n    z-index: 2;\n\n    &:focus {\n      z-index: 5;\n    }\n  }\n}\n\n\n// Textual addons\n//\n// Serves as a catch-all element for any text or radio/checkbox input you wish\n// to prepend or append to an input.\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: $input-group-addon-padding-y $input-group-addon-padding-x;\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $input-group-addon-font-weight;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n}\n\n\n// Sizing\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: $form-select-padding-x + $form-select-indicator-padding;\n}\n\n\n// Rounded corners\n//\n// These rulesets must come after the sizing ones to properly override sm and lg\n// border-radius values when extending. They're more specific than we'd like\n// with the `.input-group >` part, but without it, we cannot override the sizing.\n\n// stylelint-disable-next-line no-duplicate-selectors\n.input-group {\n  &:not(.has-validation) {\n    > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 3),\n    > .form-floating:not(:last-child) > .form-control,\n    > .form-floating:not(:last-child) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  &.has-validation {\n    > :nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 4),\n    > .form-floating:nth-last-child(n + 3) > .form-control,\n    > .form-floating:nth-last-child(n + 3) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  $validation-messages: \"\";\n  @each $state in map-keys($form-validation-states) {\n    $validation-messages: $validation-messages + \":not(.\" + unquote($state) + \"-tooltip)\" + \":not(.\" + unquote($state) + \"-feedback)\";\n  }\n\n  > :not(:first-child):not(.dropdown-menu)#{$validation-messages} {\n    margin-left: calc(#{$input-border-width} * -1); // stylelint-disable-line function-disallowed-list\n    @include border-start-radius(0);\n  }\n\n  > .form-floating:not(:first-child) > .form-control,\n  > .form-floating:not(:first-child) > .form-select {\n    @include border-start-radius(0);\n  }\n}\n","// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n\n// scss-docs-start form-validation-mixins\n@mixin form-validation-state-selector($state) {\n  @if ($state == \"valid\" or $state == \"invalid\") {\n    .was-validated #{if(&, \"&\", \"\")}:#{$state},\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  } @else {\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  }\n}\n\n@mixin form-validation-state(\n  $state,\n  $color,\n  $icon,\n  $tooltip-color: color-contrast($color),\n  $tooltip-bg-color: rgba($color, $form-feedback-tooltip-opacity),\n  $focus-box-shadow: 0 0 $input-btn-focus-blur $input-focus-width rgba($color, $input-btn-focus-color-opacity),\n  $border-color: $color\n) {\n  .#{$state}-feedback {\n    display: none;\n    width: 100%;\n    margin-top: $form-feedback-margin-top;\n    @include font-size($form-feedback-font-size);\n    font-style: $form-feedback-font-style;\n    color: $color;\n  }\n\n  .#{$state}-tooltip {\n    position: absolute;\n    top: 100%;\n    z-index: 5;\n    display: none;\n    max-width: 100%; // Contain to parent when possible\n    padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n    margin-top: .1rem;\n    @include font-size($form-feedback-tooltip-font-size);\n    line-height: $form-feedback-tooltip-line-height;\n    color: $tooltip-color;\n    background-color: $tooltip-bg-color;\n    @include border-radius($form-feedback-tooltip-border-radius);\n  }\n\n  @include form-validation-state-selector($state) {\n    ~ .#{$state}-feedback,\n    ~ .#{$state}-tooltip {\n      display: block;\n    }\n  }\n\n  .form-control {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-image: escape-svg($icon);\n        background-repeat: no-repeat;\n        background-position: right $input-height-inner-quarter center;\n        background-size: $input-height-inner-half $input-height-inner-half;\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($input-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  // stylelint-disable-next-line selector-no-qualifying-type\n  textarea.form-control {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n      }\n    }\n  }\n\n  .form-select {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        &:not([multiple]):not([size]),\n        &:not([multiple])[size=\"1\"] {\n          --#{$prefix}form-select-bg-icon: #{escape-svg($icon)};\n          padding-right: $form-select-feedback-icon-padding-end;\n          background-position: $form-select-bg-position, $form-select-feedback-icon-position;\n          background-size: $form-select-bg-size, $form-select-feedback-icon-size;\n        }\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($form-select-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  .form-control-color {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        width: add($form-color-width, $input-height-inner);\n      }\n    }\n  }\n\n  .form-check-input {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      &:checked {\n        background-color: $color;\n      }\n\n      &:focus {\n        box-shadow: $focus-box-shadow;\n      }\n\n      ~ .form-check-label {\n        color: $color;\n      }\n    }\n  }\n  .form-check-inline .form-check-input {\n    ~ .#{$state}-feedback {\n      margin-left: .5em;\n    }\n  }\n\n  .input-group {\n    > .form-control:not(:focus),\n    > .form-select:not(:focus),\n    > .form-floating:not(:focus-within) {\n      @include form-validation-state-selector($state) {\n        @if $state == \"valid\" {\n          z-index: 3;\n        } @else if $state == \"invalid\" {\n          z-index: 4;\n        }\n      }\n    }\n  }\n}\n// scss-docs-end form-validation-mixins\n","//\n// Base styles\n//\n\n.btn {\n  // scss-docs-start btn-css-vars\n  --#{$prefix}btn-padding-x: #{$btn-padding-x};\n  --#{$prefix}btn-padding-y: #{$btn-padding-y};\n  --#{$prefix}btn-font-family: #{$btn-font-family};\n  @include rfs($btn-font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-font-weight: #{$btn-font-weight};\n  --#{$prefix}btn-line-height: #{$btn-line-height};\n  --#{$prefix}btn-color: #{$btn-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-width: #{$btn-border-width};\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-border-radius: #{$btn-border-radius};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-box-shadow: #{$btn-box-shadow};\n  --#{$prefix}btn-disabled-opacity: #{$btn-disabled-opacity};\n  --#{$prefix}btn-focus-box-shadow: 0 0 0 #{$btn-focus-width} rgba(var(--#{$prefix}btn-focus-shadow-rgb), .5);\n  // scss-docs-end btn-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}btn-padding-y) var(--#{$prefix}btn-padding-x);\n  font-family: var(--#{$prefix}btn-font-family);\n  @include font-size(var(--#{$prefix}btn-font-size));\n  font-weight: var(--#{$prefix}btn-font-weight);\n  line-height: var(--#{$prefix}btn-line-height);\n  color: var(--#{$prefix}btn-color);\n  text-align: center;\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: $btn-white-space;\n  vertical-align: middle;\n  cursor: if($enable-button-pointers, pointer, null);\n  user-select: none;\n  border: var(--#{$prefix}btn-border-width) solid var(--#{$prefix}btn-border-color);\n  @include border-radius(var(--#{$prefix}btn-border-radius));\n  @include gradient-bg(var(--#{$prefix}btn-bg));\n  @include box-shadow(var(--#{$prefix}btn-box-shadow));\n  @include transition($btn-transition);\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}btn-hover-bg);\n    border-color: var(--#{$prefix}btn-hover-border-color);\n  }\n\n  .btn-check + &:hover {\n    // override for the checkbox/radio buttons\n    color: var(--#{$prefix}btn-color);\n    background-color: var(--#{$prefix}btn-bg);\n    border-color: var(--#{$prefix}btn-border-color);\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-hover-color);\n    @include gradient-bg(var(--#{$prefix}btn-hover-bg));\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:focus-visible + & {\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:checked + &,\n  :not(.btn-check) + &:active,\n  &:first-child:active,\n  &.active,\n  &.show {\n    color: var(--#{$prefix}btn-active-color);\n    background-color: var(--#{$prefix}btn-active-bg);\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-active-border-color);\n    @include box-shadow(var(--#{$prefix}btn-active-shadow));\n\n    &:focus-visible {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      @if $enable-shadows {\n        box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n      } @else {\n        box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n      }\n    }\n  }\n\n  .btn-check:checked:focus-visible + & {\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  &:disabled,\n  &.disabled,\n  fieldset:disabled & {\n    color: var(--#{$prefix}btn-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}btn-disabled-bg);\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-disabled-border-color);\n    opacity: var(--#{$prefix}btn-disabled-opacity);\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Alternate buttons\n//\n\n// scss-docs-start btn-variant-loops\n@each $color, $value in $theme-colors {\n  .btn-#{$color} {\n    @if $color == \"light\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: shade-color($value, $btn-hover-bg-shade-amount),\n        $hover-border: shade-color($value, $btn-hover-border-shade-amount),\n        $active-background: shade-color($value, $btn-active-bg-shade-amount),\n        $active-border: shade-color($value, $btn-active-border-shade-amount)\n      );\n    } @else if $color == \"dark\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: tint-color($value, $btn-hover-bg-tint-amount),\n        $hover-border: tint-color($value, $btn-hover-border-tint-amount),\n        $active-background: tint-color($value, $btn-active-bg-tint-amount),\n        $active-border: tint-color($value, $btn-active-border-tint-amount)\n      );\n    } @else {\n      @include button-variant($value, $value);\n    }\n  }\n}\n\n@each $color, $value in $theme-colors {\n  .btn-outline-#{$color} {\n    @include button-outline-variant($value);\n  }\n}\n// scss-docs-end btn-variant-loops\n\n\n//\n// Link buttons\n//\n\n// Make a button look and behave like a link\n.btn-link {\n  --#{$prefix}btn-font-weight: #{$font-weight-normal};\n  --#{$prefix}btn-color: #{$btn-link-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-hover-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-active-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-active-border-color: transparent;\n  --#{$prefix}btn-disabled-color: #{$btn-link-disabled-color};\n  --#{$prefix}btn-disabled-border-color: transparent;\n  --#{$prefix}btn-box-shadow: 0 0 0 #000; // Can't use `none` as keyword negates all values when used with multiple shadows\n  --#{$prefix}btn-focus-shadow-rgb: #{$btn-link-focus-shadow-rgb};\n\n  text-decoration: $link-decoration;\n  @if $enable-gradients {\n    background-image: none;\n  }\n\n  &:hover,\n  &:focus-visible {\n    text-decoration: $link-hover-decoration;\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-color);\n  }\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n  }\n\n  // No need for an active state here\n}\n\n\n//\n// Button Sizes\n//\n\n.btn-lg {\n  @include button-size($btn-padding-y-lg, $btn-padding-x-lg, $btn-font-size-lg, $btn-border-radius-lg);\n}\n\n.btn-sm {\n  @include button-size($btn-padding-y-sm, $btn-padding-x-sm, $btn-font-size-sm, $btn-border-radius-sm);\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n// scss-docs-start btn-variant-mixin\n@mixin button-variant(\n  $background,\n  $border,\n  $color: color-contrast($background),\n  $hover-background: if($color == $color-contrast-light, shade-color($background, $btn-hover-bg-shade-amount), tint-color($background, $btn-hover-bg-tint-amount)),\n  $hover-border: if($color == $color-contrast-light, shade-color($border, $btn-hover-border-shade-amount), tint-color($border, $btn-hover-border-tint-amount)),\n  $hover-color: color-contrast($hover-background),\n  $active-background: if($color == $color-contrast-light, shade-color($background, $btn-active-bg-shade-amount), tint-color($background, $btn-active-bg-tint-amount)),\n  $active-border: if($color == $color-contrast-light, shade-color($border, $btn-active-border-shade-amount), tint-color($border, $btn-active-border-tint-amount)),\n  $active-color: color-contrast($active-background),\n  $disabled-background: $background,\n  $disabled-border: $border,\n  $disabled-color: color-contrast($disabled-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-bg: #{$background};\n  --#{$prefix}btn-border-color: #{$border};\n  --#{$prefix}btn-hover-color: #{$hover-color};\n  --#{$prefix}btn-hover-bg: #{$hover-background};\n  --#{$prefix}btn-hover-border-color: #{$hover-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb(mix($color, $border, 15%))};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$disabled-color};\n  --#{$prefix}btn-disabled-bg: #{$disabled-background};\n  --#{$prefix}btn-disabled-border-color: #{$disabled-border};\n}\n// scss-docs-end btn-variant-mixin\n\n// scss-docs-start btn-outline-variant-mixin\n@mixin button-outline-variant(\n  $color,\n  $color-hover: color-contrast($color),\n  $active-background: $color,\n  $active-border: $color,\n  $active-color: color-contrast($active-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-border-color: #{$color};\n  --#{$prefix}btn-hover-color: #{$color-hover};\n  --#{$prefix}btn-hover-bg: #{$active-background};\n  --#{$prefix}btn-hover-border-color: #{$active-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb($color)};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$color};\n  --#{$prefix}btn-disabled-bg: transparent;\n  --#{$prefix}btn-disabled-border-color: #{$color};\n  --#{$prefix}gradient: none;\n}\n// scss-docs-end btn-outline-variant-mixin\n\n// scss-docs-start btn-size-mixin\n@mixin button-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}btn-padding-y: #{$padding-y};\n  --#{$prefix}btn-padding-x: #{$padding-x};\n  @include rfs($font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-border-radius: #{$border-radius};\n}\n// scss-docs-end btn-size-mixin\n",".fade {\n  @include transition($transition-fade);\n\n  &:not(.show) {\n    opacity: 0;\n  }\n}\n\n// scss-docs-start collapse-classes\n.collapse {\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  @include transition($transition-collapse);\n\n  &.collapse-horizontal {\n    width: 0;\n    height: auto;\n    @include transition($transition-collapse-width);\n  }\n}\n// scss-docs-end collapse-classes\n","// The dropdown wrapper (`<div>`)\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n\n  // Generate the caret automatically\n  @include caret();\n}\n\n// The dropdown menu\n.dropdown-menu {\n  // scss-docs-start dropdown-css-vars\n  --#{$prefix}dropdown-zindex: #{$zindex-dropdown};\n  --#{$prefix}dropdown-min-width: #{$dropdown-min-width};\n  --#{$prefix}dropdown-padding-x: #{$dropdown-padding-x};\n  --#{$prefix}dropdown-padding-y: #{$dropdown-padding-y};\n  --#{$prefix}dropdown-spacer: #{$dropdown-spacer};\n  @include rfs($dropdown-font-size, --#{$prefix}dropdown-font-size);\n  --#{$prefix}dropdown-color: #{$dropdown-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-border-color};\n  --#{$prefix}dropdown-border-radius: #{$dropdown-border-radius};\n  --#{$prefix}dropdown-border-width: #{$dropdown-border-width};\n  --#{$prefix}dropdown-inner-border-radius: #{$dropdown-inner-border-radius};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-divider-bg};\n  --#{$prefix}dropdown-divider-margin-y: #{$dropdown-divider-margin-y};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-link-hover-color};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-link-disabled-color};\n  --#{$prefix}dropdown-item-padding-x: #{$dropdown-item-padding-x};\n  --#{$prefix}dropdown-item-padding-y: #{$dropdown-item-padding-y};\n  --#{$prefix}dropdown-header-color: #{$dropdown-header-color};\n  --#{$prefix}dropdown-header-padding-x: #{$dropdown-header-padding-x};\n  --#{$prefix}dropdown-header-padding-y: #{$dropdown-header-padding-y};\n  // scss-docs-end dropdown-css-vars\n\n  position: absolute;\n  z-index: var(--#{$prefix}dropdown-zindex);\n  display: none; // none by default, but block on \"open\" of the menu\n  min-width: var(--#{$prefix}dropdown-min-width);\n  padding: var(--#{$prefix}dropdown-padding-y) var(--#{$prefix}dropdown-padding-x);\n  margin: 0; // Override default margin of ul\n  @include font-size(var(--#{$prefix}dropdown-font-size));\n  color: var(--#{$prefix}dropdown-color);\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: var(--#{$prefix}dropdown-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}dropdown-border-width) solid var(--#{$prefix}dropdown-border-color);\n  @include border-radius(var(--#{$prefix}dropdown-border-radius));\n  @include box-shadow(var(--#{$prefix}dropdown-box-shadow));\n\n  &[data-bs-popper] {\n    top: 100%;\n    left: 0;\n    margin-top: var(--#{$prefix}dropdown-spacer);\n  }\n\n  @if $dropdown-padding-y == 0 {\n    > .dropdown-item:first-child,\n    > li:first-child .dropdown-item {\n      @include border-top-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n    > .dropdown-item:last-child,\n    > li:last-child .dropdown-item {\n      @include border-bottom-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n\n  }\n}\n\n// scss-docs-start responsive-breakpoints\n// We deliberately hardcode the `bs-` prefix because we check\n// this custom property in JS to determine Popper's positioning\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .dropdown-menu#{$infix}-start {\n      --bs-position: start;\n\n      &[data-bs-popper] {\n        right: auto;\n        left: 0;\n      }\n    }\n\n    .dropdown-menu#{$infix}-end {\n      --bs-position: end;\n\n      &[data-bs-popper] {\n        right: 0;\n        left: auto;\n      }\n    }\n  }\n}\n// scss-docs-end responsive-breakpoints\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n// Just add .dropup after the standard .dropdown class and you're set.\n.dropup {\n  .dropdown-menu[data-bs-popper] {\n    top: auto;\n    bottom: 100%;\n    margin-top: 0;\n    margin-bottom: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(up);\n  }\n}\n\n.dropend {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: auto;\n    left: 100%;\n    margin-top: 0;\n    margin-left: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(end);\n    &::after {\n      vertical-align: 0;\n    }\n  }\n}\n\n.dropstart {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: 100%;\n    left: auto;\n    margin-top: 0;\n    margin-right: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(start);\n    &::before {\n      vertical-align: 0;\n    }\n  }\n}\n\n\n// Dividers (basically an `<hr>`) within the dropdown\n.dropdown-divider {\n  height: 0;\n  margin: var(--#{$prefix}dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--#{$prefix}dropdown-divider-bg);\n  opacity: 1; // Revisit in v6 to de-dupe styles that conflict with <hr> element\n}\n\n// Links, buttons, and more within the dropdown menu\n//\n// `<button>`-specific styles are denoted with `// For <button>s`\n.dropdown-item {\n  display: block;\n  width: 100%; // For `<button>`s\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  clear: both;\n  font-weight: $font-weight-normal;\n  color: var(--#{$prefix}dropdown-link-color);\n  text-align: inherit; // For `<button>`s\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap; // prevent links from randomly breaking onto new lines\n  background-color: transparent; // For `<button>`s\n  border: 0; // For `<button>`s\n  @include border-radius(var(--#{$prefix}dropdown-item-border-radius, 0));\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}dropdown-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    @include gradient-bg(var(--#{$prefix}dropdown-link-hover-bg));\n  }\n\n  &.active,\n  &:active {\n    color: var(--#{$prefix}dropdown-link-active-color);\n    text-decoration: none;\n    @include gradient-bg(var(--#{$prefix}dropdown-link-active-bg));\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}dropdown-link-disabled-color);\n    pointer-events: none;\n    background-color: transparent;\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n  }\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: var(--#{$prefix}dropdown-header-padding-y) var(--#{$prefix}dropdown-header-padding-x);\n  margin-bottom: 0; // for use with heading elements\n  @include font-size($font-size-sm);\n  color: var(--#{$prefix}dropdown-header-color);\n  white-space: nowrap; // as with > li > a\n}\n\n// Dropdown text\n.dropdown-item-text {\n  display: block;\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  color: var(--#{$prefix}dropdown-link-color);\n}\n\n// Dark dropdowns\n.dropdown-menu-dark {\n  // scss-docs-start dropdown-dark-css-vars\n  --#{$prefix}dropdown-color: #{$dropdown-dark-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-dark-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-dark-border-color};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-dark-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-dark-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-dark-link-hover-color};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-dark-divider-bg};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-dark-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-dark-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-dark-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-dark-link-disabled-color};\n  --#{$prefix}dropdown-header-color: #{$dropdown-dark-header-color};\n  // scss-docs-end dropdown-dark-css-vars\n}\n","// scss-docs-start caret-mixins\n@mixin caret-down($width: $caret-width) {\n  border-top: $width solid;\n  border-right: $width solid transparent;\n  border-bottom: 0;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-up($width: $caret-width) {\n  border-top: 0;\n  border-right: $width solid transparent;\n  border-bottom: $width solid;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-end($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align: $vertical-align;\n      content: \"\";\n      @if $direction == down {\n        @include caret-down($width);\n      } @else if $direction == up {\n        @include caret-up($width);\n      } @else if $direction == end {\n        @include caret-end($width);\n      }\n    }\n\n    @if $direction == start {\n      &::after {\n        display: none;\n      }\n\n      &::before {\n        display: inline-block;\n        margin-right: $spacing;\n        vertical-align: $vertical-align;\n        content: \"\";\n        @include caret-start($width);\n      }\n    }\n\n    &:empty::after {\n      margin-left: 0;\n    }\n  }\n}\n// scss-docs-end caret-mixins\n","// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n\n  > .btn {\n    position: relative;\n    flex: 1 1 auto;\n  }\n\n  // Bring the hover, focused, and \"active\" buttons to the front to overlay\n  // the borders properly\n  > .btn-check:checked + .btn,\n  > .btn-check:focus + .btn,\n  > .btn:hover,\n  > .btn:focus,\n  > .btn:active,\n  > .btn.active {\n    z-index: 1;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n\n  .input-group {\n    width: auto;\n  }\n}\n\n.btn-group {\n  @include border-radius($btn-border-radius);\n\n  // Prevent double borders when buttons are next to each other\n  > :not(.btn-check:first-child) + .btn,\n  > .btn-group:not(:first-child) {\n    margin-left: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn.dropdown-toggle-split:first-child,\n  > .btn-group:not(:last-child) > .btn {\n    @include border-end-radius(0);\n  }\n\n  // The left radius should be 0 if the button is:\n  // - the \"third or more\" child\n  // - the second child and the previous element isn't `.btn-check` (making it the first child visually)\n  // - part of a btn-group which isn't the first child\n  > .btn:nth-child(n + 3),\n  > :not(.btn-check) + .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-start-radius(0);\n  }\n}\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-sm > .btn { @extend .btn-sm; }\n.btn-group-lg > .btn { @extend .btn-lg; }\n\n\n//\n// Split button dropdowns\n//\n\n.dropdown-toggle-split {\n  padding-right: $btn-padding-x * .75;\n  padding-left: $btn-padding-x * .75;\n\n  &::after,\n  .dropup &::after,\n  .dropend &::after {\n    margin-left: 0;\n  }\n\n  .dropstart &::before {\n    margin-right: 0;\n  }\n}\n\n.btn-sm + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-sm * .75;\n  padding-left: $btn-padding-x-sm * .75;\n}\n\n.btn-lg + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-lg * .75;\n  padding-left: $btn-padding-x-lg * .75;\n}\n\n\n// The clickable button for toggling the menu\n// Set the same inset shadow as the :active state\n.btn-group.show .dropdown-toggle {\n  @include box-shadow($btn-active-box-shadow);\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Vertical button groups\n//\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n\n  > .btn,\n  > .btn-group {\n    width: 100%;\n  }\n\n  > .btn:not(:first-child),\n  > .btn-group:not(:first-child) {\n    margin-top: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn-group:not(:last-child) > .btn {\n    @include border-bottom-radius(0);\n  }\n\n  > .btn ~ .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-top-radius(0);\n  }\n}\n","// Base class\n//\n// Kickstart any navigation component with a set of style resets. Works with\n// `<nav>`s, `<ul>`s or `<ol>`s.\n\n.nav {\n  // scss-docs-start nav-css-vars\n  --#{$prefix}nav-link-padding-x: #{$nav-link-padding-x};\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: #{$nav-link-color};\n  --#{$prefix}nav-link-hover-color: #{$nav-link-hover-color};\n  --#{$prefix}nav-link-disabled-color: #{$nav-link-disabled-color};\n  // scss-docs-end nav-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--#{$prefix}nav-link-padding-y) var(--#{$prefix}nav-link-padding-x);\n  @include font-size(var(--#{$prefix}nav-link-font-size));\n  font-weight: var(--#{$prefix}nav-link-font-weight);\n  color: var(--#{$prefix}nav-link-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background: none;\n  border: 0;\n  @include transition($nav-link-transition);\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}nav-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  &:focus-visible {\n    outline: 0;\n    box-shadow: $nav-link-focus-box-shadow;\n  }\n\n  // Disabled state lightens text\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}nav-link-disabled-color);\n    pointer-events: none;\n    cursor: default;\n  }\n}\n\n//\n// Tabs\n//\n\n.nav-tabs {\n  // scss-docs-start nav-tabs-css-vars\n  --#{$prefix}nav-tabs-border-width: #{$nav-tabs-border-width};\n  --#{$prefix}nav-tabs-border-color: #{$nav-tabs-border-color};\n  --#{$prefix}nav-tabs-border-radius: #{$nav-tabs-border-radius};\n  --#{$prefix}nav-tabs-link-hover-border-color: #{$nav-tabs-link-hover-border-color};\n  --#{$prefix}nav-tabs-link-active-color: #{$nav-tabs-link-active-color};\n  --#{$prefix}nav-tabs-link-active-bg: #{$nav-tabs-link-active-bg};\n  --#{$prefix}nav-tabs-link-active-border-color: #{$nav-tabs-link-active-border-color};\n  // scss-docs-end nav-tabs-css-vars\n\n  border-bottom: var(--#{$prefix}nav-tabs-border-width) solid var(--#{$prefix}nav-tabs-border-color);\n\n  .nav-link {\n    margin-bottom: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    border: var(--#{$prefix}nav-tabs-border-width) solid transparent;\n    @include border-top-radius(var(--#{$prefix}nav-tabs-border-radius));\n\n    &:hover,\n    &:focus {\n      // Prevents active .nav-link tab overlapping focus outline of previous/next .nav-link\n      isolation: isolate;\n      border-color: var(--#{$prefix}nav-tabs-link-hover-border-color);\n    }\n  }\n\n  .nav-link.active,\n  .nav-item.show .nav-link {\n    color: var(--#{$prefix}nav-tabs-link-active-color);\n    background-color: var(--#{$prefix}nav-tabs-link-active-bg);\n    border-color: var(--#{$prefix}nav-tabs-link-active-border-color);\n  }\n\n  .dropdown-menu {\n    // Make dropdown border overlap tab border\n    margin-top: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    // Remove the top rounded corners here since there is a hard edge above the menu\n    @include border-top-radius(0);\n  }\n}\n\n\n//\n// Pills\n//\n\n.nav-pills {\n  // scss-docs-start nav-pills-css-vars\n  --#{$prefix}nav-pills-border-radius: #{$nav-pills-border-radius};\n  --#{$prefix}nav-pills-link-active-color: #{$nav-pills-link-active-color};\n  --#{$prefix}nav-pills-link-active-bg: #{$nav-pills-link-active-bg};\n  // scss-docs-end nav-pills-css-vars\n\n  .nav-link {\n    @include border-radius(var(--#{$prefix}nav-pills-border-radius));\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    color: var(--#{$prefix}nav-pills-link-active-color);\n    @include gradient-bg(var(--#{$prefix}nav-pills-link-active-bg));\n  }\n}\n\n\n//\n// Underline\n//\n\n.nav-underline {\n  // scss-docs-start nav-underline-css-vars\n  --#{$prefix}nav-underline-gap: #{$nav-underline-gap};\n  --#{$prefix}nav-underline-border-width: #{$nav-underline-border-width};\n  --#{$prefix}nav-underline-link-active-color: #{$nav-underline-link-active-color};\n  // scss-docs-end nav-underline-css-vars\n\n  gap: var(--#{$prefix}nav-underline-gap);\n\n  .nav-link {\n    padding-right: 0;\n    padding-left: 0;\n    border-bottom: var(--#{$prefix}nav-underline-border-width) solid transparent;\n\n    &:hover,\n    &:focus {\n      border-bottom-color: currentcolor;\n    }\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    font-weight: $font-weight-bold;\n    color: var(--#{$prefix}nav-underline-link-active-color);\n    border-bottom-color: currentcolor;\n  }\n}\n\n\n//\n// Justified variants\n//\n\n.nav-fill {\n  > .nav-link,\n  .nav-item {\n    flex: 1 1 auto;\n    text-align: center;\n  }\n}\n\n.nav-justified {\n  > .nav-link,\n  .nav-item {\n    flex-basis: 0;\n    flex-grow: 1;\n    text-align: center;\n  }\n}\n\n.nav-fill,\n.nav-justified {\n  .nav-item .nav-link {\n    width: 100%; // Make sure button will grow\n  }\n}\n\n\n// Tabbable tabs\n//\n// Hide tabbable panes to start, show them when `.active`\n\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n","// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  // scss-docs-start navbar-css-vars\n  --#{$prefix}navbar-padding-x: #{if($navbar-padding-x == null, 0, $navbar-padding-x)};\n  --#{$prefix}navbar-padding-y: #{$navbar-padding-y};\n  --#{$prefix}navbar-color: #{$navbar-light-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-light-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-light-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-light-active-color};\n  --#{$prefix}navbar-brand-padding-y: #{$navbar-brand-padding-y};\n  --#{$prefix}navbar-brand-margin-end: #{$navbar-brand-margin-end};\n  --#{$prefix}navbar-brand-font-size: #{$navbar-brand-font-size};\n  --#{$prefix}navbar-brand-color: #{$navbar-light-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-light-brand-hover-color};\n  --#{$prefix}navbar-nav-link-padding-x: #{$navbar-nav-link-padding-x};\n  --#{$prefix}navbar-toggler-padding-y: #{$navbar-toggler-padding-y};\n  --#{$prefix}navbar-toggler-padding-x: #{$navbar-toggler-padding-x};\n  --#{$prefix}navbar-toggler-font-size: #{$navbar-toggler-font-size};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-light-toggler-icon-bg)};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-light-toggler-border-color};\n  --#{$prefix}navbar-toggler-border-radius: #{$navbar-toggler-border-radius};\n  --#{$prefix}navbar-toggler-focus-width: #{$navbar-toggler-focus-width};\n  --#{$prefix}navbar-toggler-transition: #{$navbar-toggler-transition};\n  // scss-docs-end navbar-css-vars\n\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: var(--#{$prefix}navbar-padding-y) var(--#{$prefix}navbar-padding-x);\n  @include gradient-bg();\n\n  // Because flex properties aren't inherited, we need to redeclare these first\n  // few properties so that content nested within behave properly.\n  // The `flex-wrap` property is inherited to simplify the expanded navbars\n  %container-flex-properties {\n    display: flex;\n    flex-wrap: inherit;\n    align-items: center;\n    justify-content: space-between;\n  }\n\n  > .container,\n  > .container-fluid {\n    @extend %container-flex-properties;\n  }\n\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    > .container#{breakpoint-infix($breakpoint, $container-max-widths)} {\n      @extend %container-flex-properties;\n    }\n  }\n}\n\n\n// Navbar brand\n//\n// Used for brand, project, or site names.\n\n.navbar-brand {\n  padding-top: var(--#{$prefix}navbar-brand-padding-y);\n  padding-bottom: var(--#{$prefix}navbar-brand-padding-y);\n  margin-right: var(--#{$prefix}navbar-brand-margin-end);\n  @include font-size(var(--#{$prefix}navbar-brand-font-size));\n  color: var(--#{$prefix}navbar-brand-color);\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap;\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}navbar-brand-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n}\n\n\n// Navbar nav\n//\n// Custom navbar navigation (doesn't require `.nav`, but does make use of `.nav-link`).\n\n.navbar-nav {\n  // scss-docs-start navbar-nav-css-vars\n  --#{$prefix}nav-link-padding-x: 0;\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: var(--#{$prefix}navbar-color);\n  --#{$prefix}nav-link-hover-color: var(--#{$prefix}navbar-hover-color);\n  --#{$prefix}nav-link-disabled-color: var(--#{$prefix}navbar-disabled-color);\n  // scss-docs-end navbar-nav-css-vars\n\n  display: flex;\n  flex-direction: column; // cannot use `inherit` to get the `.navbar`s value\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  .nav-link {\n    &.active,\n    &.show {\n      color: var(--#{$prefix}navbar-active-color);\n    }\n  }\n\n  .dropdown-menu {\n    position: static;\n  }\n}\n\n\n// Navbar text\n//\n//\n\n.navbar-text {\n  padding-top: $nav-link-padding-y;\n  padding-bottom: $nav-link-padding-y;\n  color: var(--#{$prefix}navbar-color);\n\n  a,\n  a:hover,\n  a:focus  {\n    color: var(--#{$prefix}navbar-active-color);\n  }\n}\n\n\n// Responsive navbar\n//\n// Custom styles for responsive collapsing and toggling of navbar contents.\n// Powered by the collapse Bootstrap JavaScript plugin.\n\n// When collapsed, prevent the toggleable navbar contents from appearing in\n// the default flexbox row orientation. Requires the use of `flex-wrap: wrap`\n// on the `.navbar` parent.\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  // For always expanded or extra full navbars, ensure content aligns itself\n  // properly vertically. Can be easily overridden with flex utilities.\n  align-items: center;\n}\n\n// Button for toggling the navbar when in its collapsed state\n.navbar-toggler {\n  padding: var(--#{$prefix}navbar-toggler-padding-y) var(--#{$prefix}navbar-toggler-padding-x);\n  @include font-size(var(--#{$prefix}navbar-toggler-font-size));\n  line-height: 1;\n  color: var(--#{$prefix}navbar-color);\n  background-color: transparent; // remove default button style\n  border: var(--#{$prefix}border-width) solid var(--#{$prefix}navbar-toggler-border-color); // remove default button style\n  @include border-radius(var(--#{$prefix}navbar-toggler-border-radius));\n  @include transition(var(--#{$prefix}navbar-toggler-transition));\n\n  &:hover {\n    text-decoration: none;\n  }\n\n  &:focus {\n    text-decoration: none;\n    outline: 0;\n    box-shadow: 0 0 0 var(--#{$prefix}navbar-toggler-focus-width);\n  }\n}\n\n// Keep as a separate element so folks can easily override it with another icon\n// or image file as needed.\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--#{$prefix}navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--#{$prefix}scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n// scss-docs-start navbar-expand-loop\n// Generate series of `.navbar-expand-*` responsive classes for configuring\n// where your navbar collapses.\n.navbar-expand {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $next: breakpoint-next($breakpoint, $grid-breakpoints);\n    $infix: breakpoint-infix($next, $grid-breakpoints);\n\n    // stylelint-disable-next-line scss/selector-no-union-class-name\n    &#{$infix} {\n      @include media-breakpoint-up($next) {\n        flex-wrap: nowrap;\n        justify-content: flex-start;\n\n        .navbar-nav {\n          flex-direction: row;\n\n          .dropdown-menu {\n            position: absolute;\n          }\n\n          .nav-link {\n            padding-right: var(--#{$prefix}navbar-nav-link-padding-x);\n            padding-left: var(--#{$prefix}navbar-nav-link-padding-x);\n          }\n        }\n\n        .navbar-nav-scroll {\n          overflow: visible;\n        }\n\n        .navbar-collapse {\n          display: flex !important; // stylelint-disable-line declaration-no-important\n          flex-basis: auto;\n        }\n\n        .navbar-toggler {\n          display: none;\n        }\n\n        .offcanvas {\n          // stylelint-disable declaration-no-important\n          position: static;\n          z-index: auto;\n          flex-grow: 1;\n          width: auto !important;\n          height: auto !important;\n          visibility: visible !important;\n          background-color: transparent !important;\n          border: 0 !important;\n          transform: none !important;\n          @include box-shadow(none);\n          @include transition(none);\n          // stylelint-enable declaration-no-important\n\n          .offcanvas-header {\n            display: none;\n          }\n\n          .offcanvas-body {\n            display: flex;\n            flex-grow: 0;\n            padding: 0;\n            overflow-y: visible;\n          }\n        }\n      }\n    }\n  }\n}\n// scss-docs-end navbar-expand-loop\n\n// Navbar themes\n//\n// Styles for switching between navbars with light or dark background.\n\n.navbar-light {\n  @include deprecate(\"`.navbar-light`\", \"v5.2.0\", \"v6.0.0\", true);\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=\"dark\"] {\n  // scss-docs-start navbar-dark-css-vars\n  --#{$prefix}navbar-color: #{$navbar-dark-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-dark-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-dark-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-dark-active-color};\n  --#{$prefix}navbar-brand-color: #{$navbar-dark-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-dark-brand-hover-color};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-dark-toggler-border-color};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n  // scss-docs-end navbar-dark-css-vars\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .navbar-toggler-icon {\n      --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.card {\n  // scss-docs-start card-css-vars\n  --#{$prefix}card-spacer-y: #{$card-spacer-y};\n  --#{$prefix}card-spacer-x: #{$card-spacer-x};\n  --#{$prefix}card-title-spacer-y: #{$card-title-spacer-y};\n  --#{$prefix}card-title-color: #{$card-title-color};\n  --#{$prefix}card-subtitle-color: #{$card-subtitle-color};\n  --#{$prefix}card-border-width: #{$card-border-width};\n  --#{$prefix}card-border-color: #{$card-border-color};\n  --#{$prefix}card-border-radius: #{$card-border-radius};\n  --#{$prefix}card-box-shadow: #{$card-box-shadow};\n  --#{$prefix}card-inner-border-radius: #{$card-inner-border-radius};\n  --#{$prefix}card-cap-padding-y: #{$card-cap-padding-y};\n  --#{$prefix}card-cap-padding-x: #{$card-cap-padding-x};\n  --#{$prefix}card-cap-bg: #{$card-cap-bg};\n  --#{$prefix}card-cap-color: #{$card-cap-color};\n  --#{$prefix}card-height: #{$card-height};\n  --#{$prefix}card-color: #{$card-color};\n  --#{$prefix}card-bg: #{$card-bg};\n  --#{$prefix}card-img-overlay-padding: #{$card-img-overlay-padding};\n  --#{$prefix}card-group-margin: #{$card-group-margin};\n  // scss-docs-end card-css-vars\n\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0; // See https://github.com/twbs/bootstrap/pull/22740#issuecomment-305868106\n  height: var(--#{$prefix}card-height);\n  color: var(--#{$prefix}body-color);\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}card-bg);\n  background-clip: border-box;\n  border: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n  @include border-radius(var(--#{$prefix}card-border-radius));\n  @include box-shadow(var(--#{$prefix}card-box-shadow));\n\n  > hr {\n    margin-right: 0;\n    margin-left: 0;\n  }\n\n  > .list-group {\n    border-top: inherit;\n    border-bottom: inherit;\n\n    &:first-child {\n      border-top-width: 0;\n      @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n\n    &:last-child  {\n      border-bottom-width: 0;\n      @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n  }\n\n  // Due to specificity of the above selector (`.card > .list-group`), we must\n  // use a child selector here to prevent double borders.\n  > .card-header + .list-group,\n  > .list-group + .card-footer {\n    border-top: 0;\n  }\n}\n\n.card-body {\n  // Enable `flex-grow: 1` for decks and groups so that card blocks take up\n  // as much space as possible, ensuring footers are aligned to the bottom.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}card-spacer-y) var(--#{$prefix}card-spacer-x);\n  color: var(--#{$prefix}card-color);\n}\n\n.card-title {\n  margin-bottom: var(--#{$prefix}card-title-spacer-y);\n  color: var(--#{$prefix}card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-.5 * var(--#{$prefix}card-title-spacer-y)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: 0;\n  color: var(--#{$prefix}card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link {\n  &:hover {\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  + .card-link {\n    margin-left: var(--#{$prefix}card-spacer-x);\n  }\n}\n\n//\n// Optional textual caps\n//\n\n.card-header {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  margin-bottom: 0; // Removes the default margin-bottom of <hN>\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-bottom: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:first-child {\n    @include border-radius(var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius) 0 0);\n  }\n}\n\n.card-footer {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-top: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:last-child {\n    @include border-radius(0 0 var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius));\n  }\n}\n\n\n//\n// Header navs\n//\n\n.card-header-tabs {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: calc(-1 * var(--#{$prefix}card-cap-padding-y)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  border-bottom: 0;\n\n  .nav-link.active {\n    background-color: var(--#{$prefix}card-bg);\n    border-bottom-color: var(--#{$prefix}card-bg);\n  }\n}\n\n.card-header-pills {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n}\n\n// Card image\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--#{$prefix}card-img-overlay-padding);\n  @include border-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%; // Required because we use flexbox and this inherently applies align-self: stretch\n}\n\n.card-img,\n.card-img-top {\n  @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-bottom {\n  @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n\n//\n// Card groups\n//\n\n.card-group {\n  // The child selector allows nested `.card` within `.card-group`\n  // to display properly.\n  > .card {\n    margin-bottom: var(--#{$prefix}card-group-margin);\n  }\n\n  @include media-breakpoint-up(sm) {\n    display: flex;\n    flex-flow: row wrap;\n    // The child selector allows nested `.card` within `.card-group`\n    // to display properly.\n    > .card {\n      // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      flex: 1 0 0%;\n      margin-bottom: 0;\n\n      + .card {\n        margin-left: 0;\n        border-left: 0;\n      }\n\n      // Handle rounded corners\n      @if $enable-rounded {\n        &:not(:last-child) {\n          @include border-end-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-right-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-right-radius: 0;\n          }\n        }\n\n        &:not(:first-child) {\n          @include border-start-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-left-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-left-radius: 0;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.accordion {\n  // scss-docs-start accordion-css-vars\n  --#{$prefix}accordion-color: #{$accordion-color};\n  --#{$prefix}accordion-bg: #{$accordion-bg};\n  --#{$prefix}accordion-transition: #{$accordion-transition};\n  --#{$prefix}accordion-border-color: #{$accordion-border-color};\n  --#{$prefix}accordion-border-width: #{$accordion-border-width};\n  --#{$prefix}accordion-border-radius: #{$accordion-border-radius};\n  --#{$prefix}accordion-inner-border-radius: #{$accordion-inner-border-radius};\n  --#{$prefix}accordion-btn-padding-x: #{$accordion-button-padding-x};\n  --#{$prefix}accordion-btn-padding-y: #{$accordion-button-padding-y};\n  --#{$prefix}accordion-btn-color: #{$accordion-button-color};\n  --#{$prefix}accordion-btn-bg: #{$accordion-button-bg};\n  --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon)};\n  --#{$prefix}accordion-btn-icon-width: #{$accordion-icon-width};\n  --#{$prefix}accordion-btn-icon-transform: #{$accordion-icon-transform};\n  --#{$prefix}accordion-btn-icon-transition: #{$accordion-icon-transition};\n  --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon)};\n  --#{$prefix}accordion-btn-focus-box-shadow: #{$accordion-button-focus-box-shadow};\n  --#{$prefix}accordion-body-padding-x: #{$accordion-body-padding-x};\n  --#{$prefix}accordion-body-padding-y: #{$accordion-body-padding-y};\n  --#{$prefix}accordion-active-color: #{$accordion-button-active-color};\n  --#{$prefix}accordion-active-bg: #{$accordion-button-active-bg};\n  // scss-docs-end accordion-css-vars\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--#{$prefix}accordion-btn-padding-y) var(--#{$prefix}accordion-btn-padding-x);\n  @include font-size($font-size-base);\n  color: var(--#{$prefix}accordion-btn-color);\n  text-align: left; // Reset button style\n  background-color: var(--#{$prefix}accordion-btn-bg);\n  border: 0;\n  @include border-radius(0);\n  overflow-anchor: none;\n  @include transition(var(--#{$prefix}accordion-transition));\n\n  &:not(.collapsed) {\n    color: var(--#{$prefix}accordion-active-color);\n    background-color: var(--#{$prefix}accordion-active-bg);\n    box-shadow: inset 0 calc(-1 * var(--#{$prefix}accordion-border-width)) 0 var(--#{$prefix}accordion-border-color); // stylelint-disable-line function-disallowed-list\n\n    &::after {\n      background-image: var(--#{$prefix}accordion-btn-active-icon);\n      transform: var(--#{$prefix}accordion-btn-icon-transform);\n    }\n  }\n\n  // Accordion icon\n  &::after {\n    flex-shrink: 0;\n    width: var(--#{$prefix}accordion-btn-icon-width);\n    height: var(--#{$prefix}accordion-btn-icon-width);\n    margin-left: auto;\n    content: \"\";\n    background-image: var(--#{$prefix}accordion-btn-icon);\n    background-repeat: no-repeat;\n    background-size: var(--#{$prefix}accordion-btn-icon-width);\n    @include transition(var(--#{$prefix}accordion-btn-icon-transition));\n  }\n\n  &:hover {\n    z-index: 2;\n  }\n\n  &:focus {\n    z-index: 3;\n    outline: 0;\n    box-shadow: var(--#{$prefix}accordion-btn-focus-box-shadow);\n  }\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--#{$prefix}accordion-color);\n  background-color: var(--#{$prefix}accordion-bg);\n  border: var(--#{$prefix}accordion-border-width) solid var(--#{$prefix}accordion-border-color);\n\n  &:first-of-type {\n    @include border-top-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      @include border-top-radius(var(--#{$prefix}accordion-inner-border-radius));\n    }\n  }\n\n  &:not(:first-of-type) {\n    border-top: 0;\n  }\n\n  // Only set a border-radius on the last item if the accordion is collapsed\n  &:last-of-type {\n    @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      &.collapsed {\n        @include border-bottom-radius(var(--#{$prefix}accordion-inner-border-radius));\n      }\n    }\n\n    > .accordion-collapse {\n      @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n    }\n  }\n}\n\n.accordion-body {\n  padding: var(--#{$prefix}accordion-body-padding-y) var(--#{$prefix}accordion-body-padding-x);\n}\n\n\n// Flush accordion items\n//\n// Remove borders and border-radius to keep accordion items edge-to-edge.\n\n.accordion-flush {\n  > .accordion-item {\n    border-right: 0;\n    border-left: 0;\n    @include border-radius(0);\n\n    &:first-child { border-top: 0; }\n    &:last-child { border-bottom: 0; }\n\n    // stylelint-disable selector-max-class\n    > .accordion-header .accordion-button {\n      &,\n      &.collapsed {\n        @include border-radius(0);\n      }\n    }\n    // stylelint-enable selector-max-class\n\n    > .accordion-collapse {\n      @include border-radius(0);\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .accordion-button::after {\n      --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon-dark)};\n      --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon-dark)};\n    }\n  }\n}\n",".breadcrumb {\n  // scss-docs-start breadcrumb-css-vars\n  --#{$prefix}breadcrumb-padding-x: #{$breadcrumb-padding-x};\n  --#{$prefix}breadcrumb-padding-y: #{$breadcrumb-padding-y};\n  --#{$prefix}breadcrumb-margin-bottom: #{$breadcrumb-margin-bottom};\n  @include rfs($breadcrumb-font-size, --#{$prefix}breadcrumb-font-size);\n  --#{$prefix}breadcrumb-bg: #{$breadcrumb-bg};\n  --#{$prefix}breadcrumb-border-radius: #{$breadcrumb-border-radius};\n  --#{$prefix}breadcrumb-divider-color: #{$breadcrumb-divider-color};\n  --#{$prefix}breadcrumb-item-padding-x: #{$breadcrumb-item-padding-x};\n  --#{$prefix}breadcrumb-item-active-color: #{$breadcrumb-active-color};\n  // scss-docs-end breadcrumb-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--#{$prefix}breadcrumb-padding-y) var(--#{$prefix}breadcrumb-padding-x);\n  margin-bottom: var(--#{$prefix}breadcrumb-margin-bottom);\n  @include font-size(var(--#{$prefix}breadcrumb-font-size));\n  list-style: none;\n  background-color: var(--#{$prefix}breadcrumb-bg);\n  @include border-radius(var(--#{$prefix}breadcrumb-border-radius));\n}\n\n.breadcrumb-item {\n  // The separator between breadcrumbs (by default, a forward-slash: \"/\")\n  + .breadcrumb-item {\n    padding-left: var(--#{$prefix}breadcrumb-item-padding-x);\n\n    &::before {\n      float: left; // Suppress inline spacings and underlining of the separator\n      padding-right: var(--#{$prefix}breadcrumb-item-padding-x);\n      color: var(--#{$prefix}breadcrumb-divider-color);\n      content: var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider)) #{\"/* rtl:\"} var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider-flipped)) #{\"*/\"};\n    }\n  }\n\n  &.active {\n    color: var(--#{$prefix}breadcrumb-item-active-color);\n  }\n}\n",".pagination {\n  // scss-docs-start pagination-css-vars\n  --#{$prefix}pagination-padding-x: #{$pagination-padding-x};\n  --#{$prefix}pagination-padding-y: #{$pagination-padding-y};\n  @include rfs($pagination-font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-color: #{$pagination-color};\n  --#{$prefix}pagination-bg: #{$pagination-bg};\n  --#{$prefix}pagination-border-width: #{$pagination-border-width};\n  --#{$prefix}pagination-border-color: #{$pagination-border-color};\n  --#{$prefix}pagination-border-radius: #{$pagination-border-radius};\n  --#{$prefix}pagination-hover-color: #{$pagination-hover-color};\n  --#{$prefix}pagination-hover-bg: #{$pagination-hover-bg};\n  --#{$prefix}pagination-hover-border-color: #{$pagination-hover-border-color};\n  --#{$prefix}pagination-focus-color: #{$pagination-focus-color};\n  --#{$prefix}pagination-focus-bg: #{$pagination-focus-bg};\n  --#{$prefix}pagination-focus-box-shadow: #{$pagination-focus-box-shadow};\n  --#{$prefix}pagination-active-color: #{$pagination-active-color};\n  --#{$prefix}pagination-active-bg: #{$pagination-active-bg};\n  --#{$prefix}pagination-active-border-color: #{$pagination-active-border-color};\n  --#{$prefix}pagination-disabled-color: #{$pagination-disabled-color};\n  --#{$prefix}pagination-disabled-bg: #{$pagination-disabled-bg};\n  --#{$prefix}pagination-disabled-border-color: #{$pagination-disabled-border-color};\n  // scss-docs-end pagination-css-vars\n\n  display: flex;\n  @include list-unstyled();\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}pagination-padding-y) var(--#{$prefix}pagination-padding-x);\n  @include font-size(var(--#{$prefix}pagination-font-size));\n  color: var(--#{$prefix}pagination-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}pagination-bg);\n  border: var(--#{$prefix}pagination-border-width) solid var(--#{$prefix}pagination-border-color);\n  @include transition($pagination-transition);\n\n  &:hover {\n    z-index: 2;\n    color: var(--#{$prefix}pagination-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}pagination-hover-bg);\n    border-color: var(--#{$prefix}pagination-hover-border-color);\n  }\n\n  &:focus {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-focus-color);\n    background-color: var(--#{$prefix}pagination-focus-bg);\n    outline: $pagination-focus-outline;\n    box-shadow: var(--#{$prefix}pagination-focus-box-shadow);\n  }\n\n  &.active,\n  .active > & {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-active-color);\n    @include gradient-bg(var(--#{$prefix}pagination-active-bg));\n    border-color: var(--#{$prefix}pagination-active-border-color);\n  }\n\n  &.disabled,\n  .disabled > & {\n    color: var(--#{$prefix}pagination-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}pagination-disabled-bg);\n    border-color: var(--#{$prefix}pagination-disabled-border-color);\n  }\n}\n\n.page-item {\n  &:not(:first-child) .page-link {\n    margin-left: $pagination-margin-start;\n  }\n\n  @if $pagination-margin-start == calc(#{$pagination-border-width} * -1) {\n    &:first-child {\n      .page-link {\n        @include border-start-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n\n    &:last-child {\n      .page-link {\n        @include border-end-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n  } @else {\n    // Add border-radius to all pageLinks in case they have left margin\n    .page-link {\n      @include border-radius(var(--#{$prefix}pagination-border-radius));\n    }\n  }\n}\n\n\n//\n// Sizing\n//\n\n.pagination-lg {\n  @include pagination-size($pagination-padding-y-lg, $pagination-padding-x-lg, $font-size-lg, $pagination-border-radius-lg);\n}\n\n.pagination-sm {\n  @include pagination-size($pagination-padding-y-sm, $pagination-padding-x-sm, $font-size-sm, $pagination-border-radius-sm);\n}\n","// Pagination\n\n// scss-docs-start pagination-mixin\n@mixin pagination-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}pagination-padding-x: #{$padding-x};\n  --#{$prefix}pagination-padding-y: #{$padding-y};\n  @include rfs($font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-border-radius: #{$border-radius};\n}\n// scss-docs-end pagination-mixin\n","// Base class\n//\n// Requires one of the contextual, color modifier classes for `color` and\n// `background-color`.\n\n.badge {\n  // scss-docs-start badge-css-vars\n  --#{$prefix}badge-padding-x: #{$badge-padding-x};\n  --#{$prefix}badge-padding-y: #{$badge-padding-y};\n  @include rfs($badge-font-size, --#{$prefix}badge-font-size);\n  --#{$prefix}badge-font-weight: #{$badge-font-weight};\n  --#{$prefix}badge-color: #{$badge-color};\n  --#{$prefix}badge-border-radius: #{$badge-border-radius};\n  // scss-docs-end badge-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}badge-padding-y) var(--#{$prefix}badge-padding-x);\n  @include font-size(var(--#{$prefix}badge-font-size));\n  font-weight: var(--#{$prefix}badge-font-weight);\n  line-height: 1;\n  color: var(--#{$prefix}badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  @include border-radius(var(--#{$prefix}badge-border-radius));\n  @include gradient-bg();\n\n  // Empty badges collapse automatically\n  &:empty {\n    display: none;\n  }\n}\n\n// Quick fix for badges in buttons\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n","//\n// Base styles\n//\n\n.alert {\n  // scss-docs-start alert-css-vars\n  --#{$prefix}alert-bg: transparent;\n  --#{$prefix}alert-padding-x: #{$alert-padding-x};\n  --#{$prefix}alert-padding-y: #{$alert-padding-y};\n  --#{$prefix}alert-margin-bottom: #{$alert-margin-bottom};\n  --#{$prefix}alert-color: inherit;\n  --#{$prefix}alert-border-color: transparent;\n  --#{$prefix}alert-border: #{$alert-border-width} solid var(--#{$prefix}alert-border-color);\n  --#{$prefix}alert-border-radius: #{$alert-border-radius};\n  --#{$prefix}alert-link-color: inherit;\n  // scss-docs-end alert-css-vars\n\n  position: relative;\n  padding: var(--#{$prefix}alert-padding-y) var(--#{$prefix}alert-padding-x);\n  margin-bottom: var(--#{$prefix}alert-margin-bottom);\n  color: var(--#{$prefix}alert-color);\n  background-color: var(--#{$prefix}alert-bg);\n  border: var(--#{$prefix}alert-border);\n  @include border-radius(var(--#{$prefix}alert-border-radius));\n}\n\n// Headings for larger alerts\n.alert-heading {\n  // Specified to prevent conflicts of changing $headings-color\n  color: inherit;\n}\n\n// Provide class for links that match alerts\n.alert-link {\n  font-weight: $alert-link-font-weight;\n  color: var(--#{$prefix}alert-link-color);\n}\n\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissible {\n  padding-right: $alert-dismissible-padding-r;\n\n  // Adjust close link position\n  .btn-close {\n    position: absolute;\n    top: 0;\n    right: 0;\n    z-index: $stretched-link-z-index + 1;\n    padding: $alert-padding-y * 1.25 $alert-padding-x;\n  }\n}\n\n\n// scss-docs-start alert-modifiers\n// Generate contextual modifier classes for colorizing the alert\n@each $state in map-keys($theme-colors) {\n  .alert-#{$state} {\n    --#{$prefix}alert-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}alert-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}alert-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}alert-link-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end alert-modifiers\n","// Disable animation if transitions are disabled\n\n// scss-docs-start progress-keyframes\n@if $enable-transitions {\n  @keyframes progress-bar-stripes {\n    0% { background-position-x: $progress-height; }\n  }\n}\n// scss-docs-end progress-keyframes\n\n.progress,\n.progress-stacked {\n  // scss-docs-start progress-css-vars\n  --#{$prefix}progress-height: #{$progress-height};\n  @include rfs($progress-font-size, --#{$prefix}progress-font-size);\n  --#{$prefix}progress-bg: #{$progress-bg};\n  --#{$prefix}progress-border-radius: #{$progress-border-radius};\n  --#{$prefix}progress-box-shadow: #{$progress-box-shadow};\n  --#{$prefix}progress-bar-color: #{$progress-bar-color};\n  --#{$prefix}progress-bar-bg: #{$progress-bar-bg};\n  --#{$prefix}progress-bar-transition: #{$progress-bar-transition};\n  // scss-docs-end progress-css-vars\n\n  display: flex;\n  height: var(--#{$prefix}progress-height);\n  overflow: hidden; // force rounded corners by cropping it\n  @include font-size(var(--#{$prefix}progress-font-size));\n  background-color: var(--#{$prefix}progress-bg);\n  @include border-radius(var(--#{$prefix}progress-border-radius));\n  @include box-shadow(var(--#{$prefix}progress-box-shadow));\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--#{$prefix}progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--#{$prefix}progress-bar-bg);\n  @include transition(var(--#{$prefix}progress-bar-transition));\n}\n\n.progress-bar-striped {\n  @include gradient-striped();\n  background-size: var(--#{$prefix}progress-height) var(--#{$prefix}progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n@if $enable-transitions {\n  .progress-bar-animated {\n    animation: $progress-bar-animation-timing progress-bar-stripes;\n\n    @if $enable-reduced-motion {\n      @media (prefers-reduced-motion: reduce) {\n        animation: none;\n      }\n    }\n  }\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // scss-docs-start list-group-css-vars\n  --#{$prefix}list-group-color: #{$list-group-color};\n  --#{$prefix}list-group-bg: #{$list-group-bg};\n  --#{$prefix}list-group-border-color: #{$list-group-border-color};\n  --#{$prefix}list-group-border-width: #{$list-group-border-width};\n  --#{$prefix}list-group-border-radius: #{$list-group-border-radius};\n  --#{$prefix}list-group-item-padding-x: #{$list-group-item-padding-x};\n  --#{$prefix}list-group-item-padding-y: #{$list-group-item-padding-y};\n  --#{$prefix}list-group-action-color: #{$list-group-action-color};\n  --#{$prefix}list-group-action-hover-color: #{$list-group-action-hover-color};\n  --#{$prefix}list-group-action-hover-bg: #{$list-group-hover-bg};\n  --#{$prefix}list-group-action-active-color: #{$list-group-action-active-color};\n  --#{$prefix}list-group-action-active-bg: #{$list-group-action-active-bg};\n  --#{$prefix}list-group-disabled-color: #{$list-group-disabled-color};\n  --#{$prefix}list-group-disabled-bg: #{$list-group-disabled-bg};\n  --#{$prefix}list-group-active-color: #{$list-group-active-color};\n  --#{$prefix}list-group-active-bg: #{$list-group-active-bg};\n  --#{$prefix}list-group-active-border-color: #{$list-group-active-border-color};\n  // scss-docs-end list-group-css-vars\n\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n  @include border-radius(var(--#{$prefix}list-group-border-radius));\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n\n  > .list-group-item::before {\n    // Increments only this instance of the section counter\n    content: counters(section, \".\") \". \";\n    counter-increment: section;\n  }\n}\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected items.\n\n.list-group-item-action {\n  width: 100%; // For `<button>`s (anchors become 100% by default though)\n  color: var(--#{$prefix}list-group-action-color);\n  text-align: inherit; // For `<button>`s (anchors inherit)\n\n  // Hover state\n  &:hover,\n  &:focus {\n    z-index: 1; // Place hover/focus items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-action-hover-color);\n    text-decoration: none;\n    background-color: var(--#{$prefix}list-group-action-hover-bg);\n  }\n\n  &:active {\n    color: var(--#{$prefix}list-group-action-active-color);\n    background-color: var(--#{$prefix}list-group-action-active-bg);\n  }\n}\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}list-group-item-padding-y) var(--#{$prefix}list-group-item-padding-x);\n  color: var(--#{$prefix}list-group-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}list-group-bg);\n  border: var(--#{$prefix}list-group-border-width) solid var(--#{$prefix}list-group-border-color);\n\n  &:first-child {\n    @include border-top-radius(inherit);\n  }\n\n  &:last-child {\n    @include border-bottom-radius(inherit);\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}list-group-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}list-group-disabled-bg);\n  }\n\n  // Include both here for `<a>`s and `<button>`s\n  &.active {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-active-color);\n    background-color: var(--#{$prefix}list-group-active-bg);\n    border-color: var(--#{$prefix}list-group-active-border-color);\n  }\n\n  // stylelint-disable-next-line scss/selector-no-redundant-nesting-selector\n  & + .list-group-item {\n    border-top-width: 0;\n\n    &.active {\n      margin-top: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n      border-top-width: var(--#{$prefix}list-group-border-width);\n    }\n  }\n}\n\n// Horizontal\n//\n// Change the layout of list group items from vertical (default) to horizontal.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .list-group-horizontal#{$infix} {\n      flex-direction: row;\n\n      > .list-group-item {\n        &:first-child:not(:last-child) {\n          @include border-bottom-start-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-top-end-radius(0);\n        }\n\n        &:last-child:not(:first-child) {\n          @include border-top-end-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-bottom-start-radius(0);\n        }\n\n        &.active {\n          margin-top: 0;\n        }\n\n        + .list-group-item {\n          border-top-width: var(--#{$prefix}list-group-border-width);\n          border-left-width: 0;\n\n          &.active {\n            margin-left: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n            border-left-width: var(--#{$prefix}list-group-border-width);\n          }\n        }\n      }\n    }\n  }\n}\n\n\n// Flush list items\n//\n// Remove borders and border-radius to keep list group items edge-to-edge. Most\n// useful within other components (e.g., cards).\n\n.list-group-flush {\n  @include border-radius(0);\n\n  > .list-group-item {\n    border-width: 0 0 var(--#{$prefix}list-group-border-width);\n\n    &:last-child {\n      border-bottom-width: 0;\n    }\n  }\n}\n\n\n// scss-docs-start list-group-modifiers\n// List group contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n@each $state in map-keys($theme-colors) {\n  .list-group-item-#{$state} {\n    --#{$prefix}list-group-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-hover-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-hover-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-active-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-active-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-active-color: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-active-bg: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-active-border-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end list-group-modifiers\n","// Transparent background and border properties included for button version.\n// iOS requires the button element instead of an anchor tag.\n// If you want the anchor version, it requires `href=\"#\"`.\n// See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n.btn-close {\n  // scss-docs-start close-css-vars\n  --#{$prefix}btn-close-color: #{$btn-close-color};\n  --#{$prefix}btn-close-bg: #{ escape-svg($btn-close-bg) };\n  --#{$prefix}btn-close-opacity: #{$btn-close-opacity};\n  --#{$prefix}btn-close-hover-opacity: #{$btn-close-hover-opacity};\n  --#{$prefix}btn-close-focus-shadow: #{$btn-close-focus-shadow};\n  --#{$prefix}btn-close-focus-opacity: #{$btn-close-focus-opacity};\n  --#{$prefix}btn-close-disabled-opacity: #{$btn-close-disabled-opacity};\n  --#{$prefix}btn-close-white-filter: #{$btn-close-white-filter};\n  // scss-docs-end close-css-vars\n\n  box-sizing: content-box;\n  width: $btn-close-width;\n  height: $btn-close-height;\n  padding: $btn-close-padding-y $btn-close-padding-x;\n  color: var(--#{$prefix}btn-close-color);\n  background: transparent var(--#{$prefix}btn-close-bg) center / $btn-close-width auto no-repeat; // include transparent for button elements\n  border: 0; // for button elements\n  @include border-radius();\n  opacity: var(--#{$prefix}btn-close-opacity);\n\n  // Override <a>'s hover style\n  &:hover {\n    color: var(--#{$prefix}btn-close-color);\n    text-decoration: none;\n    opacity: var(--#{$prefix}btn-close-hover-opacity);\n  }\n\n  &:focus {\n    outline: 0;\n    box-shadow: var(--#{$prefix}btn-close-focus-shadow);\n    opacity: var(--#{$prefix}btn-close-focus-opacity);\n  }\n\n  &:disabled,\n  &.disabled {\n    pointer-events: none;\n    user-select: none;\n    opacity: var(--#{$prefix}btn-close-disabled-opacity);\n  }\n}\n\n@mixin btn-close-white() {\n  filter: var(--#{$prefix}btn-close-white-filter);\n}\n\n.btn-close-white {\n  @include btn-close-white();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .btn-close {\n      @include btn-close-white();\n    }\n  }\n}\n",".toast {\n  // scss-docs-start toast-css-vars\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n  --#{$prefix}toast-padding-x: #{$toast-padding-x};\n  --#{$prefix}toast-padding-y: #{$toast-padding-y};\n  --#{$prefix}toast-spacing: #{$toast-spacing};\n  --#{$prefix}toast-max-width: #{$toast-max-width};\n  @include rfs($toast-font-size, --#{$prefix}toast-font-size);\n  --#{$prefix}toast-color: #{$toast-color};\n  --#{$prefix}toast-bg: #{$toast-background-color};\n  --#{$prefix}toast-border-width: #{$toast-border-width};\n  --#{$prefix}toast-border-color: #{$toast-border-color};\n  --#{$prefix}toast-border-radius: #{$toast-border-radius};\n  --#{$prefix}toast-box-shadow: #{$toast-box-shadow};\n  --#{$prefix}toast-header-color: #{$toast-header-color};\n  --#{$prefix}toast-header-bg: #{$toast-header-background-color};\n  --#{$prefix}toast-header-border-color: #{$toast-header-border-color};\n  // scss-docs-end toast-css-vars\n\n  width: var(--#{$prefix}toast-max-width);\n  max-width: 100%;\n  @include font-size(var(--#{$prefix}toast-font-size));\n  color: var(--#{$prefix}toast-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}toast-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-border-color);\n  box-shadow: var(--#{$prefix}toast-box-shadow);\n  @include border-radius(var(--#{$prefix}toast-border-radius));\n\n  &.showing {\n    opacity: 0;\n  }\n\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.toast-container {\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n\n  position: absolute;\n  z-index: var(--#{$prefix}toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n\n  > :not(:last-child) {\n    margin-bottom: var(--#{$prefix}toast-spacing);\n  }\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}toast-padding-y) var(--#{$prefix}toast-padding-x);\n  color: var(--#{$prefix}toast-header-color);\n  background-color: var(--#{$prefix}toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-header-border-color);\n  @include border-top-radius(calc(var(--#{$prefix}toast-border-radius) - var(--#{$prefix}toast-border-width)));\n\n  .btn-close {\n    margin-right: calc(-.5 * var(--#{$prefix}toast-padding-x)); // stylelint-disable-line function-disallowed-list\n    margin-left: var(--#{$prefix}toast-padding-x);\n  }\n}\n\n.toast-body {\n  padding: var(--#{$prefix}toast-padding-x);\n  word-wrap: break-word;\n}\n","// stylelint-disable function-disallowed-list\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and stuff\n\n\n// Container that the modal scrolls within\n.modal {\n  // scss-docs-start modal-css-vars\n  --#{$prefix}modal-zindex: #{$zindex-modal};\n  --#{$prefix}modal-width: #{$modal-md};\n  --#{$prefix}modal-padding: #{$modal-inner-padding};\n  --#{$prefix}modal-margin: #{$modal-dialog-margin};\n  --#{$prefix}modal-color: #{$modal-content-color};\n  --#{$prefix}modal-bg: #{$modal-content-bg};\n  --#{$prefix}modal-border-color: #{$modal-content-border-color};\n  --#{$prefix}modal-border-width: #{$modal-content-border-width};\n  --#{$prefix}modal-border-radius: #{$modal-content-border-radius};\n  --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-xs};\n  --#{$prefix}modal-inner-border-radius: #{$modal-content-inner-border-radius};\n  --#{$prefix}modal-header-padding-x: #{$modal-header-padding-x};\n  --#{$prefix}modal-header-padding-y: #{$modal-header-padding-y};\n  --#{$prefix}modal-header-padding: #{$modal-header-padding}; // Todo in v6: Split this padding into x and y\n  --#{$prefix}modal-header-border-color: #{$modal-header-border-color};\n  --#{$prefix}modal-header-border-width: #{$modal-header-border-width};\n  --#{$prefix}modal-title-line-height: #{$modal-title-line-height};\n  --#{$prefix}modal-footer-gap: #{$modal-footer-margin-between};\n  --#{$prefix}modal-footer-bg: #{$modal-footer-bg};\n  --#{$prefix}modal-footer-border-color: #{$modal-footer-border-color};\n  --#{$prefix}modal-footer-border-width: #{$modal-footer-border-width};\n  // scss-docs-end modal-css-vars\n\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--#{$prefix}modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n  // We deliberately don't use `-webkit-overflow-scrolling: touch;` due to a\n  // gnarly iOS Safari bug: https://bugs.webkit.org/show_bug.cgi?id=158342\n  // See also https://github.com/twbs/bootstrap/issues/17695\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--#{$prefix}modal-margin);\n  // allow clicks to pass through for custom click handling to close modal\n  pointer-events: none;\n\n  // When fading in the modal, animate it to slide down\n  .modal.fade & {\n    @include transition($modal-transition);\n    transform: $modal-fade-transform;\n  }\n  .modal.show & {\n    transform: $modal-show-transform;\n  }\n\n  // When trying to close, animate focus to scale\n  .modal.modal-static & {\n    transform: $modal-scale-transform;\n  }\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n\n  .modal-content {\n    max-height: 100%;\n    overflow: hidden;\n  }\n\n  .modal-body {\n    overflow-y: auto;\n  }\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%; // Ensure `.modal-content` extends the full width of the parent `.modal-dialog`\n  // counteract the pointer-events: none; in the .modal-dialog\n  color: var(--#{$prefix}modal-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}modal-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}modal-border-width) solid var(--#{$prefix}modal-border-color);\n  @include border-radius(var(--#{$prefix}modal-border-radius));\n  @include box-shadow(var(--#{$prefix}modal-box-shadow));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  // scss-docs-start modal-backdrop-css-vars\n  --#{$prefix}backdrop-zindex: #{$zindex-modal-backdrop};\n  --#{$prefix}backdrop-bg: #{$modal-backdrop-bg};\n  --#{$prefix}backdrop-opacity: #{$modal-backdrop-opacity};\n  // scss-docs-end modal-backdrop-css-vars\n\n  @include overlay-backdrop(var(--#{$prefix}backdrop-zindex), var(--#{$prefix}backdrop-bg), var(--#{$prefix}backdrop-opacity));\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  padding: var(--#{$prefix}modal-header-padding);\n  border-bottom: var(--#{$prefix}modal-header-border-width) solid var(--#{$prefix}modal-header-border-color);\n  @include border-top-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}modal-header-padding-y) * .5) calc(var(--#{$prefix}modal-header-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}modal-header-padding-y)) calc(-.5 * var(--#{$prefix}modal-header-padding-x)) calc(-.5 * var(--#{$prefix}modal-header-padding-y)) auto;\n  }\n}\n\n// Title text within header\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}modal-title-line-height);\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  // Enable `flex-grow: 1` so that the body take up as much space as possible\n  // when there should be a fixed height on `.modal-dialog`.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}modal-padding);\n}\n\n// Footer (for actions)\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center; // vertically center\n  justify-content: flex-end; // Right align buttons with flex property because text-align doesn't work on flex items\n  padding: calc(var(--#{$prefix}modal-padding) - var(--#{$prefix}modal-footer-gap) * .5);\n  background-color: var(--#{$prefix}modal-footer-bg);\n  border-top: var(--#{$prefix}modal-footer-border-width) solid var(--#{$prefix}modal-footer-border-color);\n  @include border-bottom-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  // Place margin between footer elements\n  // This solution is far from ideal because of the universal selector usage,\n  // but is needed to fix https://github.com/twbs/bootstrap/issues/24800\n  > * {\n    margin: calc(var(--#{$prefix}modal-footer-gap) * .5); // Todo in v6: replace with gap on parent class\n  }\n}\n\n// Scale up the modal\n@include media-breakpoint-up(sm) {\n  .modal {\n    --#{$prefix}modal-margin: #{$modal-dialog-margin-y-sm-up};\n    --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-sm-up};\n  }\n\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    max-width: var(--#{$prefix}modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  .modal-sm {\n    --#{$prefix}modal-width: #{$modal-sm};\n  }\n}\n\n@include media-breakpoint-up(lg) {\n  .modal-lg,\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-lg};\n  }\n}\n\n@include media-breakpoint-up(xl) {\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-xl};\n  }\n}\n\n// scss-docs-start modal-fullscreen-loop\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n  $postfix: if($infix != \"\", $infix + \"-down\", \"\");\n\n  @include media-breakpoint-down($breakpoint) {\n    .modal-fullscreen#{$postfix} {\n      width: 100vw;\n      max-width: none;\n      height: 100%;\n      margin: 0;\n\n      .modal-content {\n        height: 100%;\n        border: 0;\n        @include border-radius(0);\n      }\n\n      .modal-header,\n      .modal-footer {\n        @include border-radius(0);\n      }\n\n      .modal-body {\n        overflow-y: auto;\n      }\n    }\n  }\n}\n// scss-docs-end modal-fullscreen-loop\n","// Shared between modals and offcanvases\n@mixin overlay-backdrop($zindex, $backdrop-bg, $backdrop-opacity) {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex;\n  width: 100vw;\n  height: 100vh;\n  background-color: $backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $backdrop-opacity; }\n}\n","// Base class\n.tooltip {\n  // scss-docs-start tooltip-css-vars\n  --#{$prefix}tooltip-zindex: #{$zindex-tooltip};\n  --#{$prefix}tooltip-max-width: #{$tooltip-max-width};\n  --#{$prefix}tooltip-padding-x: #{$tooltip-padding-x};\n  --#{$prefix}tooltip-padding-y: #{$tooltip-padding-y};\n  --#{$prefix}tooltip-margin: #{$tooltip-margin};\n  @include rfs($tooltip-font-size, --#{$prefix}tooltip-font-size);\n  --#{$prefix}tooltip-color: #{$tooltip-color};\n  --#{$prefix}tooltip-bg: #{$tooltip-bg};\n  --#{$prefix}tooltip-border-radius: #{$tooltip-border-radius};\n  --#{$prefix}tooltip-opacity: #{$tooltip-opacity};\n  --#{$prefix}tooltip-arrow-width: #{$tooltip-arrow-width};\n  --#{$prefix}tooltip-arrow-height: #{$tooltip-arrow-height};\n  // scss-docs-end tooltip-css-vars\n\n  z-index: var(--#{$prefix}tooltip-zindex);\n  display: block;\n  margin: var(--#{$prefix}tooltip-margin);\n  @include deprecate(\"`$tooltip-margin`\", \"v5\", \"v5.x\", true);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}tooltip-font-size));\n  // Allow breaking very long words so they don't overflow the tooltip's bounds\n  word-wrap: break-word;\n  opacity: 0;\n\n  &.show { opacity: var(--#{$prefix}tooltip-opacity); }\n\n  .tooltip-arrow {\n    display: block;\n    width: var(--#{$prefix}tooltip-arrow-width);\n    height: var(--#{$prefix}tooltip-arrow-height);\n\n    &::before {\n      position: absolute;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n    }\n  }\n}\n\n.bs-tooltip-top .tooltip-arrow {\n  bottom: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    top: -1px;\n    border-width: var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-top-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow {\n  left: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    right: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-right-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-bottom .tooltip-arrow {\n  top: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    bottom: -1px;\n    border-width: 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-bottom-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow {\n  right: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    left: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-left-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-tooltip-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-tooltip-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-tooltip-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-tooltip-start;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: var(--#{$prefix}tooltip-max-width);\n  padding: var(--#{$prefix}tooltip-padding-y) var(--#{$prefix}tooltip-padding-x);\n  color: var(--#{$prefix}tooltip-color);\n  text-align: center;\n  background-color: var(--#{$prefix}tooltip-bg);\n  @include border-radius(var(--#{$prefix}tooltip-border-radius));\n}\n","@mixin reset-text {\n  font-family: $font-family-base;\n  // We deliberately do NOT reset font-size or overflow-wrap / word-wrap.\n  font-style: normal;\n  font-weight: $font-weight-normal;\n  line-height: $line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n}\n",".popover {\n  // scss-docs-start popover-css-vars\n  --#{$prefix}popover-zindex: #{$zindex-popover};\n  --#{$prefix}popover-max-width: #{$popover-max-width};\n  @include rfs($popover-font-size, --#{$prefix}popover-font-size);\n  --#{$prefix}popover-bg: #{$popover-bg};\n  --#{$prefix}popover-border-width: #{$popover-border-width};\n  --#{$prefix}popover-border-color: #{$popover-border-color};\n  --#{$prefix}popover-border-radius: #{$popover-border-radius};\n  --#{$prefix}popover-inner-border-radius: #{$popover-inner-border-radius};\n  --#{$prefix}popover-box-shadow: #{$popover-box-shadow};\n  --#{$prefix}popover-header-padding-x: #{$popover-header-padding-x};\n  --#{$prefix}popover-header-padding-y: #{$popover-header-padding-y};\n  @include rfs($popover-header-font-size, --#{$prefix}popover-header-font-size);\n  --#{$prefix}popover-header-color: #{$popover-header-color};\n  --#{$prefix}popover-header-bg: #{$popover-header-bg};\n  --#{$prefix}popover-body-padding-x: #{$popover-body-padding-x};\n  --#{$prefix}popover-body-padding-y: #{$popover-body-padding-y};\n  --#{$prefix}popover-body-color: #{$popover-body-color};\n  --#{$prefix}popover-arrow-width: #{$popover-arrow-width};\n  --#{$prefix}popover-arrow-height: #{$popover-arrow-height};\n  --#{$prefix}popover-arrow-border: var(--#{$prefix}popover-border-color);\n  // scss-docs-end popover-css-vars\n\n  z-index: var(--#{$prefix}popover-zindex);\n  display: block;\n  max-width: var(--#{$prefix}popover-max-width);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}popover-font-size));\n  // Allow breaking very long words so they don't overflow the popover's bounds\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}popover-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-radius(var(--#{$prefix}popover-border-radius));\n  @include box-shadow(var(--#{$prefix}popover-box-shadow));\n\n  .popover-arrow {\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    height: var(--#{$prefix}popover-arrow-height);\n\n    &::before,\n    &::after {\n      position: absolute;\n      display: block;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n      border-width: 0;\n    }\n  }\n}\n\n.bs-popover-top {\n  > .popover-arrow {\n    bottom: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      bottom: 0;\n      border-top-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      bottom: var(--#{$prefix}popover-border-width);\n      border-top-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end {\n  > .popover-arrow {\n    left: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      left: 0;\n      border-right-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      left: var(--#{$prefix}popover-border-width);\n      border-right-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-bottom {\n  > .popover-arrow {\n    top: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      top: 0;\n      border-bottom-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      top: var(--#{$prefix}popover-border-width);\n      border-bottom-color: var(--#{$prefix}popover-bg);\n    }\n  }\n\n  // This will remove the popover-header's border just below the arrow\n  .popover-header::before {\n    position: absolute;\n    top: 0;\n    left: 50%;\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    margin-left: calc(-.5 * var(--#{$prefix}popover-arrow-width)); // stylelint-disable-line function-disallowed-list\n    content: \"\";\n    border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-header-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start {\n  > .popover-arrow {\n    right: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      right: 0;\n      border-left-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      right: var(--#{$prefix}popover-border-width);\n      border-left-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-popover-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-popover-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-popover-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-popover-start;\n  }\n}\n\n// Offset the popover to account for the popover arrow\n.popover-header {\n  padding: var(--#{$prefix}popover-header-padding-y) var(--#{$prefix}popover-header-padding-x);\n  margin-bottom: 0; // Reset the default from Reboot\n  @include font-size(var(--#{$prefix}popover-header-font-size));\n  color: var(--#{$prefix}popover-header-color);\n  background-color: var(--#{$prefix}popover-header-bg);\n  border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-top-radius(var(--#{$prefix}popover-inner-border-radius));\n\n  &:empty {\n    display: none;\n  }\n}\n\n.popover-body {\n  padding: var(--#{$prefix}popover-body-padding-y) var(--#{$prefix}popover-body-padding-x);\n  color: var(--#{$prefix}popover-body-color);\n}\n","// Notes on the classes:\n//\n// 1. .carousel.pointer-event should ideally be pan-y (to allow for users to scroll vertically)\n//    even when their scroll action started on a carousel, but for compatibility (with Firefox)\n//    we're preventing all actions instead\n// 2. The .carousel-item-start and .carousel-item-end is used to indicate where\n//    the active slide is heading.\n// 3. .active.carousel-item is the current slide.\n// 4. .active.carousel-item-start and .active.carousel-item-end is the current\n//    slide in its in-transition state. Only one of these occurs at a time.\n// 5. .carousel-item-next.carousel-item-start and .carousel-item-prev.carousel-item-end\n//    is the upcoming slide in transition.\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n  @include clearfix();\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  @include transition($carousel-transition);\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n\n//\n// Alternate transitions\n//\n\n.carousel-fade {\n  .carousel-item {\n    opacity: 0;\n    transition-property: opacity;\n    transform: none;\n  }\n\n  .carousel-item.active,\n  .carousel-item-next.carousel-item-start,\n  .carousel-item-prev.carousel-item-end {\n    z-index: 1;\n    opacity: 1;\n  }\n\n  .active.carousel-item-start,\n  .active.carousel-item-end {\n    z-index: 0;\n    opacity: 0;\n    @include transition(opacity 0s $carousel-transition-duration);\n  }\n}\n\n\n//\n// Left/right controls for nav\n//\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  // Use flex for alignment (1-3)\n  display: flex; // 1. allow flex styles\n  align-items: center; // 2. vertically center contents\n  justify-content: center; // 3. horizontally center contents\n  width: $carousel-control-width;\n  padding: 0;\n  color: $carousel-control-color;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: $carousel-control-opacity;\n  @include transition($carousel-control-transition);\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: $carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    opacity: $carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  background-image: if($enable-gradients, linear-gradient(90deg, rgba($black, .25), rgba($black, .001)), null);\n}\n.carousel-control-next {\n  right: 0;\n  background-image: if($enable-gradients, linear-gradient(270deg, rgba($black, .25), rgba($black, .001)), null);\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height: $carousel-control-icon-width;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n.carousel-control-prev-icon {\n  background-image: escape-svg($carousel-control-prev-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-next-icon-bg) + \"*/\"};\n}\n.carousel-control-next-icon {\n  background-image: escape-svg($carousel-control-next-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-prev-icon-bg) + \"*/\"};\n}\n\n// Optional indicator pips/controls\n//\n// Add a container (such as a list) with the following class and add an item (ideally a focusable control,\n// like a button) with data-bs-target for each slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  // Use the .carousel-control's width as margin so we don't overlay those\n  margin-right: $carousel-control-width;\n  margin-bottom: 1rem;\n  margin-left: $carousel-control-width;\n\n  [data-bs-target] {\n    box-sizing: content-box;\n    flex: 0 1 auto;\n    width: $carousel-indicator-width;\n    height: $carousel-indicator-height;\n    padding: 0;\n    margin-right: $carousel-indicator-spacer;\n    margin-left: $carousel-indicator-spacer;\n    text-indent: -999px;\n    cursor: pointer;\n    background-color: $carousel-indicator-active-bg;\n    background-clip: padding-box;\n    border: 0;\n    // Use transparent borders to increase the hit area by 10px on top and bottom.\n    border-top: $carousel-indicator-hit-area-height solid transparent;\n    border-bottom: $carousel-indicator-hit-area-height solid transparent;\n    opacity: $carousel-indicator-opacity;\n    @include transition($carousel-indicator-transition);\n  }\n\n  .active {\n    opacity: $carousel-indicator-active-opacity;\n  }\n}\n\n\n// Optional captions\n//\n//\n\n.carousel-caption {\n  position: absolute;\n  right: (100% - $carousel-caption-width) * .5;\n  bottom: $carousel-caption-spacer;\n  left: (100% - $carousel-caption-width) * .5;\n  padding-top: $carousel-caption-padding-y;\n  padding-bottom: $carousel-caption-padding-y;\n  color: $carousel-caption-color;\n  text-align: center;\n}\n\n// Dark mode carousel\n\n@mixin carousel-dark() {\n  .carousel-control-prev-icon,\n  .carousel-control-next-icon {\n    filter: $carousel-dark-control-icon-filter;\n  }\n\n  .carousel-indicators [data-bs-target] {\n    background-color: $carousel-dark-indicator-active-bg;\n  }\n\n  .carousel-caption {\n    color: $carousel-dark-caption-color;\n  }\n}\n\n.carousel-dark {\n  @include carousel-dark();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    @if $color-mode-type == \"media-query\" {\n      .carousel {\n        @include carousel-dark();\n      }\n    } @else {\n      .carousel,\n      &.carousel {\n        @include carousel-dark();\n      }\n    }\n  }\n}\n","// scss-docs-start clearfix\n@mixin clearfix() {\n  &::after {\n    display: block;\n    clear: both;\n    content: \"\";\n  }\n}\n// scss-docs-end clearfix\n","//\n// Rotating border\n//\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--#{$prefix}spinner-width);\n  height: var(--#{$prefix}spinner-height);\n  vertical-align: var(--#{$prefix}spinner-vertical-align);\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 50%;\n  animation: var(--#{$prefix}spinner-animation-speed) linear infinite var(--#{$prefix}spinner-animation-name);\n}\n\n// scss-docs-start spinner-border-keyframes\n@keyframes spinner-border {\n  to { transform: rotate(360deg) #{\"/* rtl:ignore */\"}; }\n}\n// scss-docs-end spinner-border-keyframes\n\n.spinner-border {\n  // scss-docs-start spinner-border-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-border;\n  // scss-docs-end spinner-border-css-vars\n\n  border: var(--#{$prefix}spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  // scss-docs-start spinner-border-sm-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width-sm};\n  // scss-docs-end spinner-border-sm-css-vars\n}\n\n//\n// Growing circle\n//\n\n// scss-docs-start spinner-grow-keyframes\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n// scss-docs-end spinner-grow-keyframes\n\n.spinner-grow {\n  // scss-docs-start spinner-grow-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-grow;\n  // scss-docs-end spinner-grow-css-vars\n\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n}\n\n@if $enable-reduced-motion {\n  @media (prefers-reduced-motion: reduce) {\n    .spinner-border,\n    .spinner-grow {\n      --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed * 2};\n    }\n  }\n}\n","// stylelint-disable function-disallowed-list\n\n%offcanvas-css-vars {\n  // scss-docs-start offcanvas-css-vars\n  --#{$prefix}offcanvas-zindex: #{$zindex-offcanvas};\n  --#{$prefix}offcanvas-width: #{$offcanvas-horizontal-width};\n  --#{$prefix}offcanvas-height: #{$offcanvas-vertical-height};\n  --#{$prefix}offcanvas-padding-x: #{$offcanvas-padding-x};\n  --#{$prefix}offcanvas-padding-y: #{$offcanvas-padding-y};\n  --#{$prefix}offcanvas-color: #{$offcanvas-color};\n  --#{$prefix}offcanvas-bg: #{$offcanvas-bg-color};\n  --#{$prefix}offcanvas-border-width: #{$offcanvas-border-width};\n  --#{$prefix}offcanvas-border-color: #{$offcanvas-border-color};\n  --#{$prefix}offcanvas-box-shadow: #{$offcanvas-box-shadow};\n  --#{$prefix}offcanvas-transition: #{transform $offcanvas-transition-duration ease-in-out};\n  --#{$prefix}offcanvas-title-line-height: #{$offcanvas-title-line-height};\n  // scss-docs-end offcanvas-css-vars\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @extend %offcanvas-css-vars;\n  }\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @include media-breakpoint-down($next) {\n      position: fixed;\n      bottom: 0;\n      z-index: var(--#{$prefix}offcanvas-zindex);\n      display: flex;\n      flex-direction: column;\n      max-width: 100%;\n      color: var(--#{$prefix}offcanvas-color);\n      visibility: hidden;\n      background-color: var(--#{$prefix}offcanvas-bg);\n      background-clip: padding-box;\n      outline: 0;\n      @include box-shadow(var(--#{$prefix}offcanvas-box-shadow));\n      @include transition(var(--#{$prefix}offcanvas-transition));\n\n      &.offcanvas-start {\n        top: 0;\n        left: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-right: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(-100%);\n      }\n\n      &.offcanvas-end {\n        top: 0;\n        right: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-left: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(100%);\n      }\n\n      &.offcanvas-top {\n        top: 0;\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-bottom: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(-100%);\n      }\n\n      &.offcanvas-bottom {\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-top: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(100%);\n      }\n\n      &.showing,\n      &.show:not(.hiding) {\n        transform: none;\n      }\n\n      &.showing,\n      &.hiding,\n      &.show {\n        visibility: visible;\n      }\n    }\n\n    @if not ($infix == \"\") {\n      @include media-breakpoint-up($next) {\n        --#{$prefix}offcanvas-height: auto;\n        --#{$prefix}offcanvas-border-width: 0;\n        background-color: transparent !important; // stylelint-disable-line declaration-no-important\n\n        .offcanvas-header {\n          display: none;\n        }\n\n        .offcanvas-body {\n          display: flex;\n          flex-grow: 0;\n          padding: 0;\n          overflow-y: visible;\n          // Reset `background-color` in case `.bg-*` classes are used in offcanvas\n          background-color: transparent !important; // stylelint-disable-line declaration-no-important\n        }\n      }\n    }\n  }\n}\n\n.offcanvas-backdrop {\n  @include overlay-backdrop($zindex-offcanvas-backdrop, $offcanvas-backdrop-bg, $offcanvas-backdrop-opacity);\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}offcanvas-padding-y) * .5) calc(var(--#{$prefix}offcanvas-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) calc(-.5 * var(--#{$prefix}offcanvas-padding-x)) calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) auto;\n  }\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n  overflow-y: auto;\n}\n",".placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: $placeholder-opacity-max;\n\n  &.btn::before {\n    display: inline-block;\n    content: \"\";\n  }\n}\n\n// Sizing\n.placeholder-xs {\n  min-height: .6em;\n}\n\n.placeholder-sm {\n  min-height: .8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n// Animation\n.placeholder-glow {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .text-bg-#{$color} {\n    color: color-contrast($value) if($enable-important-utilities, !important, null);\n    background-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}bg-opacity, 1)) if($enable-important-utilities, !important, null);\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .link-#{$color} {\n    color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n    text-decoration-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n    @if $link-shade-percentage != 0 {\n      &:hover,\n      &:focus {\n        $hover-color: if(color-contrast($value) == $color-contrast-light, shade-color($value, $link-shade-percentage), tint-color($value, $link-shade-percentage));\n        color: RGBA(#{to-rgb($hover-color)}, var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n        text-decoration-color: RGBA(to-rgb($hover-color), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n      }\n    }\n  }\n}\n\n// One-off special link helper as a bridge until v6\n.link-body-emphasis {\n  color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n  text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n  @if $link-shade-percentage != 0 {\n    &:hover,\n    &:focus {\n      color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, .75)) if($enable-important-utilities, !important, null);\n      text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, .75)) if($enable-important-utilities, !important, null);\n    }\n  }\n}\n",".focus-ring:focus {\n  outline: 0;\n  // By default, there is no `--bs-focus-ring-x`, `--bs-focus-ring-y`, or `--bs-focus-ring-blur`, but we provide CSS variables with fallbacks to initial `0` values\n  box-shadow: var(--#{$prefix}focus-ring-x, 0) var(--#{$prefix}focus-ring-y, 0) var(--#{$prefix}focus-ring-blur, 0) var(--#{$prefix}focus-ring-width) var(--#{$prefix}focus-ring-color);\n}\n",".icon-link {\n  display: inline-flex;\n  gap: $icon-link-gap;\n  align-items: center;\n  text-decoration-color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, .5));\n  text-underline-offset: $icon-link-underline-offset;\n  backface-visibility: hidden;\n\n  > .bi {\n    flex-shrink: 0;\n    width: $icon-link-icon-size;\n    height: $icon-link-icon-size;\n    fill: currentcolor;\n    @include transition($icon-link-icon-transition);\n  }\n}\n\n.icon-link-hover {\n  &:hover,\n  &:focus-visible {\n    > .bi {\n      transform: var(--#{$prefix}icon-link-transform, $icon-link-icon-transform);\n    }\n  }\n}\n","// Credit: Nicolas Gallagher and SUIT CSS.\n\n.ratio {\n  position: relative;\n  width: 100%;\n\n  &::before {\n    display: block;\n    padding-top: var(--#{$prefix}aspect-ratio);\n    content: \"\";\n  }\n\n  > * {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@each $key, $ratio in $aspect-ratios {\n  .ratio-#{$key} {\n    --#{$prefix}aspect-ratio: #{$ratio};\n  }\n}\n","// Shorthand\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n// Responsive sticky top and bottom\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .sticky#{$infix}-top {\n      position: sticky;\n      top: 0;\n      z-index: $zindex-sticky;\n    }\n\n    .sticky#{$infix}-bottom {\n      position: sticky;\n      bottom: 0;\n      z-index: $zindex-sticky;\n    }\n  }\n}\n","// scss-docs-start stacks\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n// scss-docs-end stacks\n","//\n// Visually hidden\n//\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  @include visually-hidden();\n}\n","// stylelint-disable declaration-no-important\n\n// Hide content visually while keeping it accessible to assistive technologies\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://kittygiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin visually-hidden() {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important; // Fix for https://github.com/twbs/bootstrap/issues/25686\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n\n  // Fix for positioned table caption that could become anonymous cells\n  &:not(caption) {\n    position: absolute !important;\n  }\n}\n\n// Use to only display content when it's focused, or one of its child elements is focused\n// (i.e. when focus is within the element/container that the class was applied to)\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n\n@mixin visually-hidden-focusable() {\n  &:not(:focus):not(:focus-within) {\n    @include visually-hidden();\n  }\n}\n","//\n// Stretched link\n//\n\n.stretched-link {\n  &::#{$stretched-link-pseudo-element} {\n    position: absolute;\n    top: 0;\n    right: 0;\n    bottom: 0;\n    left: 0;\n    z-index: $stretched-link-z-index;\n    content: \"\";\n  }\n}\n","//\n// Text truncation\n//\n\n.text-truncate {\n  @include text-truncate();\n}\n","// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n",".vr {\n  display: inline-block;\n  align-self: stretch;\n  width: $vr-border-width;\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: $hr-opacity;\n}\n","// Utility generator\n// Used to generate utilities & print utilities\n@mixin generate-utility($utility, $infix: \"\", $is-rfs-media-query: false) {\n  $values: map-get($utility, values);\n\n  // If the values are a list or string, convert it into a map\n  @if type-of($values) == \"string\" or type-of(nth($values, 1)) != \"list\" {\n    $values: zip($values, $values);\n  }\n\n  @each $key, $value in $values {\n    $properties: map-get($utility, property);\n\n    // Multiple properties are possible, for example with vertical or horizontal margins or paddings\n    @if type-of($properties) == \"string\" {\n      $properties: append((), $properties);\n    }\n\n    // Use custom class if present\n    $property-class: if(map-has-key($utility, class), map-get($utility, class), nth($properties, 1));\n    $property-class: if($property-class == null, \"\", $property-class);\n\n    // Use custom CSS variable name if present, otherwise default to `class`\n    $css-variable-name: if(map-has-key($utility, css-variable-name), map-get($utility, css-variable-name), map-get($utility, class));\n\n    // State params to generate pseudo-classes\n    $state: if(map-has-key($utility, state), map-get($utility, state), ());\n\n    $infix: if($property-class == \"\" and str-slice($infix, 1, 1) == \"-\", str-slice($infix, 2), $infix);\n\n    // Don't prefix if value key is null (e.g. with shadow class)\n    $property-class-modifier: if($key, if($property-class == \"\" and $infix == \"\", \"\", \"-\") + $key, \"\");\n\n    @if map-get($utility, rfs) {\n      // Inside the media query\n      @if $is-rfs-media-query {\n        $val: rfs-value($value);\n\n        // Do not render anything if fluid and non fluid values are the same\n        $value: if($val == rfs-fluid-value($value), null, $val);\n      }\n      @else {\n        $value: rfs-fluid-value($value);\n      }\n    }\n\n    $is-css-var: map-get($utility, css-var);\n    $is-local-vars: map-get($utility, local-vars);\n    $is-rtl: map-get($utility, rtl);\n\n    @if $value != null {\n      @if $is-rtl == false {\n        /* rtl:begin:remove */\n      }\n\n      @if $is-css-var {\n        .#{$property-class + $infix + $property-class-modifier} {\n          --#{$prefix}#{$css-variable-name}: #{$value};\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            --#{$prefix}#{$css-variable-name}: #{$value};\n          }\n        }\n      } @else {\n        .#{$property-class + $infix + $property-class-modifier} {\n          @each $property in $properties {\n            @if $is-local-vars {\n              @each $local-var, $variable in $is-local-vars {\n                --#{$prefix}#{$local-var}: #{$variable};\n              }\n            }\n            #{$property}: $value if($enable-important-utilities, !important, null);\n          }\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            @each $property in $properties {\n              @if $is-local-vars {\n                @each $local-var, $variable in $is-local-vars {\n                  --#{$prefix}#{$local-var}: #{$variable};\n                }\n              }\n              #{$property}: $value if($enable-important-utilities, !important, null);\n            }\n          }\n        }\n      }\n\n      @if $is-rtl == false {\n        /* rtl:end:remove */\n      }\n    }\n  }\n}\n","// Loop over each breakpoint\n@each $breakpoint in map-keys($grid-breakpoints) {\n\n  // Generate media query if needed\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    // Loop over each utility property\n    @each $key, $utility in $utilities {\n      // The utility can be disabled with `false`, thus check if the utility is a map first\n      // Only proceed if responsive media queries are enabled or if it's the base media query\n      @if type-of($utility) == \"map\" and (map-get($utility, responsive) or $infix == \"\") {\n        @include generate-utility($utility, $infix);\n      }\n    }\n  }\n}\n\n// RFS rescaling\n@media (min-width: $rfs-mq-value) {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    @if (map-get($grid-breakpoints, $breakpoint) < $rfs-breakpoint) {\n      // Loop over each utility property\n      @each $key, $utility in $utilities {\n        // The utility can be disabled with `false`, thus check if the utility is a map first\n        // Only proceed if responsive media queries are enabled or if it's the base media query\n        @if type-of($utility) == \"map\" and map-get($utility, rfs) and (map-get($utility, responsive) or $infix == \"\") {\n          @include generate-utility($utility, $infix, true);\n        }\n      }\n    }\n  }\n}\n\n\n// Print utilities\n@media print {\n  @each $key, $utility in $utilities {\n    // The utility can be disabled with `false`, thus check if the utility is a map first\n    // Then check if the utility needs print styles\n    @if type-of($utility) == \"map\" and map-get($utility, print) == true {\n      @include generate-utility($utility, \"-print\");\n    }\n  }\n}\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n// Font Awesome core compile (Web Fonts-based)\n// -------------------------\n\n@import 'functions';\n@import 'variables';\n@import 'mixins';\n@import 'core';\n@import 'sizing';\n@import 'fixed-width';\n@import 'list';\n@import 'bordered-pulled';\n@import 'animated';\n@import 'rotated-flipped';\n@import 'stacked';\n@import 'icons';\n@import 'screen-reader';\n","// base icon class definition\n// -------------------------\n\n.#{$fa-css-prefix} {\n  font-family: var(--#{$fa-css-prefix}-style-family, '#{$fa-style-family}');\n  font-weight: var(--#{$fa-css-prefix}-style, #{$fa-style});\n}\n\n.#{$fa-css-prefix},\n.#{$fa-css-prefix}-classic,\n.#{$fa-css-prefix}-sharp,\n.fas,\n.#{$fa-css-prefix}-solid,\n.far,\n.#{$fa-css-prefix}-regular,\n.fab,\n.#{$fa-css-prefix}-brands {\n  -moz-osx-font-smoothing: grayscale;\n  -webkit-font-smoothing: antialiased;\n  display: var(--#{$fa-css-prefix}-display, #{$fa-display});\n  font-style: normal;\n  font-variant: normal;\n  line-height: 1;\n  text-rendering: auto;\n}\n\n.fas,\n.#{$fa-css-prefix}-classic,\n.#{$fa-css-prefix}-solid,\n.far,\n.#{$fa-css-prefix}-regular {\n  font-family: 'Font Awesome 6 Free';\n}\n\n.fab,\n.#{$fa-css-prefix}-brands {\n  font-family: 'Font Awesome 6 Brands';\n}\n\n\n%fa-icon {\n  @include fa-icon;\n}\n","// sizing icons\n// -------------------------\n\n// literal magnification scale\n@for $i from 1 through 10 {\n  .#{$fa-css-prefix}-#{$i}x {\n    font-size: $i * 1em;\n  }\n}\n\n// step-based scale (with alignment)\n@each $size, $value in $fa-sizes {\n  .#{$fa-css-prefix}-#{$size} {\n     @include fa-size($value);\n  }\n}\n","// mixins\n// --------------------------\n\n// base rendering for an icon\n@mixin fa-icon {\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n  display: inline-block;\n  font-style: normal;\n  font-variant: normal;\n  font-weight: normal;\n  line-height: 1;\n}\n\n// sets relative font-sizing and alignment (in _sizing)\n@mixin fa-size ($font-size) {\n  font-size: fa-divide($font-size, $fa-size-scale-base) * 1em; // converts step in sizing scale into an em-based value that's relative to the scale's base\n  line-height: fa-divide(1, $font-size) * 1em; // sets the line-height of the icon back to that of it's parent\n  vertical-align: (fa-divide(6, $font-size) - fa-divide(3, 8)) * 1em; // vertically centers the icon taking into account the surrounding text's descender\n}\n\n// only display content to screen readers\n// see: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// see: https://hugogiraudel.com/2016/10/13/css-hide-and-seek/\n@mixin fa-sr-only() {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  white-space: nowrap;\n  border-width: 0;\n}\n\n// use in conjunction with .sr-only to only display content when it's focused\n@mixin fa-sr-only-focusable() {\n  &:not(:focus) {\n    @include fa-sr-only();\n  }\n}\n\n// sets a specific icon family to use alongside style + icon mixins\n\n// convenience mixins for declaring pseudo-elements by CSS variable,\n// including all style-specific font properties, and both the ::before\n// and ::after elements in the duotone case.\n@mixin fa-icon-solid($fa-var) {\n  @extend %fa-icon;\n  @extend .fa-solid;\n\n  &::before {\n    content: unquote(\"\\\"#{ $fa-var }\\\"\");\n  }\n}\n@mixin fa-icon-regular($fa-var) {\n  @extend %fa-icon;\n  @extend .fa-regular;\n\n  &::before {\n    content: unquote(\"\\\"#{ $fa-var }\\\"\");\n  }\n}\n@mixin fa-icon-brands($fa-var) {\n  @extend %fa-icon;\n  @extend .fa-brands;\n\n  &::before {\n    content: unquote(\"\\\"#{ $fa-var }\\\"\");\n  }\n}\n","// fixed-width icons\n// -------------------------\n\n.#{$fa-css-prefix}-fw {\n  text-align: center;\n  width: $fa-fw-width;\n}\n","// icons in a list\n// -------------------------\n\n.#{$fa-css-prefix}-ul {\n  list-style-type: none;\n  margin-left: var(--#{$fa-css-prefix}-li-margin, #{$fa-li-margin});\n  padding-left: 0;\n\n  > li { position: relative; }\n}\n\n.#{$fa-css-prefix}-li {\n  left: calc(var(--#{$fa-css-prefix}-li-width, #{$fa-li-width}) * -1);\n  position: absolute;\n  text-align: center;\n  width: var(--#{$fa-css-prefix}-li-width, #{$fa-li-width});\n  line-height: inherit;\n}\n","// bordered + pulled icons\n// -------------------------\n\n.#{$fa-css-prefix}-border {\n  border-color: var(--#{$fa-css-prefix}-border-color, #{$fa-border-color});\n  border-radius: var(--#{$fa-css-prefix}-border-radius, #{$fa-border-radius});\n  border-style: var(--#{$fa-css-prefix}-border-style, #{$fa-border-style});\n  border-width: var(--#{$fa-css-prefix}-border-width, #{$fa-border-width});\n  padding: var(--#{$fa-css-prefix}-border-padding, #{$fa-border-padding});\n}\n\n.#{$fa-css-prefix}-pull-left {\n  float: left;\n  margin-right: var(--#{$fa-css-prefix}-pull-margin, #{$fa-pull-margin}); \n}\n\n.#{$fa-css-prefix}-pull-right {\n  float: right;\n  margin-left: var(--#{$fa-css-prefix}-pull-margin, #{$fa-pull-margin}); \n}\n","// animating icons\n// --------------------------\n\n.#{$fa-css-prefix}-beat {\n  animation-name: #{$fa-css-prefix}-beat;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, ease-in-out);\n}\n\n.#{$fa-css-prefix}-bounce {\n  animation-name: #{$fa-css-prefix}-bounce;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, cubic-bezier(0.280, 0.840, 0.420, 1));\n}\n\n.#{$fa-css-prefix}-fade {\n  animation-name: #{$fa-css-prefix}-fade;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, cubic-bezier(.4,0,.6,1));\n}\n\n.#{$fa-css-prefix}-beat-fade {\n  animation-name: #{$fa-css-prefix}-beat-fade;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, cubic-bezier(.4,0,.6,1));\n}\n\n.#{$fa-css-prefix}-flip {\n  animation-name: #{$fa-css-prefix}-flip;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, ease-in-out);\n}\n\n.#{$fa-css-prefix}-shake {\n  animation-name: #{$fa-css-prefix}-shake;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, linear);\n}\n\n.#{$fa-css-prefix}-spin {\n  animation-name: #{$fa-css-prefix}-spin;\n  animation-delay: var(--#{$fa-css-prefix}-animation-delay, 0s);\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 2s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, linear);\n}\n\n.#{$fa-css-prefix}-spin-reverse {\n  --#{$fa-css-prefix}-animation-direction: reverse;\n}\n\n.#{$fa-css-prefix}-pulse,\n.#{$fa-css-prefix}-spin-pulse {\n  animation-name: #{$fa-css-prefix}-spin;\n  animation-direction: var(--#{$fa-css-prefix}-animation-direction, normal);\n  animation-duration: var(--#{$fa-css-prefix}-animation-duration, 1s);\n  animation-iteration-count: var(--#{$fa-css-prefix}-animation-iteration-count, infinite);\n  animation-timing-function: var(--#{$fa-css-prefix}-animation-timing, steps(8));\n}\n\n// if agent or operating system prefers reduced motion, disable animations\n// see: https://www.smashingmagazine.com/2020/09/design-reduced-motion-sensitivities/\n// see: https://developer.mozilla.org/en-US/docs/Web/CSS/@media/prefers-reduced-motion\n@media (prefers-reduced-motion: reduce) {\n  .#{$fa-css-prefix}-beat,\n  .#{$fa-css-prefix}-bounce,\n  .#{$fa-css-prefix}-fade,\n  .#{$fa-css-prefix}-beat-fade,\n  .#{$fa-css-prefix}-flip,\n  .#{$fa-css-prefix}-pulse,\n  .#{$fa-css-prefix}-shake,\n  .#{$fa-css-prefix}-spin,\n  .#{$fa-css-prefix}-spin-pulse {\n    animation-delay: -1ms;\n    animation-duration: 1ms;\n    animation-iteration-count: 1;\n    transition-delay: 0s;\n    transition-duration: 0s;\n  }\n}\n\n@keyframes #{$fa-css-prefix}-beat {\n  0%, 90% { transform: scale(1); }\n  45% { transform: scale(var(--#{$fa-css-prefix}-beat-scale, 1.25)); }\n}\n\n@keyframes #{$fa-css-prefix}-bounce {\n  0%   { transform: scale(1,1) translateY(0); }\n  10%  { transform: scale(var(--#{$fa-css-prefix}-bounce-start-scale-x, 1.1),var(--#{$fa-css-prefix}-bounce-start-scale-y, 0.9)) translateY(0); }\n  30%  { transform: scale(var(--#{$fa-css-prefix}-bounce-jump-scale-x, 0.9),var(--#{$fa-css-prefix}-bounce-jump-scale-y, 1.1)) translateY(var(--#{$fa-css-prefix}-bounce-height, -0.5em)); }\n  50%  { transform: scale(var(--#{$fa-css-prefix}-bounce-land-scale-x, 1.05),var(--#{$fa-css-prefix}-bounce-land-scale-y, 0.95)) translateY(0); }\n  57%  { transform: scale(1,1) translateY(var(--#{$fa-css-prefix}-bounce-rebound, -0.125em)); }\n  64%  { transform: scale(1,1) translateY(0); }\n  100% { transform: scale(1,1) translateY(0); }\n}\n\n@keyframes #{$fa-css-prefix}-fade {\n  50% { opacity: var(--#{$fa-css-prefix}-fade-opacity, 0.4); }\n}\n\n@keyframes #{$fa-css-prefix}-beat-fade {\n  0%, 100% {\n    opacity: var(--#{$fa-css-prefix}-beat-fade-opacity, 0.4);\n    transform: scale(1);\n  }\n  50% {\n    opacity: 1;\n    transform: scale(var(--#{$fa-css-prefix}-beat-fade-scale, 1.125));\n  }\n}\n\n@keyframes #{$fa-css-prefix}-flip {\n  50% {\n    transform: rotate3d(var(--#{$fa-css-prefix}-flip-x, 0), var(--#{$fa-css-prefix}-flip-y, 1), var(--#{$fa-css-prefix}-flip-z, 0), var(--#{$fa-css-prefix}-flip-angle, -180deg));\n  }\n}\n\n@keyframes #{$fa-css-prefix}-shake {\n  0% { transform: rotate(-15deg); }\n  4% { transform: rotate(15deg); }\n  8%, 24% { transform: rotate(-18deg); }\n  12%, 28% { transform: rotate(18deg); }\n  16% { transform: rotate(-22deg); }\n  20% { transform: rotate(22deg); }\n  32% { transform: rotate(-12deg); }\n  36% { transform: rotate(12deg); }\n  40%, 100% { transform: rotate(0deg); }\n}\n\n@keyframes #{$fa-css-prefix}-spin {\n  0% { transform: rotate(0deg); }\n  100% { transform: rotate(360deg); }\n}\n","// rotating + flipping icons\n// -------------------------\n\n.#{$fa-css-prefix}-rotate-90 {\n  transform: rotate(90deg);\n}\n\n.#{$fa-css-prefix}-rotate-180 {\n  transform: rotate(180deg);\n}\n\n.#{$fa-css-prefix}-rotate-270 {\n  transform: rotate(270deg);\n}\n\n.#{$fa-css-prefix}-flip-horizontal {\n  transform: scale(-1, 1);\n}\n\n.#{$fa-css-prefix}-flip-vertical {\n  transform: scale(1, -1);\n}\n\n.#{$fa-css-prefix}-flip-both,\n.#{$fa-css-prefix}-flip-horizontal.#{$fa-css-prefix}-flip-vertical {\n  transform: scale(-1, -1);\n}\n\n.#{$fa-css-prefix}-rotate-by {\n  transform: rotate(var(--#{$fa-css-prefix}-rotate-angle, 0));\n}\n","// stacking icons\n// -------------------------\n\n.#{$fa-css-prefix}-stack {\n  display: inline-block;\n  height: 2em;\n  line-height: 2em;\n  position: relative;\n  vertical-align: $fa-stack-vertical-align;\n  width: $fa-stack-width;\n}\n\n.#{$fa-css-prefix}-stack-1x,\n.#{$fa-css-prefix}-stack-2x {\n  left: 0;\n  position: absolute;\n  text-align: center;\n  width: 100%;\n  z-index: var(--#{$fa-css-prefix}-stack-z-index, #{$fa-stack-z-index});\n}\n\n.#{$fa-css-prefix}-stack-1x {\n  line-height: inherit;\n}\n\n.#{$fa-css-prefix}-stack-2x {\n  font-size: 2em;\n}\n\n.#{$fa-css-prefix}-inverse {\n  color: var(--#{$fa-css-prefix}-inverse, #{$fa-inverse});\n}\n","// variables\n// --------------------------\n\n$fa-css-prefix          : fa !default;\n$fa-style               : 900 !default;\n$fa-style-family        : \"Font Awesome 6 Free\" !default;\n\n$fa-display             : inline-block !default;\n\n$fa-fw-width            : fa-divide(20em, 16) !default;\n$fa-inverse             : #fff !default;\n\n$fa-border-color        : #eee !default;\n$fa-border-padding      : .2em .25em .15em !default;\n$fa-border-radius       : .1em !default;\n$fa-border-style        : solid !default;\n$fa-border-width        : .08em !default;\n\n$fa-size-scale-2xs      : 10 !default;\n$fa-size-scale-xs       : 12 !default;\n$fa-size-scale-sm       : 14 !default;\n$fa-size-scale-base     : 16 !default;\n$fa-size-scale-lg       : 20 !default;\n$fa-size-scale-xl       : 24 !default;\n$fa-size-scale-2xl      : 32 !default;\n\n$fa-sizes: (\n  \"2xs\"                 : $fa-size-scale-2xs,\n  \"xs\"                  : $fa-size-scale-xs,\n  \"sm\"                  : $fa-size-scale-sm,\n  \"lg\"                  : $fa-size-scale-lg,\n  \"xl\"                  : $fa-size-scale-xl,\n  \"2xl\"                 : $fa-size-scale-2xl\n) !default;\n\n$fa-li-width            : 2em !default;\n$fa-li-margin           : $fa-li-width * fa-divide(5, 4) !default;\n\n$fa-pull-margin         : .3em !default;\n\n$fa-primary-opacity     : 1 !default;\n$fa-secondary-opacity   : .4 !default;\n\n$fa-stack-vertical-align: middle !default;\n$fa-stack-width         : ($fa-fw-width * 2) !default;\n$fa-stack-z-index       : auto !default;\n\n$fa-font-display        : block !default;\n$fa-font-path           : \"../webfonts\" !default;\n\n$fa-var-0: \\30;\n$fa-var-1: \\31;\n$fa-var-2: \\32;\n$fa-var-3: \\33;\n$fa-var-4: \\34;\n$fa-var-5: \\35;\n$fa-var-6: \\36;\n$fa-var-7: \\37;\n$fa-var-8: \\38;\n$fa-var-9: \\39;\n$fa-var-fill-drip: \\f576;\n$fa-var-arrows-to-circle: \\e4bd;\n$fa-var-circle-chevron-right: \\f138;\n$fa-var-chevron-circle-right: \\f138;\n$fa-var-at: \\40;\n$fa-var-trash-can: \\f2ed;\n$fa-var-trash-alt: \\f2ed;\n$fa-var-text-height: \\f034;\n$fa-var-user-xmark: \\f235;\n$fa-var-user-times: \\f235;\n$fa-var-stethoscope: \\f0f1;\n$fa-var-message: \\f27a;\n$fa-var-comment-alt: \\f27a;\n$fa-var-info: \\f129;\n$fa-var-down-left-and-up-right-to-center: \\f422;\n$fa-var-compress-alt: \\f422;\n$fa-var-explosion: \\e4e9;\n$fa-var-file-lines: \\f15c;\n$fa-var-file-alt: \\f15c;\n$fa-var-file-text: \\f15c;\n$fa-var-wave-square: \\f83e;\n$fa-var-ring: \\f70b;\n$fa-var-building-un: \\e4d9;\n$fa-var-dice-three: \\f527;\n$fa-var-calendar-days: \\f073;\n$fa-var-calendar-alt: \\f073;\n$fa-var-anchor-circle-check: \\e4aa;\n$fa-var-building-circle-arrow-right: \\e4d1;\n$fa-var-volleyball: \\f45f;\n$fa-var-volleyball-ball: \\f45f;\n$fa-var-arrows-up-to-line: \\e4c2;\n$fa-var-sort-down: \\f0dd;\n$fa-var-sort-desc: \\f0dd;\n$fa-var-circle-minus: \\f056;\n$fa-var-minus-circle: \\f056;\n$fa-var-door-open: \\f52b;\n$fa-var-right-from-bracket: \\f2f5;\n$fa-var-sign-out-alt: \\f2f5;\n$fa-var-atom: \\f5d2;\n$fa-var-soap: \\e06e;\n$fa-var-icons: \\f86d;\n$fa-var-heart-music-camera-bolt: \\f86d;\n$fa-var-microphone-lines-slash: \\f539;\n$fa-var-microphone-alt-slash: \\f539;\n$fa-var-bridge-circle-check: \\e4c9;\n$fa-var-pump-medical: \\e06a;\n$fa-var-fingerprint: \\f577;\n$fa-var-hand-point-right: \\f0a4;\n$fa-var-magnifying-glass-location: \\f689;\n$fa-var-search-location: \\f689;\n$fa-var-forward-step: \\f051;\n$fa-var-step-forward: \\f051;\n$fa-var-face-smile-beam: \\f5b8;\n$fa-var-smile-beam: \\f5b8;\n$fa-var-flag-checkered: \\f11e;\n$fa-var-football: \\f44e;\n$fa-var-football-ball: \\f44e;\n$fa-var-school-circle-exclamation: \\e56c;\n$fa-var-crop: \\f125;\n$fa-var-angles-down: \\f103;\n$fa-var-angle-double-down: \\f103;\n$fa-var-users-rectangle: \\e594;\n$fa-var-people-roof: \\e537;\n$fa-var-people-line: \\e534;\n$fa-var-beer-mug-empty: \\f0fc;\n$fa-var-beer: \\f0fc;\n$fa-var-diagram-predecessor: \\e477;\n$fa-var-arrow-up-long: \\f176;\n$fa-var-long-arrow-up: \\f176;\n$fa-var-fire-flame-simple: \\f46a;\n$fa-var-burn: \\f46a;\n$fa-var-person: \\f183;\n$fa-var-male: \\f183;\n$fa-var-laptop: \\f109;\n$fa-var-file-csv: \\f6dd;\n$fa-var-menorah: \\f676;\n$fa-var-truck-plane: \\e58f;\n$fa-var-record-vinyl: \\f8d9;\n$fa-var-face-grin-stars: \\f587;\n$fa-var-grin-stars: \\f587;\n$fa-var-bong: \\f55c;\n$fa-var-spaghetti-monster-flying: \\f67b;\n$fa-var-pastafarianism: \\f67b;\n$fa-var-arrow-down-up-across-line: \\e4af;\n$fa-var-spoon: \\f2e5;\n$fa-var-utensil-spoon: \\f2e5;\n$fa-var-jar-wheat: \\e517;\n$fa-var-envelopes-bulk: \\f674;\n$fa-var-mail-bulk: \\f674;\n$fa-var-file-circle-exclamation: \\e4eb;\n$fa-var-circle-h: \\f47e;\n$fa-var-hospital-symbol: \\f47e;\n$fa-var-pager: \\f815;\n$fa-var-address-book: \\f2b9;\n$fa-var-contact-book: \\f2b9;\n$fa-var-strikethrough: \\f0cc;\n$fa-var-k: \\4b;\n$fa-var-landmark-flag: \\e51c;\n$fa-var-pencil: \\f303;\n$fa-var-pencil-alt: \\f303;\n$fa-var-backward: \\f04a;\n$fa-var-caret-right: \\f0da;\n$fa-var-comments: \\f086;\n$fa-var-paste: \\f0ea;\n$fa-var-file-clipboard: \\f0ea;\n$fa-var-code-pull-request: \\e13c;\n$fa-var-clipboard-list: \\f46d;\n$fa-var-truck-ramp-box: \\f4de;\n$fa-var-truck-loading: \\f4de;\n$fa-var-user-check: \\f4fc;\n$fa-var-vial-virus: \\e597;\n$fa-var-sheet-plastic: \\e571;\n$fa-var-blog: \\f781;\n$fa-var-user-ninja: \\f504;\n$fa-var-person-arrow-up-from-line: \\e539;\n$fa-var-scroll-torah: \\f6a0;\n$fa-var-torah: \\f6a0;\n$fa-var-broom-ball: \\f458;\n$fa-var-quidditch: \\f458;\n$fa-var-quidditch-broom-ball: \\f458;\n$fa-var-toggle-off: \\f204;\n$fa-var-box-archive: \\f187;\n$fa-var-archive: \\f187;\n$fa-var-person-drowning: \\e545;\n$fa-var-arrow-down-9-1: \\f886;\n$fa-var-sort-numeric-desc: \\f886;\n$fa-var-sort-numeric-down-alt: \\f886;\n$fa-var-face-grin-tongue-squint: \\f58a;\n$fa-var-grin-tongue-squint: \\f58a;\n$fa-var-spray-can: \\f5bd;\n$fa-var-truck-monster: \\f63b;\n$fa-var-w: \\57;\n$fa-var-earth-africa: \\f57c;\n$fa-var-globe-africa: \\f57c;\n$fa-var-rainbow: \\f75b;\n$fa-var-circle-notch: \\f1ce;\n$fa-var-tablet-screen-button: \\f3fa;\n$fa-var-tablet-alt: \\f3fa;\n$fa-var-paw: \\f1b0;\n$fa-var-cloud: \\f0c2;\n$fa-var-trowel-bricks: \\e58a;\n$fa-var-face-flushed: \\f579;\n$fa-var-flushed: \\f579;\n$fa-var-hospital-user: \\f80d;\n$fa-var-tent-arrow-left-right: \\e57f;\n$fa-var-gavel: \\f0e3;\n$fa-var-legal: \\f0e3;\n$fa-var-binoculars: \\f1e5;\n$fa-var-microphone-slash: \\f131;\n$fa-var-box-tissue: \\e05b;\n$fa-var-motorcycle: \\f21c;\n$fa-var-bell-concierge: \\f562;\n$fa-var-concierge-bell: \\f562;\n$fa-var-pen-ruler: \\f5ae;\n$fa-var-pencil-ruler: \\f5ae;\n$fa-var-people-arrows: \\e068;\n$fa-var-people-arrows-left-right: \\e068;\n$fa-var-mars-and-venus-burst: \\e523;\n$fa-var-square-caret-right: \\f152;\n$fa-var-caret-square-right: \\f152;\n$fa-var-scissors: \\f0c4;\n$fa-var-cut: \\f0c4;\n$fa-var-sun-plant-wilt: \\e57a;\n$fa-var-toilets-portable: \\e584;\n$fa-var-hockey-puck: \\f453;\n$fa-var-table: \\f0ce;\n$fa-var-magnifying-glass-arrow-right: \\e521;\n$fa-var-tachograph-digital: \\f566;\n$fa-var-digital-tachograph: \\f566;\n$fa-var-users-slash: \\e073;\n$fa-var-clover: \\e139;\n$fa-var-reply: \\f3e5;\n$fa-var-mail-reply: \\f3e5;\n$fa-var-star-and-crescent: \\f699;\n$fa-var-house-fire: \\e50c;\n$fa-var-square-minus: \\f146;\n$fa-var-minus-square: \\f146;\n$fa-var-helicopter: \\f533;\n$fa-var-compass: \\f14e;\n$fa-var-square-caret-down: \\f150;\n$fa-var-caret-square-down: \\f150;\n$fa-var-file-circle-question: \\e4ef;\n$fa-var-laptop-code: \\f5fc;\n$fa-var-swatchbook: \\f5c3;\n$fa-var-prescription-bottle: \\f485;\n$fa-var-bars: \\f0c9;\n$fa-var-navicon: \\f0c9;\n$fa-var-people-group: \\e533;\n$fa-var-hourglass-end: \\f253;\n$fa-var-hourglass-3: \\f253;\n$fa-var-heart-crack: \\f7a9;\n$fa-var-heart-broken: \\f7a9;\n$fa-var-square-up-right: \\f360;\n$fa-var-external-link-square-alt: \\f360;\n$fa-var-face-kiss-beam: \\f597;\n$fa-var-kiss-beam: \\f597;\n$fa-var-film: \\f008;\n$fa-var-ruler-horizontal: \\f547;\n$fa-var-people-robbery: \\e536;\n$fa-var-lightbulb: \\f0eb;\n$fa-var-caret-left: \\f0d9;\n$fa-var-circle-exclamation: \\f06a;\n$fa-var-exclamation-circle: \\f06a;\n$fa-var-school-circle-xmark: \\e56d;\n$fa-var-arrow-right-from-bracket: \\f08b;\n$fa-var-sign-out: \\f08b;\n$fa-var-circle-chevron-down: \\f13a;\n$fa-var-chevron-circle-down: \\f13a;\n$fa-var-unlock-keyhole: \\f13e;\n$fa-var-unlock-alt: \\f13e;\n$fa-var-cloud-showers-heavy: \\f740;\n$fa-var-headphones-simple: \\f58f;\n$fa-var-headphones-alt: \\f58f;\n$fa-var-sitemap: \\f0e8;\n$fa-var-circle-dollar-to-slot: \\f4b9;\n$fa-var-donate: \\f4b9;\n$fa-var-memory: \\f538;\n$fa-var-road-spikes: \\e568;\n$fa-var-fire-burner: \\e4f1;\n$fa-var-flag: \\f024;\n$fa-var-hanukiah: \\f6e6;\n$fa-var-feather: \\f52d;\n$fa-var-volume-low: \\f027;\n$fa-var-volume-down: \\f027;\n$fa-var-comment-slash: \\f4b3;\n$fa-var-cloud-sun-rain: \\f743;\n$fa-var-compress: \\f066;\n$fa-var-wheat-awn: \\e2cd;\n$fa-var-wheat-alt: \\e2cd;\n$fa-var-ankh: \\f644;\n$fa-var-hands-holding-child: \\e4fa;\n$fa-var-asterisk: \\2a;\n$fa-var-square-check: \\f14a;\n$fa-var-check-square: \\f14a;\n$fa-var-peseta-sign: \\e221;\n$fa-var-heading: \\f1dc;\n$fa-var-header: \\f1dc;\n$fa-var-ghost: \\f6e2;\n$fa-var-list: \\f03a;\n$fa-var-list-squares: \\f03a;\n$fa-var-square-phone-flip: \\f87b;\n$fa-var-phone-square-alt: \\f87b;\n$fa-var-cart-plus: \\f217;\n$fa-var-gamepad: \\f11b;\n$fa-var-circle-dot: \\f192;\n$fa-var-dot-circle: \\f192;\n$fa-var-face-dizzy: \\f567;\n$fa-var-dizzy: \\f567;\n$fa-var-egg: \\f7fb;\n$fa-var-house-medical-circle-xmark: \\e513;\n$fa-var-campground: \\f6bb;\n$fa-var-folder-plus: \\f65e;\n$fa-var-futbol: \\f1e3;\n$fa-var-futbol-ball: \\f1e3;\n$fa-var-soccer-ball: \\f1e3;\n$fa-var-paintbrush: \\f1fc;\n$fa-var-paint-brush: \\f1fc;\n$fa-var-lock: \\f023;\n$fa-var-gas-pump: \\f52f;\n$fa-var-hot-tub-person: \\f593;\n$fa-var-hot-tub: \\f593;\n$fa-var-map-location: \\f59f;\n$fa-var-map-marked: \\f59f;\n$fa-var-house-flood-water: \\e50e;\n$fa-var-tree: \\f1bb;\n$fa-var-bridge-lock: \\e4cc;\n$fa-var-sack-dollar: \\f81d;\n$fa-var-pen-to-square: \\f044;\n$fa-var-edit: \\f044;\n$fa-var-car-side: \\f5e4;\n$fa-var-share-nodes: \\f1e0;\n$fa-var-share-alt: \\f1e0;\n$fa-var-heart-circle-minus: \\e4ff;\n$fa-var-hourglass-half: \\f252;\n$fa-var-hourglass-2: \\f252;\n$fa-var-microscope: \\f610;\n$fa-var-sink: \\e06d;\n$fa-var-bag-shopping: \\f290;\n$fa-var-shopping-bag: \\f290;\n$fa-var-arrow-down-z-a: \\f881;\n$fa-var-sort-alpha-desc: \\f881;\n$fa-var-sort-alpha-down-alt: \\f881;\n$fa-var-mitten: \\f7b5;\n$fa-var-person-rays: \\e54d;\n$fa-var-users: \\f0c0;\n$fa-var-eye-slash: \\f070;\n$fa-var-flask-vial: \\e4f3;\n$fa-var-hand: \\f256;\n$fa-var-hand-paper: \\f256;\n$fa-var-om: \\f679;\n$fa-var-worm: \\e599;\n$fa-var-house-circle-xmark: \\e50b;\n$fa-var-plug: \\f1e6;\n$fa-var-chevron-up: \\f077;\n$fa-var-hand-spock: \\f259;\n$fa-var-stopwatch: \\f2f2;\n$fa-var-face-kiss: \\f596;\n$fa-var-kiss: \\f596;\n$fa-var-bridge-circle-xmark: \\e4cb;\n$fa-var-face-grin-tongue: \\f589;\n$fa-var-grin-tongue: \\f589;\n$fa-var-chess-bishop: \\f43a;\n$fa-var-face-grin-wink: \\f58c;\n$fa-var-grin-wink: \\f58c;\n$fa-var-ear-deaf: \\f2a4;\n$fa-var-deaf: \\f2a4;\n$fa-var-deafness: \\f2a4;\n$fa-var-hard-of-hearing: \\f2a4;\n$fa-var-road-circle-check: \\e564;\n$fa-var-dice-five: \\f523;\n$fa-var-square-rss: \\f143;\n$fa-var-rss-square: \\f143;\n$fa-var-land-mine-on: \\e51b;\n$fa-var-i-cursor: \\f246;\n$fa-var-stamp: \\f5bf;\n$fa-var-stairs: \\e289;\n$fa-var-i: \\49;\n$fa-var-hryvnia-sign: \\f6f2;\n$fa-var-hryvnia: \\f6f2;\n$fa-var-pills: \\f484;\n$fa-var-face-grin-wide: \\f581;\n$fa-var-grin-alt: \\f581;\n$fa-var-tooth: \\f5c9;\n$fa-var-v: \\56;\n$fa-var-bangladeshi-taka-sign: \\e2e6;\n$fa-var-bicycle: \\f206;\n$fa-var-staff-snake: \\e579;\n$fa-var-rod-asclepius: \\e579;\n$fa-var-rod-snake: \\e579;\n$fa-var-staff-aesculapius: \\e579;\n$fa-var-head-side-cough-slash: \\e062;\n$fa-var-truck-medical: \\f0f9;\n$fa-var-ambulance: \\f0f9;\n$fa-var-wheat-awn-circle-exclamation: \\e598;\n$fa-var-snowman: \\f7d0;\n$fa-var-mortar-pestle: \\f5a7;\n$fa-var-road-barrier: \\e562;\n$fa-var-school: \\f549;\n$fa-var-igloo: \\f7ae;\n$fa-var-joint: \\f595;\n$fa-var-angle-right: \\f105;\n$fa-var-horse: \\f6f0;\n$fa-var-q: \\51;\n$fa-var-g: \\47;\n$fa-var-notes-medical: \\f481;\n$fa-var-temperature-half: \\f2c9;\n$fa-var-temperature-2: \\f2c9;\n$fa-var-thermometer-2: \\f2c9;\n$fa-var-thermometer-half: \\f2c9;\n$fa-var-dong-sign: \\e169;\n$fa-var-capsules: \\f46b;\n$fa-var-poo-storm: \\f75a;\n$fa-var-poo-bolt: \\f75a;\n$fa-var-face-frown-open: \\f57a;\n$fa-var-frown-open: \\f57a;\n$fa-var-hand-point-up: \\f0a6;\n$fa-var-money-bill: \\f0d6;\n$fa-var-bookmark: \\f02e;\n$fa-var-align-justify: \\f039;\n$fa-var-umbrella-beach: \\f5ca;\n$fa-var-helmet-un: \\e503;\n$fa-var-bullseye: \\f140;\n$fa-var-bacon: \\f7e5;\n$fa-var-hand-point-down: \\f0a7;\n$fa-var-arrow-up-from-bracket: \\e09a;\n$fa-var-folder: \\f07b;\n$fa-var-folder-blank: \\f07b;\n$fa-var-file-waveform: \\f478;\n$fa-var-file-medical-alt: \\f478;\n$fa-var-radiation: \\f7b9;\n$fa-var-chart-simple: \\e473;\n$fa-var-mars-stroke: \\f229;\n$fa-var-vial: \\f492;\n$fa-var-gauge: \\f624;\n$fa-var-dashboard: \\f624;\n$fa-var-gauge-med: \\f624;\n$fa-var-tachometer-alt-average: \\f624;\n$fa-var-wand-magic-sparkles: \\e2ca;\n$fa-var-magic-wand-sparkles: \\e2ca;\n$fa-var-e: \\45;\n$fa-var-pen-clip: \\f305;\n$fa-var-pen-alt: \\f305;\n$fa-var-bridge-circle-exclamation: \\e4ca;\n$fa-var-user: \\f007;\n$fa-var-school-circle-check: \\e56b;\n$fa-var-dumpster: \\f793;\n$fa-var-van-shuttle: \\f5b6;\n$fa-var-shuttle-van: \\f5b6;\n$fa-var-building-user: \\e4da;\n$fa-var-square-caret-left: \\f191;\n$fa-var-caret-square-left: \\f191;\n$fa-var-highlighter: \\f591;\n$fa-var-key: \\f084;\n$fa-var-bullhorn: \\f0a1;\n$fa-var-globe: \\f0ac;\n$fa-var-synagogue: \\f69b;\n$fa-var-person-half-dress: \\e548;\n$fa-var-road-bridge: \\e563;\n$fa-var-location-arrow: \\f124;\n$fa-var-c: \\43;\n$fa-var-tablet-button: \\f10a;\n$fa-var-building-lock: \\e4d6;\n$fa-var-pizza-slice: \\f818;\n$fa-var-money-bill-wave: \\f53a;\n$fa-var-chart-area: \\f1fe;\n$fa-var-area-chart: \\f1fe;\n$fa-var-house-flag: \\e50d;\n$fa-var-person-circle-minus: \\e540;\n$fa-var-ban: \\f05e;\n$fa-var-cancel: \\f05e;\n$fa-var-camera-rotate: \\e0d8;\n$fa-var-spray-can-sparkles: \\f5d0;\n$fa-var-air-freshener: \\f5d0;\n$fa-var-star: \\f005;\n$fa-var-repeat: \\f363;\n$fa-var-cross: \\f654;\n$fa-var-box: \\f466;\n$fa-var-venus-mars: \\f228;\n$fa-var-arrow-pointer: \\f245;\n$fa-var-mouse-pointer: \\f245;\n$fa-var-maximize: \\f31e;\n$fa-var-expand-arrows-alt: \\f31e;\n$fa-var-charging-station: \\f5e7;\n$fa-var-shapes: \\f61f;\n$fa-var-triangle-circle-square: \\f61f;\n$fa-var-shuffle: \\f074;\n$fa-var-random: \\f074;\n$fa-var-person-running: \\f70c;\n$fa-var-running: \\f70c;\n$fa-var-mobile-retro: \\e527;\n$fa-var-grip-lines-vertical: \\f7a5;\n$fa-var-spider: \\f717;\n$fa-var-hands-bound: \\e4f9;\n$fa-var-file-invoice-dollar: \\f571;\n$fa-var-plane-circle-exclamation: \\e556;\n$fa-var-x-ray: \\f497;\n$fa-var-spell-check: \\f891;\n$fa-var-slash: \\f715;\n$fa-var-computer-mouse: \\f8cc;\n$fa-var-mouse: \\f8cc;\n$fa-var-arrow-right-to-bracket: \\f090;\n$fa-var-sign-in: \\f090;\n$fa-var-shop-slash: \\e070;\n$fa-var-store-alt-slash: \\e070;\n$fa-var-server: \\f233;\n$fa-var-virus-covid-slash: \\e4a9;\n$fa-var-shop-lock: \\e4a5;\n$fa-var-hourglass-start: \\f251;\n$fa-var-hourglass-1: \\f251;\n$fa-var-blender-phone: \\f6b6;\n$fa-var-building-wheat: \\e4db;\n$fa-var-person-breastfeeding: \\e53a;\n$fa-var-right-to-bracket: \\f2f6;\n$fa-var-sign-in-alt: \\f2f6;\n$fa-var-venus: \\f221;\n$fa-var-passport: \\f5ab;\n$fa-var-heart-pulse: \\f21e;\n$fa-var-heartbeat: \\f21e;\n$fa-var-people-carry-box: \\f4ce;\n$fa-var-people-carry: \\f4ce;\n$fa-var-temperature-high: \\f769;\n$fa-var-microchip: \\f2db;\n$fa-var-crown: \\f521;\n$fa-var-weight-hanging: \\f5cd;\n$fa-var-xmarks-lines: \\e59a;\n$fa-var-file-prescription: \\f572;\n$fa-var-weight-scale: \\f496;\n$fa-var-weight: \\f496;\n$fa-var-user-group: \\f500;\n$fa-var-user-friends: \\f500;\n$fa-var-arrow-up-a-z: \\f15e;\n$fa-var-sort-alpha-up: \\f15e;\n$fa-var-chess-knight: \\f441;\n$fa-var-face-laugh-squint: \\f59b;\n$fa-var-laugh-squint: \\f59b;\n$fa-var-wheelchair: \\f193;\n$fa-var-circle-arrow-up: \\f0aa;\n$fa-var-arrow-circle-up: \\f0aa;\n$fa-var-toggle-on: \\f205;\n$fa-var-person-walking: \\f554;\n$fa-var-walking: \\f554;\n$fa-var-l: \\4c;\n$fa-var-fire: \\f06d;\n$fa-var-bed-pulse: \\f487;\n$fa-var-procedures: \\f487;\n$fa-var-shuttle-space: \\f197;\n$fa-var-space-shuttle: \\f197;\n$fa-var-face-laugh: \\f599;\n$fa-var-laugh: \\f599;\n$fa-var-folder-open: \\f07c;\n$fa-var-heart-circle-plus: \\e500;\n$fa-var-code-fork: \\e13b;\n$fa-var-city: \\f64f;\n$fa-var-microphone-lines: \\f3c9;\n$fa-var-microphone-alt: \\f3c9;\n$fa-var-pepper-hot: \\f816;\n$fa-var-unlock: \\f09c;\n$fa-var-colon-sign: \\e140;\n$fa-var-headset: \\f590;\n$fa-var-store-slash: \\e071;\n$fa-var-road-circle-xmark: \\e566;\n$fa-var-user-minus: \\f503;\n$fa-var-mars-stroke-up: \\f22a;\n$fa-var-mars-stroke-v: \\f22a;\n$fa-var-champagne-glasses: \\f79f;\n$fa-var-glass-cheers: \\f79f;\n$fa-var-clipboard: \\f328;\n$fa-var-house-circle-exclamation: \\e50a;\n$fa-var-file-arrow-up: \\f574;\n$fa-var-file-upload: \\f574;\n$fa-var-wifi: \\f1eb;\n$fa-var-wifi-3: \\f1eb;\n$fa-var-wifi-strong: \\f1eb;\n$fa-var-bath: \\f2cd;\n$fa-var-bathtub: \\f2cd;\n$fa-var-underline: \\f0cd;\n$fa-var-user-pen: \\f4ff;\n$fa-var-user-edit: \\f4ff;\n$fa-var-signature: \\f5b7;\n$fa-var-stroopwafel: \\f551;\n$fa-var-bold: \\f032;\n$fa-var-anchor-lock: \\e4ad;\n$fa-var-building-ngo: \\e4d7;\n$fa-var-manat-sign: \\e1d5;\n$fa-var-not-equal: \\f53e;\n$fa-var-border-top-left: \\f853;\n$fa-var-border-style: \\f853;\n$fa-var-map-location-dot: \\f5a0;\n$fa-var-map-marked-alt: \\f5a0;\n$fa-var-jedi: \\f669;\n$fa-var-square-poll-vertical: \\f681;\n$fa-var-poll: \\f681;\n$fa-var-mug-hot: \\f7b6;\n$fa-var-car-battery: \\f5df;\n$fa-var-battery-car: \\f5df;\n$fa-var-gift: \\f06b;\n$fa-var-dice-two: \\f528;\n$fa-var-chess-queen: \\f445;\n$fa-var-glasses: \\f530;\n$fa-var-chess-board: \\f43c;\n$fa-var-building-circle-check: \\e4d2;\n$fa-var-person-chalkboard: \\e53d;\n$fa-var-mars-stroke-right: \\f22b;\n$fa-var-mars-stroke-h: \\f22b;\n$fa-var-hand-back-fist: \\f255;\n$fa-var-hand-rock: \\f255;\n$fa-var-square-caret-up: \\f151;\n$fa-var-caret-square-up: \\f151;\n$fa-var-cloud-showers-water: \\e4e4;\n$fa-var-chart-bar: \\f080;\n$fa-var-bar-chart: \\f080;\n$fa-var-hands-bubbles: \\e05e;\n$fa-var-hands-wash: \\e05e;\n$fa-var-less-than-equal: \\f537;\n$fa-var-train: \\f238;\n$fa-var-eye-low-vision: \\f2a8;\n$fa-var-low-vision: \\f2a8;\n$fa-var-crow: \\f520;\n$fa-var-sailboat: \\e445;\n$fa-var-window-restore: \\f2d2;\n$fa-var-square-plus: \\f0fe;\n$fa-var-plus-square: \\f0fe;\n$fa-var-torii-gate: \\f6a1;\n$fa-var-frog: \\f52e;\n$fa-var-bucket: \\e4cf;\n$fa-var-image: \\f03e;\n$fa-var-microphone: \\f130;\n$fa-var-cow: \\f6c8;\n$fa-var-caret-up: \\f0d8;\n$fa-var-screwdriver: \\f54a;\n$fa-var-folder-closed: \\e185;\n$fa-var-house-tsunami: \\e515;\n$fa-var-square-nfi: \\e576;\n$fa-var-arrow-up-from-ground-water: \\e4b5;\n$fa-var-martini-glass: \\f57b;\n$fa-var-glass-martini-alt: \\f57b;\n$fa-var-rotate-left: \\f2ea;\n$fa-var-rotate-back: \\f2ea;\n$fa-var-rotate-backward: \\f2ea;\n$fa-var-undo-alt: \\f2ea;\n$fa-var-table-columns: \\f0db;\n$fa-var-columns: \\f0db;\n$fa-var-lemon: \\f094;\n$fa-var-head-side-mask: \\e063;\n$fa-var-handshake: \\f2b5;\n$fa-var-gem: \\f3a5;\n$fa-var-dolly: \\f472;\n$fa-var-dolly-box: \\f472;\n$fa-var-smoking: \\f48d;\n$fa-var-minimize: \\f78c;\n$fa-var-compress-arrows-alt: \\f78c;\n$fa-var-monument: \\f5a6;\n$fa-var-snowplow: \\f7d2;\n$fa-var-angles-right: \\f101;\n$fa-var-angle-double-right: \\f101;\n$fa-var-cannabis: \\f55f;\n$fa-var-circle-play: \\f144;\n$fa-var-play-circle: \\f144;\n$fa-var-tablets: \\f490;\n$fa-var-ethernet: \\f796;\n$fa-var-euro-sign: \\f153;\n$fa-var-eur: \\f153;\n$fa-var-euro: \\f153;\n$fa-var-chair: \\f6c0;\n$fa-var-circle-check: \\f058;\n$fa-var-check-circle: \\f058;\n$fa-var-circle-stop: \\f28d;\n$fa-var-stop-circle: \\f28d;\n$fa-var-compass-drafting: \\f568;\n$fa-var-drafting-compass: \\f568;\n$fa-var-plate-wheat: \\e55a;\n$fa-var-icicles: \\f7ad;\n$fa-var-person-shelter: \\e54f;\n$fa-var-neuter: \\f22c;\n$fa-var-id-badge: \\f2c1;\n$fa-var-marker: \\f5a1;\n$fa-var-face-laugh-beam: \\f59a;\n$fa-var-laugh-beam: \\f59a;\n$fa-var-helicopter-symbol: \\e502;\n$fa-var-universal-access: \\f29a;\n$fa-var-circle-chevron-up: \\f139;\n$fa-var-chevron-circle-up: \\f139;\n$fa-var-lari-sign: \\e1c8;\n$fa-var-volcano: \\f770;\n$fa-var-person-walking-dashed-line-arrow-right: \\e553;\n$fa-var-sterling-sign: \\f154;\n$fa-var-gbp: \\f154;\n$fa-var-pound-sign: \\f154;\n$fa-var-viruses: \\e076;\n$fa-var-square-person-confined: \\e577;\n$fa-var-user-tie: \\f508;\n$fa-var-arrow-down-long: \\f175;\n$fa-var-long-arrow-down: \\f175;\n$fa-var-tent-arrow-down-to-line: \\e57e;\n$fa-var-certificate: \\f0a3;\n$fa-var-reply-all: \\f122;\n$fa-var-mail-reply-all: \\f122;\n$fa-var-suitcase: \\f0f2;\n$fa-var-person-skating: \\f7c5;\n$fa-var-skating: \\f7c5;\n$fa-var-filter-circle-dollar: \\f662;\n$fa-var-funnel-dollar: \\f662;\n$fa-var-camera-retro: \\f083;\n$fa-var-circle-arrow-down: \\f0ab;\n$fa-var-arrow-circle-down: \\f0ab;\n$fa-var-file-import: \\f56f;\n$fa-var-arrow-right-to-file: \\f56f;\n$fa-var-square-arrow-up-right: \\f14c;\n$fa-var-external-link-square: \\f14c;\n$fa-var-box-open: \\f49e;\n$fa-var-scroll: \\f70e;\n$fa-var-spa: \\f5bb;\n$fa-var-location-pin-lock: \\e51f;\n$fa-var-pause: \\f04c;\n$fa-var-hill-avalanche: \\e507;\n$fa-var-temperature-empty: \\f2cb;\n$fa-var-temperature-0: \\f2cb;\n$fa-var-thermometer-0: \\f2cb;\n$fa-var-thermometer-empty: \\f2cb;\n$fa-var-bomb: \\f1e2;\n$fa-var-registered: \\f25d;\n$fa-var-address-card: \\f2bb;\n$fa-var-contact-card: \\f2bb;\n$fa-var-vcard: \\f2bb;\n$fa-var-scale-unbalanced-flip: \\f516;\n$fa-var-balance-scale-right: \\f516;\n$fa-var-subscript: \\f12c;\n$fa-var-diamond-turn-right: \\f5eb;\n$fa-var-directions: \\f5eb;\n$fa-var-burst: \\e4dc;\n$fa-var-house-laptop: \\e066;\n$fa-var-laptop-house: \\e066;\n$fa-var-face-tired: \\f5c8;\n$fa-var-tired: \\f5c8;\n$fa-var-money-bills: \\e1f3;\n$fa-var-smog: \\f75f;\n$fa-var-crutch: \\f7f7;\n$fa-var-cloud-arrow-up: \\f0ee;\n$fa-var-cloud-upload: \\f0ee;\n$fa-var-cloud-upload-alt: \\f0ee;\n$fa-var-palette: \\f53f;\n$fa-var-arrows-turn-right: \\e4c0;\n$fa-var-vest: \\e085;\n$fa-var-ferry: \\e4ea;\n$fa-var-arrows-down-to-people: \\e4b9;\n$fa-var-seedling: \\f4d8;\n$fa-var-sprout: \\f4d8;\n$fa-var-left-right: \\f337;\n$fa-var-arrows-alt-h: \\f337;\n$fa-var-boxes-packing: \\e4c7;\n$fa-var-circle-arrow-left: \\f0a8;\n$fa-var-arrow-circle-left: \\f0a8;\n$fa-var-group-arrows-rotate: \\e4f6;\n$fa-var-bowl-food: \\e4c6;\n$fa-var-candy-cane: \\f786;\n$fa-var-arrow-down-wide-short: \\f160;\n$fa-var-sort-amount-asc: \\f160;\n$fa-var-sort-amount-down: \\f160;\n$fa-var-cloud-bolt: \\f76c;\n$fa-var-thunderstorm: \\f76c;\n$fa-var-text-slash: \\f87d;\n$fa-var-remove-format: \\f87d;\n$fa-var-face-smile-wink: \\f4da;\n$fa-var-smile-wink: \\f4da;\n$fa-var-file-word: \\f1c2;\n$fa-var-file-powerpoint: \\f1c4;\n$fa-var-arrows-left-right: \\f07e;\n$fa-var-arrows-h: \\f07e;\n$fa-var-house-lock: \\e510;\n$fa-var-cloud-arrow-down: \\f0ed;\n$fa-var-cloud-download: \\f0ed;\n$fa-var-cloud-download-alt: \\f0ed;\n$fa-var-children: \\e4e1;\n$fa-var-chalkboard: \\f51b;\n$fa-var-blackboard: \\f51b;\n$fa-var-user-large-slash: \\f4fa;\n$fa-var-user-alt-slash: \\f4fa;\n$fa-var-envelope-open: \\f2b6;\n$fa-var-handshake-simple-slash: \\e05f;\n$fa-var-handshake-alt-slash: \\e05f;\n$fa-var-mattress-pillow: \\e525;\n$fa-var-guarani-sign: \\e19a;\n$fa-var-arrows-rotate: \\f021;\n$fa-var-refresh: \\f021;\n$fa-var-sync: \\f021;\n$fa-var-fire-extinguisher: \\f134;\n$fa-var-cruzeiro-sign: \\e152;\n$fa-var-greater-than-equal: \\f532;\n$fa-var-shield-halved: \\f3ed;\n$fa-var-shield-alt: \\f3ed;\n$fa-var-book-atlas: \\f558;\n$fa-var-atlas: \\f558;\n$fa-var-virus: \\e074;\n$fa-var-envelope-circle-check: \\e4e8;\n$fa-var-layer-group: \\f5fd;\n$fa-var-arrows-to-dot: \\e4be;\n$fa-var-archway: \\f557;\n$fa-var-heart-circle-check: \\e4fd;\n$fa-var-house-chimney-crack: \\f6f1;\n$fa-var-house-damage: \\f6f1;\n$fa-var-file-zipper: \\f1c6;\n$fa-var-file-archive: \\f1c6;\n$fa-var-square: \\f0c8;\n$fa-var-martini-glass-empty: \\f000;\n$fa-var-glass-martini: \\f000;\n$fa-var-couch: \\f4b8;\n$fa-var-cedi-sign: \\e0df;\n$fa-var-italic: \\f033;\n$fa-var-table-cells-column-lock: \\e678;\n$fa-var-church: \\f51d;\n$fa-var-comments-dollar: \\f653;\n$fa-var-democrat: \\f747;\n$fa-var-z: \\5a;\n$fa-var-person-skiing: \\f7c9;\n$fa-var-skiing: \\f7c9;\n$fa-var-road-lock: \\e567;\n$fa-var-a: \\41;\n$fa-var-temperature-arrow-down: \\e03f;\n$fa-var-temperature-down: \\e03f;\n$fa-var-feather-pointed: \\f56b;\n$fa-var-feather-alt: \\f56b;\n$fa-var-p: \\50;\n$fa-var-snowflake: \\f2dc;\n$fa-var-newspaper: \\f1ea;\n$fa-var-rectangle-ad: \\f641;\n$fa-var-ad: \\f641;\n$fa-var-circle-arrow-right: \\f0a9;\n$fa-var-arrow-circle-right: \\f0a9;\n$fa-var-filter-circle-xmark: \\e17b;\n$fa-var-locust: \\e520;\n$fa-var-sort: \\f0dc;\n$fa-var-unsorted: \\f0dc;\n$fa-var-list-ol: \\f0cb;\n$fa-var-list-1-2: \\f0cb;\n$fa-var-list-numeric: \\f0cb;\n$fa-var-person-dress-burst: \\e544;\n$fa-var-money-check-dollar: \\f53d;\n$fa-var-money-check-alt: \\f53d;\n$fa-var-vector-square: \\f5cb;\n$fa-var-bread-slice: \\f7ec;\n$fa-var-language: \\f1ab;\n$fa-var-face-kiss-wink-heart: \\f598;\n$fa-var-kiss-wink-heart: \\f598;\n$fa-var-filter: \\f0b0;\n$fa-var-question: \\3f;\n$fa-var-file-signature: \\f573;\n$fa-var-up-down-left-right: \\f0b2;\n$fa-var-arrows-alt: \\f0b2;\n$fa-var-house-chimney-user: \\e065;\n$fa-var-hand-holding-heart: \\f4be;\n$fa-var-puzzle-piece: \\f12e;\n$fa-var-money-check: \\f53c;\n$fa-var-star-half-stroke: \\f5c0;\n$fa-var-star-half-alt: \\f5c0;\n$fa-var-code: \\f121;\n$fa-var-whiskey-glass: \\f7a0;\n$fa-var-glass-whiskey: \\f7a0;\n$fa-var-building-circle-exclamation: \\e4d3;\n$fa-var-magnifying-glass-chart: \\e522;\n$fa-var-arrow-up-right-from-square: \\f08e;\n$fa-var-external-link: \\f08e;\n$fa-var-cubes-stacked: \\e4e6;\n$fa-var-won-sign: \\f159;\n$fa-var-krw: \\f159;\n$fa-var-won: \\f159;\n$fa-var-virus-covid: \\e4a8;\n$fa-var-austral-sign: \\e0a9;\n$fa-var-f: \\46;\n$fa-var-leaf: \\f06c;\n$fa-var-road: \\f018;\n$fa-var-taxi: \\f1ba;\n$fa-var-cab: \\f1ba;\n$fa-var-person-circle-plus: \\e541;\n$fa-var-chart-pie: \\f200;\n$fa-var-pie-chart: \\f200;\n$fa-var-bolt-lightning: \\e0b7;\n$fa-var-sack-xmark: \\e56a;\n$fa-var-file-excel: \\f1c3;\n$fa-var-file-contract: \\f56c;\n$fa-var-fish-fins: \\e4f2;\n$fa-var-building-flag: \\e4d5;\n$fa-var-face-grin-beam: \\f582;\n$fa-var-grin-beam: \\f582;\n$fa-var-object-ungroup: \\f248;\n$fa-var-poop: \\f619;\n$fa-var-location-pin: \\f041;\n$fa-var-map-marker: \\f041;\n$fa-var-kaaba: \\f66b;\n$fa-var-toilet-paper: \\f71e;\n$fa-var-helmet-safety: \\f807;\n$fa-var-hard-hat: \\f807;\n$fa-var-hat-hard: \\f807;\n$fa-var-eject: \\f052;\n$fa-var-circle-right: \\f35a;\n$fa-var-arrow-alt-circle-right: \\f35a;\n$fa-var-plane-circle-check: \\e555;\n$fa-var-face-rolling-eyes: \\f5a5;\n$fa-var-meh-rolling-eyes: \\f5a5;\n$fa-var-object-group: \\f247;\n$fa-var-chart-line: \\f201;\n$fa-var-line-chart: \\f201;\n$fa-var-mask-ventilator: \\e524;\n$fa-var-arrow-right: \\f061;\n$fa-var-signs-post: \\f277;\n$fa-var-map-signs: \\f277;\n$fa-var-cash-register: \\f788;\n$fa-var-person-circle-question: \\e542;\n$fa-var-h: \\48;\n$fa-var-tarp: \\e57b;\n$fa-var-screwdriver-wrench: \\f7d9;\n$fa-var-tools: \\f7d9;\n$fa-var-arrows-to-eye: \\e4bf;\n$fa-var-plug-circle-bolt: \\e55b;\n$fa-var-heart: \\f004;\n$fa-var-mars-and-venus: \\f224;\n$fa-var-house-user: \\e1b0;\n$fa-var-home-user: \\e1b0;\n$fa-var-dumpster-fire: \\f794;\n$fa-var-house-crack: \\e3b1;\n$fa-var-martini-glass-citrus: \\f561;\n$fa-var-cocktail: \\f561;\n$fa-var-face-surprise: \\f5c2;\n$fa-var-surprise: \\f5c2;\n$fa-var-bottle-water: \\e4c5;\n$fa-var-circle-pause: \\f28b;\n$fa-var-pause-circle: \\f28b;\n$fa-var-toilet-paper-slash: \\e072;\n$fa-var-apple-whole: \\f5d1;\n$fa-var-apple-alt: \\f5d1;\n$fa-var-kitchen-set: \\e51a;\n$fa-var-r: \\52;\n$fa-var-temperature-quarter: \\f2ca;\n$fa-var-temperature-1: \\f2ca;\n$fa-var-thermometer-1: \\f2ca;\n$fa-var-thermometer-quarter: \\f2ca;\n$fa-var-cube: \\f1b2;\n$fa-var-bitcoin-sign: \\e0b4;\n$fa-var-shield-dog: \\e573;\n$fa-var-solar-panel: \\f5ba;\n$fa-var-lock-open: \\f3c1;\n$fa-var-elevator: \\e16d;\n$fa-var-money-bill-transfer: \\e528;\n$fa-var-money-bill-trend-up: \\e529;\n$fa-var-house-flood-water-circle-arrow-right: \\e50f;\n$fa-var-square-poll-horizontal: \\f682;\n$fa-var-poll-h: \\f682;\n$fa-var-circle: \\f111;\n$fa-var-backward-fast: \\f049;\n$fa-var-fast-backward: \\f049;\n$fa-var-recycle: \\f1b8;\n$fa-var-user-astronaut: \\f4fb;\n$fa-var-plane-slash: \\e069;\n$fa-var-trademark: \\f25c;\n$fa-var-basketball: \\f434;\n$fa-var-basketball-ball: \\f434;\n$fa-var-satellite-dish: \\f7c0;\n$fa-var-circle-up: \\f35b;\n$fa-var-arrow-alt-circle-up: \\f35b;\n$fa-var-mobile-screen-button: \\f3cd;\n$fa-var-mobile-alt: \\f3cd;\n$fa-var-volume-high: \\f028;\n$fa-var-volume-up: \\f028;\n$fa-var-users-rays: \\e593;\n$fa-var-wallet: \\f555;\n$fa-var-clipboard-check: \\f46c;\n$fa-var-file-audio: \\f1c7;\n$fa-var-burger: \\f805;\n$fa-var-hamburger: \\f805;\n$fa-var-wrench: \\f0ad;\n$fa-var-bugs: \\e4d0;\n$fa-var-rupee-sign: \\f156;\n$fa-var-rupee: \\f156;\n$fa-var-file-image: \\f1c5;\n$fa-var-circle-question: \\f059;\n$fa-var-question-circle: \\f059;\n$fa-var-plane-departure: \\f5b0;\n$fa-var-handshake-slash: \\e060;\n$fa-var-book-bookmark: \\e0bb;\n$fa-var-code-branch: \\f126;\n$fa-var-hat-cowboy: \\f8c0;\n$fa-var-bridge: \\e4c8;\n$fa-var-phone-flip: \\f879;\n$fa-var-phone-alt: \\f879;\n$fa-var-truck-front: \\e2b7;\n$fa-var-cat: \\f6be;\n$fa-var-anchor-circle-exclamation: \\e4ab;\n$fa-var-truck-field: \\e58d;\n$fa-var-route: \\f4d7;\n$fa-var-clipboard-question: \\e4e3;\n$fa-var-panorama: \\e209;\n$fa-var-comment-medical: \\f7f5;\n$fa-var-teeth-open: \\f62f;\n$fa-var-file-circle-minus: \\e4ed;\n$fa-var-tags: \\f02c;\n$fa-var-wine-glass: \\f4e3;\n$fa-var-forward-fast: \\f050;\n$fa-var-fast-forward: \\f050;\n$fa-var-face-meh-blank: \\f5a4;\n$fa-var-meh-blank: \\f5a4;\n$fa-var-square-parking: \\f540;\n$fa-var-parking: \\f540;\n$fa-var-house-signal: \\e012;\n$fa-var-bars-progress: \\f828;\n$fa-var-tasks-alt: \\f828;\n$fa-var-faucet-drip: \\e006;\n$fa-var-cart-flatbed: \\f474;\n$fa-var-dolly-flatbed: \\f474;\n$fa-var-ban-smoking: \\f54d;\n$fa-var-smoking-ban: \\f54d;\n$fa-var-terminal: \\f120;\n$fa-var-mobile-button: \\f10b;\n$fa-var-house-medical-flag: \\e514;\n$fa-var-basket-shopping: \\f291;\n$fa-var-shopping-basket: \\f291;\n$fa-var-tape: \\f4db;\n$fa-var-bus-simple: \\f55e;\n$fa-var-bus-alt: \\f55e;\n$fa-var-eye: \\f06e;\n$fa-var-face-sad-cry: \\f5b3;\n$fa-var-sad-cry: \\f5b3;\n$fa-var-audio-description: \\f29e;\n$fa-var-person-military-to-person: \\e54c;\n$fa-var-file-shield: \\e4f0;\n$fa-var-user-slash: \\f506;\n$fa-var-pen: \\f304;\n$fa-var-tower-observation: \\e586;\n$fa-var-file-code: \\f1c9;\n$fa-var-signal: \\f012;\n$fa-var-signal-5: \\f012;\n$fa-var-signal-perfect: \\f012;\n$fa-var-bus: \\f207;\n$fa-var-heart-circle-xmark: \\e501;\n$fa-var-house-chimney: \\e3af;\n$fa-var-home-lg: \\e3af;\n$fa-var-window-maximize: \\f2d0;\n$fa-var-face-frown: \\f119;\n$fa-var-frown: \\f119;\n$fa-var-prescription: \\f5b1;\n$fa-var-shop: \\f54f;\n$fa-var-store-alt: \\f54f;\n$fa-var-floppy-disk: \\f0c7;\n$fa-var-save: \\f0c7;\n$fa-var-vihara: \\f6a7;\n$fa-var-scale-unbalanced: \\f515;\n$fa-var-balance-scale-left: \\f515;\n$fa-var-sort-up: \\f0de;\n$fa-var-sort-asc: \\f0de;\n$fa-var-comment-dots: \\f4ad;\n$fa-var-commenting: \\f4ad;\n$fa-var-plant-wilt: \\e5aa;\n$fa-var-diamond: \\f219;\n$fa-var-face-grin-squint: \\f585;\n$fa-var-grin-squint: \\f585;\n$fa-var-hand-holding-dollar: \\f4c0;\n$fa-var-hand-holding-usd: \\f4c0;\n$fa-var-bacterium: \\e05a;\n$fa-var-hand-pointer: \\f25a;\n$fa-var-drum-steelpan: \\f56a;\n$fa-var-hand-scissors: \\f257;\n$fa-var-hands-praying: \\f684;\n$fa-var-praying-hands: \\f684;\n$fa-var-arrow-rotate-right: \\f01e;\n$fa-var-arrow-right-rotate: \\f01e;\n$fa-var-arrow-rotate-forward: \\f01e;\n$fa-var-redo: \\f01e;\n$fa-var-biohazard: \\f780;\n$fa-var-location-crosshairs: \\f601;\n$fa-var-location: \\f601;\n$fa-var-mars-double: \\f227;\n$fa-var-child-dress: \\e59c;\n$fa-var-users-between-lines: \\e591;\n$fa-var-lungs-virus: \\e067;\n$fa-var-face-grin-tears: \\f588;\n$fa-var-grin-tears: \\f588;\n$fa-var-phone: \\f095;\n$fa-var-calendar-xmark: \\f273;\n$fa-var-calendar-times: \\f273;\n$fa-var-child-reaching: \\e59d;\n$fa-var-head-side-virus: \\e064;\n$fa-var-user-gear: \\f4fe;\n$fa-var-user-cog: \\f4fe;\n$fa-var-arrow-up-1-9: \\f163;\n$fa-var-sort-numeric-up: \\f163;\n$fa-var-door-closed: \\f52a;\n$fa-var-shield-virus: \\e06c;\n$fa-var-dice-six: \\f526;\n$fa-var-mosquito-net: \\e52c;\n$fa-var-bridge-water: \\e4ce;\n$fa-var-person-booth: \\f756;\n$fa-var-text-width: \\f035;\n$fa-var-hat-wizard: \\f6e8;\n$fa-var-pen-fancy: \\f5ac;\n$fa-var-person-digging: \\f85e;\n$fa-var-digging: \\f85e;\n$fa-var-trash: \\f1f8;\n$fa-var-gauge-simple: \\f629;\n$fa-var-gauge-simple-med: \\f629;\n$fa-var-tachometer-average: \\f629;\n$fa-var-book-medical: \\f7e6;\n$fa-var-poo: \\f2fe;\n$fa-var-quote-right: \\f10e;\n$fa-var-quote-right-alt: \\f10e;\n$fa-var-shirt: \\f553;\n$fa-var-t-shirt: \\f553;\n$fa-var-tshirt: \\f553;\n$fa-var-cubes: \\f1b3;\n$fa-var-divide: \\f529;\n$fa-var-tenge-sign: \\f7d7;\n$fa-var-tenge: \\f7d7;\n$fa-var-headphones: \\f025;\n$fa-var-hands-holding: \\f4c2;\n$fa-var-hands-clapping: \\e1a8;\n$fa-var-republican: \\f75e;\n$fa-var-arrow-left: \\f060;\n$fa-var-person-circle-xmark: \\e543;\n$fa-var-ruler: \\f545;\n$fa-var-align-left: \\f036;\n$fa-var-dice-d6: \\f6d1;\n$fa-var-restroom: \\f7bd;\n$fa-var-j: \\4a;\n$fa-var-users-viewfinder: \\e595;\n$fa-var-file-video: \\f1c8;\n$fa-var-up-right-from-square: \\f35d;\n$fa-var-external-link-alt: \\f35d;\n$fa-var-table-cells: \\f00a;\n$fa-var-th: \\f00a;\n$fa-var-file-pdf: \\f1c1;\n$fa-var-book-bible: \\f647;\n$fa-var-bible: \\f647;\n$fa-var-o: \\4f;\n$fa-var-suitcase-medical: \\f0fa;\n$fa-var-medkit: \\f0fa;\n$fa-var-user-secret: \\f21b;\n$fa-var-otter: \\f700;\n$fa-var-person-dress: \\f182;\n$fa-var-female: \\f182;\n$fa-var-comment-dollar: \\f651;\n$fa-var-business-time: \\f64a;\n$fa-var-briefcase-clock: \\f64a;\n$fa-var-table-cells-large: \\f009;\n$fa-var-th-large: \\f009;\n$fa-var-book-tanakh: \\f827;\n$fa-var-tanakh: \\f827;\n$fa-var-phone-volume: \\f2a0;\n$fa-var-volume-control-phone: \\f2a0;\n$fa-var-hat-cowboy-side: \\f8c1;\n$fa-var-clipboard-user: \\f7f3;\n$fa-var-child: \\f1ae;\n$fa-var-lira-sign: \\f195;\n$fa-var-satellite: \\f7bf;\n$fa-var-plane-lock: \\e558;\n$fa-var-tag: \\f02b;\n$fa-var-comment: \\f075;\n$fa-var-cake-candles: \\f1fd;\n$fa-var-birthday-cake: \\f1fd;\n$fa-var-cake: \\f1fd;\n$fa-var-envelope: \\f0e0;\n$fa-var-angles-up: \\f102;\n$fa-var-angle-double-up: \\f102;\n$fa-var-paperclip: \\f0c6;\n$fa-var-arrow-right-to-city: \\e4b3;\n$fa-var-ribbon: \\f4d6;\n$fa-var-lungs: \\f604;\n$fa-var-arrow-up-9-1: \\f887;\n$fa-var-sort-numeric-up-alt: \\f887;\n$fa-var-litecoin-sign: \\e1d3;\n$fa-var-border-none: \\f850;\n$fa-var-circle-nodes: \\e4e2;\n$fa-var-parachute-box: \\f4cd;\n$fa-var-indent: \\f03c;\n$fa-var-truck-field-un: \\e58e;\n$fa-var-hourglass: \\f254;\n$fa-var-hourglass-empty: \\f254;\n$fa-var-mountain: \\f6fc;\n$fa-var-user-doctor: \\f0f0;\n$fa-var-user-md: \\f0f0;\n$fa-var-circle-info: \\f05a;\n$fa-var-info-circle: \\f05a;\n$fa-var-cloud-meatball: \\f73b;\n$fa-var-camera: \\f030;\n$fa-var-camera-alt: \\f030;\n$fa-var-square-virus: \\e578;\n$fa-var-meteor: \\f753;\n$fa-var-car-on: \\e4dd;\n$fa-var-sleigh: \\f7cc;\n$fa-var-arrow-down-1-9: \\f162;\n$fa-var-sort-numeric-asc: \\f162;\n$fa-var-sort-numeric-down: \\f162;\n$fa-var-hand-holding-droplet: \\f4c1;\n$fa-var-hand-holding-water: \\f4c1;\n$fa-var-water: \\f773;\n$fa-var-calendar-check: \\f274;\n$fa-var-braille: \\f2a1;\n$fa-var-prescription-bottle-medical: \\f486;\n$fa-var-prescription-bottle-alt: \\f486;\n$fa-var-landmark: \\f66f;\n$fa-var-truck: \\f0d1;\n$fa-var-crosshairs: \\f05b;\n$fa-var-person-cane: \\e53c;\n$fa-var-tent: \\e57d;\n$fa-var-vest-patches: \\e086;\n$fa-var-check-double: \\f560;\n$fa-var-arrow-down-a-z: \\f15d;\n$fa-var-sort-alpha-asc: \\f15d;\n$fa-var-sort-alpha-down: \\f15d;\n$fa-var-money-bill-wheat: \\e52a;\n$fa-var-cookie: \\f563;\n$fa-var-arrow-rotate-left: \\f0e2;\n$fa-var-arrow-left-rotate: \\f0e2;\n$fa-var-arrow-rotate-back: \\f0e2;\n$fa-var-arrow-rotate-backward: \\f0e2;\n$fa-var-undo: \\f0e2;\n$fa-var-hard-drive: \\f0a0;\n$fa-var-hdd: \\f0a0;\n$fa-var-face-grin-squint-tears: \\f586;\n$fa-var-grin-squint-tears: \\f586;\n$fa-var-dumbbell: \\f44b;\n$fa-var-rectangle-list: \\f022;\n$fa-var-list-alt: \\f022;\n$fa-var-tarp-droplet: \\e57c;\n$fa-var-house-medical-circle-check: \\e511;\n$fa-var-person-skiing-nordic: \\f7ca;\n$fa-var-skiing-nordic: \\f7ca;\n$fa-var-calendar-plus: \\f271;\n$fa-var-plane-arrival: \\f5af;\n$fa-var-circle-left: \\f359;\n$fa-var-arrow-alt-circle-left: \\f359;\n$fa-var-train-subway: \\f239;\n$fa-var-subway: \\f239;\n$fa-var-chart-gantt: \\e0e4;\n$fa-var-indian-rupee-sign: \\e1bc;\n$fa-var-indian-rupee: \\e1bc;\n$fa-var-inr: \\e1bc;\n$fa-var-crop-simple: \\f565;\n$fa-var-crop-alt: \\f565;\n$fa-var-money-bill-1: \\f3d1;\n$fa-var-money-bill-alt: \\f3d1;\n$fa-var-left-long: \\f30a;\n$fa-var-long-arrow-alt-left: \\f30a;\n$fa-var-dna: \\f471;\n$fa-var-virus-slash: \\e075;\n$fa-var-minus: \\f068;\n$fa-var-subtract: \\f068;\n$fa-var-chess: \\f439;\n$fa-var-arrow-left-long: \\f177;\n$fa-var-long-arrow-left: \\f177;\n$fa-var-plug-circle-check: \\e55c;\n$fa-var-street-view: \\f21d;\n$fa-var-franc-sign: \\e18f;\n$fa-var-volume-off: \\f026;\n$fa-var-hands-asl-interpreting: \\f2a3;\n$fa-var-american-sign-language-interpreting: \\f2a3;\n$fa-var-asl-interpreting: \\f2a3;\n$fa-var-hands-american-sign-language-interpreting: \\f2a3;\n$fa-var-gear: \\f013;\n$fa-var-cog: \\f013;\n$fa-var-droplet-slash: \\f5c7;\n$fa-var-tint-slash: \\f5c7;\n$fa-var-mosque: \\f678;\n$fa-var-mosquito: \\e52b;\n$fa-var-star-of-david: \\f69a;\n$fa-var-person-military-rifle: \\e54b;\n$fa-var-cart-shopping: \\f07a;\n$fa-var-shopping-cart: \\f07a;\n$fa-var-vials: \\f493;\n$fa-var-plug-circle-plus: \\e55f;\n$fa-var-place-of-worship: \\f67f;\n$fa-var-grip-vertical: \\f58e;\n$fa-var-arrow-turn-up: \\f148;\n$fa-var-level-up: \\f148;\n$fa-var-u: \\55;\n$fa-var-square-root-variable: \\f698;\n$fa-var-square-root-alt: \\f698;\n$fa-var-clock: \\f017;\n$fa-var-clock-four: \\f017;\n$fa-var-backward-step: \\f048;\n$fa-var-step-backward: \\f048;\n$fa-var-pallet: \\f482;\n$fa-var-faucet: \\e005;\n$fa-var-baseball-bat-ball: \\f432;\n$fa-var-s: \\53;\n$fa-var-timeline: \\e29c;\n$fa-var-keyboard: \\f11c;\n$fa-var-caret-down: \\f0d7;\n$fa-var-house-chimney-medical: \\f7f2;\n$fa-var-clinic-medical: \\f7f2;\n$fa-var-temperature-three-quarters: \\f2c8;\n$fa-var-temperature-3: \\f2c8;\n$fa-var-thermometer-3: \\f2c8;\n$fa-var-thermometer-three-quarters: \\f2c8;\n$fa-var-mobile-screen: \\f3cf;\n$fa-var-mobile-android-alt: \\f3cf;\n$fa-var-plane-up: \\e22d;\n$fa-var-piggy-bank: \\f4d3;\n$fa-var-battery-half: \\f242;\n$fa-var-battery-3: \\f242;\n$fa-var-mountain-city: \\e52e;\n$fa-var-coins: \\f51e;\n$fa-var-khanda: \\f66d;\n$fa-var-sliders: \\f1de;\n$fa-var-sliders-h: \\f1de;\n$fa-var-folder-tree: \\f802;\n$fa-var-network-wired: \\f6ff;\n$fa-var-map-pin: \\f276;\n$fa-var-hamsa: \\f665;\n$fa-var-cent-sign: \\e3f5;\n$fa-var-flask: \\f0c3;\n$fa-var-person-pregnant: \\e31e;\n$fa-var-wand-sparkles: \\f72b;\n$fa-var-ellipsis-vertical: \\f142;\n$fa-var-ellipsis-v: \\f142;\n$fa-var-ticket: \\f145;\n$fa-var-power-off: \\f011;\n$fa-var-right-long: \\f30b;\n$fa-var-long-arrow-alt-right: \\f30b;\n$fa-var-flag-usa: \\f74d;\n$fa-var-laptop-file: \\e51d;\n$fa-var-tty: \\f1e4;\n$fa-var-teletype: \\f1e4;\n$fa-var-diagram-next: \\e476;\n$fa-var-person-rifle: \\e54e;\n$fa-var-house-medical-circle-exclamation: \\e512;\n$fa-var-closed-captioning: \\f20a;\n$fa-var-person-hiking: \\f6ec;\n$fa-var-hiking: \\f6ec;\n$fa-var-venus-double: \\f226;\n$fa-var-images: \\f302;\n$fa-var-calculator: \\f1ec;\n$fa-var-people-pulling: \\e535;\n$fa-var-n: \\4e;\n$fa-var-cable-car: \\f7da;\n$fa-var-tram: \\f7da;\n$fa-var-cloud-rain: \\f73d;\n$fa-var-building-circle-xmark: \\e4d4;\n$fa-var-ship: \\f21a;\n$fa-var-arrows-down-to-line: \\e4b8;\n$fa-var-download: \\f019;\n$fa-var-face-grin: \\f580;\n$fa-var-grin: \\f580;\n$fa-var-delete-left: \\f55a;\n$fa-var-backspace: \\f55a;\n$fa-var-eye-dropper: \\f1fb;\n$fa-var-eye-dropper-empty: \\f1fb;\n$fa-var-eyedropper: \\f1fb;\n$fa-var-file-circle-check: \\e5a0;\n$fa-var-forward: \\f04e;\n$fa-var-mobile: \\f3ce;\n$fa-var-mobile-android: \\f3ce;\n$fa-var-mobile-phone: \\f3ce;\n$fa-var-face-meh: \\f11a;\n$fa-var-meh: \\f11a;\n$fa-var-align-center: \\f037;\n$fa-var-book-skull: \\f6b7;\n$fa-var-book-dead: \\f6b7;\n$fa-var-id-card: \\f2c2;\n$fa-var-drivers-license: \\f2c2;\n$fa-var-outdent: \\f03b;\n$fa-var-dedent: \\f03b;\n$fa-var-heart-circle-exclamation: \\e4fe;\n$fa-var-house: \\f015;\n$fa-var-home: \\f015;\n$fa-var-home-alt: \\f015;\n$fa-var-home-lg-alt: \\f015;\n$fa-var-calendar-week: \\f784;\n$fa-var-laptop-medical: \\f812;\n$fa-var-b: \\42;\n$fa-var-file-medical: \\f477;\n$fa-var-dice-one: \\f525;\n$fa-var-kiwi-bird: \\f535;\n$fa-var-arrow-right-arrow-left: \\f0ec;\n$fa-var-exchange: \\f0ec;\n$fa-var-rotate-right: \\f2f9;\n$fa-var-redo-alt: \\f2f9;\n$fa-var-rotate-forward: \\f2f9;\n$fa-var-utensils: \\f2e7;\n$fa-var-cutlery: \\f2e7;\n$fa-var-arrow-up-wide-short: \\f161;\n$fa-var-sort-amount-up: \\f161;\n$fa-var-mill-sign: \\e1ed;\n$fa-var-bowl-rice: \\e2eb;\n$fa-var-skull: \\f54c;\n$fa-var-tower-broadcast: \\f519;\n$fa-var-broadcast-tower: \\f519;\n$fa-var-truck-pickup: \\f63c;\n$fa-var-up-long: \\f30c;\n$fa-var-long-arrow-alt-up: \\f30c;\n$fa-var-stop: \\f04d;\n$fa-var-code-merge: \\f387;\n$fa-var-upload: \\f093;\n$fa-var-hurricane: \\f751;\n$fa-var-mound: \\e52d;\n$fa-var-toilet-portable: \\e583;\n$fa-var-compact-disc: \\f51f;\n$fa-var-file-arrow-down: \\f56d;\n$fa-var-file-download: \\f56d;\n$fa-var-caravan: \\f8ff;\n$fa-var-shield-cat: \\e572;\n$fa-var-bolt: \\f0e7;\n$fa-var-zap: \\f0e7;\n$fa-var-glass-water: \\e4f4;\n$fa-var-oil-well: \\e532;\n$fa-var-vault: \\e2c5;\n$fa-var-mars: \\f222;\n$fa-var-toilet: \\f7d8;\n$fa-var-plane-circle-xmark: \\e557;\n$fa-var-yen-sign: \\f157;\n$fa-var-cny: \\f157;\n$fa-var-jpy: \\f157;\n$fa-var-rmb: \\f157;\n$fa-var-yen: \\f157;\n$fa-var-ruble-sign: \\f158;\n$fa-var-rouble: \\f158;\n$fa-var-rub: \\f158;\n$fa-var-ruble: \\f158;\n$fa-var-sun: \\f185;\n$fa-var-guitar: \\f7a6;\n$fa-var-face-laugh-wink: \\f59c;\n$fa-var-laugh-wink: \\f59c;\n$fa-var-horse-head: \\f7ab;\n$fa-var-bore-hole: \\e4c3;\n$fa-var-industry: \\f275;\n$fa-var-circle-down: \\f358;\n$fa-var-arrow-alt-circle-down: \\f358;\n$fa-var-arrows-turn-to-dots: \\e4c1;\n$fa-var-florin-sign: \\e184;\n$fa-var-arrow-down-short-wide: \\f884;\n$fa-var-sort-amount-desc: \\f884;\n$fa-var-sort-amount-down-alt: \\f884;\n$fa-var-less-than: \\3c;\n$fa-var-angle-down: \\f107;\n$fa-var-car-tunnel: \\e4de;\n$fa-var-head-side-cough: \\e061;\n$fa-var-grip-lines: \\f7a4;\n$fa-var-thumbs-down: \\f165;\n$fa-var-user-lock: \\f502;\n$fa-var-arrow-right-long: \\f178;\n$fa-var-long-arrow-right: \\f178;\n$fa-var-anchor-circle-xmark: \\e4ac;\n$fa-var-ellipsis: \\f141;\n$fa-var-ellipsis-h: \\f141;\n$fa-var-chess-pawn: \\f443;\n$fa-var-kit-medical: \\f479;\n$fa-var-first-aid: \\f479;\n$fa-var-person-through-window: \\e5a9;\n$fa-var-toolbox: \\f552;\n$fa-var-hands-holding-circle: \\e4fb;\n$fa-var-bug: \\f188;\n$fa-var-credit-card: \\f09d;\n$fa-var-credit-card-alt: \\f09d;\n$fa-var-car: \\f1b9;\n$fa-var-automobile: \\f1b9;\n$fa-var-hand-holding-hand: \\e4f7;\n$fa-var-book-open-reader: \\f5da;\n$fa-var-book-reader: \\f5da;\n$fa-var-mountain-sun: \\e52f;\n$fa-var-arrows-left-right-to-line: \\e4ba;\n$fa-var-dice-d20: \\f6cf;\n$fa-var-truck-droplet: \\e58c;\n$fa-var-file-circle-xmark: \\e5a1;\n$fa-var-temperature-arrow-up: \\e040;\n$fa-var-temperature-up: \\e040;\n$fa-var-medal: \\f5a2;\n$fa-var-bed: \\f236;\n$fa-var-square-h: \\f0fd;\n$fa-var-h-square: \\f0fd;\n$fa-var-podcast: \\f2ce;\n$fa-var-temperature-full: \\f2c7;\n$fa-var-temperature-4: \\f2c7;\n$fa-var-thermometer-4: \\f2c7;\n$fa-var-thermometer-full: \\f2c7;\n$fa-var-bell: \\f0f3;\n$fa-var-superscript: \\f12b;\n$fa-var-plug-circle-xmark: \\e560;\n$fa-var-star-of-life: \\f621;\n$fa-var-phone-slash: \\f3dd;\n$fa-var-paint-roller: \\f5aa;\n$fa-var-handshake-angle: \\f4c4;\n$fa-var-hands-helping: \\f4c4;\n$fa-var-location-dot: \\f3c5;\n$fa-var-map-marker-alt: \\f3c5;\n$fa-var-file: \\f15b;\n$fa-var-greater-than: \\3e;\n$fa-var-person-swimming: \\f5c4;\n$fa-var-swimmer: \\f5c4;\n$fa-var-arrow-down: \\f063;\n$fa-var-droplet: \\f043;\n$fa-var-tint: \\f043;\n$fa-var-eraser: \\f12d;\n$fa-var-earth-americas: \\f57d;\n$fa-var-earth: \\f57d;\n$fa-var-earth-america: \\f57d;\n$fa-var-globe-americas: \\f57d;\n$fa-var-person-burst: \\e53b;\n$fa-var-dove: \\f4ba;\n$fa-var-battery-empty: \\f244;\n$fa-var-battery-0: \\f244;\n$fa-var-socks: \\f696;\n$fa-var-inbox: \\f01c;\n$fa-var-section: \\e447;\n$fa-var-gauge-high: \\f625;\n$fa-var-tachometer-alt: \\f625;\n$fa-var-tachometer-alt-fast: \\f625;\n$fa-var-envelope-open-text: \\f658;\n$fa-var-hospital: \\f0f8;\n$fa-var-hospital-alt: \\f0f8;\n$fa-var-hospital-wide: \\f0f8;\n$fa-var-wine-bottle: \\f72f;\n$fa-var-chess-rook: \\f447;\n$fa-var-bars-staggered: \\f550;\n$fa-var-reorder: \\f550;\n$fa-var-stream: \\f550;\n$fa-var-dharmachakra: \\f655;\n$fa-var-hotdog: \\f80f;\n$fa-var-person-walking-with-cane: \\f29d;\n$fa-var-blind: \\f29d;\n$fa-var-drum: \\f569;\n$fa-var-ice-cream: \\f810;\n$fa-var-heart-circle-bolt: \\e4fc;\n$fa-var-fax: \\f1ac;\n$fa-var-paragraph: \\f1dd;\n$fa-var-check-to-slot: \\f772;\n$fa-var-vote-yea: \\f772;\n$fa-var-star-half: \\f089;\n$fa-var-boxes-stacked: \\f468;\n$fa-var-boxes: \\f468;\n$fa-var-boxes-alt: \\f468;\n$fa-var-link: \\f0c1;\n$fa-var-chain: \\f0c1;\n$fa-var-ear-listen: \\f2a2;\n$fa-var-assistive-listening-systems: \\f2a2;\n$fa-var-tree-city: \\e587;\n$fa-var-play: \\f04b;\n$fa-var-font: \\f031;\n$fa-var-table-cells-row-lock: \\e67a;\n$fa-var-rupiah-sign: \\e23d;\n$fa-var-magnifying-glass: \\f002;\n$fa-var-search: \\f002;\n$fa-var-table-tennis-paddle-ball: \\f45d;\n$fa-var-ping-pong-paddle-ball: \\f45d;\n$fa-var-table-tennis: \\f45d;\n$fa-var-person-dots-from-line: \\f470;\n$fa-var-diagnoses: \\f470;\n$fa-var-trash-can-arrow-up: \\f82a;\n$fa-var-trash-restore-alt: \\f82a;\n$fa-var-naira-sign: \\e1f6;\n$fa-var-cart-arrow-down: \\f218;\n$fa-var-walkie-talkie: \\f8ef;\n$fa-var-file-pen: \\f31c;\n$fa-var-file-edit: \\f31c;\n$fa-var-receipt: \\f543;\n$fa-var-square-pen: \\f14b;\n$fa-var-pen-square: \\f14b;\n$fa-var-pencil-square: \\f14b;\n$fa-var-suitcase-rolling: \\f5c1;\n$fa-var-person-circle-exclamation: \\e53f;\n$fa-var-chevron-down: \\f078;\n$fa-var-battery-full: \\f240;\n$fa-var-battery: \\f240;\n$fa-var-battery-5: \\f240;\n$fa-var-skull-crossbones: \\f714;\n$fa-var-code-compare: \\e13a;\n$fa-var-list-ul: \\f0ca;\n$fa-var-list-dots: \\f0ca;\n$fa-var-school-lock: \\e56f;\n$fa-var-tower-cell: \\e585;\n$fa-var-down-long: \\f309;\n$fa-var-long-arrow-alt-down: \\f309;\n$fa-var-ranking-star: \\e561;\n$fa-var-chess-king: \\f43f;\n$fa-var-person-harassing: \\e549;\n$fa-var-brazilian-real-sign: \\e46c;\n$fa-var-landmark-dome: \\f752;\n$fa-var-landmark-alt: \\f752;\n$fa-var-arrow-up: \\f062;\n$fa-var-tv: \\f26c;\n$fa-var-television: \\f26c;\n$fa-var-tv-alt: \\f26c;\n$fa-var-shrimp: \\e448;\n$fa-var-list-check: \\f0ae;\n$fa-var-tasks: \\f0ae;\n$fa-var-jug-detergent: \\e519;\n$fa-var-circle-user: \\f2bd;\n$fa-var-user-circle: \\f2bd;\n$fa-var-user-shield: \\f505;\n$fa-var-wind: \\f72e;\n$fa-var-car-burst: \\f5e1;\n$fa-var-car-crash: \\f5e1;\n$fa-var-y: \\59;\n$fa-var-person-snowboarding: \\f7ce;\n$fa-var-snowboarding: \\f7ce;\n$fa-var-truck-fast: \\f48b;\n$fa-var-shipping-fast: \\f48b;\n$fa-var-fish: \\f578;\n$fa-var-user-graduate: \\f501;\n$fa-var-circle-half-stroke: \\f042;\n$fa-var-adjust: \\f042;\n$fa-var-clapperboard: \\e131;\n$fa-var-circle-radiation: \\f7ba;\n$fa-var-radiation-alt: \\f7ba;\n$fa-var-baseball: \\f433;\n$fa-var-baseball-ball: \\f433;\n$fa-var-jet-fighter-up: \\e518;\n$fa-var-diagram-project: \\f542;\n$fa-var-project-diagram: \\f542;\n$fa-var-copy: \\f0c5;\n$fa-var-volume-xmark: \\f6a9;\n$fa-var-volume-mute: \\f6a9;\n$fa-var-volume-times: \\f6a9;\n$fa-var-hand-sparkles: \\e05d;\n$fa-var-grip: \\f58d;\n$fa-var-grip-horizontal: \\f58d;\n$fa-var-share-from-square: \\f14d;\n$fa-var-share-square: \\f14d;\n$fa-var-child-combatant: \\e4e0;\n$fa-var-child-rifle: \\e4e0;\n$fa-var-gun: \\e19b;\n$fa-var-square-phone: \\f098;\n$fa-var-phone-square: \\f098;\n$fa-var-plus: \\2b;\n$fa-var-add: \\2b;\n$fa-var-expand: \\f065;\n$fa-var-computer: \\e4e5;\n$fa-var-xmark: \\f00d;\n$fa-var-close: \\f00d;\n$fa-var-multiply: \\f00d;\n$fa-var-remove: \\f00d;\n$fa-var-times: \\f00d;\n$fa-var-arrows-up-down-left-right: \\f047;\n$fa-var-arrows: \\f047;\n$fa-var-chalkboard-user: \\f51c;\n$fa-var-chalkboard-teacher: \\f51c;\n$fa-var-peso-sign: \\e222;\n$fa-var-building-shield: \\e4d8;\n$fa-var-baby: \\f77c;\n$fa-var-users-line: \\e592;\n$fa-var-quote-left: \\f10d;\n$fa-var-quote-left-alt: \\f10d;\n$fa-var-tractor: \\f722;\n$fa-var-trash-arrow-up: \\f829;\n$fa-var-trash-restore: \\f829;\n$fa-var-arrow-down-up-lock: \\e4b0;\n$fa-var-lines-leaning: \\e51e;\n$fa-var-ruler-combined: \\f546;\n$fa-var-copyright: \\f1f9;\n$fa-var-equals: \\3d;\n$fa-var-blender: \\f517;\n$fa-var-teeth: \\f62e;\n$fa-var-shekel-sign: \\f20b;\n$fa-var-ils: \\f20b;\n$fa-var-shekel: \\f20b;\n$fa-var-sheqel: \\f20b;\n$fa-var-sheqel-sign: \\f20b;\n$fa-var-map: \\f279;\n$fa-var-rocket: \\f135;\n$fa-var-photo-film: \\f87c;\n$fa-var-photo-video: \\f87c;\n$fa-var-folder-minus: \\f65d;\n$fa-var-store: \\f54e;\n$fa-var-arrow-trend-up: \\e098;\n$fa-var-plug-circle-minus: \\e55e;\n$fa-var-sign-hanging: \\f4d9;\n$fa-var-sign: \\f4d9;\n$fa-var-bezier-curve: \\f55b;\n$fa-var-bell-slash: \\f1f6;\n$fa-var-tablet: \\f3fb;\n$fa-var-tablet-android: \\f3fb;\n$fa-var-school-flag: \\e56e;\n$fa-var-fill: \\f575;\n$fa-var-angle-up: \\f106;\n$fa-var-drumstick-bite: \\f6d7;\n$fa-var-holly-berry: \\f7aa;\n$fa-var-chevron-left: \\f053;\n$fa-var-bacteria: \\e059;\n$fa-var-hand-lizard: \\f258;\n$fa-var-notdef: \\e1fe;\n$fa-var-disease: \\f7fa;\n$fa-var-briefcase-medical: \\f469;\n$fa-var-genderless: \\f22d;\n$fa-var-chevron-right: \\f054;\n$fa-var-retweet: \\f079;\n$fa-var-car-rear: \\f5de;\n$fa-var-car-alt: \\f5de;\n$fa-var-pump-soap: \\e06b;\n$fa-var-video-slash: \\f4e2;\n$fa-var-battery-quarter: \\f243;\n$fa-var-battery-2: \\f243;\n$fa-var-radio: \\f8d7;\n$fa-var-baby-carriage: \\f77d;\n$fa-var-carriage-baby: \\f77d;\n$fa-var-traffic-light: \\f637;\n$fa-var-thermometer: \\f491;\n$fa-var-vr-cardboard: \\f729;\n$fa-var-hand-middle-finger: \\f806;\n$fa-var-percent: \\25;\n$fa-var-percentage: \\25;\n$fa-var-truck-moving: \\f4df;\n$fa-var-glass-water-droplet: \\e4f5;\n$fa-var-display: \\e163;\n$fa-var-face-smile: \\f118;\n$fa-var-smile: \\f118;\n$fa-var-thumbtack: \\f08d;\n$fa-var-thumb-tack: \\f08d;\n$fa-var-trophy: \\f091;\n$fa-var-person-praying: \\f683;\n$fa-var-pray: \\f683;\n$fa-var-hammer: \\f6e3;\n$fa-var-hand-peace: \\f25b;\n$fa-var-rotate: \\f2f1;\n$fa-var-sync-alt: \\f2f1;\n$fa-var-spinner: \\f110;\n$fa-var-robot: \\f544;\n$fa-var-peace: \\f67c;\n$fa-var-gears: \\f085;\n$fa-var-cogs: \\f085;\n$fa-var-warehouse: \\f494;\n$fa-var-arrow-up-right-dots: \\e4b7;\n$fa-var-splotch: \\f5bc;\n$fa-var-face-grin-hearts: \\f584;\n$fa-var-grin-hearts: \\f584;\n$fa-var-dice-four: \\f524;\n$fa-var-sim-card: \\f7c4;\n$fa-var-transgender: \\f225;\n$fa-var-transgender-alt: \\f225;\n$fa-var-mercury: \\f223;\n$fa-var-arrow-turn-down: \\f149;\n$fa-var-level-down: \\f149;\n$fa-var-person-falling-burst: \\e547;\n$fa-var-award: \\f559;\n$fa-var-ticket-simple: \\f3ff;\n$fa-var-ticket-alt: \\f3ff;\n$fa-var-building: \\f1ad;\n$fa-var-angles-left: \\f100;\n$fa-var-angle-double-left: \\f100;\n$fa-var-qrcode: \\f029;\n$fa-var-clock-rotate-left: \\f1da;\n$fa-var-history: \\f1da;\n$fa-var-face-grin-beam-sweat: \\f583;\n$fa-var-grin-beam-sweat: \\f583;\n$fa-var-file-export: \\f56e;\n$fa-var-arrow-right-from-file: \\f56e;\n$fa-var-shield: 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\\f7cd;\n$fa-var-book: \\f02d;\n$fa-var-user-plus: \\f234;\n$fa-var-check: \\f00c;\n$fa-var-battery-three-quarters: \\f241;\n$fa-var-battery-4: \\f241;\n$fa-var-house-circle-check: \\e509;\n$fa-var-angle-left: \\f104;\n$fa-var-diagram-successor: \\e47a;\n$fa-var-truck-arrow-right: \\e58b;\n$fa-var-arrows-split-up-and-left: \\e4bc;\n$fa-var-hand-fist: \\f6de;\n$fa-var-fist-raised: \\f6de;\n$fa-var-cloud-moon: \\f6c3;\n$fa-var-briefcase: \\f0b1;\n$fa-var-person-falling: \\e546;\n$fa-var-image-portrait: \\f3e0;\n$fa-var-portrait: \\f3e0;\n$fa-var-user-tag: \\f507;\n$fa-var-rug: \\e569;\n$fa-var-earth-europe: \\f7a2;\n$fa-var-globe-europe: \\f7a2;\n$fa-var-cart-flatbed-suitcase: \\f59d;\n$fa-var-luggage-cart: \\f59d;\n$fa-var-rectangle-xmark: \\f410;\n$fa-var-rectangle-times: \\f410;\n$fa-var-times-rectangle: \\f410;\n$fa-var-window-close: \\f410;\n$fa-var-baht-sign: \\e0ac;\n$fa-var-book-open: \\f518;\n$fa-var-book-journal-whills: \\f66a;\n$fa-var-journal-whills: \\f66a;\n$fa-var-handcuffs: \\e4f8;\n$fa-var-triangle-exclamation: \\f071;\n$fa-var-exclamation-triangle: \\f071;\n$fa-var-warning: \\f071;\n$fa-var-database: \\f1c0;\n$fa-var-share: \\f064;\n$fa-var-mail-forward: \\f064;\n$fa-var-bottle-droplet: \\e4c4;\n$fa-var-mask-face: \\e1d7;\n$fa-var-hill-rockslide: \\e508;\n$fa-var-right-left: \\f362;\n$fa-var-exchange-alt: \\f362;\n$fa-var-paper-plane: \\f1d8;\n$fa-var-road-circle-exclamation: \\e565;\n$fa-var-dungeon: \\f6d9;\n$fa-var-align-right: \\f038;\n$fa-var-money-bill-1-wave: \\f53b;\n$fa-var-money-bill-wave-alt: \\f53b;\n$fa-var-life-ring: \\f1cd;\n$fa-var-hands: \\f2a7;\n$fa-var-sign-language: \\f2a7;\n$fa-var-signing: \\f2a7;\n$fa-var-calendar-day: \\f783;\n$fa-var-water-ladder: \\f5c5;\n$fa-var-ladder-water: \\f5c5;\n$fa-var-swimming-pool: \\f5c5;\n$fa-var-arrows-up-down: \\f07d;\n$fa-var-arrows-v: \\f07d;\n$fa-var-face-grimace: \\f57f;\n$fa-var-grimace: \\f57f;\n$fa-var-wheelchair-move: \\e2ce;\n$fa-var-wheelchair-alt: \\e2ce;\n$fa-var-turn-down: \\f3be;\n$fa-var-level-down-alt: \\f3be;\n$fa-var-person-walking-arrow-right: \\e552;\n$fa-var-square-envelope: \\f199;\n$fa-var-envelope-square: \\f199;\n$fa-var-dice: \\f522;\n$fa-var-bowling-ball: \\f436;\n$fa-var-brain: \\f5dc;\n$fa-var-bandage: \\f462;\n$fa-var-band-aid: \\f462;\n$fa-var-calendar-minus: \\f272;\n$fa-var-circle-xmark: \\f057;\n$fa-var-times-circle: \\f057;\n$fa-var-xmark-circle: \\f057;\n$fa-var-gifts: \\f79c;\n$fa-var-hotel: \\f594;\n$fa-var-earth-asia: \\f57e;\n$fa-var-globe-asia: \\f57e;\n$fa-var-id-card-clip: \\f47f;\n$fa-var-id-card-alt: \\f47f;\n$fa-var-magnifying-glass-plus: \\f00e;\n$fa-var-search-plus: \\f00e;\n$fa-var-thumbs-up: \\f164;\n$fa-var-user-clock: \\f4fd;\n$fa-var-hand-dots: \\f461;\n$fa-var-allergies: \\f461;\n$fa-var-file-invoice: \\f570;\n$fa-var-window-minimize: \\f2d1;\n$fa-var-mug-saucer: \\f0f4;\n$fa-var-coffee: \\f0f4;\n$fa-var-brush: \\f55d;\n$fa-var-mask: \\f6fa;\n$fa-var-magnifying-glass-minus: \\f010;\n$fa-var-search-minus: \\f010;\n$fa-var-ruler-vertical: \\f548;\n$fa-var-user-large: \\f406;\n$fa-var-user-alt: \\f406;\n$fa-var-train-tram: \\e5b4;\n$fa-var-user-nurse: \\f82f;\n$fa-var-syringe: \\f48e;\n$fa-var-cloud-sun: \\f6c4;\n$fa-var-stopwatch-20: \\e06f;\n$fa-var-square-full: \\f45c;\n$fa-var-magnet: \\f076;\n$fa-var-jar: \\e516;\n$fa-var-note-sticky: \\f249;\n$fa-var-sticky-note: \\f249;\n$fa-var-bug-slash: \\e490;\n$fa-var-arrow-up-from-water-pump: \\e4b6;\n$fa-var-bone: \\f5d7;\n$fa-var-user-injured: \\f728;\n$fa-var-face-sad-tear: \\f5b4;\n$fa-var-sad-tear: \\f5b4;\n$fa-var-plane: \\f072;\n$fa-var-tent-arrows-down: \\e581;\n$fa-var-exclamation: \\21;\n$fa-var-arrows-spin: \\e4bb;\n$fa-var-print: \\f02f;\n$fa-var-turkish-lira-sign: \\e2bb;\n$fa-var-try: \\e2bb;\n$fa-var-turkish-lira: \\e2bb;\n$fa-var-dollar-sign: \\24;\n$fa-var-dollar: \\24;\n$fa-var-usd: \\24;\n$fa-var-x: \\58;\n$fa-var-magnifying-glass-dollar: \\f688;\n$fa-var-search-dollar: \\f688;\n$fa-var-users-gear: \\f509;\n$fa-var-users-cog: \\f509;\n$fa-var-person-military-pointing: \\e54a;\n$fa-var-building-columns: \\f19c;\n$fa-var-bank: \\f19c;\n$fa-var-institution: \\f19c;\n$fa-var-museum: \\f19c;\n$fa-var-university: \\f19c;\n$fa-var-umbrella: \\f0e9;\n$fa-var-trowel: \\e589;\n$fa-var-d: \\44;\n$fa-var-stapler: \\e5af;\n$fa-var-masks-theater: \\f630;\n$fa-var-theater-masks: \\f630;\n$fa-var-kip-sign: \\e1c4;\n$fa-var-hand-point-left: \\f0a5;\n$fa-var-handshake-simple: \\f4c6;\n$fa-var-handshake-alt: \\f4c6;\n$fa-var-jet-fighter: \\f0fb;\n$fa-var-fighter-jet: \\f0fb;\n$fa-var-square-share-nodes: \\f1e1;\n$fa-var-share-alt-square: \\f1e1;\n$fa-var-barcode: \\f02a;\n$fa-var-plus-minus: \\e43c;\n$fa-var-video: \\f03d;\n$fa-var-video-camera: \\f03d;\n$fa-var-graduation-cap: \\f19d;\n$fa-var-mortar-board: \\f19d;\n$fa-var-hand-holding-medical: \\e05c;\n$fa-var-person-circle-check: \\e53e;\n$fa-var-turn-up: \\f3bf;\n$fa-var-level-up-alt: \\f3bf;\n\n$fa-var-monero: \\f3d0;\n$fa-var-hooli: \\f427;\n$fa-var-yelp: \\f1e9;\n$fa-var-cc-visa: \\f1f0;\n$fa-var-lastfm: \\f202;\n$fa-var-shopware: \\f5b5;\n$fa-var-creative-commons-nc: \\f4e8;\n$fa-var-aws: \\f375;\n$fa-var-redhat: \\f7bc;\n$fa-var-yoast: \\f2b1;\n$fa-var-cloudflare: \\e07d;\n$fa-var-ups: \\f7e0;\n$fa-var-pixiv: \\e640;\n$fa-var-wpexplorer: \\f2de;\n$fa-var-dyalog: \\f399;\n$fa-var-bity: \\f37a;\n$fa-var-stackpath: \\f842;\n$fa-var-buysellads: \\f20d;\n$fa-var-first-order: \\f2b0;\n$fa-var-modx: \\f285;\n$fa-var-guilded: \\e07e;\n$fa-var-vnv: \\f40b;\n$fa-var-square-js: \\f3b9;\n$fa-var-js-square: \\f3b9;\n$fa-var-microsoft: \\f3ca;\n$fa-var-qq: \\f1d6;\n$fa-var-orcid: \\f8d2;\n$fa-var-java: \\f4e4;\n$fa-var-invision: \\f7b0;\n$fa-var-creative-commons-pd-alt: \\f4ed;\n$fa-var-centercode: \\f380;\n$fa-var-glide-g: \\f2a6;\n$fa-var-drupal: \\f1a9;\n$fa-var-jxl: \\e67b;\n$fa-var-hire-a-helper: \\f3b0;\n$fa-var-creative-commons-by: \\f4e7;\n$fa-var-unity: \\e049;\n$fa-var-whmcs: \\f40d;\n$fa-var-rocketchat: \\f3e8;\n$fa-var-vk: \\f189;\n$fa-var-untappd: \\f405;\n$fa-var-mailchimp: \\f59e;\n$fa-var-css3-alt: \\f38b;\n$fa-var-square-reddit: \\f1a2;\n$fa-var-reddit-square: \\f1a2;\n$fa-var-vimeo-v: \\f27d;\n$fa-var-contao: \\f26d;\n$fa-var-square-font-awesome: \\e5ad;\n$fa-var-deskpro: \\f38f;\n$fa-var-brave: \\e63c;\n$fa-var-sistrix: \\f3ee;\n$fa-var-square-instagram: \\e055;\n$fa-var-instagram-square: \\e055;\n$fa-var-battle-net: \\f835;\n$fa-var-the-red-yeti: \\f69d;\n$fa-var-square-hacker-news: \\f3af;\n$fa-var-hacker-news-square: \\f3af;\n$fa-var-edge: \\f282;\n$fa-var-threads: \\e618;\n$fa-var-napster: \\f3d2;\n$fa-var-square-snapchat: \\f2ad;\n$fa-var-snapchat-square: \\f2ad;\n$fa-var-google-plus-g: \\f0d5;\n$fa-var-artstation: \\f77a;\n$fa-var-markdown: \\f60f;\n$fa-var-sourcetree: \\f7d3;\n$fa-var-google-plus: \\f2b3;\n$fa-var-diaspora: \\f791;\n$fa-var-foursquare: \\f180;\n$fa-var-stack-overflow: \\f16c;\n$fa-var-github-alt: \\f113;\n$fa-var-phoenix-squadron: \\f511;\n$fa-var-pagelines: \\f18c;\n$fa-var-algolia: \\f36c;\n$fa-var-red-river: \\f3e3;\n$fa-var-creative-commons-sa: \\f4ef;\n$fa-var-safari: \\f267;\n$fa-var-google: \\f1a0;\n$fa-var-square-font-awesome-stroke: \\f35c;\n$fa-var-font-awesome-alt: \\f35c;\n$fa-var-atlassian: \\f77b;\n$fa-var-linkedin-in: \\f0e1;\n$fa-var-digital-ocean: \\f391;\n$fa-var-nimblr: \\f5a8;\n$fa-var-chromecast: \\f838;\n$fa-var-evernote: \\f839;\n$fa-var-hacker-news: \\f1d4;\n$fa-var-creative-commons-sampling: \\f4f0;\n$fa-var-adversal: \\f36a;\n$fa-var-creative-commons: \\f25e;\n$fa-var-watchman-monitoring: \\e087;\n$fa-var-fonticons: \\f280;\n$fa-var-weixin: \\f1d7;\n$fa-var-shirtsinbulk: \\f214;\n$fa-var-codepen: \\f1cb;\n$fa-var-git-alt: \\f841;\n$fa-var-lyft: \\f3c3;\n$fa-var-rev: \\f5b2;\n$fa-var-windows: \\f17a;\n$fa-var-wizards-of-the-coast: \\f730;\n$fa-var-square-viadeo: \\f2aa;\n$fa-var-viadeo-square: \\f2aa;\n$fa-var-meetup: \\f2e0;\n$fa-var-centos: \\f789;\n$fa-var-adn: \\f170;\n$fa-var-cloudsmith: \\f384;\n$fa-var-opensuse: \\e62b;\n$fa-var-pied-piper-alt: \\f1a8;\n$fa-var-square-dribbble: \\f397;\n$fa-var-dribbble-square: \\f397;\n$fa-var-codiepie: \\f284;\n$fa-var-node: \\f419;\n$fa-var-mix: \\f3cb;\n$fa-var-steam: \\f1b6;\n$fa-var-cc-apple-pay: \\f416;\n$fa-var-scribd: \\f28a;\n$fa-var-debian: \\e60b;\n$fa-var-openid: \\f19b;\n$fa-var-instalod: \\e081;\n$fa-var-expeditedssl: \\f23e;\n$fa-var-sellcast: \\f2da;\n$fa-var-square-twitter: \\f081;\n$fa-var-twitter-square: \\f081;\n$fa-var-r-project: \\f4f7;\n$fa-var-delicious: \\f1a5;\n$fa-var-freebsd: \\f3a4;\n$fa-var-vuejs: \\f41f;\n$fa-var-accusoft: \\f369;\n$fa-var-ioxhost: \\f208;\n$fa-var-fonticons-fi: \\f3a2;\n$fa-var-app-store: \\f36f;\n$fa-var-cc-mastercard: \\f1f1;\n$fa-var-itunes-note: \\f3b5;\n$fa-var-golang: \\e40f;\n$fa-var-kickstarter: \\f3bb;\n$fa-var-square-kickstarter: \\f3bb;\n$fa-var-grav: \\f2d6;\n$fa-var-weibo: \\f18a;\n$fa-var-uncharted: \\e084;\n$fa-var-firstdraft: \\f3a1;\n$fa-var-square-youtube: \\f431;\n$fa-var-youtube-square: \\f431;\n$fa-var-wikipedia-w: \\f266;\n$fa-var-wpressr: \\f3e4;\n$fa-var-rendact: \\f3e4;\n$fa-var-angellist: \\f209;\n$fa-var-galactic-republic: \\f50c;\n$fa-var-nfc-directional: \\e530;\n$fa-var-skype: \\f17e;\n$fa-var-joget: \\f3b7;\n$fa-var-fedora: \\f798;\n$fa-var-stripe-s: \\f42a;\n$fa-var-meta: \\e49b;\n$fa-var-laravel: \\f3bd;\n$fa-var-hotjar: \\f3b1;\n$fa-var-bluetooth-b: \\f294;\n$fa-var-square-letterboxd: \\e62e;\n$fa-var-sticker-mule: \\f3f7;\n$fa-var-creative-commons-zero: \\f4f3;\n$fa-var-hips: \\f452;\n$fa-var-behance: \\f1b4;\n$fa-var-reddit: \\f1a1;\n$fa-var-discord: \\f392;\n$fa-var-chrome: \\f268;\n$fa-var-app-store-ios: \\f370;\n$fa-var-cc-discover: \\f1f2;\n$fa-var-wpbeginner: \\f297;\n$fa-var-confluence: \\f78d;\n$fa-var-shoelace: \\e60c;\n$fa-var-mdb: \\f8ca;\n$fa-var-dochub: \\f394;\n$fa-var-accessible-icon: \\f368;\n$fa-var-ebay: \\f4f4;\n$fa-var-amazon: \\f270;\n$fa-var-unsplash: \\e07c;\n$fa-var-yarn: \\f7e3;\n$fa-var-square-steam: \\f1b7;\n$fa-var-steam-square: \\f1b7;\n$fa-var-500px: \\f26e;\n$fa-var-square-vimeo: \\f194;\n$fa-var-vimeo-square: \\f194;\n$fa-var-asymmetrik: \\f372;\n$fa-var-font-awesome: \\f2b4;\n$fa-var-font-awesome-flag: \\f2b4;\n$fa-var-font-awesome-logo-full: \\f2b4;\n$fa-var-gratipay: \\f184;\n$fa-var-apple: \\f179;\n$fa-var-hive: \\e07f;\n$fa-var-gitkraken: \\f3a6;\n$fa-var-keybase: \\f4f5;\n$fa-var-apple-pay: \\f415;\n$fa-var-padlet: \\e4a0;\n$fa-var-amazon-pay: \\f42c;\n$fa-var-square-github: \\f092;\n$fa-var-github-square: \\f092;\n$fa-var-stumbleupon: \\f1a4;\n$fa-var-fedex: \\f797;\n$fa-var-phoenix-framework: \\f3dc;\n$fa-var-shopify: \\e057;\n$fa-var-neos: \\f612;\n$fa-var-square-threads: \\e619;\n$fa-var-hackerrank: \\f5f7;\n$fa-var-researchgate: \\f4f8;\n$fa-var-swift: \\f8e1;\n$fa-var-angular: \\f420;\n$fa-var-speakap: \\f3f3;\n$fa-var-angrycreative: \\f36e;\n$fa-var-y-combinator: \\f23b;\n$fa-var-empire: \\f1d1;\n$fa-var-envira: \\f299;\n$fa-var-google-scholar: \\e63b;\n$fa-var-square-gitlab: \\e5ae;\n$fa-var-gitlab-square: \\e5ae;\n$fa-var-studiovinari: \\f3f8;\n$fa-var-pied-piper: \\f2ae;\n$fa-var-wordpress: \\f19a;\n$fa-var-product-hunt: \\f288;\n$fa-var-firefox: \\f269;\n$fa-var-linode: \\f2b8;\n$fa-var-goodreads: \\f3a8;\n$fa-var-square-odnoklassniki: \\f264;\n$fa-var-odnoklassniki-square: \\f264;\n$fa-var-jsfiddle: \\f1cc;\n$fa-var-sith: \\f512;\n$fa-var-themeisle: \\f2b2;\n$fa-var-page4: \\f3d7;\n$fa-var-hashnode: \\e499;\n$fa-var-react: \\f41b;\n$fa-var-cc-paypal: \\f1f4;\n$fa-var-squarespace: \\f5be;\n$fa-var-cc-stripe: \\f1f5;\n$fa-var-creative-commons-share: \\f4f2;\n$fa-var-bitcoin: \\f379;\n$fa-var-keycdn: \\f3ba;\n$fa-var-opera: \\f26a;\n$fa-var-itch-io: \\f83a;\n$fa-var-umbraco: \\f8e8;\n$fa-var-galactic-senate: \\f50d;\n$fa-var-ubuntu: \\f7df;\n$fa-var-draft2digital: \\f396;\n$fa-var-stripe: \\f429;\n$fa-var-houzz: \\f27c;\n$fa-var-gg: \\f260;\n$fa-var-dhl: \\f790;\n$fa-var-square-pinterest: \\f0d3;\n$fa-var-pinterest-square: \\f0d3;\n$fa-var-xing: \\f168;\n$fa-var-blackberry: \\f37b;\n$fa-var-creative-commons-pd: \\f4ec;\n$fa-var-playstation: \\f3df;\n$fa-var-quinscape: \\f459;\n$fa-var-less: \\f41d;\n$fa-var-blogger-b: \\f37d;\n$fa-var-opencart: \\f23d;\n$fa-var-vine: \\f1ca;\n$fa-var-signal-messenger: \\e663;\n$fa-var-paypal: \\f1ed;\n$fa-var-gitlab: \\f296;\n$fa-var-typo3: \\f42b;\n$fa-var-reddit-alien: \\f281;\n$fa-var-yahoo: \\f19e;\n$fa-var-dailymotion: \\e052;\n$fa-var-affiliatetheme: \\f36b;\n$fa-var-pied-piper-pp: \\f1a7;\n$fa-var-bootstrap: \\f836;\n$fa-var-odnoklassniki: \\f263;\n$fa-var-nfc-symbol: \\e531;\n$fa-var-mintbit: \\e62f;\n$fa-var-ethereum: \\f42e;\n$fa-var-speaker-deck: \\f83c;\n$fa-var-creative-commons-nc-eu: \\f4e9;\n$fa-var-patreon: \\f3d9;\n$fa-var-avianex: \\f374;\n$fa-var-ello: \\f5f1;\n$fa-var-gofore: \\f3a7;\n$fa-var-bimobject: \\f378;\n$fa-var-brave-reverse: \\e63d;\n$fa-var-facebook-f: \\f39e;\n$fa-var-square-google-plus: \\f0d4;\n$fa-var-google-plus-square: \\f0d4;\n$fa-var-web-awesome: \\e682;\n$fa-var-mandalorian: \\f50f;\n$fa-var-first-order-alt: \\f50a;\n$fa-var-osi: \\f41a;\n$fa-var-google-wallet: \\f1ee;\n$fa-var-d-and-d-beyond: \\f6ca;\n$fa-var-periscope: \\f3da;\n$fa-var-fulcrum: \\f50b;\n$fa-var-cloudscale: \\f383;\n$fa-var-forumbee: \\f211;\n$fa-var-mizuni: \\f3cc;\n$fa-var-schlix: \\f3ea;\n$fa-var-square-xing: \\f169;\n$fa-var-xing-square: \\f169;\n$fa-var-bandcamp: \\f2d5;\n$fa-var-wpforms: \\f298;\n$fa-var-cloudversify: \\f385;\n$fa-var-usps: \\f7e1;\n$fa-var-megaport: \\f5a3;\n$fa-var-magento: \\f3c4;\n$fa-var-spotify: \\f1bc;\n$fa-var-optin-monster: \\f23c;\n$fa-var-fly: \\f417;\n$fa-var-aviato: \\f421;\n$fa-var-itunes: \\f3b4;\n$fa-var-cuttlefish: \\f38c;\n$fa-var-blogger: \\f37c;\n$fa-var-flickr: \\f16e;\n$fa-var-viber: \\f409;\n$fa-var-soundcloud: \\f1be;\n$fa-var-digg: \\f1a6;\n$fa-var-tencent-weibo: \\f1d5;\n$fa-var-letterboxd: \\e62d;\n$fa-var-symfony: \\f83d;\n$fa-var-maxcdn: \\f136;\n$fa-var-etsy: \\f2d7;\n$fa-var-facebook-messenger: \\f39f;\n$fa-var-audible: \\f373;\n$fa-var-think-peaks: \\f731;\n$fa-var-bilibili: \\e3d9;\n$fa-var-erlang: \\f39d;\n$fa-var-x-twitter: \\e61b;\n$fa-var-cotton-bureau: \\f89e;\n$fa-var-dashcube: \\f210;\n$fa-var-42-group: \\e080;\n$fa-var-innosoft: \\e080;\n$fa-var-stack-exchange: \\f18d;\n$fa-var-elementor: \\f430;\n$fa-var-square-pied-piper: \\e01e;\n$fa-var-pied-piper-square: \\e01e;\n$fa-var-creative-commons-nd: \\f4eb;\n$fa-var-palfed: \\f3d8;\n$fa-var-superpowers: \\f2dd;\n$fa-var-resolving: \\f3e7;\n$fa-var-xbox: \\f412;\n$fa-var-square-web-awesome-stroke: \\e684;\n$fa-var-searchengin: \\f3eb;\n$fa-var-tiktok: \\e07b;\n$fa-var-square-facebook: \\f082;\n$fa-var-facebook-square: \\f082;\n$fa-var-renren: \\f18b;\n$fa-var-linux: \\f17c;\n$fa-var-glide: \\f2a5;\n$fa-var-linkedin: \\f08c;\n$fa-var-hubspot: \\f3b2;\n$fa-var-deploydog: \\f38e;\n$fa-var-twitch: \\f1e8;\n$fa-var-ravelry: \\f2d9;\n$fa-var-mixer: \\e056;\n$fa-var-square-lastfm: \\f203;\n$fa-var-lastfm-square: \\f203;\n$fa-var-vimeo: \\f40a;\n$fa-var-mendeley: \\f7b3;\n$fa-var-uniregistry: \\f404;\n$fa-var-figma: \\f799;\n$fa-var-creative-commons-remix: \\f4ee;\n$fa-var-cc-amazon-pay: \\f42d;\n$fa-var-dropbox: \\f16b;\n$fa-var-instagram: \\f16d;\n$fa-var-cmplid: \\e360;\n$fa-var-upwork: \\e641;\n$fa-var-facebook: \\f09a;\n$fa-var-gripfire: \\f3ac;\n$fa-var-jedi-order: \\f50e;\n$fa-var-uikit: \\f403;\n$fa-var-fort-awesome-alt: \\f3a3;\n$fa-var-phabricator: \\f3db;\n$fa-var-ussunnah: \\f407;\n$fa-var-earlybirds: \\f39a;\n$fa-var-trade-federation: \\f513;\n$fa-var-autoprefixer: \\f41c;\n$fa-var-whatsapp: \\f232;\n$fa-var-square-upwork: \\e67c;\n$fa-var-slideshare: \\f1e7;\n$fa-var-google-play: \\f3ab;\n$fa-var-viadeo: \\f2a9;\n$fa-var-line: \\f3c0;\n$fa-var-google-drive: \\f3aa;\n$fa-var-servicestack: \\f3ec;\n$fa-var-simplybuilt: \\f215;\n$fa-var-bitbucket: \\f171;\n$fa-var-imdb: \\f2d8;\n$fa-var-deezer: \\e077;\n$fa-var-raspberry-pi: \\f7bb;\n$fa-var-jira: \\f7b1;\n$fa-var-docker: \\f395;\n$fa-var-screenpal: \\e570;\n$fa-var-bluetooth: \\f293;\n$fa-var-gitter: \\f426;\n$fa-var-d-and-d: \\f38d;\n$fa-var-microblog: \\e01a;\n$fa-var-cc-diners-club: \\f24c;\n$fa-var-gg-circle: \\f261;\n$fa-var-pied-piper-hat: \\f4e5;\n$fa-var-kickstarter-k: \\f3bc;\n$fa-var-yandex: \\f413;\n$fa-var-readme: \\f4d5;\n$fa-var-html5: \\f13b;\n$fa-var-sellsy: \\f213;\n$fa-var-square-web-awesome: \\e683;\n$fa-var-sass: \\f41e;\n$fa-var-wirsindhandwerk: \\e2d0;\n$fa-var-wsh: \\e2d0;\n$fa-var-buromobelexperte: \\f37f;\n$fa-var-salesforce: \\f83b;\n$fa-var-octopus-deploy: \\e082;\n$fa-var-medapps: \\f3c6;\n$fa-var-ns8: \\f3d5;\n$fa-var-pinterest-p: \\f231;\n$fa-var-apper: \\f371;\n$fa-var-fort-awesome: \\f286;\n$fa-var-waze: \\f83f;\n$fa-var-bluesky: \\e671;\n$fa-var-cc-jcb: \\f24b;\n$fa-var-snapchat: \\f2ab;\n$fa-var-snapchat-ghost: \\f2ab;\n$fa-var-fantasy-flight-games: \\f6dc;\n$fa-var-rust: \\e07a;\n$fa-var-wix: \\f5cf;\n$fa-var-square-behance: \\f1b5;\n$fa-var-behance-square: \\f1b5;\n$fa-var-supple: \\f3f9;\n$fa-var-webflow: \\e65c;\n$fa-var-rebel: \\f1d0;\n$fa-var-css3: \\f13c;\n$fa-var-staylinked: \\f3f5;\n$fa-var-kaggle: \\f5fa;\n$fa-var-space-awesome: \\e5ac;\n$fa-var-deviantart: \\f1bd;\n$fa-var-cpanel: \\f388;\n$fa-var-goodreads-g: \\f3a9;\n$fa-var-square-git: \\f1d2;\n$fa-var-git-square: \\f1d2;\n$fa-var-square-tumblr: \\f174;\n$fa-var-tumblr-square: \\f174;\n$fa-var-trello: \\f181;\n$fa-var-creative-commons-nc-jp: \\f4ea;\n$fa-var-get-pocket: \\f265;\n$fa-var-perbyte: \\e083;\n$fa-var-grunt: \\f3ad;\n$fa-var-weebly: \\f5cc;\n$fa-var-connectdevelop: \\f20e;\n$fa-var-leanpub: \\f212;\n$fa-var-black-tie: \\f27e;\n$fa-var-themeco: \\f5c6;\n$fa-var-python: \\f3e2;\n$fa-var-android: \\f17b;\n$fa-var-bots: \\e340;\n$fa-var-free-code-camp: \\f2c5;\n$fa-var-hornbill: \\f592;\n$fa-var-js: \\f3b8;\n$fa-var-ideal: \\e013;\n$fa-var-git: \\f1d3;\n$fa-var-dev: \\f6cc;\n$fa-var-sketch: \\f7c6;\n$fa-var-yandex-international: \\f414;\n$fa-var-cc-amex: \\f1f3;\n$fa-var-uber: \\f402;\n$fa-var-github: \\f09b;\n$fa-var-php: \\f457;\n$fa-var-alipay: \\f642;\n$fa-var-youtube: \\f167;\n$fa-var-skyatlas: \\f216;\n$fa-var-firefox-browser: \\e007;\n$fa-var-replyd: \\f3e6;\n$fa-var-suse: \\f7d6;\n$fa-var-jenkins: \\f3b6;\n$fa-var-twitter: \\f099;\n$fa-var-rockrms: \\f3e9;\n$fa-var-pinterest: \\f0d2;\n$fa-var-buffer: \\f837;\n$fa-var-npm: \\f3d4;\n$fa-var-yammer: \\f840;\n$fa-var-btc: \\f15a;\n$fa-var-dribbble: \\f17d;\n$fa-var-stumbleupon-circle: \\f1a3;\n$fa-var-internet-explorer: \\f26b;\n$fa-var-stubber: \\e5c7;\n$fa-var-telegram: \\f2c6;\n$fa-var-telegram-plane: \\f2c6;\n$fa-var-old-republic: \\f510;\n$fa-var-odysee: \\e5c6;\n$fa-var-square-whatsapp: \\f40c;\n$fa-var-whatsapp-square: \\f40c;\n$fa-var-node-js: \\f3d3;\n$fa-var-edge-legacy: \\e078;\n$fa-var-slack: \\f198;\n$fa-var-slack-hash: \\f198;\n$fa-var-medrt: \\f3c8;\n$fa-var-usb: \\f287;\n$fa-var-tumblr: \\f173;\n$fa-var-vaadin: \\f408;\n$fa-var-quora: \\f2c4;\n$fa-var-square-x-twitter: \\e61a;\n$fa-var-reacteurope: \\f75d;\n$fa-var-medium: \\f23a;\n$fa-var-medium-m: \\f23a;\n$fa-var-amilia: \\f36d;\n$fa-var-mixcloud: \\f289;\n$fa-var-flipboard: \\f44d;\n$fa-var-viacoin: \\f237;\n$fa-var-critical-role: \\f6c9;\n$fa-var-sitrox: \\e44a;\n$fa-var-discourse: \\f393;\n$fa-var-joomla: \\f1aa;\n$fa-var-mastodon: \\f4f6;\n$fa-var-airbnb: \\f834;\n$fa-var-wolf-pack-battalion: \\f514;\n$fa-var-buy-n-large: \\f8a6;\n$fa-var-gulp: \\f3ae;\n$fa-var-creative-commons-sampling-plus: \\f4f1;\n$fa-var-strava: \\f428;\n$fa-var-ember: \\f423;\n$fa-var-canadian-maple-leaf: \\f785;\n$fa-var-teamspeak: \\f4f9;\n$fa-var-pushed: \\f3e1;\n$fa-var-wordpress-simple: \\f411;\n$fa-var-nutritionix: \\f3d6;\n$fa-var-wodu: \\e088;\n$fa-var-google-pay: \\e079;\n$fa-var-intercom: \\f7af;\n$fa-var-zhihu: \\f63f;\n$fa-var-korvue: \\f42f;\n$fa-var-pix: \\e43a;\n$fa-var-steam-symbol: \\f3f6;\n\n$fa-icons: (\n  \"0\": $fa-var-0,\n  \"1\": $fa-var-1,\n  \"2\": $fa-var-2,\n  \"3\": $fa-var-3,\n  \"4\": $fa-var-4,\n  \"5\": $fa-var-5,\n  \"6\": $fa-var-6,\n  \"7\": $fa-var-7,\n  \"8\": $fa-var-8,\n  \"9\": $fa-var-9,\n  \"fill-drip\": $fa-var-fill-drip,\n  \"arrows-to-circle\": $fa-var-arrows-to-circle,\n  \"circle-chevron-right\": $fa-var-circle-chevron-right,\n  \"chevron-circle-right\": $fa-var-chevron-circle-right,\n  \"at\": $fa-var-at,\n  \"trash-can\": $fa-var-trash-can,\n  \"trash-alt\": $fa-var-trash-alt,\n  \"text-height\": $fa-var-text-height,\n  \"user-xmark\": $fa-var-user-xmark,\n  \"user-times\": $fa-var-user-times,\n  \"stethoscope\": $fa-var-stethoscope,\n  \"message\": $fa-var-message,\n  \"comment-alt\": $fa-var-comment-alt,\n  \"info\": $fa-var-info,\n  \"down-left-and-up-right-to-center\": $fa-var-down-left-and-up-right-to-center,\n  \"compress-alt\": $fa-var-compress-alt,\n  \"explosion\": $fa-var-explosion,\n  \"file-lines\": $fa-var-file-lines,\n  \"file-alt\": $fa-var-file-alt,\n  \"file-text\": $fa-var-file-text,\n  \"wave-square\": $fa-var-wave-square,\n  \"ring\": $fa-var-ring,\n  \"building-un\": $fa-var-building-un,\n  \"dice-three\": $fa-var-dice-three,\n  \"calendar-days\": $fa-var-calendar-days,\n  \"calendar-alt\": $fa-var-calendar-alt,\n  \"anchor-circle-check\": $fa-var-anchor-circle-check,\n  \"building-circle-arrow-right\": $fa-var-building-circle-arrow-right,\n  \"volleyball\": $fa-var-volleyball,\n  \"volleyball-ball\": $fa-var-volleyball-ball,\n  \"arrows-up-to-line\": $fa-var-arrows-up-to-line,\n  \"sort-down\": $fa-var-sort-down,\n  \"sort-desc\": $fa-var-sort-desc,\n  \"circle-minus\": $fa-var-circle-minus,\n  \"minus-circle\": $fa-var-minus-circle,\n  \"door-open\": $fa-var-door-open,\n  \"right-from-bracket\": $fa-var-right-from-bracket,\n  \"sign-out-alt\": $fa-var-sign-out-alt,\n  \"atom\": $fa-var-atom,\n  \"soap\": $fa-var-soap,\n  \"icons\": $fa-var-icons,\n  \"heart-music-camera-bolt\": $fa-var-heart-music-camera-bolt,\n  \"microphone-lines-slash\": $fa-var-microphone-lines-slash,\n  \"microphone-alt-slash\": $fa-var-microphone-alt-slash,\n  \"bridge-circle-check\": $fa-var-bridge-circle-check,\n  \"pump-medical\": $fa-var-pump-medical,\n  \"fingerprint\": $fa-var-fingerprint,\n  \"hand-point-right\": $fa-var-hand-point-right,\n  \"magnifying-glass-location\": $fa-var-magnifying-glass-location,\n  \"search-location\": $fa-var-search-location,\n  \"forward-step\": $fa-var-forward-step,\n  \"step-forward\": $fa-var-step-forward,\n  \"face-smile-beam\": $fa-var-face-smile-beam,\n  \"smile-beam\": $fa-var-smile-beam,\n  \"flag-checkered\": $fa-var-flag-checkered,\n  \"football\": $fa-var-football,\n  \"football-ball\": $fa-var-football-ball,\n  \"school-circle-exclamation\": $fa-var-school-circle-exclamation,\n  \"crop\": $fa-var-crop,\n  \"angles-down\": $fa-var-angles-down,\n  \"angle-double-down\": $fa-var-angle-double-down,\n  \"users-rectangle\": $fa-var-users-rectangle,\n  \"people-roof\": $fa-var-people-roof,\n  \"people-line\": $fa-var-people-line,\n  \"beer-mug-empty\": $fa-var-beer-mug-empty,\n  \"beer\": $fa-var-beer,\n  \"diagram-predecessor\": $fa-var-diagram-predecessor,\n  \"arrow-up-long\": $fa-var-arrow-up-long,\n  \"long-arrow-up\": $fa-var-long-arrow-up,\n  \"fire-flame-simple\": $fa-var-fire-flame-simple,\n  \"burn\": $fa-var-burn,\n  \"person\": $fa-var-person,\n  \"male\": $fa-var-male,\n  \"laptop\": $fa-var-laptop,\n  \"file-csv\": $fa-var-file-csv,\n  \"menorah\": $fa-var-menorah,\n  \"truck-plane\": $fa-var-truck-plane,\n  \"record-vinyl\": $fa-var-record-vinyl,\n  \"face-grin-stars\": $fa-var-face-grin-stars,\n  \"grin-stars\": $fa-var-grin-stars,\n  \"bong\": $fa-var-bong,\n  \"spaghetti-monster-flying\": $fa-var-spaghetti-monster-flying,\n  \"pastafarianism\": $fa-var-pastafarianism,\n  \"arrow-down-up-across-line\": $fa-var-arrow-down-up-across-line,\n  \"spoon\": $fa-var-spoon,\n  \"utensil-spoon\": $fa-var-utensil-spoon,\n  \"jar-wheat\": $fa-var-jar-wheat,\n  \"envelopes-bulk\": $fa-var-envelopes-bulk,\n  \"mail-bulk\": $fa-var-mail-bulk,\n  \"file-circle-exclamation\": $fa-var-file-circle-exclamation,\n  \"circle-h\": $fa-var-circle-h,\n  \"hospital-symbol\": $fa-var-hospital-symbol,\n  \"pager\": $fa-var-pager,\n  \"address-book\": $fa-var-address-book,\n  \"contact-book\": $fa-var-contact-book,\n  \"strikethrough\": $fa-var-strikethrough,\n  \"k\": $fa-var-k,\n  \"landmark-flag\": $fa-var-landmark-flag,\n  \"pencil\": $fa-var-pencil,\n  \"pencil-alt\": $fa-var-pencil-alt,\n  \"backward\": $fa-var-backward,\n  \"caret-right\": $fa-var-caret-right,\n  \"comments\": $fa-var-comments,\n  \"paste\": $fa-var-paste,\n  \"file-clipboard\": $fa-var-file-clipboard,\n  \"code-pull-request\": $fa-var-code-pull-request,\n  \"clipboard-list\": $fa-var-clipboard-list,\n  \"truck-ramp-box\": $fa-var-truck-ramp-box,\n  \"truck-loading\": $fa-var-truck-loading,\n  \"user-check\": $fa-var-user-check,\n  \"vial-virus\": $fa-var-vial-virus,\n  \"sheet-plastic\": $fa-var-sheet-plastic,\n  \"blog\": $fa-var-blog,\n  \"user-ninja\": $fa-var-user-ninja,\n  \"person-arrow-up-from-line\": $fa-var-person-arrow-up-from-line,\n  \"scroll-torah\": $fa-var-scroll-torah,\n  \"torah\": $fa-var-torah,\n  \"broom-ball\": $fa-var-broom-ball,\n  \"quidditch\": $fa-var-quidditch,\n  \"quidditch-broom-ball\": $fa-var-quidditch-broom-ball,\n  \"toggle-off\": $fa-var-toggle-off,\n  \"box-archive\": $fa-var-box-archive,\n  \"archive\": $fa-var-archive,\n  \"person-drowning\": $fa-var-person-drowning,\n  \"arrow-down-9-1\": $fa-var-arrow-down-9-1,\n  \"sort-numeric-desc\": $fa-var-sort-numeric-desc,\n  \"sort-numeric-down-alt\": $fa-var-sort-numeric-down-alt,\n  \"face-grin-tongue-squint\": $fa-var-face-grin-tongue-squint,\n  \"grin-tongue-squint\": $fa-var-grin-tongue-squint,\n  \"spray-can\": $fa-var-spray-can,\n  \"truck-monster\": $fa-var-truck-monster,\n  \"w\": $fa-var-w,\n  \"earth-africa\": $fa-var-earth-africa,\n  \"globe-africa\": $fa-var-globe-africa,\n  \"rainbow\": $fa-var-rainbow,\n  \"circle-notch\": $fa-var-circle-notch,\n  \"tablet-screen-button\": $fa-var-tablet-screen-button,\n  \"tablet-alt\": $fa-var-tablet-alt,\n  \"paw\": $fa-var-paw,\n  \"cloud\": $fa-var-cloud,\n  \"trowel-bricks\": $fa-var-trowel-bricks,\n  \"face-flushed\": $fa-var-face-flushed,\n  \"flushed\": $fa-var-flushed,\n  \"hospital-user\": $fa-var-hospital-user,\n  \"tent-arrow-left-right\": $fa-var-tent-arrow-left-right,\n  \"gavel\": $fa-var-gavel,\n  \"legal\": $fa-var-legal,\n  \"binoculars\": $fa-var-binoculars,\n  \"microphone-slash\": $fa-var-microphone-slash,\n  \"box-tissue\": $fa-var-box-tissue,\n  \"motorcycle\": $fa-var-motorcycle,\n  \"bell-concierge\": $fa-var-bell-concierge,\n  \"concierge-bell\": $fa-var-concierge-bell,\n  \"pen-ruler\": $fa-var-pen-ruler,\n  \"pencil-ruler\": $fa-var-pencil-ruler,\n  \"people-arrows\": $fa-var-people-arrows,\n  \"people-arrows-left-right\": $fa-var-people-arrows-left-right,\n  \"mars-and-venus-burst\": $fa-var-mars-and-venus-burst,\n  \"square-caret-right\": $fa-var-square-caret-right,\n  \"caret-square-right\": $fa-var-caret-square-right,\n  \"scissors\": $fa-var-scissors,\n  \"cut\": $fa-var-cut,\n  \"sun-plant-wilt\": $fa-var-sun-plant-wilt,\n  \"toilets-portable\": $fa-var-toilets-portable,\n  \"hockey-puck\": $fa-var-hockey-puck,\n  \"table\": $fa-var-table,\n  \"magnifying-glass-arrow-right\": $fa-var-magnifying-glass-arrow-right,\n  \"tachograph-digital\": $fa-var-tachograph-digital,\n  \"digital-tachograph\": $fa-var-digital-tachograph,\n  \"users-slash\": $fa-var-users-slash,\n  \"clover\": $fa-var-clover,\n  \"reply\": $fa-var-reply,\n  \"mail-reply\": $fa-var-mail-reply,\n  \"star-and-crescent\": $fa-var-star-and-crescent,\n  \"house-fire\": $fa-var-house-fire,\n  \"square-minus\": $fa-var-square-minus,\n  \"minus-square\": $fa-var-minus-square,\n  \"helicopter\": $fa-var-helicopter,\n  \"compass\": $fa-var-compass,\n  \"square-caret-down\": $fa-var-square-caret-down,\n  \"caret-square-down\": $fa-var-caret-square-down,\n  \"file-circle-question\": $fa-var-file-circle-question,\n  \"laptop-code\": $fa-var-laptop-code,\n  \"swatchbook\": $fa-var-swatchbook,\n  \"prescription-bottle\": $fa-var-prescription-bottle,\n  \"bars\": $fa-var-bars,\n  \"navicon\": $fa-var-navicon,\n  \"people-group\": $fa-var-people-group,\n  \"hourglass-end\": $fa-var-hourglass-end,\n  \"hourglass-3\": $fa-var-hourglass-3,\n  \"heart-crack\": $fa-var-heart-crack,\n  \"heart-broken\": $fa-var-heart-broken,\n  \"square-up-right\": $fa-var-square-up-right,\n  \"external-link-square-alt\": $fa-var-external-link-square-alt,\n  \"face-kiss-beam\": $fa-var-face-kiss-beam,\n  \"kiss-beam\": $fa-var-kiss-beam,\n  \"film\": $fa-var-film,\n  \"ruler-horizontal\": $fa-var-ruler-horizontal,\n  \"people-robbery\": $fa-var-people-robbery,\n  \"lightbulb\": $fa-var-lightbulb,\n  \"caret-left\": $fa-var-caret-left,\n  \"circle-exclamation\": $fa-var-circle-exclamation,\n  \"exclamation-circle\": $fa-var-exclamation-circle,\n  \"school-circle-xmark\": $fa-var-school-circle-xmark,\n  \"arrow-right-from-bracket\": $fa-var-arrow-right-from-bracket,\n  \"sign-out\": $fa-var-sign-out,\n  \"circle-chevron-down\": $fa-var-circle-chevron-down,\n  \"chevron-circle-down\": $fa-var-chevron-circle-down,\n  \"unlock-keyhole\": $fa-var-unlock-keyhole,\n  \"unlock-alt\": $fa-var-unlock-alt,\n  \"cloud-showers-heavy\": $fa-var-cloud-showers-heavy,\n  \"headphones-simple\": $fa-var-headphones-simple,\n  \"headphones-alt\": $fa-var-headphones-alt,\n  \"sitemap\": $fa-var-sitemap,\n  \"circle-dollar-to-slot\": $fa-var-circle-dollar-to-slot,\n  \"donate\": $fa-var-donate,\n  \"memory\": $fa-var-memory,\n  \"road-spikes\": $fa-var-road-spikes,\n  \"fire-burner\": $fa-var-fire-burner,\n  \"flag\": $fa-var-flag,\n  \"hanukiah\": $fa-var-hanukiah,\n  \"feather\": $fa-var-feather,\n  \"volume-low\": $fa-var-volume-low,\n  \"volume-down\": $fa-var-volume-down,\n  \"comment-slash\": $fa-var-comment-slash,\n  \"cloud-sun-rain\": $fa-var-cloud-sun-rain,\n  \"compress\": $fa-var-compress,\n  \"wheat-awn\": $fa-var-wheat-awn,\n  \"wheat-alt\": $fa-var-wheat-alt,\n  \"ankh\": $fa-var-ankh,\n  \"hands-holding-child\": $fa-var-hands-holding-child,\n  \"asterisk\": $fa-var-asterisk,\n  \"square-check\": $fa-var-square-check,\n  \"check-square\": $fa-var-check-square,\n  \"peseta-sign\": $fa-var-peseta-sign,\n  \"heading\": $fa-var-heading,\n  \"header\": $fa-var-header,\n  \"ghost\": $fa-var-ghost,\n  \"list\": $fa-var-list,\n  \"list-squares\": $fa-var-list-squares,\n  \"square-phone-flip\": $fa-var-square-phone-flip,\n  \"phone-square-alt\": $fa-var-phone-square-alt,\n  \"cart-plus\": $fa-var-cart-plus,\n  \"gamepad\": $fa-var-gamepad,\n  \"circle-dot\": $fa-var-circle-dot,\n  \"dot-circle\": $fa-var-dot-circle,\n  \"face-dizzy\": $fa-var-face-dizzy,\n  \"dizzy\": $fa-var-dizzy,\n  \"egg\": $fa-var-egg,\n  \"house-medical-circle-xmark\": $fa-var-house-medical-circle-xmark,\n  \"campground\": $fa-var-campground,\n  \"folder-plus\": $fa-var-folder-plus,\n  \"futbol\": $fa-var-futbol,\n  \"futbol-ball\": $fa-var-futbol-ball,\n  \"soccer-ball\": $fa-var-soccer-ball,\n  \"paintbrush\": $fa-var-paintbrush,\n  \"paint-brush\": $fa-var-paint-brush,\n  \"lock\": $fa-var-lock,\n  \"gas-pump\": $fa-var-gas-pump,\n  \"hot-tub-person\": $fa-var-hot-tub-person,\n  \"hot-tub\": $fa-var-hot-tub,\n  \"map-location\": $fa-var-map-location,\n  \"map-marked\": $fa-var-map-marked,\n  \"house-flood-water\": $fa-var-house-flood-water,\n  \"tree\": $fa-var-tree,\n  \"bridge-lock\": $fa-var-bridge-lock,\n  \"sack-dollar\": $fa-var-sack-dollar,\n  \"pen-to-square\": $fa-var-pen-to-square,\n  \"edit\": $fa-var-edit,\n  \"car-side\": $fa-var-car-side,\n  \"share-nodes\": $fa-var-share-nodes,\n  \"share-alt\": $fa-var-share-alt,\n  \"heart-circle-minus\": $fa-var-heart-circle-minus,\n  \"hourglass-half\": $fa-var-hourglass-half,\n  \"hourglass-2\": $fa-var-hourglass-2,\n  \"microscope\": $fa-var-microscope,\n  \"sink\": $fa-var-sink,\n  \"bag-shopping\": $fa-var-bag-shopping,\n  \"shopping-bag\": $fa-var-shopping-bag,\n  \"arrow-down-z-a\": $fa-var-arrow-down-z-a,\n  \"sort-alpha-desc\": $fa-var-sort-alpha-desc,\n  \"sort-alpha-down-alt\": $fa-var-sort-alpha-down-alt,\n  \"mitten\": $fa-var-mitten,\n  \"person-rays\": $fa-var-person-rays,\n  \"users\": $fa-var-users,\n  \"eye-slash\": $fa-var-eye-slash,\n  \"flask-vial\": $fa-var-flask-vial,\n  \"hand\": $fa-var-hand,\n  \"hand-paper\": $fa-var-hand-paper,\n  \"om\": $fa-var-om,\n  \"worm\": $fa-var-worm,\n  \"house-circle-xmark\": $fa-var-house-circle-xmark,\n  \"plug\": $fa-var-plug,\n  \"chevron-up\": $fa-var-chevron-up,\n  \"hand-spock\": $fa-var-hand-spock,\n  \"stopwatch\": $fa-var-stopwatch,\n  \"face-kiss\": $fa-var-face-kiss,\n  \"kiss\": $fa-var-kiss,\n  \"bridge-circle-xmark\": $fa-var-bridge-circle-xmark,\n  \"face-grin-tongue\": $fa-var-face-grin-tongue,\n  \"grin-tongue\": $fa-var-grin-tongue,\n  \"chess-bishop\": $fa-var-chess-bishop,\n  \"face-grin-wink\": $fa-var-face-grin-wink,\n  \"grin-wink\": $fa-var-grin-wink,\n  \"ear-deaf\": $fa-var-ear-deaf,\n  \"deaf\": $fa-var-deaf,\n  \"deafness\": $fa-var-deafness,\n  \"hard-of-hearing\": $fa-var-hard-of-hearing,\n  \"road-circle-check\": $fa-var-road-circle-check,\n  \"dice-five\": $fa-var-dice-five,\n  \"square-rss\": $fa-var-square-rss,\n  \"rss-square\": $fa-var-rss-square,\n  \"land-mine-on\": $fa-var-land-mine-on,\n  \"i-cursor\": $fa-var-i-cursor,\n  \"stamp\": $fa-var-stamp,\n  \"stairs\": $fa-var-stairs,\n  \"i\": $fa-var-i,\n  \"hryvnia-sign\": $fa-var-hryvnia-sign,\n  \"hryvnia\": $fa-var-hryvnia,\n  \"pills\": $fa-var-pills,\n  \"face-grin-wide\": $fa-var-face-grin-wide,\n  \"grin-alt\": $fa-var-grin-alt,\n  \"tooth\": $fa-var-tooth,\n  \"v\": $fa-var-v,\n  \"bangladeshi-taka-sign\": $fa-var-bangladeshi-taka-sign,\n  \"bicycle\": $fa-var-bicycle,\n  \"staff-snake\": $fa-var-staff-snake,\n  \"rod-asclepius\": $fa-var-rod-asclepius,\n  \"rod-snake\": $fa-var-rod-snake,\n  \"staff-aesculapius\": $fa-var-staff-aesculapius,\n  \"head-side-cough-slash\": $fa-var-head-side-cough-slash,\n  \"truck-medical\": $fa-var-truck-medical,\n  \"ambulance\": $fa-var-ambulance,\n  \"wheat-awn-circle-exclamation\": $fa-var-wheat-awn-circle-exclamation,\n  \"snowman\": $fa-var-snowman,\n  \"mortar-pestle\": $fa-var-mortar-pestle,\n  \"road-barrier\": $fa-var-road-barrier,\n  \"school\": $fa-var-school,\n  \"igloo\": $fa-var-igloo,\n  \"joint\": $fa-var-joint,\n  \"angle-right\": $fa-var-angle-right,\n  \"horse\": $fa-var-horse,\n  \"q\": $fa-var-q,\n  \"g\": $fa-var-g,\n  \"notes-medical\": $fa-var-notes-medical,\n  \"temperature-half\": $fa-var-temperature-half,\n  \"temperature-2\": $fa-var-temperature-2,\n  \"thermometer-2\": $fa-var-thermometer-2,\n  \"thermometer-half\": $fa-var-thermometer-half,\n  \"dong-sign\": $fa-var-dong-sign,\n  \"capsules\": $fa-var-capsules,\n  \"poo-storm\": $fa-var-poo-storm,\n  \"poo-bolt\": $fa-var-poo-bolt,\n  \"face-frown-open\": $fa-var-face-frown-open,\n  \"frown-open\": $fa-var-frown-open,\n  \"hand-point-up\": $fa-var-hand-point-up,\n  \"money-bill\": $fa-var-money-bill,\n  \"bookmark\": $fa-var-bookmark,\n  \"align-justify\": $fa-var-align-justify,\n  \"umbrella-beach\": $fa-var-umbrella-beach,\n  \"helmet-un\": $fa-var-helmet-un,\n  \"bullseye\": $fa-var-bullseye,\n  \"bacon\": $fa-var-bacon,\n  \"hand-point-down\": $fa-var-hand-point-down,\n  \"arrow-up-from-bracket\": $fa-var-arrow-up-from-bracket,\n  \"folder\": $fa-var-folder,\n  \"folder-blank\": $fa-var-folder-blank,\n  \"file-waveform\": $fa-var-file-waveform,\n  \"file-medical-alt\": $fa-var-file-medical-alt,\n  \"radiation\": $fa-var-radiation,\n  \"chart-simple\": $fa-var-chart-simple,\n  \"mars-stroke\": $fa-var-mars-stroke,\n  \"vial\": $fa-var-vial,\n  \"gauge\": $fa-var-gauge,\n  \"dashboard\": $fa-var-dashboard,\n  \"gauge-med\": $fa-var-gauge-med,\n  \"tachometer-alt-average\": $fa-var-tachometer-alt-average,\n  \"wand-magic-sparkles\": $fa-var-wand-magic-sparkles,\n  \"magic-wand-sparkles\": $fa-var-magic-wand-sparkles,\n  \"e\": $fa-var-e,\n  \"pen-clip\": $fa-var-pen-clip,\n  \"pen-alt\": $fa-var-pen-alt,\n  \"bridge-circle-exclamation\": $fa-var-bridge-circle-exclamation,\n  \"user\": $fa-var-user,\n  \"school-circle-check\": $fa-var-school-circle-check,\n  \"dumpster\": $fa-var-dumpster,\n  \"van-shuttle\": $fa-var-van-shuttle,\n  \"shuttle-van\": $fa-var-shuttle-van,\n  \"building-user\": $fa-var-building-user,\n  \"square-caret-left\": $fa-var-square-caret-left,\n  \"caret-square-left\": $fa-var-caret-square-left,\n  \"highlighter\": $fa-var-highlighter,\n  \"key\": $fa-var-key,\n  \"bullhorn\": $fa-var-bullhorn,\n  \"globe\": $fa-var-globe,\n  \"synagogue\": $fa-var-synagogue,\n  \"person-half-dress\": $fa-var-person-half-dress,\n  \"road-bridge\": $fa-var-road-bridge,\n  \"location-arrow\": $fa-var-location-arrow,\n  \"c\": $fa-var-c,\n  \"tablet-button\": $fa-var-tablet-button,\n  \"building-lock\": $fa-var-building-lock,\n  \"pizza-slice\": $fa-var-pizza-slice,\n  \"money-bill-wave\": $fa-var-money-bill-wave,\n  \"chart-area\": $fa-var-chart-area,\n  \"area-chart\": $fa-var-area-chart,\n  \"house-flag\": $fa-var-house-flag,\n  \"person-circle-minus\": $fa-var-person-circle-minus,\n  \"ban\": $fa-var-ban,\n  \"cancel\": $fa-var-cancel,\n  \"camera-rotate\": $fa-var-camera-rotate,\n  \"spray-can-sparkles\": $fa-var-spray-can-sparkles,\n  \"air-freshener\": $fa-var-air-freshener,\n  \"star\": $fa-var-star,\n  \"repeat\": $fa-var-repeat,\n  \"cross\": $fa-var-cross,\n  \"box\": $fa-var-box,\n  \"venus-mars\": $fa-var-venus-mars,\n  \"arrow-pointer\": $fa-var-arrow-pointer,\n  \"mouse-pointer\": $fa-var-mouse-pointer,\n  \"maximize\": $fa-var-maximize,\n  \"expand-arrows-alt\": $fa-var-expand-arrows-alt,\n  \"charging-station\": $fa-var-charging-station,\n  \"shapes\": $fa-var-shapes,\n  \"triangle-circle-square\": $fa-var-triangle-circle-square,\n  \"shuffle\": $fa-var-shuffle,\n  \"random\": $fa-var-random,\n  \"person-running\": $fa-var-person-running,\n  \"running\": $fa-var-running,\n  \"mobile-retro\": $fa-var-mobile-retro,\n  \"grip-lines-vertical\": $fa-var-grip-lines-vertical,\n  \"spider\": $fa-var-spider,\n  \"hands-bound\": $fa-var-hands-bound,\n  \"file-invoice-dollar\": $fa-var-file-invoice-dollar,\n  \"plane-circle-exclamation\": $fa-var-plane-circle-exclamation,\n  \"x-ray\": $fa-var-x-ray,\n  \"spell-check\": $fa-var-spell-check,\n  \"slash\": $fa-var-slash,\n  \"computer-mouse\": $fa-var-computer-mouse,\n  \"mouse\": $fa-var-mouse,\n  \"arrow-right-to-bracket\": $fa-var-arrow-right-to-bracket,\n  \"sign-in\": $fa-var-sign-in,\n  \"shop-slash\": $fa-var-shop-slash,\n  \"store-alt-slash\": $fa-var-store-alt-slash,\n  \"server\": $fa-var-server,\n  \"virus-covid-slash\": $fa-var-virus-covid-slash,\n  \"shop-lock\": $fa-var-shop-lock,\n  \"hourglass-start\": $fa-var-hourglass-start,\n  \"hourglass-1\": $fa-var-hourglass-1,\n  \"blender-phone\": $fa-var-blender-phone,\n  \"building-wheat\": $fa-var-building-wheat,\n  \"person-breastfeeding\": $fa-var-person-breastfeeding,\n  \"right-to-bracket\": $fa-var-right-to-bracket,\n  \"sign-in-alt\": $fa-var-sign-in-alt,\n  \"venus\": $fa-var-venus,\n  \"passport\": $fa-var-passport,\n  \"heart-pulse\": $fa-var-heart-pulse,\n  \"heartbeat\": $fa-var-heartbeat,\n  \"people-carry-box\": $fa-var-people-carry-box,\n  \"people-carry\": $fa-var-people-carry,\n  \"temperature-high\": $fa-var-temperature-high,\n  \"microchip\": $fa-var-microchip,\n  \"crown\": $fa-var-crown,\n  \"weight-hanging\": $fa-var-weight-hanging,\n  \"xmarks-lines\": $fa-var-xmarks-lines,\n  \"file-prescription\": $fa-var-file-prescription,\n  \"weight-scale\": $fa-var-weight-scale,\n  \"weight\": $fa-var-weight,\n  \"user-group\": $fa-var-user-group,\n  \"user-friends\": $fa-var-user-friends,\n  \"arrow-up-a-z\": $fa-var-arrow-up-a-z,\n  \"sort-alpha-up\": $fa-var-sort-alpha-up,\n  \"chess-knight\": $fa-var-chess-knight,\n  \"face-laugh-squint\": $fa-var-face-laugh-squint,\n  \"laugh-squint\": $fa-var-laugh-squint,\n  \"wheelchair\": $fa-var-wheelchair,\n  \"circle-arrow-up\": $fa-var-circle-arrow-up,\n  \"arrow-circle-up\": $fa-var-arrow-circle-up,\n  \"toggle-on\": $fa-var-toggle-on,\n  \"person-walking\": $fa-var-person-walking,\n  \"walking\": $fa-var-walking,\n  \"l\": $fa-var-l,\n  \"fire\": $fa-var-fire,\n  \"bed-pulse\": $fa-var-bed-pulse,\n  \"procedures\": $fa-var-procedures,\n  \"shuttle-space\": $fa-var-shuttle-space,\n  \"space-shuttle\": $fa-var-space-shuttle,\n  \"face-laugh\": $fa-var-face-laugh,\n  \"laugh\": $fa-var-laugh,\n  \"folder-open\": $fa-var-folder-open,\n  \"heart-circle-plus\": $fa-var-heart-circle-plus,\n  \"code-fork\": $fa-var-code-fork,\n  \"city\": $fa-var-city,\n  \"microphone-lines\": $fa-var-microphone-lines,\n  \"microphone-alt\": $fa-var-microphone-alt,\n  \"pepper-hot\": $fa-var-pepper-hot,\n  \"unlock\": $fa-var-unlock,\n  \"colon-sign\": $fa-var-colon-sign,\n  \"headset\": $fa-var-headset,\n  \"store-slash\": $fa-var-store-slash,\n  \"road-circle-xmark\": $fa-var-road-circle-xmark,\n  \"user-minus\": $fa-var-user-minus,\n  \"mars-stroke-up\": $fa-var-mars-stroke-up,\n  \"mars-stroke-v\": $fa-var-mars-stroke-v,\n  \"champagne-glasses\": $fa-var-champagne-glasses,\n  \"glass-cheers\": $fa-var-glass-cheers,\n  \"clipboard\": $fa-var-clipboard,\n  \"house-circle-exclamation\": $fa-var-house-circle-exclamation,\n  \"file-arrow-up\": $fa-var-file-arrow-up,\n  \"file-upload\": $fa-var-file-upload,\n  \"wifi\": $fa-var-wifi,\n  \"wifi-3\": $fa-var-wifi-3,\n  \"wifi-strong\": $fa-var-wifi-strong,\n  \"bath\": $fa-var-bath,\n  \"bathtub\": $fa-var-bathtub,\n  \"underline\": $fa-var-underline,\n  \"user-pen\": $fa-var-user-pen,\n  \"user-edit\": $fa-var-user-edit,\n  \"signature\": $fa-var-signature,\n  \"stroopwafel\": $fa-var-stroopwafel,\n  \"bold\": $fa-var-bold,\n  \"anchor-lock\": $fa-var-anchor-lock,\n  \"building-ngo\": $fa-var-building-ngo,\n  \"manat-sign\": $fa-var-manat-sign,\n  \"not-equal\": $fa-var-not-equal,\n  \"border-top-left\": $fa-var-border-top-left,\n  \"border-style\": $fa-var-border-style,\n  \"map-location-dot\": $fa-var-map-location-dot,\n  \"map-marked-alt\": $fa-var-map-marked-alt,\n  \"jedi\": $fa-var-jedi,\n  \"square-poll-vertical\": $fa-var-square-poll-vertical,\n  \"poll\": $fa-var-poll,\n  \"mug-hot\": $fa-var-mug-hot,\n  \"car-battery\": $fa-var-car-battery,\n  \"battery-car\": $fa-var-battery-car,\n  \"gift\": $fa-var-gift,\n  \"dice-two\": $fa-var-dice-two,\n  \"chess-queen\": $fa-var-chess-queen,\n  \"glasses\": $fa-var-glasses,\n  \"chess-board\": $fa-var-chess-board,\n  \"building-circle-check\": $fa-var-building-circle-check,\n  \"person-chalkboard\": $fa-var-person-chalkboard,\n  \"mars-stroke-right\": $fa-var-mars-stroke-right,\n  \"mars-stroke-h\": $fa-var-mars-stroke-h,\n  \"hand-back-fist\": $fa-var-hand-back-fist,\n  \"hand-rock\": $fa-var-hand-rock,\n  \"square-caret-up\": $fa-var-square-caret-up,\n  \"caret-square-up\": $fa-var-caret-square-up,\n  \"cloud-showers-water\": $fa-var-cloud-showers-water,\n  \"chart-bar\": $fa-var-chart-bar,\n  \"bar-chart\": $fa-var-bar-chart,\n  \"hands-bubbles\": $fa-var-hands-bubbles,\n  \"hands-wash\": $fa-var-hands-wash,\n  \"less-than-equal\": $fa-var-less-than-equal,\n  \"train\": $fa-var-train,\n  \"eye-low-vision\": $fa-var-eye-low-vision,\n  \"low-vision\": $fa-var-low-vision,\n  \"crow\": $fa-var-crow,\n  \"sailboat\": $fa-var-sailboat,\n  \"window-restore\": $fa-var-window-restore,\n  \"square-plus\": $fa-var-square-plus,\n  \"plus-square\": $fa-var-plus-square,\n  \"torii-gate\": $fa-var-torii-gate,\n  \"frog\": $fa-var-frog,\n  \"bucket\": $fa-var-bucket,\n  \"image\": $fa-var-image,\n  \"microphone\": $fa-var-microphone,\n  \"cow\": $fa-var-cow,\n  \"caret-up\": $fa-var-caret-up,\n  \"screwdriver\": $fa-var-screwdriver,\n  \"folder-closed\": $fa-var-folder-closed,\n  \"house-tsunami\": $fa-var-house-tsunami,\n  \"square-nfi\": $fa-var-square-nfi,\n  \"arrow-up-from-ground-water\": $fa-var-arrow-up-from-ground-water,\n  \"martini-glass\": $fa-var-martini-glass,\n  \"glass-martini-alt\": $fa-var-glass-martini-alt,\n  \"rotate-left\": $fa-var-rotate-left,\n  \"rotate-back\": $fa-var-rotate-back,\n  \"rotate-backward\": $fa-var-rotate-backward,\n  \"undo-alt\": $fa-var-undo-alt,\n  \"table-columns\": $fa-var-table-columns,\n  \"columns\": $fa-var-columns,\n  \"lemon\": $fa-var-lemon,\n  \"head-side-mask\": $fa-var-head-side-mask,\n  \"handshake\": $fa-var-handshake,\n  \"gem\": $fa-var-gem,\n  \"dolly\": $fa-var-dolly,\n  \"dolly-box\": $fa-var-dolly-box,\n  \"smoking\": $fa-var-smoking,\n  \"minimize\": $fa-var-minimize,\n  \"compress-arrows-alt\": $fa-var-compress-arrows-alt,\n  \"monument\": $fa-var-monument,\n  \"snowplow\": $fa-var-snowplow,\n  \"angles-right\": $fa-var-angles-right,\n  \"angle-double-right\": $fa-var-angle-double-right,\n  \"cannabis\": $fa-var-cannabis,\n  \"circle-play\": $fa-var-circle-play,\n  \"play-circle\": $fa-var-play-circle,\n  \"tablets\": $fa-var-tablets,\n  \"ethernet\": $fa-var-ethernet,\n  \"euro-sign\": $fa-var-euro-sign,\n  \"eur\": $fa-var-eur,\n  \"euro\": $fa-var-euro,\n  \"chair\": $fa-var-chair,\n  \"circle-check\": $fa-var-circle-check,\n  \"check-circle\": $fa-var-check-circle,\n  \"circle-stop\": $fa-var-circle-stop,\n  \"stop-circle\": $fa-var-stop-circle,\n  \"compass-drafting\": $fa-var-compass-drafting,\n  \"drafting-compass\": $fa-var-drafting-compass,\n  \"plate-wheat\": $fa-var-plate-wheat,\n  \"icicles\": $fa-var-icicles,\n  \"person-shelter\": $fa-var-person-shelter,\n  \"neuter\": $fa-var-neuter,\n  \"id-badge\": $fa-var-id-badge,\n  \"marker\": $fa-var-marker,\n  \"face-laugh-beam\": $fa-var-face-laugh-beam,\n  \"laugh-beam\": $fa-var-laugh-beam,\n  \"helicopter-symbol\": $fa-var-helicopter-symbol,\n  \"universal-access\": $fa-var-universal-access,\n  \"circle-chevron-up\": $fa-var-circle-chevron-up,\n  \"chevron-circle-up\": $fa-var-chevron-circle-up,\n  \"lari-sign\": $fa-var-lari-sign,\n  \"volcano\": $fa-var-volcano,\n  \"person-walking-dashed-line-arrow-right\": $fa-var-person-walking-dashed-line-arrow-right,\n  \"sterling-sign\": $fa-var-sterling-sign,\n  \"gbp\": $fa-var-gbp,\n  \"pound-sign\": $fa-var-pound-sign,\n  \"viruses\": $fa-var-viruses,\n  \"square-person-confined\": $fa-var-square-person-confined,\n  \"user-tie\": $fa-var-user-tie,\n  \"arrow-down-long\": $fa-var-arrow-down-long,\n  \"long-arrow-down\": $fa-var-long-arrow-down,\n  \"tent-arrow-down-to-line\": $fa-var-tent-arrow-down-to-line,\n  \"certificate\": $fa-var-certificate,\n  \"reply-all\": $fa-var-reply-all,\n  \"mail-reply-all\": $fa-var-mail-reply-all,\n  \"suitcase\": $fa-var-suitcase,\n  \"person-skating\": $fa-var-person-skating,\n  \"skating\": $fa-var-skating,\n  \"filter-circle-dollar\": $fa-var-filter-circle-dollar,\n  \"funnel-dollar\": $fa-var-funnel-dollar,\n  \"camera-retro\": $fa-var-camera-retro,\n  \"circle-arrow-down\": $fa-var-circle-arrow-down,\n  \"arrow-circle-down\": $fa-var-arrow-circle-down,\n  \"file-import\": $fa-var-file-import,\n  \"arrow-right-to-file\": $fa-var-arrow-right-to-file,\n  \"square-arrow-up-right\": $fa-var-square-arrow-up-right,\n  \"external-link-square\": $fa-var-external-link-square,\n  \"box-open\": $fa-var-box-open,\n  \"scroll\": $fa-var-scroll,\n  \"spa\": $fa-var-spa,\n  \"location-pin-lock\": $fa-var-location-pin-lock,\n  \"pause\": $fa-var-pause,\n  \"hill-avalanche\": $fa-var-hill-avalanche,\n  \"temperature-empty\": $fa-var-temperature-empty,\n  \"temperature-0\": $fa-var-temperature-0,\n  \"thermometer-0\": $fa-var-thermometer-0,\n  \"thermometer-empty\": $fa-var-thermometer-empty,\n  \"bomb\": $fa-var-bomb,\n  \"registered\": $fa-var-registered,\n  \"address-card\": $fa-var-address-card,\n  \"contact-card\": $fa-var-contact-card,\n  \"vcard\": $fa-var-vcard,\n  \"scale-unbalanced-flip\": $fa-var-scale-unbalanced-flip,\n  \"balance-scale-right\": $fa-var-balance-scale-right,\n  \"subscript\": $fa-var-subscript,\n  \"diamond-turn-right\": $fa-var-diamond-turn-right,\n  \"directions\": $fa-var-directions,\n  \"burst\": $fa-var-burst,\n  \"house-laptop\": $fa-var-house-laptop,\n  \"laptop-house\": $fa-var-laptop-house,\n  \"face-tired\": $fa-var-face-tired,\n  \"tired\": $fa-var-tired,\n  \"money-bills\": $fa-var-money-bills,\n  \"smog\": $fa-var-smog,\n  \"crutch\": $fa-var-crutch,\n  \"cloud-arrow-up\": $fa-var-cloud-arrow-up,\n  \"cloud-upload\": $fa-var-cloud-upload,\n  \"cloud-upload-alt\": $fa-var-cloud-upload-alt,\n  \"palette\": $fa-var-palette,\n  \"arrows-turn-right\": $fa-var-arrows-turn-right,\n  \"vest\": $fa-var-vest,\n  \"ferry\": $fa-var-ferry,\n  \"arrows-down-to-people\": $fa-var-arrows-down-to-people,\n  \"seedling\": $fa-var-seedling,\n  \"sprout\": $fa-var-sprout,\n  \"left-right\": $fa-var-left-right,\n  \"arrows-alt-h\": $fa-var-arrows-alt-h,\n  \"boxes-packing\": $fa-var-boxes-packing,\n  \"circle-arrow-left\": $fa-var-circle-arrow-left,\n  \"arrow-circle-left\": $fa-var-arrow-circle-left,\n  \"group-arrows-rotate\": $fa-var-group-arrows-rotate,\n  \"bowl-food\": $fa-var-bowl-food,\n  \"candy-cane\": $fa-var-candy-cane,\n  \"arrow-down-wide-short\": $fa-var-arrow-down-wide-short,\n  \"sort-amount-asc\": $fa-var-sort-amount-asc,\n  \"sort-amount-down\": $fa-var-sort-amount-down,\n  \"cloud-bolt\": $fa-var-cloud-bolt,\n  \"thunderstorm\": $fa-var-thunderstorm,\n  \"text-slash\": $fa-var-text-slash,\n  \"remove-format\": $fa-var-remove-format,\n  \"face-smile-wink\": $fa-var-face-smile-wink,\n  \"smile-wink\": $fa-var-smile-wink,\n  \"file-word\": $fa-var-file-word,\n  \"file-powerpoint\": $fa-var-file-powerpoint,\n  \"arrows-left-right\": $fa-var-arrows-left-right,\n  \"arrows-h\": $fa-var-arrows-h,\n  \"house-lock\": $fa-var-house-lock,\n  \"cloud-arrow-down\": $fa-var-cloud-arrow-down,\n  \"cloud-download\": $fa-var-cloud-download,\n  \"cloud-download-alt\": $fa-var-cloud-download-alt,\n  \"children\": $fa-var-children,\n  \"chalkboard\": $fa-var-chalkboard,\n  \"blackboard\": $fa-var-blackboard,\n  \"user-large-slash\": $fa-var-user-large-slash,\n  \"user-alt-slash\": $fa-var-user-alt-slash,\n  \"envelope-open\": $fa-var-envelope-open,\n  \"handshake-simple-slash\": $fa-var-handshake-simple-slash,\n  \"handshake-alt-slash\": $fa-var-handshake-alt-slash,\n  \"mattress-pillow\": $fa-var-mattress-pillow,\n  \"guarani-sign\": $fa-var-guarani-sign,\n  \"arrows-rotate\": $fa-var-arrows-rotate,\n  \"refresh\": $fa-var-refresh,\n  \"sync\": $fa-var-sync,\n  \"fire-extinguisher\": $fa-var-fire-extinguisher,\n  \"cruzeiro-sign\": $fa-var-cruzeiro-sign,\n  \"greater-than-equal\": $fa-var-greater-than-equal,\n  \"shield-halved\": $fa-var-shield-halved,\n  \"shield-alt\": $fa-var-shield-alt,\n  \"book-atlas\": $fa-var-book-atlas,\n  \"atlas\": $fa-var-atlas,\n  \"virus\": $fa-var-virus,\n  \"envelope-circle-check\": $fa-var-envelope-circle-check,\n  \"layer-group\": $fa-var-layer-group,\n  \"arrows-to-dot\": $fa-var-arrows-to-dot,\n  \"archway\": $fa-var-archway,\n  \"heart-circle-check\": $fa-var-heart-circle-check,\n  \"house-chimney-crack\": $fa-var-house-chimney-crack,\n  \"house-damage\": $fa-var-house-damage,\n  \"file-zipper\": $fa-var-file-zipper,\n  \"file-archive\": $fa-var-file-archive,\n  \"square\": $fa-var-square,\n  \"martini-glass-empty\": $fa-var-martini-glass-empty,\n  \"glass-martini\": $fa-var-glass-martini,\n  \"couch\": $fa-var-couch,\n  \"cedi-sign\": $fa-var-cedi-sign,\n  \"italic\": $fa-var-italic,\n  \"table-cells-column-lock\": $fa-var-table-cells-column-lock,\n  \"church\": $fa-var-church,\n  \"comments-dollar\": $fa-var-comments-dollar,\n  \"democrat\": $fa-var-democrat,\n  \"z\": $fa-var-z,\n  \"person-skiing\": $fa-var-person-skiing,\n  \"skiing\": $fa-var-skiing,\n  \"road-lock\": $fa-var-road-lock,\n  \"a\": $fa-var-a,\n  \"temperature-arrow-down\": $fa-var-temperature-arrow-down,\n  \"temperature-down\": $fa-var-temperature-down,\n  \"feather-pointed\": $fa-var-feather-pointed,\n  \"feather-alt\": $fa-var-feather-alt,\n  \"p\": $fa-var-p,\n  \"snowflake\": $fa-var-snowflake,\n  \"newspaper\": $fa-var-newspaper,\n  \"rectangle-ad\": $fa-var-rectangle-ad,\n  \"ad\": $fa-var-ad,\n  \"circle-arrow-right\": $fa-var-circle-arrow-right,\n  \"arrow-circle-right\": $fa-var-arrow-circle-right,\n  \"filter-circle-xmark\": $fa-var-filter-circle-xmark,\n  \"locust\": $fa-var-locust,\n  \"sort\": $fa-var-sort,\n  \"unsorted\": $fa-var-unsorted,\n  \"list-ol\": $fa-var-list-ol,\n  \"list-1-2\": $fa-var-list-1-2,\n  \"list-numeric\": $fa-var-list-numeric,\n  \"person-dress-burst\": $fa-var-person-dress-burst,\n  \"money-check-dollar\": $fa-var-money-check-dollar,\n  \"money-check-alt\": $fa-var-money-check-alt,\n  \"vector-square\": $fa-var-vector-square,\n  \"bread-slice\": $fa-var-bread-slice,\n  \"language\": $fa-var-language,\n  \"face-kiss-wink-heart\": $fa-var-face-kiss-wink-heart,\n  \"kiss-wink-heart\": $fa-var-kiss-wink-heart,\n  \"filter\": $fa-var-filter,\n  \"question\": $fa-var-question,\n  \"file-signature\": $fa-var-file-signature,\n  \"up-down-left-right\": $fa-var-up-down-left-right,\n  \"arrows-alt\": $fa-var-arrows-alt,\n  \"house-chimney-user\": $fa-var-house-chimney-user,\n  \"hand-holding-heart\": $fa-var-hand-holding-heart,\n  \"puzzle-piece\": $fa-var-puzzle-piece,\n  \"money-check\": $fa-var-money-check,\n  \"star-half-stroke\": $fa-var-star-half-stroke,\n  \"star-half-alt\": $fa-var-star-half-alt,\n  \"code\": $fa-var-code,\n  \"whiskey-glass\": $fa-var-whiskey-glass,\n  \"glass-whiskey\": $fa-var-glass-whiskey,\n  \"building-circle-exclamation\": $fa-var-building-circle-exclamation,\n  \"magnifying-glass-chart\": $fa-var-magnifying-glass-chart,\n  \"arrow-up-right-from-square\": $fa-var-arrow-up-right-from-square,\n  \"external-link\": $fa-var-external-link,\n  \"cubes-stacked\": $fa-var-cubes-stacked,\n  \"won-sign\": $fa-var-won-sign,\n  \"krw\": $fa-var-krw,\n  \"won\": $fa-var-won,\n  \"virus-covid\": $fa-var-virus-covid,\n  \"austral-sign\": $fa-var-austral-sign,\n  \"f\": $fa-var-f,\n  \"leaf\": $fa-var-leaf,\n  \"road\": $fa-var-road,\n  \"taxi\": $fa-var-taxi,\n  \"cab\": $fa-var-cab,\n  \"person-circle-plus\": $fa-var-person-circle-plus,\n  \"chart-pie\": $fa-var-chart-pie,\n  \"pie-chart\": $fa-var-pie-chart,\n  \"bolt-lightning\": $fa-var-bolt-lightning,\n  \"sack-xmark\": $fa-var-sack-xmark,\n  \"file-excel\": $fa-var-file-excel,\n  \"file-contract\": $fa-var-file-contract,\n  \"fish-fins\": $fa-var-fish-fins,\n  \"building-flag\": $fa-var-building-flag,\n  \"face-grin-beam\": $fa-var-face-grin-beam,\n  \"grin-beam\": $fa-var-grin-beam,\n  \"object-ungroup\": $fa-var-object-ungroup,\n  \"poop\": $fa-var-poop,\n  \"location-pin\": $fa-var-location-pin,\n  \"map-marker\": $fa-var-map-marker,\n  \"kaaba\": $fa-var-kaaba,\n  \"toilet-paper\": $fa-var-toilet-paper,\n  \"helmet-safety\": $fa-var-helmet-safety,\n  \"hard-hat\": $fa-var-hard-hat,\n  \"hat-hard\": $fa-var-hat-hard,\n  \"eject\": $fa-var-eject,\n  \"circle-right\": $fa-var-circle-right,\n  \"arrow-alt-circle-right\": $fa-var-arrow-alt-circle-right,\n  \"plane-circle-check\": $fa-var-plane-circle-check,\n  \"face-rolling-eyes\": $fa-var-face-rolling-eyes,\n  \"meh-rolling-eyes\": $fa-var-meh-rolling-eyes,\n  \"object-group\": $fa-var-object-group,\n  \"chart-line\": $fa-var-chart-line,\n  \"line-chart\": $fa-var-line-chart,\n  \"mask-ventilator\": $fa-var-mask-ventilator,\n  \"arrow-right\": $fa-var-arrow-right,\n  \"signs-post\": $fa-var-signs-post,\n  \"map-signs\": $fa-var-map-signs,\n  \"cash-register\": $fa-var-cash-register,\n  \"person-circle-question\": $fa-var-person-circle-question,\n  \"h\": $fa-var-h,\n  \"tarp\": $fa-var-tarp,\n  \"screwdriver-wrench\": $fa-var-screwdriver-wrench,\n  \"tools\": $fa-var-tools,\n  \"arrows-to-eye\": $fa-var-arrows-to-eye,\n  \"plug-circle-bolt\": $fa-var-plug-circle-bolt,\n  \"heart\": $fa-var-heart,\n  \"mars-and-venus\": $fa-var-mars-and-venus,\n  \"house-user\": $fa-var-house-user,\n  \"home-user\": $fa-var-home-user,\n  \"dumpster-fire\": $fa-var-dumpster-fire,\n  \"house-crack\": $fa-var-house-crack,\n  \"martini-glass-citrus\": $fa-var-martini-glass-citrus,\n  \"cocktail\": $fa-var-cocktail,\n  \"face-surprise\": $fa-var-face-surprise,\n  \"surprise\": $fa-var-surprise,\n  \"bottle-water\": $fa-var-bottle-water,\n  \"circle-pause\": $fa-var-circle-pause,\n  \"pause-circle\": $fa-var-pause-circle,\n  \"toilet-paper-slash\": $fa-var-toilet-paper-slash,\n  \"apple-whole\": $fa-var-apple-whole,\n  \"apple-alt\": $fa-var-apple-alt,\n  \"kitchen-set\": $fa-var-kitchen-set,\n  \"r\": $fa-var-r,\n  \"temperature-quarter\": $fa-var-temperature-quarter,\n  \"temperature-1\": $fa-var-temperature-1,\n  \"thermometer-1\": $fa-var-thermometer-1,\n  \"thermometer-quarter\": $fa-var-thermometer-quarter,\n  \"cube\": $fa-var-cube,\n  \"bitcoin-sign\": $fa-var-bitcoin-sign,\n  \"shield-dog\": $fa-var-shield-dog,\n  \"solar-panel\": $fa-var-solar-panel,\n  \"lock-open\": $fa-var-lock-open,\n  \"elevator\": $fa-var-elevator,\n  \"money-bill-transfer\": $fa-var-money-bill-transfer,\n  \"money-bill-trend-up\": $fa-var-money-bill-trend-up,\n  \"house-flood-water-circle-arrow-right\": $fa-var-house-flood-water-circle-arrow-right,\n  \"square-poll-horizontal\": $fa-var-square-poll-horizontal,\n  \"poll-h\": $fa-var-poll-h,\n  \"circle\": $fa-var-circle,\n  \"backward-fast\": $fa-var-backward-fast,\n  \"fast-backward\": $fa-var-fast-backward,\n  \"recycle\": $fa-var-recycle,\n  \"user-astronaut\": $fa-var-user-astronaut,\n  \"plane-slash\": $fa-var-plane-slash,\n  \"trademark\": $fa-var-trademark,\n  \"basketball\": $fa-var-basketball,\n  \"basketball-ball\": $fa-var-basketball-ball,\n  \"satellite-dish\": $fa-var-satellite-dish,\n  \"circle-up\": $fa-var-circle-up,\n  \"arrow-alt-circle-up\": $fa-var-arrow-alt-circle-up,\n  \"mobile-screen-button\": $fa-var-mobile-screen-button,\n  \"mobile-alt\": $fa-var-mobile-alt,\n  \"volume-high\": $fa-var-volume-high,\n  \"volume-up\": $fa-var-volume-up,\n  \"users-rays\": $fa-var-users-rays,\n  \"wallet\": $fa-var-wallet,\n  \"clipboard-check\": $fa-var-clipboard-check,\n  \"file-audio\": $fa-var-file-audio,\n  \"burger\": $fa-var-burger,\n  \"hamburger\": $fa-var-hamburger,\n  \"wrench\": $fa-var-wrench,\n  \"bugs\": $fa-var-bugs,\n  \"rupee-sign\": $fa-var-rupee-sign,\n  \"rupee\": $fa-var-rupee,\n  \"file-image\": $fa-var-file-image,\n  \"circle-question\": $fa-var-circle-question,\n  \"question-circle\": $fa-var-question-circle,\n  \"plane-departure\": $fa-var-plane-departure,\n  \"handshake-slash\": $fa-var-handshake-slash,\n  \"book-bookmark\": $fa-var-book-bookmark,\n  \"code-branch\": $fa-var-code-branch,\n  \"hat-cowboy\": $fa-var-hat-cowboy,\n  \"bridge\": $fa-var-bridge,\n  \"phone-flip\": $fa-var-phone-flip,\n  \"phone-alt\": $fa-var-phone-alt,\n  \"truck-front\": $fa-var-truck-front,\n  \"cat\": $fa-var-cat,\n  \"anchor-circle-exclamation\": $fa-var-anchor-circle-exclamation,\n  \"truck-field\": $fa-var-truck-field,\n  \"route\": $fa-var-route,\n  \"clipboard-question\": $fa-var-clipboard-question,\n  \"panorama\": $fa-var-panorama,\n  \"comment-medical\": $fa-var-comment-medical,\n  \"teeth-open\": $fa-var-teeth-open,\n  \"file-circle-minus\": $fa-var-file-circle-minus,\n  \"tags\": $fa-var-tags,\n  \"wine-glass\": $fa-var-wine-glass,\n  \"forward-fast\": $fa-var-forward-fast,\n  \"fast-forward\": $fa-var-fast-forward,\n  \"face-meh-blank\": $fa-var-face-meh-blank,\n  \"meh-blank\": $fa-var-meh-blank,\n  \"square-parking\": $fa-var-square-parking,\n  \"parking\": $fa-var-parking,\n  \"house-signal\": $fa-var-house-signal,\n  \"bars-progress\": $fa-var-bars-progress,\n  \"tasks-alt\": $fa-var-tasks-alt,\n  \"faucet-drip\": $fa-var-faucet-drip,\n  \"cart-flatbed\": $fa-var-cart-flatbed,\n  \"dolly-flatbed\": $fa-var-dolly-flatbed,\n  \"ban-smoking\": $fa-var-ban-smoking,\n  \"smoking-ban\": $fa-var-smoking-ban,\n  \"terminal\": $fa-var-terminal,\n  \"mobile-button\": $fa-var-mobile-button,\n  \"house-medical-flag\": $fa-var-house-medical-flag,\n  \"basket-shopping\": $fa-var-basket-shopping,\n  \"shopping-basket\": $fa-var-shopping-basket,\n  \"tape\": $fa-var-tape,\n  \"bus-simple\": $fa-var-bus-simple,\n  \"bus-alt\": $fa-var-bus-alt,\n  \"eye\": $fa-var-eye,\n  \"face-sad-cry\": $fa-var-face-sad-cry,\n  \"sad-cry\": $fa-var-sad-cry,\n  \"audio-description\": $fa-var-audio-description,\n  \"person-military-to-person\": $fa-var-person-military-to-person,\n  \"file-shield\": $fa-var-file-shield,\n  \"user-slash\": $fa-var-user-slash,\n  \"pen\": $fa-var-pen,\n  \"tower-observation\": $fa-var-tower-observation,\n  \"file-code\": $fa-var-file-code,\n  \"signal\": $fa-var-signal,\n  \"signal-5\": $fa-var-signal-5,\n  \"signal-perfect\": $fa-var-signal-perfect,\n  \"bus\": $fa-var-bus,\n  \"heart-circle-xmark\": $fa-var-heart-circle-xmark,\n  \"house-chimney\": $fa-var-house-chimney,\n  \"home-lg\": $fa-var-home-lg,\n  \"window-maximize\": $fa-var-window-maximize,\n  \"face-frown\": $fa-var-face-frown,\n  \"frown\": $fa-var-frown,\n  \"prescription\": $fa-var-prescription,\n  \"shop\": $fa-var-shop,\n  \"store-alt\": $fa-var-store-alt,\n  \"floppy-disk\": $fa-var-floppy-disk,\n  \"save\": $fa-var-save,\n  \"vihara\": $fa-var-vihara,\n  \"scale-unbalanced\": $fa-var-scale-unbalanced,\n  \"balance-scale-left\": $fa-var-balance-scale-left,\n  \"sort-up\": $fa-var-sort-up,\n  \"sort-asc\": $fa-var-sort-asc,\n  \"comment-dots\": $fa-var-comment-dots,\n  \"commenting\": $fa-var-commenting,\n  \"plant-wilt\": $fa-var-plant-wilt,\n  \"diamond\": $fa-var-diamond,\n  \"face-grin-squint\": $fa-var-face-grin-squint,\n  \"grin-squint\": $fa-var-grin-squint,\n  \"hand-holding-dollar\": $fa-var-hand-holding-dollar,\n  \"hand-holding-usd\": $fa-var-hand-holding-usd,\n  \"bacterium\": $fa-var-bacterium,\n  \"hand-pointer\": $fa-var-hand-pointer,\n  \"drum-steelpan\": $fa-var-drum-steelpan,\n  \"hand-scissors\": $fa-var-hand-scissors,\n  \"hands-praying\": $fa-var-hands-praying,\n  \"praying-hands\": $fa-var-praying-hands,\n  \"arrow-rotate-right\": $fa-var-arrow-rotate-right,\n  \"arrow-right-rotate\": $fa-var-arrow-right-rotate,\n  \"arrow-rotate-forward\": $fa-var-arrow-rotate-forward,\n  \"redo\": $fa-var-redo,\n  \"biohazard\": $fa-var-biohazard,\n  \"location-crosshairs\": $fa-var-location-crosshairs,\n  \"location\": $fa-var-location,\n  \"mars-double\": $fa-var-mars-double,\n  \"child-dress\": $fa-var-child-dress,\n  \"users-between-lines\": $fa-var-users-between-lines,\n  \"lungs-virus\": $fa-var-lungs-virus,\n  \"face-grin-tears\": $fa-var-face-grin-tears,\n  \"grin-tears\": $fa-var-grin-tears,\n  \"phone\": $fa-var-phone,\n  \"calendar-xmark\": $fa-var-calendar-xmark,\n  \"calendar-times\": $fa-var-calendar-times,\n  \"child-reaching\": $fa-var-child-reaching,\n  \"head-side-virus\": $fa-var-head-side-virus,\n  \"user-gear\": $fa-var-user-gear,\n  \"user-cog\": $fa-var-user-cog,\n  \"arrow-up-1-9\": $fa-var-arrow-up-1-9,\n  \"sort-numeric-up\": $fa-var-sort-numeric-up,\n  \"door-closed\": $fa-var-door-closed,\n  \"shield-virus\": $fa-var-shield-virus,\n  \"dice-six\": $fa-var-dice-six,\n  \"mosquito-net\": $fa-var-mosquito-net,\n  \"bridge-water\": $fa-var-bridge-water,\n  \"person-booth\": $fa-var-person-booth,\n  \"text-width\": $fa-var-text-width,\n  \"hat-wizard\": $fa-var-hat-wizard,\n  \"pen-fancy\": $fa-var-pen-fancy,\n  \"person-digging\": $fa-var-person-digging,\n  \"digging\": $fa-var-digging,\n  \"trash\": $fa-var-trash,\n  \"gauge-simple\": $fa-var-gauge-simple,\n  \"gauge-simple-med\": $fa-var-gauge-simple-med,\n  \"tachometer-average\": $fa-var-tachometer-average,\n  \"book-medical\": $fa-var-book-medical,\n  \"poo\": $fa-var-poo,\n  \"quote-right\": $fa-var-quote-right,\n  \"quote-right-alt\": $fa-var-quote-right-alt,\n  \"shirt\": $fa-var-shirt,\n  \"t-shirt\": $fa-var-t-shirt,\n  \"tshirt\": $fa-var-tshirt,\n  \"cubes\": $fa-var-cubes,\n  \"divide\": $fa-var-divide,\n  \"tenge-sign\": $fa-var-tenge-sign,\n  \"tenge\": $fa-var-tenge,\n  \"headphones\": $fa-var-headphones,\n  \"hands-holding\": $fa-var-hands-holding,\n  \"hands-clapping\": $fa-var-hands-clapping,\n  \"republican\": $fa-var-republican,\n  \"arrow-left\": $fa-var-arrow-left,\n  \"person-circle-xmark\": $fa-var-person-circle-xmark,\n  \"ruler\": $fa-var-ruler,\n  \"align-left\": $fa-var-align-left,\n  \"dice-d6\": $fa-var-dice-d6,\n  \"restroom\": $fa-var-restroom,\n  \"j\": $fa-var-j,\n  \"users-viewfinder\": $fa-var-users-viewfinder,\n  \"file-video\": $fa-var-file-video,\n  \"up-right-from-square\": $fa-var-up-right-from-square,\n  \"external-link-alt\": $fa-var-external-link-alt,\n  \"table-cells\": $fa-var-table-cells,\n  \"th\": $fa-var-th,\n  \"file-pdf\": $fa-var-file-pdf,\n  \"book-bible\": $fa-var-book-bible,\n  \"bible\": $fa-var-bible,\n  \"o\": $fa-var-o,\n  \"suitcase-medical\": $fa-var-suitcase-medical,\n  \"medkit\": $fa-var-medkit,\n  \"user-secret\": $fa-var-user-secret,\n  \"otter\": $fa-var-otter,\n  \"person-dress\": $fa-var-person-dress,\n  \"female\": $fa-var-female,\n  \"comment-dollar\": $fa-var-comment-dollar,\n  \"business-time\": $fa-var-business-time,\n  \"briefcase-clock\": $fa-var-briefcase-clock,\n  \"table-cells-large\": $fa-var-table-cells-large,\n  \"th-large\": $fa-var-th-large,\n  \"book-tanakh\": $fa-var-book-tanakh,\n  \"tanakh\": $fa-var-tanakh,\n  \"phone-volume\": $fa-var-phone-volume,\n  \"volume-control-phone\": $fa-var-volume-control-phone,\n  \"hat-cowboy-side\": $fa-var-hat-cowboy-side,\n  \"clipboard-user\": $fa-var-clipboard-user,\n  \"child\": $fa-var-child,\n  \"lira-sign\": $fa-var-lira-sign,\n  \"satellite\": $fa-var-satellite,\n  \"plane-lock\": $fa-var-plane-lock,\n  \"tag\": $fa-var-tag,\n  \"comment\": $fa-var-comment,\n  \"cake-candles\": $fa-var-cake-candles,\n  \"birthday-cake\": $fa-var-birthday-cake,\n  \"cake\": $fa-var-cake,\n  \"envelope\": $fa-var-envelope,\n  \"angles-up\": $fa-var-angles-up,\n  \"angle-double-up\": $fa-var-angle-double-up,\n  \"paperclip\": $fa-var-paperclip,\n  \"arrow-right-to-city\": $fa-var-arrow-right-to-city,\n  \"ribbon\": $fa-var-ribbon,\n  \"lungs\": $fa-var-lungs,\n  \"arrow-up-9-1\": $fa-var-arrow-up-9-1,\n  \"sort-numeric-up-alt\": $fa-var-sort-numeric-up-alt,\n  \"litecoin-sign\": $fa-var-litecoin-sign,\n  \"border-none\": $fa-var-border-none,\n  \"circle-nodes\": $fa-var-circle-nodes,\n  \"parachute-box\": $fa-var-parachute-box,\n  \"indent\": $fa-var-indent,\n  \"truck-field-un\": $fa-var-truck-field-un,\n  \"hourglass\": $fa-var-hourglass,\n  \"hourglass-empty\": $fa-var-hourglass-empty,\n  \"mountain\": $fa-var-mountain,\n  \"user-doctor\": $fa-var-user-doctor,\n  \"user-md\": $fa-var-user-md,\n  \"circle-info\": $fa-var-circle-info,\n  \"info-circle\": $fa-var-info-circle,\n  \"cloud-meatball\": $fa-var-cloud-meatball,\n  \"camera\": $fa-var-camera,\n  \"camera-alt\": $fa-var-camera-alt,\n  \"square-virus\": $fa-var-square-virus,\n  \"meteor\": $fa-var-meteor,\n  \"car-on\": $fa-var-car-on,\n  \"sleigh\": $fa-var-sleigh,\n  \"arrow-down-1-9\": $fa-var-arrow-down-1-9,\n  \"sort-numeric-asc\": $fa-var-sort-numeric-asc,\n  \"sort-numeric-down\": $fa-var-sort-numeric-down,\n  \"hand-holding-droplet\": $fa-var-hand-holding-droplet,\n  \"hand-holding-water\": $fa-var-hand-holding-water,\n  \"water\": $fa-var-water,\n  \"calendar-check\": $fa-var-calendar-check,\n  \"braille\": $fa-var-braille,\n  \"prescription-bottle-medical\": $fa-var-prescription-bottle-medical,\n  \"prescription-bottle-alt\": $fa-var-prescription-bottle-alt,\n  \"landmark\": $fa-var-landmark,\n  \"truck\": $fa-var-truck,\n  \"crosshairs\": $fa-var-crosshairs,\n  \"person-cane\": $fa-var-person-cane,\n  \"tent\": $fa-var-tent,\n  \"vest-patches\": $fa-var-vest-patches,\n  \"check-double\": $fa-var-check-double,\n  \"arrow-down-a-z\": $fa-var-arrow-down-a-z,\n  \"sort-alpha-asc\": $fa-var-sort-alpha-asc,\n  \"sort-alpha-down\": $fa-var-sort-alpha-down,\n  \"money-bill-wheat\": $fa-var-money-bill-wheat,\n  \"cookie\": $fa-var-cookie,\n  \"arrow-rotate-left\": $fa-var-arrow-rotate-left,\n  \"arrow-left-rotate\": $fa-var-arrow-left-rotate,\n  \"arrow-rotate-back\": $fa-var-arrow-rotate-back,\n  \"arrow-rotate-backward\": $fa-var-arrow-rotate-backward,\n  \"undo\": $fa-var-undo,\n  \"hard-drive\": $fa-var-hard-drive,\n  \"hdd\": $fa-var-hdd,\n  \"face-grin-squint-tears\": $fa-var-face-grin-squint-tears,\n  \"grin-squint-tears\": $fa-var-grin-squint-tears,\n  \"dumbbell\": $fa-var-dumbbell,\n  \"rectangle-list\": $fa-var-rectangle-list,\n  \"list-alt\": $fa-var-list-alt,\n  \"tarp-droplet\": $fa-var-tarp-droplet,\n  \"house-medical-circle-check\": $fa-var-house-medical-circle-check,\n  \"person-skiing-nordic\": $fa-var-person-skiing-nordic,\n  \"skiing-nordic\": $fa-var-skiing-nordic,\n  \"calendar-plus\": $fa-var-calendar-plus,\n  \"plane-arrival\": $fa-var-plane-arrival,\n  \"circle-left\": $fa-var-circle-left,\n  \"arrow-alt-circle-left\": $fa-var-arrow-alt-circle-left,\n  \"train-subway\": $fa-var-train-subway,\n  \"subway\": $fa-var-subway,\n  \"chart-gantt\": $fa-var-chart-gantt,\n  \"indian-rupee-sign\": $fa-var-indian-rupee-sign,\n  \"indian-rupee\": $fa-var-indian-rupee,\n  \"inr\": $fa-var-inr,\n  \"crop-simple\": $fa-var-crop-simple,\n  \"crop-alt\": $fa-var-crop-alt,\n  \"money-bill-1\": $fa-var-money-bill-1,\n  \"money-bill-alt\": $fa-var-money-bill-alt,\n  \"left-long\": $fa-var-left-long,\n  \"long-arrow-alt-left\": $fa-var-long-arrow-alt-left,\n  \"dna\": $fa-var-dna,\n  \"virus-slash\": $fa-var-virus-slash,\n  \"minus\": $fa-var-minus,\n  \"subtract\": $fa-var-subtract,\n  \"chess\": $fa-var-chess,\n  \"arrow-left-long\": $fa-var-arrow-left-long,\n  \"long-arrow-left\": $fa-var-long-arrow-left,\n  \"plug-circle-check\": $fa-var-plug-circle-check,\n  \"street-view\": $fa-var-street-view,\n  \"franc-sign\": $fa-var-franc-sign,\n  \"volume-off\": $fa-var-volume-off,\n  \"hands-asl-interpreting\": $fa-var-hands-asl-interpreting,\n  \"american-sign-language-interpreting\": $fa-var-american-sign-language-interpreting,\n  \"asl-interpreting\": $fa-var-asl-interpreting,\n  \"hands-american-sign-language-interpreting\": $fa-var-hands-american-sign-language-interpreting,\n  \"gear\": $fa-var-gear,\n  \"cog\": $fa-var-cog,\n  \"droplet-slash\": $fa-var-droplet-slash,\n  \"tint-slash\": $fa-var-tint-slash,\n  \"mosque\": $fa-var-mosque,\n  \"mosquito\": $fa-var-mosquito,\n  \"star-of-david\": $fa-var-star-of-david,\n  \"person-military-rifle\": $fa-var-person-military-rifle,\n  \"cart-shopping\": $fa-var-cart-shopping,\n  \"shopping-cart\": $fa-var-shopping-cart,\n  \"vials\": $fa-var-vials,\n  \"plug-circle-plus\": $fa-var-plug-circle-plus,\n  \"place-of-worship\": $fa-var-place-of-worship,\n  \"grip-vertical\": $fa-var-grip-vertical,\n  \"arrow-turn-up\": $fa-var-arrow-turn-up,\n  \"level-up\": $fa-var-level-up,\n  \"u\": $fa-var-u,\n  \"square-root-variable\": $fa-var-square-root-variable,\n  \"square-root-alt\": $fa-var-square-root-alt,\n  \"clock\": $fa-var-clock,\n  \"clock-four\": $fa-var-clock-four,\n  \"backward-step\": $fa-var-backward-step,\n  \"step-backward\": $fa-var-step-backward,\n  \"pallet\": $fa-var-pallet,\n  \"faucet\": $fa-var-faucet,\n  \"baseball-bat-ball\": $fa-var-baseball-bat-ball,\n  \"s\": $fa-var-s,\n  \"timeline\": $fa-var-timeline,\n  \"keyboard\": $fa-var-keyboard,\n  \"caret-down\": $fa-var-caret-down,\n  \"house-chimney-medical\": $fa-var-house-chimney-medical,\n  \"clinic-medical\": $fa-var-clinic-medical,\n  \"temperature-three-quarters\": $fa-var-temperature-three-quarters,\n  \"temperature-3\": $fa-var-temperature-3,\n  \"thermometer-3\": $fa-var-thermometer-3,\n  \"thermometer-three-quarters\": $fa-var-thermometer-three-quarters,\n  \"mobile-screen\": $fa-var-mobile-screen,\n  \"mobile-android-alt\": $fa-var-mobile-android-alt,\n  \"plane-up\": $fa-var-plane-up,\n  \"piggy-bank\": $fa-var-piggy-bank,\n  \"battery-half\": $fa-var-battery-half,\n  \"battery-3\": $fa-var-battery-3,\n  \"mountain-city\": $fa-var-mountain-city,\n  \"coins\": $fa-var-coins,\n  \"khanda\": $fa-var-khanda,\n  \"sliders\": $fa-var-sliders,\n  \"sliders-h\": $fa-var-sliders-h,\n  \"folder-tree\": $fa-var-folder-tree,\n  \"network-wired\": $fa-var-network-wired,\n  \"map-pin\": $fa-var-map-pin,\n  \"hamsa\": $fa-var-hamsa,\n  \"cent-sign\": $fa-var-cent-sign,\n  \"flask\": $fa-var-flask,\n  \"person-pregnant\": $fa-var-person-pregnant,\n  \"wand-sparkles\": $fa-var-wand-sparkles,\n  \"ellipsis-vertical\": $fa-var-ellipsis-vertical,\n  \"ellipsis-v\": $fa-var-ellipsis-v,\n  \"ticket\": $fa-var-ticket,\n  \"power-off\": $fa-var-power-off,\n  \"right-long\": $fa-var-right-long,\n  \"long-arrow-alt-right\": $fa-var-long-arrow-alt-right,\n  \"flag-usa\": $fa-var-flag-usa,\n  \"laptop-file\": $fa-var-laptop-file,\n  \"tty\": $fa-var-tty,\n  \"teletype\": $fa-var-teletype,\n  \"diagram-next\": $fa-var-diagram-next,\n  \"person-rifle\": $fa-var-person-rifle,\n  \"house-medical-circle-exclamation\": $fa-var-house-medical-circle-exclamation,\n  \"closed-captioning\": $fa-var-closed-captioning,\n  \"person-hiking\": $fa-var-person-hiking,\n  \"hiking\": $fa-var-hiking,\n  \"venus-double\": $fa-var-venus-double,\n  \"images\": $fa-var-images,\n  \"calculator\": $fa-var-calculator,\n  \"people-pulling\": $fa-var-people-pulling,\n  \"n\": $fa-var-n,\n  \"cable-car\": $fa-var-cable-car,\n  \"tram\": $fa-var-tram,\n  \"cloud-rain\": $fa-var-cloud-rain,\n  \"building-circle-xmark\": $fa-var-building-circle-xmark,\n  \"ship\": $fa-var-ship,\n  \"arrows-down-to-line\": $fa-var-arrows-down-to-line,\n  \"download\": $fa-var-download,\n  \"face-grin\": $fa-var-face-grin,\n  \"grin\": $fa-var-grin,\n  \"delete-left\": $fa-var-delete-left,\n  \"backspace\": $fa-var-backspace,\n  \"eye-dropper\": $fa-var-eye-dropper,\n  \"eye-dropper-empty\": $fa-var-eye-dropper-empty,\n  \"eyedropper\": $fa-var-eyedropper,\n  \"file-circle-check\": $fa-var-file-circle-check,\n  \"forward\": $fa-var-forward,\n  \"mobile\": $fa-var-mobile,\n  \"mobile-android\": $fa-var-mobile-android,\n  \"mobile-phone\": $fa-var-mobile-phone,\n  \"face-meh\": $fa-var-face-meh,\n  \"meh\": $fa-var-meh,\n  \"align-center\": $fa-var-align-center,\n  \"book-skull\": $fa-var-book-skull,\n  \"book-dead\": $fa-var-book-dead,\n  \"id-card\": $fa-var-id-card,\n  \"drivers-license\": $fa-var-drivers-license,\n  \"outdent\": $fa-var-outdent,\n  \"dedent\": $fa-var-dedent,\n  \"heart-circle-exclamation\": $fa-var-heart-circle-exclamation,\n  \"house\": $fa-var-house,\n  \"home\": $fa-var-home,\n  \"home-alt\": $fa-var-home-alt,\n  \"home-lg-alt\": $fa-var-home-lg-alt,\n  \"calendar-week\": $fa-var-calendar-week,\n  \"laptop-medical\": $fa-var-laptop-medical,\n  \"b\": $fa-var-b,\n  \"file-medical\": $fa-var-file-medical,\n  \"dice-one\": $fa-var-dice-one,\n  \"kiwi-bird\": $fa-var-kiwi-bird,\n  \"arrow-right-arrow-left\": $fa-var-arrow-right-arrow-left,\n  \"exchange\": $fa-var-exchange,\n  \"rotate-right\": $fa-var-rotate-right,\n  \"redo-alt\": $fa-var-redo-alt,\n  \"rotate-forward\": $fa-var-rotate-forward,\n  \"utensils\": $fa-var-utensils,\n  \"cutlery\": $fa-var-cutlery,\n  \"arrow-up-wide-short\": $fa-var-arrow-up-wide-short,\n  \"sort-amount-up\": $fa-var-sort-amount-up,\n  \"mill-sign\": $fa-var-mill-sign,\n  \"bowl-rice\": $fa-var-bowl-rice,\n  \"skull\": $fa-var-skull,\n  \"tower-broadcast\": $fa-var-tower-broadcast,\n  \"broadcast-tower\": $fa-var-broadcast-tower,\n  \"truck-pickup\": $fa-var-truck-pickup,\n  \"up-long\": $fa-var-up-long,\n  \"long-arrow-alt-up\": $fa-var-long-arrow-alt-up,\n  \"stop\": $fa-var-stop,\n  \"code-merge\": $fa-var-code-merge,\n  \"upload\": $fa-var-upload,\n  \"hurricane\": $fa-var-hurricane,\n  \"mound\": $fa-var-mound,\n  \"toilet-portable\": $fa-var-toilet-portable,\n  \"compact-disc\": $fa-var-compact-disc,\n  \"file-arrow-down\": $fa-var-file-arrow-down,\n  \"file-download\": $fa-var-file-download,\n  \"caravan\": $fa-var-caravan,\n  \"shield-cat\": $fa-var-shield-cat,\n  \"bolt\": $fa-var-bolt,\n  \"zap\": $fa-var-zap,\n  \"glass-water\": $fa-var-glass-water,\n  \"oil-well\": $fa-var-oil-well,\n  \"vault\": $fa-var-vault,\n  \"mars\": $fa-var-mars,\n  \"toilet\": $fa-var-toilet,\n  \"plane-circle-xmark\": $fa-var-plane-circle-xmark,\n  \"yen-sign\": $fa-var-yen-sign,\n  \"cny\": $fa-var-cny,\n  \"jpy\": $fa-var-jpy,\n  \"rmb\": $fa-var-rmb,\n  \"yen\": $fa-var-yen,\n  \"ruble-sign\": $fa-var-ruble-sign,\n  \"rouble\": $fa-var-rouble,\n  \"rub\": $fa-var-rub,\n  \"ruble\": $fa-var-ruble,\n  \"sun\": $fa-var-sun,\n  \"guitar\": $fa-var-guitar,\n  \"face-laugh-wink\": $fa-var-face-laugh-wink,\n  \"laugh-wink\": $fa-var-laugh-wink,\n  \"horse-head\": $fa-var-horse-head,\n  \"bore-hole\": $fa-var-bore-hole,\n  \"industry\": $fa-var-industry,\n  \"circle-down\": $fa-var-circle-down,\n  \"arrow-alt-circle-down\": $fa-var-arrow-alt-circle-down,\n  \"arrows-turn-to-dots\": $fa-var-arrows-turn-to-dots,\n  \"florin-sign\": $fa-var-florin-sign,\n  \"arrow-down-short-wide\": $fa-var-arrow-down-short-wide,\n  \"sort-amount-desc\": $fa-var-sort-amount-desc,\n  \"sort-amount-down-alt\": $fa-var-sort-amount-down-alt,\n  \"less-than\": $fa-var-less-than,\n  \"angle-down\": $fa-var-angle-down,\n  \"car-tunnel\": $fa-var-car-tunnel,\n  \"head-side-cough\": $fa-var-head-side-cough,\n  \"grip-lines\": $fa-var-grip-lines,\n  \"thumbs-down\": $fa-var-thumbs-down,\n  \"user-lock\": $fa-var-user-lock,\n  \"arrow-right-long\": $fa-var-arrow-right-long,\n  \"long-arrow-right\": $fa-var-long-arrow-right,\n  \"anchor-circle-xmark\": $fa-var-anchor-circle-xmark,\n  \"ellipsis\": $fa-var-ellipsis,\n  \"ellipsis-h\": $fa-var-ellipsis-h,\n  \"chess-pawn\": $fa-var-chess-pawn,\n  \"kit-medical\": $fa-var-kit-medical,\n  \"first-aid\": $fa-var-first-aid,\n  \"person-through-window\": $fa-var-person-through-window,\n  \"toolbox\": $fa-var-toolbox,\n  \"hands-holding-circle\": $fa-var-hands-holding-circle,\n  \"bug\": $fa-var-bug,\n  \"credit-card\": $fa-var-credit-card,\n  \"credit-card-alt\": $fa-var-credit-card-alt,\n  \"car\": $fa-var-car,\n  \"automobile\": $fa-var-automobile,\n  \"hand-holding-hand\": $fa-var-hand-holding-hand,\n  \"book-open-reader\": $fa-var-book-open-reader,\n  \"book-reader\": $fa-var-book-reader,\n  \"mountain-sun\": $fa-var-mountain-sun,\n  \"arrows-left-right-to-line\": $fa-var-arrows-left-right-to-line,\n  \"dice-d20\": $fa-var-dice-d20,\n  \"truck-droplet\": $fa-var-truck-droplet,\n  \"file-circle-xmark\": $fa-var-file-circle-xmark,\n  \"temperature-arrow-up\": $fa-var-temperature-arrow-up,\n  \"temperature-up\": $fa-var-temperature-up,\n  \"medal\": $fa-var-medal,\n  \"bed\": $fa-var-bed,\n  \"square-h\": $fa-var-square-h,\n  \"h-square\": $fa-var-h-square,\n  \"podcast\": $fa-var-podcast,\n  \"temperature-full\": $fa-var-temperature-full,\n  \"temperature-4\": $fa-var-temperature-4,\n  \"thermometer-4\": $fa-var-thermometer-4,\n  \"thermometer-full\": $fa-var-thermometer-full,\n  \"bell\": $fa-var-bell,\n  \"superscript\": $fa-var-superscript,\n  \"plug-circle-xmark\": $fa-var-plug-circle-xmark,\n  \"star-of-life\": $fa-var-star-of-life,\n  \"phone-slash\": $fa-var-phone-slash,\n  \"paint-roller\": $fa-var-paint-roller,\n  \"handshake-angle\": $fa-var-handshake-angle,\n  \"hands-helping\": $fa-var-hands-helping,\n  \"location-dot\": $fa-var-location-dot,\n  \"map-marker-alt\": $fa-var-map-marker-alt,\n  \"file\": $fa-var-file,\n  \"greater-than\": $fa-var-greater-than,\n  \"person-swimming\": $fa-var-person-swimming,\n  \"swimmer\": $fa-var-swimmer,\n  \"arrow-down\": $fa-var-arrow-down,\n  \"droplet\": $fa-var-droplet,\n  \"tint\": $fa-var-tint,\n  \"eraser\": $fa-var-eraser,\n  \"earth-americas\": $fa-var-earth-americas,\n  \"earth\": $fa-var-earth,\n  \"earth-america\": $fa-var-earth-america,\n  \"globe-americas\": $fa-var-globe-americas,\n  \"person-burst\": $fa-var-person-burst,\n  \"dove\": $fa-var-dove,\n  \"battery-empty\": $fa-var-battery-empty,\n  \"battery-0\": $fa-var-battery-0,\n  \"socks\": $fa-var-socks,\n  \"inbox\": $fa-var-inbox,\n  \"section\": $fa-var-section,\n  \"gauge-high\": $fa-var-gauge-high,\n  \"tachometer-alt\": $fa-var-tachometer-alt,\n  \"tachometer-alt-fast\": $fa-var-tachometer-alt-fast,\n  \"envelope-open-text\": $fa-var-envelope-open-text,\n  \"hospital\": $fa-var-hospital,\n  \"hospital-alt\": $fa-var-hospital-alt,\n  \"hospital-wide\": $fa-var-hospital-wide,\n  \"wine-bottle\": $fa-var-wine-bottle,\n  \"chess-rook\": $fa-var-chess-rook,\n  \"bars-staggered\": $fa-var-bars-staggered,\n  \"reorder\": $fa-var-reorder,\n  \"stream\": $fa-var-stream,\n  \"dharmachakra\": $fa-var-dharmachakra,\n  \"hotdog\": $fa-var-hotdog,\n  \"person-walking-with-cane\": $fa-var-person-walking-with-cane,\n  \"blind\": $fa-var-blind,\n  \"drum\": $fa-var-drum,\n  \"ice-cream\": $fa-var-ice-cream,\n  \"heart-circle-bolt\": $fa-var-heart-circle-bolt,\n  \"fax\": $fa-var-fax,\n  \"paragraph\": $fa-var-paragraph,\n  \"check-to-slot\": $fa-var-check-to-slot,\n  \"vote-yea\": $fa-var-vote-yea,\n  \"star-half\": $fa-var-star-half,\n  \"boxes-stacked\": $fa-var-boxes-stacked,\n  \"boxes\": $fa-var-boxes,\n  \"boxes-alt\": $fa-var-boxes-alt,\n  \"link\": $fa-var-link,\n  \"chain\": $fa-var-chain,\n  \"ear-listen\": $fa-var-ear-listen,\n  \"assistive-listening-systems\": $fa-var-assistive-listening-systems,\n  \"tree-city\": $fa-var-tree-city,\n  \"play\": $fa-var-play,\n  \"font\": $fa-var-font,\n  \"table-cells-row-lock\": $fa-var-table-cells-row-lock,\n  \"rupiah-sign\": $fa-var-rupiah-sign,\n  \"magnifying-glass\": $fa-var-magnifying-glass,\n  \"search\": $fa-var-search,\n  \"table-tennis-paddle-ball\": $fa-var-table-tennis-paddle-ball,\n  \"ping-pong-paddle-ball\": $fa-var-ping-pong-paddle-ball,\n  \"table-tennis\": $fa-var-table-tennis,\n  \"person-dots-from-line\": $fa-var-person-dots-from-line,\n  \"diagnoses\": $fa-var-diagnoses,\n  \"trash-can-arrow-up\": $fa-var-trash-can-arrow-up,\n  \"trash-restore-alt\": $fa-var-trash-restore-alt,\n  \"naira-sign\": $fa-var-naira-sign,\n  \"cart-arrow-down\": $fa-var-cart-arrow-down,\n  \"walkie-talkie\": $fa-var-walkie-talkie,\n  \"file-pen\": $fa-var-file-pen,\n  \"file-edit\": $fa-var-file-edit,\n  \"receipt\": $fa-var-receipt,\n  \"square-pen\": $fa-var-square-pen,\n  \"pen-square\": $fa-var-pen-square,\n  \"pencil-square\": $fa-var-pencil-square,\n  \"suitcase-rolling\": $fa-var-suitcase-rolling,\n  \"person-circle-exclamation\": $fa-var-person-circle-exclamation,\n  \"chevron-down\": $fa-var-chevron-down,\n  \"battery-full\": $fa-var-battery-full,\n  \"battery\": $fa-var-battery,\n  \"battery-5\": $fa-var-battery-5,\n  \"skull-crossbones\": $fa-var-skull-crossbones,\n  \"code-compare\": $fa-var-code-compare,\n  \"list-ul\": $fa-var-list-ul,\n  \"list-dots\": $fa-var-list-dots,\n  \"school-lock\": $fa-var-school-lock,\n  \"tower-cell\": $fa-var-tower-cell,\n  \"down-long\": $fa-var-down-long,\n  \"long-arrow-alt-down\": $fa-var-long-arrow-alt-down,\n  \"ranking-star\": $fa-var-ranking-star,\n  \"chess-king\": $fa-var-chess-king,\n  \"person-harassing\": $fa-var-person-harassing,\n  \"brazilian-real-sign\": $fa-var-brazilian-real-sign,\n  \"landmark-dome\": $fa-var-landmark-dome,\n  \"landmark-alt\": $fa-var-landmark-alt,\n  \"arrow-up\": $fa-var-arrow-up,\n  \"tv\": $fa-var-tv,\n  \"television\": $fa-var-television,\n  \"tv-alt\": $fa-var-tv-alt,\n  \"shrimp\": $fa-var-shrimp,\n  \"list-check\": $fa-var-list-check,\n  \"tasks\": $fa-var-tasks,\n  \"jug-detergent\": $fa-var-jug-detergent,\n  \"circle-user\": $fa-var-circle-user,\n  \"user-circle\": $fa-var-user-circle,\n  \"user-shield\": $fa-var-user-shield,\n  \"wind\": $fa-var-wind,\n  \"car-burst\": $fa-var-car-burst,\n  \"car-crash\": $fa-var-car-crash,\n  \"y\": $fa-var-y,\n  \"person-snowboarding\": $fa-var-person-snowboarding,\n  \"snowboarding\": $fa-var-snowboarding,\n  \"truck-fast\": $fa-var-truck-fast,\n  \"shipping-fast\": $fa-var-shipping-fast,\n  \"fish\": $fa-var-fish,\n  \"user-graduate\": $fa-var-user-graduate,\n  \"circle-half-stroke\": $fa-var-circle-half-stroke,\n  \"adjust\": $fa-var-adjust,\n  \"clapperboard\": $fa-var-clapperboard,\n  \"circle-radiation\": $fa-var-circle-radiation,\n  \"radiation-alt\": $fa-var-radiation-alt,\n  \"baseball\": $fa-var-baseball,\n  \"baseball-ball\": $fa-var-baseball-ball,\n  \"jet-fighter-up\": $fa-var-jet-fighter-up,\n  \"diagram-project\": $fa-var-diagram-project,\n  \"project-diagram\": $fa-var-project-diagram,\n  \"copy\": $fa-var-copy,\n  \"volume-xmark\": $fa-var-volume-xmark,\n  \"volume-mute\": $fa-var-volume-mute,\n  \"volume-times\": $fa-var-volume-times,\n  \"hand-sparkles\": $fa-var-hand-sparkles,\n  \"grip\": $fa-var-grip,\n  \"grip-horizontal\": $fa-var-grip-horizontal,\n  \"share-from-square\": $fa-var-share-from-square,\n  \"share-square\": $fa-var-share-square,\n  \"child-combatant\": $fa-var-child-combatant,\n  \"child-rifle\": $fa-var-child-rifle,\n  \"gun\": $fa-var-gun,\n  \"square-phone\": $fa-var-square-phone,\n  \"phone-square\": $fa-var-phone-square,\n  \"plus\": $fa-var-plus,\n  \"add\": $fa-var-add,\n  \"expand\": $fa-var-expand,\n  \"computer\": $fa-var-computer,\n  \"xmark\": $fa-var-xmark,\n  \"close\": $fa-var-close,\n  \"multiply\": $fa-var-multiply,\n  \"remove\": $fa-var-remove,\n  \"times\": $fa-var-times,\n  \"arrows-up-down-left-right\": $fa-var-arrows-up-down-left-right,\n  \"arrows\": $fa-var-arrows,\n  \"chalkboard-user\": $fa-var-chalkboard-user,\n  \"chalkboard-teacher\": $fa-var-chalkboard-teacher,\n  \"peso-sign\": $fa-var-peso-sign,\n  \"building-shield\": $fa-var-building-shield,\n  \"baby\": $fa-var-baby,\n  \"users-line\": $fa-var-users-line,\n  \"quote-left\": $fa-var-quote-left,\n  \"quote-left-alt\": $fa-var-quote-left-alt,\n  \"tractor\": $fa-var-tractor,\n  \"trash-arrow-up\": $fa-var-trash-arrow-up,\n  \"trash-restore\": $fa-var-trash-restore,\n  \"arrow-down-up-lock\": $fa-var-arrow-down-up-lock,\n  \"lines-leaning\": $fa-var-lines-leaning,\n  \"ruler-combined\": $fa-var-ruler-combined,\n  \"copyright\": $fa-var-copyright,\n  \"equals\": $fa-var-equals,\n  \"blender\": $fa-var-blender,\n  \"teeth\": $fa-var-teeth,\n  \"shekel-sign\": $fa-var-shekel-sign,\n  \"ils\": $fa-var-ils,\n  \"shekel\": $fa-var-shekel,\n  \"sheqel\": $fa-var-sheqel,\n  \"sheqel-sign\": $fa-var-sheqel-sign,\n  \"map\": $fa-var-map,\n  \"rocket\": $fa-var-rocket,\n  \"photo-film\": $fa-var-photo-film,\n  \"photo-video\": $fa-var-photo-video,\n  \"folder-minus\": $fa-var-folder-minus,\n  \"store\": $fa-var-store,\n  \"arrow-trend-up\": $fa-var-arrow-trend-up,\n  \"plug-circle-minus\": $fa-var-plug-circle-minus,\n  \"sign-hanging\": $fa-var-sign-hanging,\n  \"sign\": $fa-var-sign,\n  \"bezier-curve\": $fa-var-bezier-curve,\n  \"bell-slash\": $fa-var-bell-slash,\n  \"tablet\": $fa-var-tablet,\n  \"tablet-android\": $fa-var-tablet-android,\n  \"school-flag\": $fa-var-school-flag,\n  \"fill\": $fa-var-fill,\n  \"angle-up\": $fa-var-angle-up,\n  \"drumstick-bite\": $fa-var-drumstick-bite,\n  \"holly-berry\": $fa-var-holly-berry,\n  \"chevron-left\": $fa-var-chevron-left,\n  \"bacteria\": $fa-var-bacteria,\n  \"hand-lizard\": $fa-var-hand-lizard,\n  \"notdef\": $fa-var-notdef,\n  \"disease\": $fa-var-disease,\n  \"briefcase-medical\": $fa-var-briefcase-medical,\n  \"genderless\": $fa-var-genderless,\n  \"chevron-right\": $fa-var-chevron-right,\n  \"retweet\": $fa-var-retweet,\n  \"car-rear\": $fa-var-car-rear,\n  \"car-alt\": $fa-var-car-alt,\n  \"pump-soap\": $fa-var-pump-soap,\n  \"video-slash\": $fa-var-video-slash,\n  \"battery-quarter\": $fa-var-battery-quarter,\n  \"battery-2\": $fa-var-battery-2,\n  \"radio\": $fa-var-radio,\n  \"baby-carriage\": $fa-var-baby-carriage,\n  \"carriage-baby\": $fa-var-carriage-baby,\n  \"traffic-light\": $fa-var-traffic-light,\n  \"thermometer\": $fa-var-thermometer,\n  \"vr-cardboard\": $fa-var-vr-cardboard,\n  \"hand-middle-finger\": $fa-var-hand-middle-finger,\n  \"percent\": $fa-var-percent,\n  \"percentage\": $fa-var-percentage,\n  \"truck-moving\": $fa-var-truck-moving,\n  \"glass-water-droplet\": $fa-var-glass-water-droplet,\n  \"display\": $fa-var-display,\n  \"face-smile\": $fa-var-face-smile,\n  \"smile\": $fa-var-smile,\n  \"thumbtack\": $fa-var-thumbtack,\n  \"thumb-tack\": $fa-var-thumb-tack,\n  \"trophy\": $fa-var-trophy,\n  \"person-praying\": $fa-var-person-praying,\n  \"pray\": $fa-var-pray,\n  \"hammer\": $fa-var-hammer,\n  \"hand-peace\": $fa-var-hand-peace,\n  \"rotate\": $fa-var-rotate,\n  \"sync-alt\": $fa-var-sync-alt,\n  \"spinner\": $fa-var-spinner,\n  \"robot\": $fa-var-robot,\n  \"peace\": $fa-var-peace,\n  \"gears\": $fa-var-gears,\n  \"cogs\": $fa-var-cogs,\n  \"warehouse\": $fa-var-warehouse,\n  \"arrow-up-right-dots\": $fa-var-arrow-up-right-dots,\n  \"splotch\": $fa-var-splotch,\n  \"face-grin-hearts\": $fa-var-face-grin-hearts,\n  \"grin-hearts\": $fa-var-grin-hearts,\n  \"dice-four\": $fa-var-dice-four,\n  \"sim-card\": $fa-var-sim-card,\n  \"transgender\": $fa-var-transgender,\n  \"transgender-alt\": $fa-var-transgender-alt,\n  \"mercury\": $fa-var-mercury,\n  \"arrow-turn-down\": $fa-var-arrow-turn-down,\n  \"level-down\": $fa-var-level-down,\n  \"person-falling-burst\": $fa-var-person-falling-burst,\n  \"award\": $fa-var-award,\n  \"ticket-simple\": $fa-var-ticket-simple,\n  \"ticket-alt\": $fa-var-ticket-alt,\n  \"building\": $fa-var-building,\n  \"angles-left\": $fa-var-angles-left,\n  \"angle-double-left\": $fa-var-angle-double-left,\n  \"qrcode\": $fa-var-qrcode,\n  \"clock-rotate-left\": $fa-var-clock-rotate-left,\n  \"history\": $fa-var-history,\n  \"face-grin-beam-sweat\": $fa-var-face-grin-beam-sweat,\n  \"grin-beam-sweat\": $fa-var-grin-beam-sweat,\n  \"file-export\": $fa-var-file-export,\n  \"arrow-right-from-file\": $fa-var-arrow-right-from-file,\n  \"shield\": $fa-var-shield,\n  \"shield-blank\": $fa-var-shield-blank,\n  \"arrow-up-short-wide\": $fa-var-arrow-up-short-wide,\n  \"sort-amount-up-alt\": $fa-var-sort-amount-up-alt,\n  \"house-medical\": $fa-var-house-medical,\n  \"golf-ball-tee\": $fa-var-golf-ball-tee,\n  \"golf-ball\": $fa-var-golf-ball,\n  \"circle-chevron-left\": $fa-var-circle-chevron-left,\n  \"chevron-circle-left\": $fa-var-chevron-circle-left,\n  \"house-chimney-window\": $fa-var-house-chimney-window,\n  \"pen-nib\": $fa-var-pen-nib,\n  \"tent-arrow-turn-left\": $fa-var-tent-arrow-turn-left,\n  \"tents\": $fa-var-tents,\n  \"wand-magic\": $fa-var-wand-magic,\n  \"magic\": $fa-var-magic,\n  \"dog\": $fa-var-dog,\n  \"carrot\": $fa-var-carrot,\n  \"moon\": $fa-var-moon,\n  \"wine-glass-empty\": $fa-var-wine-glass-empty,\n  \"wine-glass-alt\": $fa-var-wine-glass-alt,\n  \"cheese\": $fa-var-cheese,\n  \"yin-yang\": $fa-var-yin-yang,\n  \"music\": $fa-var-music,\n  \"code-commit\": $fa-var-code-commit,\n  \"temperature-low\": $fa-var-temperature-low,\n  \"person-biking\": $fa-var-person-biking,\n  \"biking\": $fa-var-biking,\n  \"broom\": $fa-var-broom,\n  \"shield-heart\": $fa-var-shield-heart,\n  \"gopuram\": $fa-var-gopuram,\n  \"earth-oceania\": $fa-var-earth-oceania,\n  \"globe-oceania\": $fa-var-globe-oceania,\n  \"square-xmark\": $fa-var-square-xmark,\n  \"times-square\": $fa-var-times-square,\n  \"xmark-square\": $fa-var-xmark-square,\n  \"hashtag\": $fa-var-hashtag,\n  \"up-right-and-down-left-from-center\": $fa-var-up-right-and-down-left-from-center,\n  \"expand-alt\": $fa-var-expand-alt,\n  \"oil-can\": $fa-var-oil-can,\n  \"t\": $fa-var-t,\n  \"hippo\": $fa-var-hippo,\n  \"chart-column\": $fa-var-chart-column,\n  \"infinity\": $fa-var-infinity,\n  \"vial-circle-check\": $fa-var-vial-circle-check,\n  \"person-arrow-down-to-line\": $fa-var-person-arrow-down-to-line,\n  \"voicemail\": $fa-var-voicemail,\n  \"fan\": $fa-var-fan,\n  \"person-walking-luggage\": $fa-var-person-walking-luggage,\n  \"up-down\": $fa-var-up-down,\n  \"arrows-alt-v\": $fa-var-arrows-alt-v,\n  \"cloud-moon-rain\": $fa-var-cloud-moon-rain,\n  \"calendar\": $fa-var-calendar,\n  \"trailer\": $fa-var-trailer,\n  \"bahai\": $fa-var-bahai,\n  \"haykal\": $fa-var-haykal,\n  \"sd-card\": $fa-var-sd-card,\n  \"dragon\": $fa-var-dragon,\n  \"shoe-prints\": $fa-var-shoe-prints,\n  \"circle-plus\": $fa-var-circle-plus,\n  \"plus-circle\": $fa-var-plus-circle,\n  \"face-grin-tongue-wink\": $fa-var-face-grin-tongue-wink,\n  \"grin-tongue-wink\": $fa-var-grin-tongue-wink,\n  \"hand-holding\": $fa-var-hand-holding,\n  \"plug-circle-exclamation\": $fa-var-plug-circle-exclamation,\n  \"link-slash\": $fa-var-link-slash,\n  \"chain-broken\": $fa-var-chain-broken,\n  \"chain-slash\": $fa-var-chain-slash,\n  \"unlink\": $fa-var-unlink,\n  \"clone\": $fa-var-clone,\n  \"person-walking-arrow-loop-left\": $fa-var-person-walking-arrow-loop-left,\n  \"arrow-up-z-a\": $fa-var-arrow-up-z-a,\n  \"sort-alpha-up-alt\": $fa-var-sort-alpha-up-alt,\n  \"fire-flame-curved\": $fa-var-fire-flame-curved,\n  \"fire-alt\": $fa-var-fire-alt,\n  \"tornado\": $fa-var-tornado,\n  \"file-circle-plus\": $fa-var-file-circle-plus,\n  \"book-quran\": $fa-var-book-quran,\n  \"quran\": $fa-var-quran,\n  \"anchor\": $fa-var-anchor,\n  \"border-all\": $fa-var-border-all,\n  \"face-angry\": $fa-var-face-angry,\n  \"angry\": $fa-var-angry,\n  \"cookie-bite\": $fa-var-cookie-bite,\n  \"arrow-trend-down\": $fa-var-arrow-trend-down,\n  \"rss\": $fa-var-rss,\n  \"feed\": $fa-var-feed,\n  \"draw-polygon\": $fa-var-draw-polygon,\n  \"scale-balanced\": $fa-var-scale-balanced,\n  \"balance-scale\": $fa-var-balance-scale,\n  \"gauge-simple-high\": $fa-var-gauge-simple-high,\n  \"tachometer\": $fa-var-tachometer,\n  \"tachometer-fast\": $fa-var-tachometer-fast,\n  \"shower\": $fa-var-shower,\n  \"desktop\": $fa-var-desktop,\n  \"desktop-alt\": $fa-var-desktop-alt,\n  \"m\": $fa-var-m,\n  \"table-list\": $fa-var-table-list,\n  \"th-list\": $fa-var-th-list,\n  \"comment-sms\": $fa-var-comment-sms,\n  \"sms\": $fa-var-sms,\n  \"book\": $fa-var-book,\n  \"user-plus\": $fa-var-user-plus,\n  \"check\": $fa-var-check,\n  \"battery-three-quarters\": $fa-var-battery-three-quarters,\n  \"battery-4\": $fa-var-battery-4,\n  \"house-circle-check\": $fa-var-house-circle-check,\n  \"angle-left\": $fa-var-angle-left,\n  \"diagram-successor\": $fa-var-diagram-successor,\n  \"truck-arrow-right\": $fa-var-truck-arrow-right,\n  \"arrows-split-up-and-left\": $fa-var-arrows-split-up-and-left,\n  \"hand-fist\": $fa-var-hand-fist,\n  \"fist-raised\": $fa-var-fist-raised,\n  \"cloud-moon\": $fa-var-cloud-moon,\n  \"briefcase\": $fa-var-briefcase,\n  \"person-falling\": $fa-var-person-falling,\n  \"image-portrait\": $fa-var-image-portrait,\n  \"portrait\": $fa-var-portrait,\n  \"user-tag\": $fa-var-user-tag,\n  \"rug\": $fa-var-rug,\n  \"earth-europe\": $fa-var-earth-europe,\n  \"globe-europe\": $fa-var-globe-europe,\n  \"cart-flatbed-suitcase\": $fa-var-cart-flatbed-suitcase,\n  \"luggage-cart\": $fa-var-luggage-cart,\n  \"rectangle-xmark\": $fa-var-rectangle-xmark,\n  \"rectangle-times\": $fa-var-rectangle-times,\n  \"times-rectangle\": $fa-var-times-rectangle,\n  \"window-close\": $fa-var-window-close,\n  \"baht-sign\": $fa-var-baht-sign,\n  \"book-open\": $fa-var-book-open,\n  \"book-journal-whills\": $fa-var-book-journal-whills,\n  \"journal-whills\": $fa-var-journal-whills,\n  \"handcuffs\": $fa-var-handcuffs,\n  \"triangle-exclamation\": $fa-var-triangle-exclamation,\n  \"exclamation-triangle\": $fa-var-exclamation-triangle,\n  \"warning\": $fa-var-warning,\n  \"database\": $fa-var-database,\n  \"share\": $fa-var-share,\n  \"mail-forward\": $fa-var-mail-forward,\n  \"bottle-droplet\": $fa-var-bottle-droplet,\n  \"mask-face\": $fa-var-mask-face,\n  \"hill-rockslide\": $fa-var-hill-rockslide,\n  \"right-left\": $fa-var-right-left,\n  \"exchange-alt\": $fa-var-exchange-alt,\n  \"paper-plane\": $fa-var-paper-plane,\n  \"road-circle-exclamation\": $fa-var-road-circle-exclamation,\n  \"dungeon\": $fa-var-dungeon,\n  \"align-right\": $fa-var-align-right,\n  \"money-bill-1-wave\": $fa-var-money-bill-1-wave,\n  \"money-bill-wave-alt\": $fa-var-money-bill-wave-alt,\n  \"life-ring\": $fa-var-life-ring,\n  \"hands\": $fa-var-hands,\n  \"sign-language\": $fa-var-sign-language,\n  \"signing\": $fa-var-signing,\n  \"calendar-day\": $fa-var-calendar-day,\n  \"water-ladder\": $fa-var-water-ladder,\n  \"ladder-water\": $fa-var-ladder-water,\n  \"swimming-pool\": $fa-var-swimming-pool,\n  \"arrows-up-down\": $fa-var-arrows-up-down,\n  \"arrows-v\": $fa-var-arrows-v,\n  \"face-grimace\": $fa-var-face-grimace,\n  \"grimace\": $fa-var-grimace,\n  \"wheelchair-move\": $fa-var-wheelchair-move,\n  \"wheelchair-alt\": $fa-var-wheelchair-alt,\n  \"turn-down\": $fa-var-turn-down,\n  \"level-down-alt\": $fa-var-level-down-alt,\n  \"person-walking-arrow-right\": $fa-var-person-walking-arrow-right,\n  \"square-envelope\": $fa-var-square-envelope,\n  \"envelope-square\": $fa-var-envelope-square,\n  \"dice\": $fa-var-dice,\n  \"bowling-ball\": $fa-var-bowling-ball,\n  \"brain\": $fa-var-brain,\n  \"bandage\": $fa-var-bandage,\n  \"band-aid\": $fa-var-band-aid,\n  \"calendar-minus\": $fa-var-calendar-minus,\n  \"circle-xmark\": $fa-var-circle-xmark,\n  \"times-circle\": $fa-var-times-circle,\n  \"xmark-circle\": $fa-var-xmark-circle,\n  \"gifts\": $fa-var-gifts,\n  \"hotel\": $fa-var-hotel,\n  \"earth-asia\": $fa-var-earth-asia,\n  \"globe-asia\": $fa-var-globe-asia,\n  \"id-card-clip\": $fa-var-id-card-clip,\n  \"id-card-alt\": $fa-var-id-card-alt,\n  \"magnifying-glass-plus\": $fa-var-magnifying-glass-plus,\n  \"search-plus\": $fa-var-search-plus,\n  \"thumbs-up\": $fa-var-thumbs-up,\n  \"user-clock\": $fa-var-user-clock,\n  \"hand-dots\": $fa-var-hand-dots,\n  \"allergies\": $fa-var-allergies,\n  \"file-invoice\": $fa-var-file-invoice,\n  \"window-minimize\": $fa-var-window-minimize,\n  \"mug-saucer\": $fa-var-mug-saucer,\n  \"coffee\": $fa-var-coffee,\n  \"brush\": $fa-var-brush,\n  \"mask\": $fa-var-mask,\n  \"magnifying-glass-minus\": $fa-var-magnifying-glass-minus,\n  \"search-minus\": $fa-var-search-minus,\n  \"ruler-vertical\": $fa-var-ruler-vertical,\n  \"user-large\": $fa-var-user-large,\n  \"user-alt\": $fa-var-user-alt,\n  \"train-tram\": $fa-var-train-tram,\n  \"user-nurse\": $fa-var-user-nurse,\n  \"syringe\": $fa-var-syringe,\n  \"cloud-sun\": $fa-var-cloud-sun,\n  \"stopwatch-20\": $fa-var-stopwatch-20,\n  \"square-full\": $fa-var-square-full,\n  \"magnet\": $fa-var-magnet,\n  \"jar\": $fa-var-jar,\n  \"note-sticky\": $fa-var-note-sticky,\n  \"sticky-note\": $fa-var-sticky-note,\n  \"bug-slash\": $fa-var-bug-slash,\n  \"arrow-up-from-water-pump\": $fa-var-arrow-up-from-water-pump,\n  \"bone\": $fa-var-bone,\n  \"user-injured\": $fa-var-user-injured,\n  \"face-sad-tear\": $fa-var-face-sad-tear,\n  \"sad-tear\": $fa-var-sad-tear,\n  \"plane\": $fa-var-plane,\n  \"tent-arrows-down\": $fa-var-tent-arrows-down,\n  \"exclamation\": $fa-var-exclamation,\n  \"arrows-spin\": $fa-var-arrows-spin,\n  \"print\": $fa-var-print,\n  \"turkish-lira-sign\": $fa-var-turkish-lira-sign,\n  \"try\": $fa-var-try,\n  \"turkish-lira\": $fa-var-turkish-lira,\n  \"dollar-sign\": $fa-var-dollar-sign,\n  \"dollar\": $fa-var-dollar,\n  \"usd\": $fa-var-usd,\n  \"x\": $fa-var-x,\n  \"magnifying-glass-dollar\": $fa-var-magnifying-glass-dollar,\n  \"search-dollar\": $fa-var-search-dollar,\n  \"users-gear\": $fa-var-users-gear,\n  \"users-cog\": $fa-var-users-cog,\n  \"person-military-pointing\": $fa-var-person-military-pointing,\n  \"building-columns\": $fa-var-building-columns,\n  \"bank\": $fa-var-bank,\n  \"institution\": $fa-var-institution,\n  \"museum\": $fa-var-museum,\n  \"university\": $fa-var-university,\n  \"umbrella\": $fa-var-umbrella,\n  \"trowel\": $fa-var-trowel,\n  \"d\": $fa-var-d,\n  \"stapler\": $fa-var-stapler,\n  \"masks-theater\": $fa-var-masks-theater,\n  \"theater-masks\": $fa-var-theater-masks,\n  \"kip-sign\": $fa-var-kip-sign,\n  \"hand-point-left\": $fa-var-hand-point-left,\n  \"handshake-simple\": $fa-var-handshake-simple,\n  \"handshake-alt\": $fa-var-handshake-alt,\n  \"jet-fighter\": $fa-var-jet-fighter,\n  \"fighter-jet\": $fa-var-fighter-jet,\n  \"square-share-nodes\": $fa-var-square-share-nodes,\n  \"share-alt-square\": $fa-var-share-alt-square,\n  \"barcode\": $fa-var-barcode,\n  \"plus-minus\": $fa-var-plus-minus,\n  \"video\": $fa-var-video,\n  \"video-camera\": $fa-var-video-camera,\n  \"graduation-cap\": $fa-var-graduation-cap,\n  \"mortar-board\": $fa-var-mortar-board,\n  \"hand-holding-medical\": $fa-var-hand-holding-medical,\n  \"person-circle-check\": $fa-var-person-circle-check,\n  \"turn-up\": $fa-var-turn-up,\n  \"level-up-alt\": $fa-var-level-up-alt,\n);\n\n$fa-brand-icons: (\n  \"monero\": $fa-var-monero,\n  \"hooli\": $fa-var-hooli,\n  \"yelp\": $fa-var-yelp,\n  \"cc-visa\": $fa-var-cc-visa,\n  \"lastfm\": $fa-var-lastfm,\n  \"shopware\": $fa-var-shopware,\n  \"creative-commons-nc\": $fa-var-creative-commons-nc,\n  \"aws\": $fa-var-aws,\n  \"redhat\": $fa-var-redhat,\n  \"yoast\": $fa-var-yoast,\n  \"cloudflare\": $fa-var-cloudflare,\n  \"ups\": $fa-var-ups,\n  \"pixiv\": $fa-var-pixiv,\n  \"wpexplorer\": $fa-var-wpexplorer,\n  \"dyalog\": $fa-var-dyalog,\n  \"bity\": $fa-var-bity,\n  \"stackpath\": $fa-var-stackpath,\n  \"buysellads\": $fa-var-buysellads,\n  \"first-order\": $fa-var-first-order,\n  \"modx\": $fa-var-modx,\n  \"guilded\": $fa-var-guilded,\n  \"vnv\": $fa-var-vnv,\n  \"square-js\": $fa-var-square-js,\n  \"js-square\": $fa-var-js-square,\n  \"microsoft\": $fa-var-microsoft,\n  \"qq\": $fa-var-qq,\n  \"orcid\": $fa-var-orcid,\n  \"java\": $fa-var-java,\n  \"invision\": $fa-var-invision,\n  \"creative-commons-pd-alt\": $fa-var-creative-commons-pd-alt,\n  \"centercode\": $fa-var-centercode,\n  \"glide-g\": $fa-var-glide-g,\n  \"drupal\": $fa-var-drupal,\n  \"jxl\": $fa-var-jxl,\n  \"hire-a-helper\": $fa-var-hire-a-helper,\n  \"creative-commons-by\": $fa-var-creative-commons-by,\n  \"unity\": $fa-var-unity,\n  \"whmcs\": $fa-var-whmcs,\n  \"rocketchat\": $fa-var-rocketchat,\n  \"vk\": $fa-var-vk,\n  \"untappd\": $fa-var-untappd,\n  \"mailchimp\": $fa-var-mailchimp,\n  \"css3-alt\": $fa-var-css3-alt,\n  \"square-reddit\": $fa-var-square-reddit,\n  \"reddit-square\": $fa-var-reddit-square,\n  \"vimeo-v\": $fa-var-vimeo-v,\n  \"contao\": $fa-var-contao,\n  \"square-font-awesome\": $fa-var-square-font-awesome,\n  \"deskpro\": $fa-var-deskpro,\n  \"brave\": $fa-var-brave,\n  \"sistrix\": $fa-var-sistrix,\n  \"square-instagram\": $fa-var-square-instagram,\n  \"instagram-square\": $fa-var-instagram-square,\n  \"battle-net\": $fa-var-battle-net,\n  \"the-red-yeti\": $fa-var-the-red-yeti,\n  \"square-hacker-news\": $fa-var-square-hacker-news,\n  \"hacker-news-square\": $fa-var-hacker-news-square,\n  \"edge\": $fa-var-edge,\n  \"threads\": $fa-var-threads,\n  \"napster\": $fa-var-napster,\n  \"square-snapchat\": $fa-var-square-snapchat,\n  \"snapchat-square\": $fa-var-snapchat-square,\n  \"google-plus-g\": $fa-var-google-plus-g,\n  \"artstation\": $fa-var-artstation,\n  \"markdown\": $fa-var-markdown,\n  \"sourcetree\": $fa-var-sourcetree,\n  \"google-plus\": $fa-var-google-plus,\n  \"diaspora\": $fa-var-diaspora,\n  \"foursquare\": $fa-var-foursquare,\n  \"stack-overflow\": $fa-var-stack-overflow,\n  \"github-alt\": $fa-var-github-alt,\n  \"phoenix-squadron\": $fa-var-phoenix-squadron,\n  \"pagelines\": $fa-var-pagelines,\n  \"algolia\": $fa-var-algolia,\n  \"red-river\": $fa-var-red-river,\n  \"creative-commons-sa\": $fa-var-creative-commons-sa,\n  \"safari\": $fa-var-safari,\n  \"google\": $fa-var-google,\n  \"square-font-awesome-stroke\": $fa-var-square-font-awesome-stroke,\n  \"font-awesome-alt\": $fa-var-font-awesome-alt,\n  \"atlassian\": $fa-var-atlassian,\n  \"linkedin-in\": $fa-var-linkedin-in,\n  \"digital-ocean\": $fa-var-digital-ocean,\n  \"nimblr\": $fa-var-nimblr,\n  \"chromecast\": $fa-var-chromecast,\n  \"evernote\": $fa-var-evernote,\n  \"hacker-news\": $fa-var-hacker-news,\n  \"creative-commons-sampling\": $fa-var-creative-commons-sampling,\n  \"adversal\": $fa-var-adversal,\n  \"creative-commons\": $fa-var-creative-commons,\n  \"watchman-monitoring\": $fa-var-watchman-monitoring,\n  \"fonticons\": $fa-var-fonticons,\n  \"weixin\": $fa-var-weixin,\n  \"shirtsinbulk\": $fa-var-shirtsinbulk,\n  \"codepen\": $fa-var-codepen,\n  \"git-alt\": $fa-var-git-alt,\n  \"lyft\": $fa-var-lyft,\n  \"rev\": $fa-var-rev,\n  \"windows\": $fa-var-windows,\n  \"wizards-of-the-coast\": $fa-var-wizards-of-the-coast,\n  \"square-viadeo\": $fa-var-square-viadeo,\n  \"viadeo-square\": $fa-var-viadeo-square,\n  \"meetup\": $fa-var-meetup,\n  \"centos\": $fa-var-centos,\n  \"adn\": $fa-var-adn,\n  \"cloudsmith\": $fa-var-cloudsmith,\n  \"opensuse\": $fa-var-opensuse,\n  \"pied-piper-alt\": $fa-var-pied-piper-alt,\n  \"square-dribbble\": $fa-var-square-dribbble,\n  \"dribbble-square\": $fa-var-dribbble-square,\n  \"codiepie\": $fa-var-codiepie,\n  \"node\": $fa-var-node,\n  \"mix\": $fa-var-mix,\n  \"steam\": $fa-var-steam,\n  \"cc-apple-pay\": $fa-var-cc-apple-pay,\n  \"scribd\": $fa-var-scribd,\n  \"debian\": $fa-var-debian,\n  \"openid\": $fa-var-openid,\n  \"instalod\": $fa-var-instalod,\n  \"expeditedssl\": $fa-var-expeditedssl,\n  \"sellcast\": $fa-var-sellcast,\n  \"square-twitter\": $fa-var-square-twitter,\n  \"twitter-square\": $fa-var-twitter-square,\n  \"r-project\": $fa-var-r-project,\n  \"delicious\": $fa-var-delicious,\n  \"freebsd\": $fa-var-freebsd,\n  \"vuejs\": $fa-var-vuejs,\n  \"accusoft\": $fa-var-accusoft,\n  \"ioxhost\": $fa-var-ioxhost,\n  \"fonticons-fi\": $fa-var-fonticons-fi,\n  \"app-store\": $fa-var-app-store,\n  \"cc-mastercard\": $fa-var-cc-mastercard,\n  \"itunes-note\": $fa-var-itunes-note,\n  \"golang\": $fa-var-golang,\n  \"kickstarter\": $fa-var-kickstarter,\n  \"square-kickstarter\": $fa-var-square-kickstarter,\n  \"grav\": $fa-var-grav,\n  \"weibo\": $fa-var-weibo,\n  \"uncharted\": $fa-var-uncharted,\n  \"firstdraft\": $fa-var-firstdraft,\n  \"square-youtube\": $fa-var-square-youtube,\n  \"youtube-square\": $fa-var-youtube-square,\n  \"wikipedia-w\": $fa-var-wikipedia-w,\n  \"wpressr\": $fa-var-wpressr,\n  \"rendact\": $fa-var-rendact,\n  \"angellist\": $fa-var-angellist,\n  \"galactic-republic\": $fa-var-galactic-republic,\n  \"nfc-directional\": $fa-var-nfc-directional,\n  \"skype\": $fa-var-skype,\n  \"joget\": $fa-var-joget,\n  \"fedora\": $fa-var-fedora,\n  \"stripe-s\": $fa-var-stripe-s,\n  \"meta\": $fa-var-meta,\n  \"laravel\": $fa-var-laravel,\n  \"hotjar\": $fa-var-hotjar,\n  \"bluetooth-b\": $fa-var-bluetooth-b,\n  \"square-letterboxd\": $fa-var-square-letterboxd,\n  \"sticker-mule\": $fa-var-sticker-mule,\n  \"creative-commons-zero\": $fa-var-creative-commons-zero,\n  \"hips\": $fa-var-hips,\n  \"behance\": $fa-var-behance,\n  \"reddit\": $fa-var-reddit,\n  \"discord\": $fa-var-discord,\n  \"chrome\": $fa-var-chrome,\n  \"app-store-ios\": $fa-var-app-store-ios,\n  \"cc-discover\": $fa-var-cc-discover,\n  \"wpbeginner\": $fa-var-wpbeginner,\n  \"confluence\": $fa-var-confluence,\n  \"shoelace\": $fa-var-shoelace,\n  \"mdb\": $fa-var-mdb,\n  \"dochub\": $fa-var-dochub,\n  \"accessible-icon\": $fa-var-accessible-icon,\n  \"ebay\": $fa-var-ebay,\n  \"amazon\": $fa-var-amazon,\n  \"unsplash\": $fa-var-unsplash,\n  \"yarn\": $fa-var-yarn,\n  \"square-steam\": $fa-var-square-steam,\n  \"steam-square\": $fa-var-steam-square,\n  \"500px\": $fa-var-500px,\n  \"square-vimeo\": $fa-var-square-vimeo,\n  \"vimeo-square\": $fa-var-vimeo-square,\n  \"asymmetrik\": $fa-var-asymmetrik,\n  \"font-awesome\": $fa-var-font-awesome,\n  \"font-awesome-flag\": $fa-var-font-awesome-flag,\n  \"font-awesome-logo-full\": $fa-var-font-awesome-logo-full,\n  \"gratipay\": $fa-var-gratipay,\n  \"apple\": $fa-var-apple,\n  \"hive\": $fa-var-hive,\n  \"gitkraken\": $fa-var-gitkraken,\n  \"keybase\": $fa-var-keybase,\n  \"apple-pay\": $fa-var-apple-pay,\n  \"padlet\": $fa-var-padlet,\n  \"amazon-pay\": $fa-var-amazon-pay,\n  \"square-github\": $fa-var-square-github,\n  \"github-square\": $fa-var-github-square,\n  \"stumbleupon\": $fa-var-stumbleupon,\n  \"fedex\": $fa-var-fedex,\n  \"phoenix-framework\": $fa-var-phoenix-framework,\n  \"shopify\": $fa-var-shopify,\n  \"neos\": $fa-var-neos,\n  \"square-threads\": $fa-var-square-threads,\n  \"hackerrank\": $fa-var-hackerrank,\n  \"researchgate\": $fa-var-researchgate,\n  \"swift\": $fa-var-swift,\n  \"angular\": $fa-var-angular,\n  \"speakap\": $fa-var-speakap,\n  \"angrycreative\": $fa-var-angrycreative,\n  \"y-combinator\": $fa-var-y-combinator,\n  \"empire\": $fa-var-empire,\n  \"envira\": $fa-var-envira,\n  \"google-scholar\": $fa-var-google-scholar,\n  \"square-gitlab\": $fa-var-square-gitlab,\n  \"gitlab-square\": $fa-var-gitlab-square,\n  \"studiovinari\": $fa-var-studiovinari,\n  \"pied-piper\": $fa-var-pied-piper,\n  \"wordpress\": $fa-var-wordpress,\n  \"product-hunt\": $fa-var-product-hunt,\n  \"firefox\": $fa-var-firefox,\n  \"linode\": $fa-var-linode,\n  \"goodreads\": $fa-var-goodreads,\n  \"square-odnoklassniki\": $fa-var-square-odnoklassniki,\n  \"odnoklassniki-square\": $fa-var-odnoklassniki-square,\n  \"jsfiddle\": $fa-var-jsfiddle,\n  \"sith\": $fa-var-sith,\n  \"themeisle\": $fa-var-themeisle,\n  \"page4\": $fa-var-page4,\n  \"hashnode\": $fa-var-hashnode,\n  \"react\": $fa-var-react,\n  \"cc-paypal\": $fa-var-cc-paypal,\n  \"squarespace\": $fa-var-squarespace,\n  \"cc-stripe\": $fa-var-cc-stripe,\n  \"creative-commons-share\": $fa-var-creative-commons-share,\n  \"bitcoin\": $fa-var-bitcoin,\n  \"keycdn\": $fa-var-keycdn,\n  \"opera\": $fa-var-opera,\n  \"itch-io\": $fa-var-itch-io,\n  \"umbraco\": $fa-var-umbraco,\n  \"galactic-senate\": $fa-var-galactic-senate,\n  \"ubuntu\": $fa-var-ubuntu,\n  \"draft2digital\": $fa-var-draft2digital,\n  \"stripe\": $fa-var-stripe,\n  \"houzz\": $fa-var-houzz,\n  \"gg\": $fa-var-gg,\n  \"dhl\": $fa-var-dhl,\n  \"square-pinterest\": $fa-var-square-pinterest,\n  \"pinterest-square\": $fa-var-pinterest-square,\n  \"xing\": $fa-var-xing,\n  \"blackberry\": $fa-var-blackberry,\n  \"creative-commons-pd\": $fa-var-creative-commons-pd,\n  \"playstation\": $fa-var-playstation,\n  \"quinscape\": $fa-var-quinscape,\n  \"less\": $fa-var-less,\n  \"blogger-b\": $fa-var-blogger-b,\n  \"opencart\": $fa-var-opencart,\n  \"vine\": $fa-var-vine,\n  \"signal-messenger\": $fa-var-signal-messenger,\n  \"paypal\": $fa-var-paypal,\n  \"gitlab\": $fa-var-gitlab,\n  \"typo3\": $fa-var-typo3,\n  \"reddit-alien\": $fa-var-reddit-alien,\n  \"yahoo\": $fa-var-yahoo,\n  \"dailymotion\": $fa-var-dailymotion,\n  \"affiliatetheme\": $fa-var-affiliatetheme,\n  \"pied-piper-pp\": $fa-var-pied-piper-pp,\n  \"bootstrap\": $fa-var-bootstrap,\n  \"odnoklassniki\": $fa-var-odnoklassniki,\n  \"nfc-symbol\": $fa-var-nfc-symbol,\n  \"mintbit\": $fa-var-mintbit,\n  \"ethereum\": $fa-var-ethereum,\n  \"speaker-deck\": $fa-var-speaker-deck,\n  \"creative-commons-nc-eu\": $fa-var-creative-commons-nc-eu,\n  \"patreon\": $fa-var-patreon,\n  \"avianex\": $fa-var-avianex,\n  \"ello\": $fa-var-ello,\n  \"gofore\": $fa-var-gofore,\n  \"bimobject\": $fa-var-bimobject,\n  \"brave-reverse\": $fa-var-brave-reverse,\n  \"facebook-f\": $fa-var-facebook-f,\n  \"square-google-plus\": $fa-var-square-google-plus,\n  \"google-plus-square\": $fa-var-google-plus-square,\n  \"web-awesome\": $fa-var-web-awesome,\n  \"mandalorian\": $fa-var-mandalorian,\n  \"first-order-alt\": $fa-var-first-order-alt,\n  \"osi\": $fa-var-osi,\n  \"google-wallet\": $fa-var-google-wallet,\n  \"d-and-d-beyond\": $fa-var-d-and-d-beyond,\n  \"periscope\": $fa-var-periscope,\n  \"fulcrum\": $fa-var-fulcrum,\n  \"cloudscale\": $fa-var-cloudscale,\n  \"forumbee\": $fa-var-forumbee,\n  \"mizuni\": $fa-var-mizuni,\n  \"schlix\": $fa-var-schlix,\n  \"square-xing\": $fa-var-square-xing,\n  \"xing-square\": $fa-var-xing-square,\n  \"bandcamp\": $fa-var-bandcamp,\n  \"wpforms\": $fa-var-wpforms,\n  \"cloudversify\": $fa-var-cloudversify,\n  \"usps\": $fa-var-usps,\n  \"megaport\": $fa-var-megaport,\n  \"magento\": $fa-var-magento,\n  \"spotify\": $fa-var-spotify,\n  \"optin-monster\": $fa-var-optin-monster,\n  \"fly\": $fa-var-fly,\n  \"aviato\": $fa-var-aviato,\n  \"itunes\": $fa-var-itunes,\n  \"cuttlefish\": $fa-var-cuttlefish,\n  \"blogger\": $fa-var-blogger,\n  \"flickr\": $fa-var-flickr,\n  \"viber\": $fa-var-viber,\n  \"soundcloud\": $fa-var-soundcloud,\n  \"digg\": $fa-var-digg,\n  \"tencent-weibo\": $fa-var-tencent-weibo,\n  \"letterboxd\": $fa-var-letterboxd,\n  \"symfony\": $fa-var-symfony,\n  \"maxcdn\": $fa-var-maxcdn,\n  \"etsy\": $fa-var-etsy,\n  \"facebook-messenger\": $fa-var-facebook-messenger,\n  \"audible\": $fa-var-audible,\n  \"think-peaks\": $fa-var-think-peaks,\n  \"bilibili\": $fa-var-bilibili,\n  \"erlang\": $fa-var-erlang,\n  \"x-twitter\": $fa-var-x-twitter,\n  \"cotton-bureau\": $fa-var-cotton-bureau,\n  \"dashcube\": $fa-var-dashcube,\n  \"42-group\": $fa-var-42-group,\n  \"innosoft\": $fa-var-innosoft,\n  \"stack-exchange\": $fa-var-stack-exchange,\n  \"elementor\": $fa-var-elementor,\n  \"square-pied-piper\": $fa-var-square-pied-piper,\n  \"pied-piper-square\": $fa-var-pied-piper-square,\n  \"creative-commons-nd\": $fa-var-creative-commons-nd,\n  \"palfed\": $fa-var-palfed,\n  \"superpowers\": $fa-var-superpowers,\n  \"resolving\": $fa-var-resolving,\n  \"xbox\": $fa-var-xbox,\n  \"square-web-awesome-stroke\": $fa-var-square-web-awesome-stroke,\n  \"searchengin\": $fa-var-searchengin,\n  \"tiktok\": $fa-var-tiktok,\n  \"square-facebook\": $fa-var-square-facebook,\n  \"facebook-square\": $fa-var-facebook-square,\n  \"renren\": $fa-var-renren,\n  \"linux\": $fa-var-linux,\n  \"glide\": $fa-var-glide,\n  \"linkedin\": $fa-var-linkedin,\n  \"hubspot\": $fa-var-hubspot,\n  \"deploydog\": $fa-var-deploydog,\n  \"twitch\": $fa-var-twitch,\n  \"ravelry\": $fa-var-ravelry,\n  \"mixer\": $fa-var-mixer,\n  \"square-lastfm\": $fa-var-square-lastfm,\n  \"lastfm-square\": $fa-var-lastfm-square,\n  \"vimeo\": $fa-var-vimeo,\n  \"mendeley\": $fa-var-mendeley,\n  \"uniregistry\": $fa-var-uniregistry,\n  \"figma\": $fa-var-figma,\n  \"creative-commons-remix\": $fa-var-creative-commons-remix,\n  \"cc-amazon-pay\": $fa-var-cc-amazon-pay,\n  \"dropbox\": $fa-var-dropbox,\n  \"instagram\": $fa-var-instagram,\n  \"cmplid\": $fa-var-cmplid,\n  \"upwork\": $fa-var-upwork,\n  \"facebook\": $fa-var-facebook,\n  \"gripfire\": $fa-var-gripfire,\n  \"jedi-order\": $fa-var-jedi-order,\n  \"uikit\": $fa-var-uikit,\n  \"fort-awesome-alt\": $fa-var-fort-awesome-alt,\n  \"phabricator\": $fa-var-phabricator,\n  \"ussunnah\": $fa-var-ussunnah,\n  \"earlybirds\": $fa-var-earlybirds,\n  \"trade-federation\": $fa-var-trade-federation,\n  \"autoprefixer\": $fa-var-autoprefixer,\n  \"whatsapp\": $fa-var-whatsapp,\n  \"square-upwork\": $fa-var-square-upwork,\n  \"slideshare\": $fa-var-slideshare,\n  \"google-play\": $fa-var-google-play,\n  \"viadeo\": $fa-var-viadeo,\n  \"line\": $fa-var-line,\n  \"google-drive\": $fa-var-google-drive,\n  \"servicestack\": $fa-var-servicestack,\n  \"simplybuilt\": $fa-var-simplybuilt,\n  \"bitbucket\": $fa-var-bitbucket,\n  \"imdb\": $fa-var-imdb,\n  \"deezer\": $fa-var-deezer,\n  \"raspberry-pi\": $fa-var-raspberry-pi,\n  \"jira\": $fa-var-jira,\n  \"docker\": $fa-var-docker,\n  \"screenpal\": $fa-var-screenpal,\n  \"bluetooth\": $fa-var-bluetooth,\n  \"gitter\": $fa-var-gitter,\n  \"d-and-d\": $fa-var-d-and-d,\n  \"microblog\": $fa-var-microblog,\n  \"cc-diners-club\": $fa-var-cc-diners-club,\n  \"gg-circle\": $fa-var-gg-circle,\n  \"pied-piper-hat\": $fa-var-pied-piper-hat,\n  \"kickstarter-k\": $fa-var-kickstarter-k,\n  \"yandex\": $fa-var-yandex,\n  \"readme\": $fa-var-readme,\n  \"html5\": $fa-var-html5,\n  \"sellsy\": $fa-var-sellsy,\n  \"square-web-awesome\": $fa-var-square-web-awesome,\n  \"sass\": $fa-var-sass,\n  \"wirsindhandwerk\": $fa-var-wirsindhandwerk,\n  \"wsh\": $fa-var-wsh,\n  \"buromobelexperte\": $fa-var-buromobelexperte,\n  \"salesforce\": $fa-var-salesforce,\n  \"octopus-deploy\": $fa-var-octopus-deploy,\n  \"medapps\": $fa-var-medapps,\n  \"ns8\": $fa-var-ns8,\n  \"pinterest-p\": $fa-var-pinterest-p,\n  \"apper\": $fa-var-apper,\n  \"fort-awesome\": $fa-var-fort-awesome,\n  \"waze\": $fa-var-waze,\n  \"bluesky\": $fa-var-bluesky,\n  \"cc-jcb\": $fa-var-cc-jcb,\n  \"snapchat\": $fa-var-snapchat,\n  \"snapchat-ghost\": $fa-var-snapchat-ghost,\n  \"fantasy-flight-games\": $fa-var-fantasy-flight-games,\n  \"rust\": $fa-var-rust,\n  \"wix\": $fa-var-wix,\n  \"square-behance\": $fa-var-square-behance,\n  \"behance-square\": $fa-var-behance-square,\n  \"supple\": $fa-var-supple,\n  \"webflow\": $fa-var-webflow,\n  \"rebel\": $fa-var-rebel,\n  \"css3\": $fa-var-css3,\n  \"staylinked\": $fa-var-staylinked,\n  \"kaggle\": $fa-var-kaggle,\n  \"space-awesome\": $fa-var-space-awesome,\n  \"deviantart\": $fa-var-deviantart,\n  \"cpanel\": $fa-var-cpanel,\n  \"goodreads-g\": $fa-var-goodreads-g,\n  \"square-git\": $fa-var-square-git,\n  \"git-square\": $fa-var-git-square,\n  \"square-tumblr\": $fa-var-square-tumblr,\n  \"tumblr-square\": $fa-var-tumblr-square,\n  \"trello\": $fa-var-trello,\n  \"creative-commons-nc-jp\": $fa-var-creative-commons-nc-jp,\n  \"get-pocket\": $fa-var-get-pocket,\n  \"perbyte\": $fa-var-perbyte,\n  \"grunt\": $fa-var-grunt,\n  \"weebly\": $fa-var-weebly,\n  \"connectdevelop\": $fa-var-connectdevelop,\n  \"leanpub\": $fa-var-leanpub,\n  \"black-tie\": $fa-var-black-tie,\n  \"themeco\": $fa-var-themeco,\n  \"python\": $fa-var-python,\n  \"android\": $fa-var-android,\n  \"bots\": $fa-var-bots,\n  \"free-code-camp\": $fa-var-free-code-camp,\n  \"hornbill\": $fa-var-hornbill,\n  \"js\": $fa-var-js,\n  \"ideal\": $fa-var-ideal,\n  \"git\": $fa-var-git,\n  \"dev\": $fa-var-dev,\n  \"sketch\": $fa-var-sketch,\n  \"yandex-international\": $fa-var-yandex-international,\n  \"cc-amex\": $fa-var-cc-amex,\n  \"uber\": $fa-var-uber,\n  \"github\": $fa-var-github,\n  \"php\": $fa-var-php,\n  \"alipay\": $fa-var-alipay,\n  \"youtube\": $fa-var-youtube,\n  \"skyatlas\": $fa-var-skyatlas,\n  \"firefox-browser\": $fa-var-firefox-browser,\n  \"replyd\": $fa-var-replyd,\n  \"suse\": $fa-var-suse,\n  \"jenkins\": $fa-var-jenkins,\n  \"twitter\": $fa-var-twitter,\n  \"rockrms\": $fa-var-rockrms,\n  \"pinterest\": $fa-var-pinterest,\n  \"buffer\": $fa-var-buffer,\n  \"npm\": $fa-var-npm,\n  \"yammer\": $fa-var-yammer,\n  \"btc\": $fa-var-btc,\n  \"dribbble\": $fa-var-dribbble,\n  \"stumbleupon-circle\": $fa-var-stumbleupon-circle,\n  \"internet-explorer\": $fa-var-internet-explorer,\n  \"stubber\": $fa-var-stubber,\n  \"telegram\": $fa-var-telegram,\n  \"telegram-plane\": $fa-var-telegram-plane,\n  \"old-republic\": $fa-var-old-republic,\n  \"odysee\": $fa-var-odysee,\n  \"square-whatsapp\": $fa-var-square-whatsapp,\n  \"whatsapp-square\": $fa-var-whatsapp-square,\n  \"node-js\": $fa-var-node-js,\n  \"edge-legacy\": $fa-var-edge-legacy,\n  \"slack\": $fa-var-slack,\n  \"slack-hash\": $fa-var-slack-hash,\n  \"medrt\": $fa-var-medrt,\n  \"usb\": $fa-var-usb,\n  \"tumblr\": $fa-var-tumblr,\n  \"vaadin\": $fa-var-vaadin,\n  \"quora\": $fa-var-quora,\n  \"square-x-twitter\": $fa-var-square-x-twitter,\n  \"reacteurope\": $fa-var-reacteurope,\n  \"medium\": $fa-var-medium,\n  \"medium-m\": $fa-var-medium-m,\n  \"amilia\": $fa-var-amilia,\n  \"mixcloud\": $fa-var-mixcloud,\n  \"flipboard\": $fa-var-flipboard,\n  \"viacoin\": $fa-var-viacoin,\n  \"critical-role\": $fa-var-critical-role,\n  \"sitrox\": $fa-var-sitrox,\n  \"discourse\": $fa-var-discourse,\n  \"joomla\": $fa-var-joomla,\n  \"mastodon\": $fa-var-mastodon,\n  \"airbnb\": $fa-var-airbnb,\n  \"wolf-pack-battalion\": $fa-var-wolf-pack-battalion,\n  \"buy-n-large\": $fa-var-buy-n-large,\n  \"gulp\": $fa-var-gulp,\n  \"creative-commons-sampling-plus\": $fa-var-creative-commons-sampling-plus,\n  \"strava\": $fa-var-strava,\n  \"ember\": $fa-var-ember,\n  \"canadian-maple-leaf\": $fa-var-canadian-maple-leaf,\n  \"teamspeak\": $fa-var-teamspeak,\n  \"pushed\": $fa-var-pushed,\n  \"wordpress-simple\": $fa-var-wordpress-simple,\n  \"nutritionix\": $fa-var-nutritionix,\n  \"wodu\": $fa-var-wodu,\n  \"google-pay\": $fa-var-google-pay,\n  \"intercom\": $fa-var-intercom,\n  \"zhihu\": $fa-var-zhihu,\n  \"korvue\": $fa-var-korvue,\n  \"pix\": $fa-var-pix,\n  \"steam-symbol\": $fa-var-steam-symbol,\n);\n","// specific icon class definition\n// -------------------------\n\n/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen\nreaders do not read off random characters that represent icons */\n\n@each $name, $icon in $fa-icons {\n  .#{$fa-css-prefix}-#{$name}::before { content: unquote(\"\\\"#{ $icon }\\\"\"); }\n}\n\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n@import 'functions';\n@import 'variables';\n\n:root, :host {\n  --#{$fa-css-prefix}-style-family-classic: '#{ $fa-style-family }';\n  --#{$fa-css-prefix}-font-solid: normal 900 1em/1 '#{ $fa-style-family }';\n}\n\n@font-face {\n  font-family: 'Font Awesome 6 Free';\n  font-style: normal;\n  font-weight: 900;\n  font-display: $fa-font-display;\n  src: url('#{$fa-font-path}/fa-solid-900.woff2') format('woff2'),\n    url('#{$fa-font-path}/fa-solid-900.ttf') format('truetype');\n}\n\n.fas,\n.#{$fa-css-prefix}-solid {\n  font-weight: 900;\n}\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n@import 'functions';\n@import 'variables';\n\n:root, :host {\n  --#{$fa-css-prefix}-style-family-classic: '#{ $fa-style-family }';\n  --#{$fa-css-prefix}-font-regular: normal 400 1em/1 '#{ $fa-style-family }';\n}\n\n@font-face {\n  font-family: 'Font Awesome 6 Free';\n  font-style: normal;\n  font-weight: 400;\n  font-display: $fa-font-display;\n  src: url('#{$fa-font-path}/fa-regular-400.woff2') format('woff2'),\n    url('#{$fa-font-path}/fa-regular-400.ttf') format('truetype');\n}\n\n.far,\n.#{$fa-css-prefix}-regular {\n  font-weight: 400;\n}\n","/*!\n * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com\n * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)\n * Copyright 2024 Fonticons, Inc.\n */\n@import 'functions';\n@import 'variables';\n\n:root, :host {\n  --#{$fa-css-prefix}-style-family-brands: 'Font Awesome 6 Brands';\n  --#{$fa-css-prefix}-font-brands: normal 400 1em/1 'Font Awesome 6 Brands';\n}\n\n@font-face {\n  font-family: 'Font Awesome 6 Brands';\n  font-style: normal;\n  font-weight: 400;\n  font-display: $fa-font-display;\n  src: url('#{$fa-font-path}/fa-brands-400.woff2') format('woff2'),\n    url('#{$fa-font-path}/fa-brands-400.ttf') format('truetype');\n}\n\n.fab,\n.#{$fa-css-prefix}-brands {\n  font-weight: 400;\n}\n\n@each $name, $icon in $fa-brand-icons {\n  .#{$fa-css-prefix}-#{$name}:before { content: unquote(\"\\\"#{ $icon }\\\"\"); }\n}\n","html {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n\n  // Header height will impact the top offset for many sections\n  // Article header is 66% of Header\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n$breakpoint-sidebar-primary: lg; // When we collapse the primary sidebar\n$breakpoint-sidebar-secondary: xl; // When we collapse the secondary sidebar\n$breakpoint-page-width: 88rem; // taken from sphinx-basic-ng, which we are ultimately going to inherit\n\n/*******************************************************************************\n* Define the animation behaviour\n*/\n$animation-time: 200ms;\n\n/*******************************************************************************\n* UI shaping and padding\n*/\n$admonition-border-radius: 0.25rem;\n\n// In this theme, some focus rings have rounded corners while others do not.\n// This variable sets the border radius for the rounded focus rings.\n$focus-ring-radius: 0.125rem; // 2px at 100% zoom and 16px base font.\n\n$navbar-link-padding-y: 0.25rem;\n\n// Ensure that there is no overlap of bumper disks (smallest circle that\n// contains the bounding box of the interactive element).\n// - https://github.com/Quansight-Labs/czi-scientific-python-mgmt/issues/81#issuecomment-2251325783\n$nav-icon-column-gap: 1.12rem;\n\n// Determines vertical space between entries in both the section (left/primary\n// sidebar) and page (right/secondary sidebar) table of contents\n$toc-item-padding-y: 0.25rem;\n","html {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n\n  // base font size - applied at body/html level\n  --pst-font-size-base: 1rem;\n\n  // heading font sizes based on a medium contrast type scale\n  // - see: https://github.com/Quansight-Labs/czi-scientific-python-mgmt/issues/97#issuecomment-2310531483\n  --pst-font-size-h1: 2.625rem;\n  --pst-font-size-h2: 2.125rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1rem;\n\n  // smaller than heading font sizes\n  --pst-font-size-milli: 0.9rem;\n\n  // Sidebar styles\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n\n  // Admonition styles\n  --pst-admonition-font-weight-heading: 600;\n\n  // Font weights\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 600;\n\n  // Font family\n  // These are adapted from https://systemfontstack.com/ */\n  --pst-font-family-base-system: -apple-system, \"BlinkMacSystemFont\", \"Segoe UI\",\n    \"Helvetica Neue\", \"Arial\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\",\n    \"Segoe UI Symbol\";\n  --pst-font-family-monospace-system: \"SFMono-Regular\", \"Menlo\", \"Consolas\",\n    \"Monaco\", \"Liberation Mono\", \"Lucida Console\", monospace;\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n\n$line-height-body: 1.65;\n$fa-font-path: \"vendor/fontawesome/webfonts/\";\n","html {\n  /*****************************************************************************\n  * Icon\n  */\n\n  // Font size across all icons\n  --pst-font-size-icon: 1.5rem;\n\n  // Font Awesome default icons\n  --pst-icon-check-circle: \"\\f058\"; // fa-solid fa-circle-check\n  --pst-icon-info-circle: \"\\f05a\"; // fa-solid fa-circle-info\n  --pst-icon-exclamation-triangle: \"\\f071\"; // fa-solid fa-triangle-exclamation\n  --pst-icon-exclamation-circle: \"\\f06a\"; // fa-solid fa-circle-exclamation\n  --pst-icon-times-circle: \"\\f057\"; // fa-solid fa-circle-xmark\n  --pst-icon-lightbulb: \"\\f0eb\"; // fa-solid fa-lightbulb\n  --pst-icon-download: \"\\f019\"; // fa-solid fa-download\n  --pst-icon-angle-left: \"\\f104\"; // fa-solid fa-angle-left\n  --pst-icon-angle-right: \"\\f105\"; // fa-solid fa-angle-right\n  --pst-icon-external-link: \"\\f35d\"; // fa-solid fa-up-right-from-square\n  --pst-icon-search-minus: \"\\f010\"; // fa-solid fa-magnifying-glass-minus\n  --pst-icon-github: \"\\f09b\"; // fa-brands fa-github\n  --pst-icon-gitlab: \"\\f296\"; // fa-brands fa-gitlab\n  --pst-icon-share: \"\\f064\"; // fa-solid fa-share\n  --pst-icon-bell: \"\\f0f3\"; // fa-solid fa-bell\n  --pst-icon-pencil: \"\\f303\"; // fa-solid fa-pencil\n\n  // Bootstrap icons\n  --pst-breadcrumb-divider: \"\\f105\";\n}\n","html {\n  /*****************************************************************************\n  * Admonitions\n  **/\n\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n","html {\n  /*****************************************************************************\n  * versionmodified\n  **/\n\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n","html {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: $line-height-body;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n\n  // hack to avoid the black background on some browser including Safari\n  &::-webkit-scrollbar-track {\n    background-color: var(--pst-color-background);\n  }\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n\n  /* section header in docstring pages */\n  &.rubric {\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  &.centered {\n    text-align: center;\n  }\n}\n\na {\n  @include link-style-default;\n\n  // The # anchor that appears on hover over headings\n  &.headerlink {\n    color: var(--pst-color-secondary);\n    font-size: 0.8em;\n    padding: 0 4px;\n    margin-left: 0.2em;\n    text-decoration: none;\n    user-select: none;\n  }\n\n  // set up a icon next to the shorten links from github and gitlab\n  &.github,\n  &.gitlab {\n    &::before {\n      color: var(--pst-color-text-muted);\n      font: var(--fa-font-brands);\n      margin-right: 0.25rem;\n    }\n  }\n\n  &.github::before {\n    content: var(--pst-icon-github);\n  }\n\n  &.gitlab::before {\n    content: var(--pst-icon-gitlab);\n  }\n}\n\n%heading-style {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1 {\n  @extend %heading-style;\n\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-color-heading);\n}\n\nh2 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-color-heading);\n}\n\nh3 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-color-heading);\n}\n\nh4 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-color-heading);\n}\n\nh5 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  // use theme negative\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n\n  // Compound keyboard elements will have nested kbd in them, so this prevents double lines\n  &:not(.compound) {\n    border: 1px solid var(--pst-color-border);\n    margin: 0 0.1rem;\n    padding: 0.1rem 0.4rem;\n    box-shadow: 1px 1px 1px var(--pst-color-shadow);\n  }\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  .linenos {\n    // minimum opacity to make the line numbers WCAG AA conformant\n    opacity: 0.8;\n    padding-right: 10px;\n  }\n}\n\n// the back to top btn\n#pst-back-to-top {\n  // zindex-tooltip comes from Bootstrap https://getbootstrap.com/docs/5.2/layout/z-index/\n  z-index: $zindex-tooltip;\n  position: fixed;\n  display: none;\n  top: 90vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n\n  .fa-arrow-up {\n    // Using margin instead of a space character prevents the space between the\n    // icon and the text from being underlined when the button is hovered.\n    margin-inline-end: 0.5em;\n  }\n\n  &:hover {\n    @include link-style-hover;\n\n    text-decoration-thickness: 1px;\n    background-color: var(--pst-color-secondary-highlight);\n    color: var(--pst-color-secondary-highlight-text);\n  }\n\n  &:focus-visible {\n    box-shadow: none;\n    outline: $focus-ring-outline;\n    outline-color: var(--pst-color-secondary);\n    outline-offset: $focus-ring-width;\n  }\n}\n\n// Focus ring\n// ----------\n// Note: The Bootstrap stylesheet provides the focus ring (customized by this\n// theme via Sass variables in _bootstrap.scss) in some cases. This rule covers\n// all other cases.\n:focus-visible {\n  outline: $focus-ring-outline;\n  box-shadow: none; // override Bootstrap\n}\n","// Consistent styling for links\n// ============================\n\n@use \"sass:string\";\n\n// Define some useful variables for links styling consistency\n\n// The default thickness of the underline for links will be either:\n//  - 1px\n//  - 0.0625rem if it's thicker than 1px because the user has changed the text\n//    size in their browser\n$link-underline-thickness: string.unquote(\"max(1px, .0625rem)\") !default;\n\n// Offset of link underlines from text baseline\n// The default is 3px expressed as ems, as calculated against the default body\n// font size (on desktop).\n$link-underline-offset: 0.1578em !default;\n\n// Thickness of link underlines in hover state\n// The default for each link will be the thickest of the following:\n//  - 3px\n//  - 0.1875rem, if it's thicker than 3px because the user has changed the text\n//    size in their browser\n//  - 0.12em (relative to the link's text size)\n$link-hover-decoration-thickness: string.unquote(\n  \"max(3px, .1875rem, .12em)\"\n) !default;\n\n// Ensures links have an underline decoration by default - needed to meet\n// WCAG SC 1.4.1\n@mixin link-decoration {\n  text-decoration: underline;\n\n  @if $link-underline-thickness {\n    text-decoration-thickness: $link-underline-thickness;\n  }\n\n  @if $link-underline-offset {\n    text-underline-offset: $link-underline-offset;\n  }\n}\n\n// Ensures links have an underline decoration on hover - distinct from the\n// default behaviour\n@mixin link-decoration-hover {\n  @if $link-hover-decoration-thickness {\n    text-decoration-thickness: $link-hover-decoration-thickness;\n\n    // Disable ink skipping on underlines on hover.\n    text-decoration-skip-ink: none;\n  }\n}\n\n// Simple hover style - can be used alone or in conjunction with other mixins\n// Add the text underline and change in thickness on hover.\n// Intended for use with the `:hover` pseudo-class.\n@mixin link-style-hover {\n  @include link-decoration;\n  @include link-decoration-hover;\n\n  color: var(--pst-color-link-hover);\n}\n\n// Default link styles\n// -------------------\n// Defines: default unvisited, visited, hover, and active.\n// TODO: @trallard to improve focus styles in subsequent PR\n@mixin link-style-default {\n  // So that really long links don't spill out of their container\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n\n  @include link-decoration;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to add active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link);\n  }\n\n  // Visited should still be hoverable\n  &:visited {\n    color: var(--pst-color-link);\n\n    &:hover {\n      color: var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Text link styles\n// ----------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation bar links.\n@mixin link-style-text {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration;\n    @include link-decoration-hover;\n  }\n}\n\n// Sidebar and TOC links\n// ---------------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation the primary sidebar and table of contents.\n// Active and hover should work together rather than one overriding the other.\n@mixin link-sidebar {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    text-decoration: underline;\n    background-color: transparent;\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to update active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    box-shadow: $focus-ring-box-shadow;\n    outline: none;\n    z-index: 10; // keep focus ring on top (prevent the link-sidebar-current notch from lying on top of the ring)\n  }\n}\n\n// Sidebar current page link styles\n// --------------------------------\n// Adds a vertical line on the left hand side of the link to indicate that\n// it's the current page. Note this is distinct from an active state.\n// Used on the primary sidebar and the TOC.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-sidebar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n\n  @if $link-hover-decoration-thickness {\n    $notch-shadow: inset\n      $link-hover-decoration-thickness\n      0\n      0\n      var(--pst-color-primary);\n\n    box-shadow: $notch-shadow;\n\n    &:focus-visible {\n      box-shadow: $notch-shadow, $focus-ring-box-shadow;\n      outline: none;\n    }\n  }\n}\n\n// Header navbar text and icon links\n// ---------------------------------\n// (includes light/dark mode button)\n\n// This mixin makes it possible to show hover/underline and focus/ring styles at\n// the same time. The trick is to use:\n//    - a pseudo-element with bottom border for the hover underline\n//    - a CSS outline for the focus ring.\n\n// Normally we use box-shadow for underline and outline for focus ring. But we\n// cannot apply box-shadow and outline together on the same element because the\n// border-radius value that we use to round the outline will also round the\n// box-shadow used for the underline. We also cannot use text-underline because\n// it does not work on non-text links, nor do we want to use it on text links\n// that we want to treat as blocks, such as the header nav links because the\n// underline will wrap across two lines if the link text also wraps across two\n// lines.\n@mixin link-style-block {\n  color: var(--pst-color-text-muted);\n\n  // Set position relative so that the child ::before pseudo-element's absolute\n  // position is relative to this element.\n  position: relative;\n\n  // Set up pseudo-element used for hover underline styles\n  &::before {\n    content: \"\";\n    display: block;\n    position: absolute;\n    inset: 0;\n    background-color: transparent;\n\n    @if $link-hover-decoration-thickness {\n      bottom: calc(-1 * $link-hover-decoration-thickness);\n      margin: $link-hover-decoration-thickness 0;\n    }\n  }\n\n  &:hover {\n    color: var(--pst-color-secondary);\n    text-decoration: none; // override the link-style-hover mixin\n    &::before {\n      @if $link-hover-decoration-thickness {\n        border-bottom: $link-hover-decoration-thickness\n          solid\n          var(--pst-color-secondary);\n      }\n    }\n  }\n\n  &:focus-visible {\n    box-shadow: none; // override Bootstrap\n    outline: 3px solid var(--pst-color-accent);\n    outline-offset: $focus-ring-width;\n  }\n}\n","/***\n * Rules for the UX/UI of skip navigation link btn.\n * It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  padding: 0.5rem;\n  z-index: $zindex-modal;\n  border-bottom: 1px solid var(--pst-color-border);\n\n  // This shows / hides the button\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n\n  &:focus-within {\n    transform: translateY(0%);\n  }\n\n  a {\n    // Ensure we are using a WCAG conformant colour\n    color: var(--pst-color-warning-text) !important;\n\n    &:focus-visible {\n      // use color with sufficient contrast\n      outline-color: $foundation-black;\n    }\n  }\n}\n",".bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n\n  .bd-container__inner {\n    display: flex;\n  }\n}\n\n.bd-page-width {\n  width: 100%;\n\n  @include media-breakpoint-up(lg) {\n    max-width: $breakpoint-page-width;\n  }\n}\n",".pst-async-banner-revealer {\n  // Setting height to 0 and overflow to hidden allows us to add up the heights\n  // of this element's children before revealing them.\n  height: 0;\n  overflow: hidden;\n\n  // Height to be set by JavaScript, which should trigger the following\n  // transition rule (unless the user has set their system to reduce motion).\n  transition: height 300ms ease-in-out;\n\n  @media (prefers-reduced-motion) {\n    transition: none;\n  }\n}\n\n#bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%; // Horizontal padding so the width is 75%\n  // One breakpoint less than $breakpoint-sidebar-primary. See variables/_layout.scss for more info.\n  @include media-breakpoint-down(lg) {\n    // Announcements can take a bit more width on mobile\n    padding: 0.5rem 2%;\n  }\n\n  p {\n    font-weight: bold;\n    margin: 0;\n  }\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // The \"Switch to stable version\" link (styled like a button)\n  .pst-button-link-to-stable-version {\n    @include box-shadow;\n\n    $background-color: var(--pst-color-danger);\n    $hover-background-color: var(--pst-color-danger-highlight);\n    $color: var(--pst-color-danger-text);\n\n    background-color: $background-color;\n    border-color: $background-color;\n    color: $color;\n    border-radius: 0.25rem;\n\n    &:hover {\n      // applying the same style as the SD \"buttons\"\n      @include link-style-hover;\n\n      text-decoration-thickness: 1px;\n      background-color: $hover-background-color;\n      border-color: $hover-background-color;\n      color: var(--pst-color-danger-highlight-text);\n    }\n\n    &:focus-visible {\n      outline: $focus-ring-width solid $background-color;\n      outline-offset: $focus-ring-width;\n    }\n  }\n}\n\n// Bg color is now defined in the theme color palette - using our secondary color\n.bd-header-announcement {\n  background-color: var(--pst-color-secondary-bg);\n}\n\n#bd-header-version-warning {\n  background-color: var(--pst-color-danger-bg);\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n\n/**\n * A consistent box shadow style we apply across elements.\n */\n@mixin box-shadow() {\n  box-shadow:\n    0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\n@mixin cell-output-background {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n\n@mixin table-colors {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n\n  th,\n  td {\n    ~ th,\n    ~ td {\n      border-left: 1px solid var(--pst-color-table-inner-border);\n    }\n  }\n\n  thead {\n    tr {\n      background-color: var(--pst-color-table-heading-bg);\n      border-bottom: 2px solid var(--pst-color-primary);\n    }\n  }\n\n  tbody {\n    tr {\n      &:nth-child(odd) {\n        background-color: var(--pst-color-table-row-zebra-low-bg);\n      }\n\n      &:nth-child(even) {\n        background-color: var(--pst-color-table-row-zebra-high-bg);\n      }\n    }\n  }\n}\n\n// Minimum mouse hit area\n// ----------------------\n// Ensures that the element has a minimum hit area that conforms to\n// accessibility guidelines. For WCAG AA, we need 24px x 24px, see:\n// https://www.w3.org/WAI/WCAG22/Understanding/target-size-minimum.html\n@mixin min-hit-area() {\n  box-sizing: border-box;\n  min-width: 24px;\n  min-height: 24px;\n}\n\n// Meant to darken the element on hover in light mode, or\n// lighten on hover in dark mode.\n@mixin hover-darken-lighten {\n  &:hover {\n    filter: brightness(0.9);\n\n    html[data-theme=\"dark\"] & {\n      filter: brightness(1.1);\n    }\n  }\n}\n","/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n\n  .bd-content {\n    display: flex;\n    justify-content: center;\n    height: 100%;\n\n    .bd-article-container {\n      justify-content: start;\n      display: flex;\n      flex-direction: column;\n\n      // Max-width is slightly more than the W3 since our docs often have images.\n      // We shoot for about 100 characters per line instead of 80.\n      // ref: https://www.w3.org/WAI/tutorials/page-structure/styling/#line-length\n      width: 100%;\n      max-width: 60em;\n      overflow-x: auto; // Prevent wide content from pushing off the secondary sidebar\n      padding: 1rem;\n\n      .bd-article {\n        // Give a bit more verticle spacing on wide screens\n        @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n          padding-top: 1.5rem;\n          padding-left: 2rem;\n        }\n      }\n    }\n  }\n}\n",".bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n\n  .bd-footer__inner {\n    display: flex;\n    flex-grow: 1;\n    padding: 1rem;\n    margin: auto;\n  }\n\n  .footer-items__start,\n  .footer-items__center,\n  .footer-items__end {\n    display: flex;\n    flex-direction: column;\n    gap: 0.5rem;\n    justify-content: center;\n    flex-grow: 1;\n  }\n\n  .footer-items__center {\n    text-align: center;\n  }\n\n  .footer-items__end {\n    text-align: end;\n  }\n\n  // So that paragraphs don't take up extra room\n  .footer-item p {\n    margin-bottom: 0;\n  }\n}\n",".bd-footer-article {\n  margin-top: auto;\n\n  .footer-article-items {\n    display: flex;\n    flex-direction: column;\n  }\n}\n",".bd-footer-content {\n  .footer-content-items {\n    display: flex;\n    flex-direction: column;\n    margin-top: auto;\n  }\n}\n","/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n\n// Styling for the Icon links can be found in components/_icon-links.scss\n\n// If we want the shadow to only point downward in the future, set\n// box-shadow to: 0 0.125rem 0.25rem -0.125rem rgba(0, 0, 0, 0.11);\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: $zindex-fixed;\n\n  // Overrides bootstrap\n  background-color: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n\n  .bd-header__inner {\n    display: flex;\n    align-items: center;\n    height: fit-content;\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // These items will define the height of the header\n  .navbar-item {\n    height: var(--pst-header-height);\n    max-height: var(--pst-header-height);\n    display: flex;\n    align-items: center;\n  }\n\n  // Hide the navbar header items on mobile because they're in the sidebar\n  .navbar-header-items {\n    display: none;\n    flex-shrink: 1;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: inherit;\n      flex-grow: 1;\n      padding: 0 0 0 0.5rem;\n    }\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center,\n  .navbar-header-items__start {\n    display: flex;\n    align-items: center;\n    flex-flow: wrap;\n\n    // In case we wrap our items to multiple rows on small screens\n    row-gap: 0;\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center {\n    column-gap: $nav-icon-column-gap;\n  }\n\n  // A little smaller because this is displayed by default on mobile\n  .navbar-header-items__start {\n    flex-shrink: 0;\n    margin-right: auto;\n    gap: 0.5rem;\n  }\n\n  .navbar-header-items__end {\n    // End navbar items should snap to the right\n    justify-content: end;\n  }\n\n  // Contains the navigation links within the navbar\n  ul.navbar-nav {\n    display: flex;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      // Align on wide screens so the dropdown button is centered properly\n      align-items: baseline;\n    }\n\n    > li.nav-item {\n      margin-inline: 2px; // breathing room so hover and focus styles do not overlap\n\n      > .nav-link {\n        @include link-style-block;\n\n        padding-inline: 6px;\n      }\n\n      &.current {\n        > .nav-link {\n          color: var(--pst-color-primary);\n\n          // Underline the current navbar item\n          &::before {\n            border-bottom: 3px solid var(--pst-color-primary);\n          }\n        }\n      }\n\n      &.dropdown {\n        margin-inline: 4px;\n\n        button {\n          padding-inline: 8px;\n        }\n\n        > .dropdown-toggle {\n          border-radius: $focus-ring-radius; // make border radius the same for both hover ring and focus ring\n          color: var(--pst-color-text-muted);\n\n          &:focus-visible {\n            box-shadow: $focus-ring-box-shadow;\n          }\n\n          &:hover {\n            text-decoration: none;\n            box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover); // purple focus ring\n            // Brighten the text on hover (muted -> base)\n            color: var(--pst-color-text-base);\n          }\n        }\n      }\n    }\n\n    li a.nav-link.dropdown-item {\n      @include link-style-text;\n    }\n\n    // Dropdowns for the extra links\n    .dropdown {\n      button {\n        display: unset;\n        border: none;\n\n        &:hover {\n          @include link-style-hover;\n        }\n      }\n\n      .dropdown-menu {\n        z-index: $zindex-popover;\n        border: 1px solid var(--pst-color-border);\n        box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n        background-color: var(--pst-color-on-background);\n        padding: 0.5rem 0;\n        margin: 0.5rem 0;\n        min-width: 20rem;\n\n        .dropdown-item {\n          // Give the items in the dropdown some breathing room but let the hit\n          // and hover area of the items extend to the edges of the menu\n          padding: 0.25rem 1.5rem;\n\n          // Override Bootstrap\n          &:focus:not(:hover, :active) {\n            background-color: inherit;\n          }\n\n          &:focus-visible {\n            z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n          }\n        }\n\n        // Hide the menu unless show has been clicked\n        &:not(.show) {\n          display: none;\n        }\n      }\n    }\n  }\n\n  // **************************************************************\n  // Showing and hiding the sidebar toggle buttons and header items\n  // **************************************************************\n\n  // Toggle buttons\n  button.sidebar-toggle {\n    font-size: var(--pst-font-size-icon);\n    color: var(--pst-color-muted);\n    margin-bottom: 0;\n    background-color: inherit;\n    padding: 0.5rem;\n  }\n\n  button.primary-toggle {\n    margin-right: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  button.secondary-toggle {\n    margin-left: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      display: none;\n    }\n  }\n}\n\n// inline the element in the navbar as long as they fit and use display block when collapsing\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n\n.nav-link {\n  &:hover {\n    @include link-style-hover;\n  }\n\n  // Override Bootstrap\n  transition: none;\n\n  &.nav-external::after {\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-external-link);\n    font-size: 0.75em;\n    margin-left: 0.3em;\n  }\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n// THe elements next to the hamburger menu only show on narrow screens\n.navbar-persistent--mobile {\n  margin-left: auto;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: none;\n  }\n}\n\n// The navbar-persistent content should only show on wide screens\n.navbar-persistent--container {\n  display: none;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: flex;\n  }\n}\n",".header-article__inner {\n  padding: 0 0.5rem;\n\n  // The items define the height so that it disappears if there are no items\n  .header-article-item {\n    min-height: var(--pst-header-article-height);\n  }\n\n  .header-article-items__end {\n    margin-left: auto;\n  }\n}\n","/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n\n$sidebar-padding-right: 1rem;\n\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n\n  @include make-col(3);\n\n  // Borders padding and whitespace\n  padding: 2rem $sidebar-padding-right 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // override bootstrap when navlink are displayed in the sidebar\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n  }\n\n  &.no-sidebar {\n    border-right: 0;\n  }\n\n  &.hide-on-wide {\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  // Headers shouldn't be colored in the sidebars and some extensions add headers\n  h1,\n  h2,\n  h3,\n  h4 {\n    color: var(--pst-color-text-base);\n  }\n\n  .sidebar-primary-items__start,\n  .sidebar-primary-items__end {\n    .sidebar-primary-item {\n      padding: 0.5rem 0;\n    }\n  }\n\n  // Hide the sidebar header items on widescreen since they are visible in the header\n  .sidebar-header-items {\n    display: flex;\n    flex-direction: column;\n\n    .sidebar-header-items__title {\n      font-weight: var(--pst-sidebar-header-font-weight);\n      font-size: var(--pst-sidebar-header-font-size);\n      color: var(--pst-color-text-base);\n      margin-bottom: 0.5rem;\n    }\n\n    .bd-navbar-elements {\n      .nav-link {\n        &:focus-visible {\n          box-shadow: none; // Override Bootstrap\n          outline: $focus-ring-outline;\n          outline-offset: $focus-ring-width;\n        }\n      }\n    }\n\n    // Center header items get displayed vertically, end items are displayed horizontally\n    .sidebar-header-items__center {\n      display: flex;\n      flex-direction: column;\n    }\n\n    // Positioning end items\n    .sidebar-header-items__end {\n      display: flex;\n      align-items: center;\n      gap: 1rem;\n    }\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  .sidebar-primary-items__start {\n    // Add a border on mobile to separate it from the header sidebar area\n    border-top: 1px solid var(--pst-color-border);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      border-top: none;\n    }\n  }\n\n  .sidebar-primary-items__end {\n    margin-top: auto;\n    margin-bottom: 1em;\n  }\n\n  .list-caption {\n    list-style: none;\n    padding-left: 0;\n\n    // Level 0 TOC heading is put inside the <summary> tag\n    // so let the <summary> tag take up more space\n    li.toctree-l0.has-children {\n      > details {\n        > summary {\n          position: relative;\n          height: auto;\n          width: auto;\n          display: flex;\n          justify-content: space-between;\n          align-items: baseline;\n\n          .toctree-toggle {\n            // Prevent toggle icon from getting squished by summary being a\n            // flexbox\n            flex: 0 0 auto;\n\n            // Make the level 0 chevron icon slightly bigger than descendant\n            // levels\n            .fa-chevron-down {\n              font-size: 1rem;\n            }\n          }\n        }\n      }\n    }\n  }\n\n  li.has-children {\n    $toctree-toggle-width: 30px;\n\n    position: relative;\n\n    > .reference,\n    .caption {\n      margin-right: calc(\n        $toctree-toggle-width + $focus-ring-width\n      ); // keep clear of the toggle icon\n\n      padding-top: 0.25rem; // align caption text with toggle chevron\n    }\n\n    > details {\n      > summary {\n        // Remove browser default toggle icon\n        list-style: none;\n\n        &::-webkit-details-marker {\n          display: none;\n        }\n\n        // The summary element is natively focusable, but delegate the focus state to the toggle icon\n        &:focus-visible {\n          outline: none;\n\n          > .toctree-toggle {\n            outline: $focus-ring-outline;\n            outline-offset: -$focus-ring-width; // Prevent right side of focus ring from disappearing underneath the sidebar's right edge\n          }\n        }\n\n        // Container for expand/collapse chevron icon\n        .toctree-toggle {\n          cursor: pointer;\n\n          // Position it so that it's aligned with the top right corner of the\n          // last positioned element, in this case the li.has-children\n          position: absolute;\n          top: 0;\n          right: 0;\n\n          // Give it dimensions\n          width: $toctree-toggle-width;\n          height: $toctree-toggle-width; // make it square\n\n          // Vertically and horizontally center the icon within the container\n          display: inline-flex;\n          justify-content: center;\n          align-items: center;\n\n          .fa-chevron-down {\n            font-size: 0.75rem;\n          }\n        }\n      }\n\n      // The section is open/expanded, rotate the toggle icon (chevron) so it\n      // points up instead of down\n      &[open] {\n        > summary {\n          .fa-chevron-down {\n            transform: rotate(180deg);\n          }\n        }\n      }\n    }\n  }\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -$sidebar-padding-right; // align toctree toggle chevrons with right edge of sidebar and allow text to flow closer to the right edge\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: block;\n  }\n\n  ul {\n    display: block;\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  li > a {\n    display: block;\n    padding: $toc-item-padding-y 0.65rem;\n\n    @include link-sidebar;\n\n    box-shadow: none;\n    margin-right: $focus-ring-width; // prevent the right side focus ring from disappearing under the sidebar right edge\n\n    &.reference.external {\n      &::after {\n        font: var(--fa-font-solid);\n        content: var(--pst-icon-external-link);\n        font-size: 0.75em;\n        margin-left: 0.3em;\n      }\n    }\n  }\n\n  .current > a {\n    @include link-sidebar-current;\n\n    background-color: transparent;\n  }\n\n  // Title\n  p.bd-links__title {\n    font-size: var(--pst-sidebar-header-font-size);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n\n  // Toctree captions\n  p.caption {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    position: relative;\n    margin-top: 1.25rem;\n    margin-bottom: 0.5rem;\n    color: var(--pst-color-text-base);\n\n    &:first-child {\n      margin-top: 0;\n    }\n\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n}\n","/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // Color and border\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem;\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n\n  i {\n    padding-right: 0.5rem;\n  }\n}\n","/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n\n/**\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n@mixin sliding-drawer($side: \"left\") {\n  position: fixed;\n  top: 0;\n  z-index: $zindex-modal;\n  height: 100vh;\n  max-height: 100vh;\n  width: 75%;\n  flex-grow: 0.75;\n  max-width: 350px;\n  transition:\n    visibility $animation-time ease-out,\n    margin $animation-time ease-out;\n  visibility: hidden;\n  border: 0;\n\n  @if $side == \"right\" {\n    margin-right: -75%;\n    right: 0;\n  } @else {\n    margin-left: -75%;\n    left: 0;\n  }\n}\n\n.bd-sidebar::backdrop {\n  background-color: black;\n  opacity: 0.5;\n}\n\n.bd-sidebar-primary {\n  @include media-breakpoint-down($breakpoint-sidebar-primary) {\n    @include sliding-drawer(\"left\");\n  }\n\n  &[open] {\n    margin-left: 0;\n    visibility: visible;\n  }\n}\n\n.bd-sidebar-secondary {\n  @include media-breakpoint-down($breakpoint-sidebar-secondary) {\n    @include sliding-drawer(\"right\");\n  }\n\n  &[open] {\n    margin-right: 0;\n    visibility: visible;\n  }\n}\n","/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n\n  // Font size slightly smaller to avoid cluttering up space too much\n  font-size: 0.8rem;\n\n  li.breadcrumb-item {\n    display: flex;\n    align-items: center;\n    white-space: nowrap;\n    overflow-x: hidden;\n\n    a,\n    .ellipsis {\n      overflow-x: hidden;\n      text-overflow: ellipsis;\n\n      // Need to add margin, otherwise the overflow: hidden rule on the parent\n      // will hide the focus ring\n      margin: $focus-ring-width;\n    }\n\n    // Style should look like heavier in-page links\n    // keeping visited in the default link colour\n    font-weight: bold;\n\n    a {\n      @include link-style-text;\n    }\n\n    // Items that aren't the home have a caret to the left\n    &:not(.breadcrumb-home)::before {\n      font: var(--fa-font-solid);\n      font-size: 0.8rem;\n      content: var(--pst-breadcrumb-divider);\n      color: var(--pst-color-text-muted);\n      padding: 0 0.5rem;\n    }\n  }\n}\n","/**\n * Icon links in the navbar\n */\n\n.pst-navbar-icon {\n  // Extra specificity needed for overrides\n  html & {\n    @include min-hit-area;\n    @include link-style-block;\n\n    display: flex;\n    align-items: center;\n    justify-content: center;\n\n    // Bootstrap overrides\n    border-radius: 0;\n    border: none;\n    font-size: 1rem;\n    line-height: $line-height-body; // Override Boostrap, which defines a separate line-height for buttons\n    padding: $navbar-link-padding-y 0; // Horizontal white space in nav bar between items is controlled via column gap rule on the container.\n\n    // Make the navbar icon links have the same size as the navbar text links\n    height: calc(2 * $navbar-link-padding-y + $line-height-body * 1rem);\n  }\n}\n\nul.navbar-icon-links {\n  display: flex;\n  flex-flow: row wrap;\n  column-gap: $nav-icon-column-gap;\n  justify-content: space-evenly;\n  align-items: center;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  // Icons styling\n  i {\n    &.fa-brands,\n    &.fa-regular,\n    &.fa-solid {\n      vertical-align: middle;\n      font-style: normal;\n      font-size: var(--pst-font-size-icon);\n    }\n\n    /* Social media buttons hard-code the brand color */\n    &.fa-square-twitter::before {\n      color: #55acee;\n    }\n\n    &.fa-square-gitlab::before {\n      color: #548;\n    }\n\n    &.fa-bitbucket::before {\n      color: #0052cc;\n    }\n  }\n\n  // Force images to be icon-sized\n  img.icon-link-image {\n    height: 1.5em;\n    border-radius: 0.2rem;\n  }\n\n  .fa-pydata {\n    stroke: var(--pst-color-background);\n    stroke-linejoin: round;\n    stroke-width: 0.35;\n  }\n}\n","/**\n * Logo in the navbar\n */\n\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n\n  // Ensure that the logo stays the same length while other content shrinks\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n\n  // If there's no logo image, we use a p element w/ the site title\n  p {\n    color: var(--pst-color-text-base);\n    margin-bottom: 0;\n  }\n\n  // If there's a logo, it'll be in an img block\n  img {\n    max-width: 100%;\n    height: 100%;\n    width: auto;\n  }\n\n  &:hover,\n  &:visited:hover {\n    @include link-style-hover;\n\n    color: var(--pst-color-text-base);\n  }\n}\n","/**\n * Navigation links in the navbar and icon links\n */\nul.navbar-nav {\n  // Reduce padding of nested `ul` items a bit\n  ul {\n    padding: 0 0 0 1rem;\n  }\n\n  // Navbar links - do not have an underline by default\n  li {\n    display: flex;\n    flex-direction: column;\n\n    a {\n      display: flex;\n      align-items: center;\n      height: 100%;\n      padding-top: $navbar-link-padding-y;\n      padding-bottom: $navbar-link-padding-y;\n\n      @include link-style-text;\n    }\n  }\n}\n","/**\n * The list of in-page TOC links\n */\n.page-toc {\n  .section-nav {\n    padding-left: 0;\n    border-bottom: none;\n\n    ul {\n      padding-left: 1rem;\n    }\n  }\n\n  // override bootstrap settings\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n\n  .onthispage {\n    color: var(--pst-color-text-base);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n}\n","/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n\n  p {\n    color: var(--pst-color-text-muted);\n    margin: 0 0.3em;\n    line-height: 1.3em;\n  }\n\n  i {\n    font-size: 1.2em;\n  }\n\n  a {\n    // So that buttons align with icons\n    display: flex;\n    align-items: center;\n    border: none;\n    padding: 10px;\n    max-width: 45%;\n    overflow-x: hidden;\n    color: var(--pst-color-text-muted);\n    text-decoration: none;\n\n    p.prev-next-title {\n      @include link-style-default;\n\n      font-weight: var(--pst-admonition-font-weight-heading);\n      font-size: 1.1em;\n    }\n\n    &:hover,\n    &:visited:hover {\n      p.prev-next-title {\n        @include link-style-hover;\n      }\n    }\n\n    .prev-next-info {\n      flex-direction: column;\n      margin: 0 0.5em;\n\n      .prev-next-subtitle {\n        text-transform: capitalize;\n      }\n    }\n\n    &.left-prev {\n      float: left;\n    }\n\n    &.right-next {\n      float: right;\n\n      div.prev-next-info {\n        text-align: right;\n      }\n    }\n  }\n}\n","/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: $admonition-border-radius;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Background should always be same color regardless of active or not\n  &:active {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n\n  // Hoist the focus ring from the input field to its parent\n  &:focus-within {\n    box-shadow: $focus-ring-box-shadow;\n\n    input:focus {\n      box-shadow: none;\n    }\n  }\n\n  .icon {\n    position: absolute;\n    color: var(--pst-color-border);\n    left: 25px;\n  }\n\n  .fa-solid.fa-magnifying-glass {\n    position: absolute;\n    left: calc((2.5rem - 0.7em) / 2);\n    color: var(--pst-color-text-muted);\n  }\n\n  input.form-control {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-base);\n    border: none;\n\n    // Inner-text of the search bar\n    &::placeholder {\n      color: var(--pst-color-text-muted);\n    }\n\n    // Remove the little \"x\" that pops up when you start typing\n    &::-webkit-search-cancel-button,\n    &::-webkit-search-decoration {\n      appearance: none;\n    }\n\n    &:focus,\n    &:focus-visible {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  // Shows off the keyboard shortcuts for the button\n  .search-button__kbd-shortcut {\n    display: none;\n    margin-inline-end: 0.5rem;\n    color: var(--pst-color-border);\n\n    @include media-breakpoint-up(md) {\n      display: flex;\n    }\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n.search-button i {\n  // Search link icon should be a bit bigger since it is separate from icon links\n  font-size: 1.3rem;\n}\n\n/**\n * The search modal <dialog>\n */\n#pst-search-dialog {\n  display: none;\n\n  &[open] {\n    display: flex;\n\n    // Center in middle of screen just underneath header\n    position: fixed;\n    z-index: $zindex-modal;\n    top: 30%;\n    left: 50%;\n    transform: translate(-50%, -50%);\n    right: 1rem;\n    margin-top: 0.5rem;\n    width: 90%;\n    max-width: 800px;\n    background-color: transparent;\n    padding: $focus-ring-width;\n    border: none;\n\n    &::backdrop {\n      background-color: black;\n      opacity: 0.5;\n    }\n\n    form.bd-search {\n      flex-grow: 1;\n\n      // Font and input text a bit bigger\n      svg,\n      input {\n        font-size: var(--pst-font-size-icon);\n      }\n    }\n  }\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  $search-button-border-radius: 1.5em;\n\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: $search-button-border-radius;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    border-radius: $search-button-border-radius;\n  }\n\n  // The keyboard shortcut text\n  .search-button__default-text {\n    font-size: var(--bs-nav-link-font-size);\n    font-weight: var(--bs-nav-link-font-weight);\n    margin-right: 0.5em;\n    margin-left: 0.5em;\n  }\n\n  .kbd-shortcut__modifier {\n    font-size: 0.75em;\n  }\n\n  // Ensures that all the text lines up in the middle\n  > * {\n    align-items: center;\n  }\n\n  // Only the icon should be visible on narrow screens\n  > :not(svg) {\n    display: none;\n\n    @include media-breakpoint-up(lg) {\n      display: flex;\n    }\n  }\n}\n","/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox {\n  // Leave `#searchbox` rules empty so that it doesn't show at all when it is empty\n  p.highlight-link {\n    margin: 1rem 0;\n    width: fit-content;\n\n    // A bit more margin on wide screens to mimic article behavior\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      margin-left: 2rem;\n    }\n\n    // Put outer shadow on this one so that we can darken the link w/ an inner shadow\n    @include box-shadow;\n\n    // Style the button to look like a Sphinx Design button\n    a {\n      border-radius: 0.25rem;\n      font-size: 1.25rem;\n      padding: 0.75rem;\n      background-color: var(--pst-color-primary);\n      color: var(--pst-color-primary-text);\n      text-decoration: none;\n\n      // The box shadow is inset so that it darkens the button on hover\n      transition: box-shadow 0.25s ease-out;\n\n      &:hover {\n        box-shadow: inset 0 0 50px 50px rgb(0 0 0 / 25%);\n      }\n\n      &::before {\n        content: var(--pst-icon-search-minus);\n        color: unset;\n        font: var(--fa-font-solid);\n        margin-right: 0.5rem;\n      }\n    }\n  }\n}\n","/**\n * Light/dark theme switcher\n */\n\n.theme-switch-button {\n  .theme-switch {\n    display: none;\n\n    &:active {\n      text-decoration: none;\n      color: var(--pst-color-link-hover);\n    }\n\n    .fa-lg {\n      aspect-ratio: 1 / 1;\n    }\n  }\n}\n\n@each $mode in auto, light, dark {\n  html[data-mode=\"#{$mode}\"]\n    .theme-switch-button\n    .theme-switch[data-mode=\"#{$mode}\"] {\n    display: inline; // inline needed for span height to be calculated using inherited font size and line height\n  }\n}\n","button.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Add a margin on narrow screens to avoid feeling cramped\n  margin-bottom: 1em;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    margin-bottom: unset;\n  }\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-secondary);\n    border-color: transparent;\n  }\n\n  &:active {\n    color: var(--pst-color-text-base);\n    border-color: var(--pst-color-border);\n  }\n\n  &:focus-visible {\n    border-color: transparent;\n  }\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n\n  a.list-group-item {\n    background-color: var(--pst-color-on-background);\n    color: var(--pst-color-text-base);\n    padding: 0.75rem 1.25rem;\n\n    &:not(:last-child) {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n\n    &:hover {\n      @include link-style-hover;\n\n      background-color: var(--pst-color-surface);\n    }\n\n    &.active {\n      @include link-sidebar-current;\n\n      position: relative;\n      z-index: 1;\n\n      span::before {\n        content: \"\";\n        width: 100%;\n        height: 100%;\n        position: absolute;\n        z-index: -1;\n        left: 0;\n        top: 0;\n      }\n    }\n\n    &:focus-visible {\n      z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n    }\n  }\n}\n\n// Font behavior on mobile\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em; // A bit smaller than other menu font\n  z-index: $zindex-modal; // higher than the sidebars\n\n  // Make sure it meets WCAG target size requirement no matter the version\n  // string displayed in the button\n  @include min-hit-area;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: unset;\n  }\n}\n","/* Collapsing of the TOC sidebar while scrolling */\n\n/* Nav: hide second level (shown on .active) */\n\nnav.page-toc {\n  // A little extra space before the buttons\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav {\n  .nav {\n    display: none;\n\n    // So we can manually specify a level as visible in the config\n    &.visible {\n      display: block;\n    }\n  }\n\n  > .active > ul {\n    display: block;\n  }\n}\n\n// Each entry of the in-page TOC\n.toc-entry {\n  display: block;\n\n  a > code {\n    color: var(--pst-color-text-muted);\n  }\n\n  a.nav-link {\n    display: block;\n    padding: $toc-item-padding-y 0;\n\n    // Padding w/ negative margin so the top TOC item highlight overlaps w/ the TOC border\n    padding-left: 1rem;\n    margin-left: -1rem;\n\n    @include link-sidebar;\n\n    &.active {\n      @include link-sidebar-current;\n\n      background-color: transparent;\n\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n\n    &:focus-visible {\n      border-radius: $focus-ring-radius;\n    }\n  }\n}\n","div.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  position: relative;\n\n  > p {\n    margin-bottom: 0.6rem;\n    margin-top: 0.6rem;\n  }\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n\n  &::before {\n    margin-right: 0.6rem;\n    color: var(--pst-color-info);\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-versionmodified-default);\n  }\n}\n\nspan.versionmodified.added {\n  &::before {\n    color: var(--pst-color-success);\n    content: var(--pst-icon-versionmodified-added);\n  }\n}\n\nspan.versionmodified.changed {\n  &::before {\n    color: var(--pst-color-warning);\n    content: var(--pst-icon-versionmodified-changed);\n  }\n}\n\nspan.versionmodified.deprecated {\n  &::before {\n    color: var(--pst-color-danger);\n    content: var(--pst-icon-versionmodified-deprecated);\n  }\n}\n",".sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    border-top: none;\n  }\n\n  .sidebar-indices-items__title {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    font-size: var(--pst-sidebar-header-font-size);\n    color: var(--pst-color-text-base);\n    margin-bottom: 0.5rem;\n  }\n\n  ul.indices-link {\n    margin-right: -1rem;\n    list-style: none;\n    padding: 0;\n\n    li > a {\n      display: block;\n      padding: 0.25rem 0;\n      color: var(--pst-color-text-muted);\n\n      &:hover {\n        color: var(--pst-color-primary);\n        text-decoration: none;\n        background-color: transparent;\n      }\n    }\n  }\n}\n","/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\n$admonition-border-radius: 0.25rem;\n$admonition-left-border-width: 0.2rem;\n\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: $admonition-left-border-width solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  // Last item should have no spacing since we'll control that w/ padding\n  *:last-child {\n    margin-bottom: 0;\n  }\n\n  // Items after the title should be indented\n  p.admonition-title ~ * {\n    margin-left: 1.4rem;\n    margin-right: 1.4rem;\n  }\n\n  // Lists need to have left margin so they don't spill into it\n  > ol,\n  > ul {\n    margin-left: 1em;\n  }\n\n  // Defaults for all admonitions\n  > .admonition-title {\n    margin: 0 -0.6rem;\n    padding: 0.4rem 0.6rem 0.4rem 2rem;\n    font-weight: var(--pst-admonition-font-weight-heading);\n    position: relative;\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-color-info-bg);\n\n    // now that we use solid colors we want the title on top\n    z-index: 1;\n\n    &::after {\n      position: absolute;\n      left: 0.5rem;\n      width: 1rem;\n      height: 1rem;\n      color: var(--pst-color-info);\n      font: var(--fa-font-solid);\n      line-height: inherit;\n      content: var(--pst-icon-admonition-default);\n      opacity: 1;\n    }\n\n    // Next element after title needs some extra upper-space\n    + * {\n      margin-top: 0.4em;\n    }\n  }\n\n  &.attention {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-attention);\n      }\n    }\n  }\n\n  &.caution {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-caution);\n      }\n    }\n  }\n\n  &.warning {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-warning);\n      }\n    }\n  }\n\n  &.danger {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-danger);\n      }\n    }\n  }\n\n  &.error {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-error);\n      }\n    }\n  }\n\n  &.hint {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-hint);\n      }\n    }\n  }\n\n  &.tip {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-tip);\n      }\n    }\n  }\n\n  &.important {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-important);\n      }\n    }\n  }\n\n  &.note {\n    border-color: var(--pst-color-info);\n\n    > .admonition-title {\n      background-color: var(--pst-color-info-bg);\n\n      &::after {\n        color: var(--pst-color-info);\n        content: var(--pst-icon-admonition-note);\n      }\n    }\n  }\n\n  &.seealso {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-seealso);\n      }\n    }\n  }\n\n  &.admonition-todo {\n    border-color: var(--pst-color-secondary);\n\n    > .admonition-title {\n      background-color: var(--pst-color-secondary-bg);\n\n      &::after {\n        color: var(--pst-color-secondary);\n        content: var(--pst-icon-admonition-todo);\n      }\n    }\n  }\n\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n  &.sidebar {\n    max-width: 40%;\n    float: right;\n    clear: both;\n    margin-left: 0.5rem;\n    margin-top: 0;\n\n    // Undo the .sidebar directive border\n    border-width: 0 0 0 $admonition-left-border-width;\n\n    // TODO: these semantic-color-names border-color rules might no longer be\n    //       needed when we drop support for Sphinx 4 / docutils 0.17\n    &.attention,\n    &.important {\n      border-color: var(--pst-color-attention);\n    }\n\n    &.caution,\n    &.warning {\n      border-color: var(--pst-color-warning);\n    }\n\n    &.danger,\n    &.error {\n      border-color: var(--pst-color-danger);\n    }\n\n    &.hint,\n    &.tip,\n    &.seealso {\n      border-color: var(--pst-color-success);\n    }\n\n    &.note,\n    &.todo {\n      border-color: var(--pst-color-info);\n    }\n\n    // No inner margin since we have less horizontal space w/ the sidebar\n    p.admonition-title ~ * {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n\n/**\n * Topics and the {contents} directive\n */\n// Docutils <= 0.17\ndiv.topic,\ndiv.topic.contents,\n// Docutils >= 0.18\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  padding: 1rem 1.25rem;\n\n  @include box-shadow;\n\n  .topic-title {\n    margin: 0 0 0.5rem;\n  }\n\n  // Over-ride text color to ensure enough contrast\n  p {\n    color: var(--pst-color-on-surface) !important;\n  }\n\n  // Over-ride large default padding\n  ul.simple {\n    padding-left: 1rem;\n\n    ul {\n      // So that sub-lists will have a bit less padding\n      padding-left: 2em;\n    }\n  }\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: $admonition-border-radius;\n\n  // to match the admonition-styled sidebars:\n  margin-left: 0.5rem;\n  padding: 0;\n\n  > *:last-child {\n    padding-bottom: 1rem;\n  }\n\n  p.sidebar-title {\n    position: relative;\n    margin-bottom: 0;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n    border-bottom: 1px solid var(--pst-color-border);\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-admonition-font-weight-heading);\n  }\n\n  // Add margin to the first non-`.sidebar-title` item\n  > *:not(.sidebar-title):first-child,\n  > p.sidebar-title + * {\n    margin-top: 1rem;\n  }\n\n  > * {\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n","/**\n * Miscellaneous color functions and mixins\n**/\n\n@use \"sass:list\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:math\";\n@use \"sass:string\";\n\n// We must add ::before pseudo-element to some theme components (such as admonitions)\n// because users were instructed to customize the background color this way.\n@mixin legacy-backdrop-placeholder {\n  &::before {\n    content: \"\";\n    width: 100%;\n    height: 100%;\n    position: absolute;\n    left: 0;\n    top: 0;\n    z-index: -1;\n\n    // So that hovering over the text within background is still possible.\n    // Otherwise the background overlays the text and you cannot click or select easily.\n    // ref: https://developer.mozilla.org/en-US/docs/Web/CSS/pointer-events\n    pointer-events: none;\n  }\n}\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetc\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n// @param {String/Color} $color - Color definition from map\n// @return {Color} - Color type (in hex)\n@function check-color($color) {\n  @if meta.type-of($color) == string {\n    $color: from-hex($color);\n  }\n\n  @return $color;\n}\n\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n// @param {String} $string - String representation of a color\n@function from-hex($string) {\n  $string-lower: string.to-lower-case($string);\n  $r: \"\";\n  $g: \"\";\n  $b: \"\";\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $length: string.length($string);\n  $max: if($length == 4, 1, 2);\n\n  // Check for length accuracy\n  @if $length != 4 and $length != 7 {\n    @return $string;\n  }\n\n  // Loop from the second character (omitting #)\n  @for $i from 2 through $length {\n    $c: string.slice($string-lower, $i, $i);\n\n    // If wrong character, return\n    @if not list.index($hex, $c) {\n      @return $string;\n    }\n\n    @if string.length($r) < $max {\n      $r: $r + $c;\n    } @else if string.length($g) < $max {\n      $g: $g + $c;\n    } @else if string.length($b) < $max {\n      $b: $b + $c;\n    }\n  }\n\n  @if $length == 4 {\n    $r: $r + $r;\n    $g: $g + $g;\n    $b: $b + $b;\n  }\n\n  @return rgb(hex-to-dec($r), hex-to-dec($g), hex-to-dec($b));\n}\n\n@function hex-to-dec($string) {\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $string: string.to-lower-case($string);\n  $length: string.length($string);\n  $dec: 0;\n\n  @for $i from 1 through $length {\n    $factor: 1 + (15 * ($length - $i));\n    $index: list.index($hex, string.slice($string, $i, $i));\n    $dec: $dec + $factor * ($index - 1);\n  }\n\n  @return $dec;\n}\n","// Style API docs from sphinx' autodoc / autosummary\n\n/*******************************************************************************\n* Styling for field lists\n*/\n\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n\n  th.field-name {\n    padding: 1px 8px 1px 5px;\n    white-space: nowrap;\n    background-color: var(--pst-color-surface);\n  }\n\n  /* italic font for parameter types */\n  td.field-body {\n    p {\n      font-style: italic;\n\n      > strong {\n        font-style: normal;\n      }\n    }\n\n    /* reduced space around parameter description */\n    blockquote {\n      border-left: none;\n      margin: 0 0 0.3em;\n      padding-left: 30px;\n    }\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n\n.table.autosummary {\n  // The first column (with the signature) should not wrap\n  td:first-child {\n    white-space: nowrap;\n  }\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors */\n\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n// addition\n\n// .sig.c .sig-name .n,\n// .sig.cpp .sig-name .n {\n//   color: var(--pst-color-inline-code);\n// }\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\n// change target color for dark theme\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\ndl > dt > a:has(.viewcode-link) {\n  // Sphinx applies a `float:right` rule to the .viewcode-line span, which\n  // exposes a browser glitch in the focus ring. It seems the browser creates\n  // two separate boxes, an empty box where the anchor element gets laid out and\n  // then another box around the anchor's contents that have been floated right.\n  // Firefox draws the focus ring around the empty anchor element box. Chrome\n  // draws two focus rings: one around the empty anchor and one around the\n  // floated-right element. To fix the glitch, we apply the float rule on the\n  // parent rather than the child.\n  float: right;\n\n  .viewcode-link {\n    float: none;\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\n\n// the API selector\n// from https://github.com/pradyunsg/furo/blob/main/src/furo/assets/styles/content/_api.sass#L6)\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) {\n  // increase margin bottom after the dl elements\n  margin-bottom: 3rem;\n\n  dd {\n    margin-left: 2rem;\n\n    // Fix until this will be solved to Sphinx https://github.com/sphinx-doc/sphinx/issues/10815\n    & > dl.simple > dt {\n      display: flex;\n    }\n  }\n\n  dl.field-list {\n    display: grid;\n    grid-template-columns: unset;\n  }\n\n  dt.field-odd,\n  dt.field-even {\n    margin-top: 0.2rem;\n    margin-bottom: 0.1rem;\n    background-color: var(--pst-color-surface);\n  }\n}\n","/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\n\n// General code block behavior\n// Unset bootstrap behavior\ndiv[class*=\"highlight-\"],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: $admonition-border-radius;\n  break-inside: avoid;\n}\n\n// Code blocks with captions\n// There's a wrapper when the code block has a title\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  // This is where the title goes\n  div.code-block-caption {\n    margin: 0;\n    border-bottom: 1px solid var(--pst-color-border);\n    padding: 0.5rem;\n    font-size: 1rem;\n    font-weight: var(--pst-font-weight-caption);\n\n    a.headerlink {\n      font-size: inherit;\n    }\n  }\n\n  // Remove the upper border radius since we want it to connect with the title\n  // Remove the box shadow so the wrapper gets the shadow instead\n  div[class*=\"highlight-\"] {\n    margin: 0;\n    border-radius: 0;\n\n    pre {\n      border: none;\n      box-shadow: none;\n    }\n  }\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n  }\n}\n\n// Minimum opacity needed for linenos to be WCAG AA conformant\nspan.linenos {\n  opacity: 0.8 !important;\n}\n","figure > a,\nfigure > a > img,\nfigure > img,\nfigure > video {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\nfigure {\n  a.headerlink {\n    // So that header link doesn't push caption to be off-center.\n    position: absolute;\n    font-size: inherit;\n  }\n\n  // Default headerlink hover doesn't trigger on figures\n  &:hover a.headerlink {\n    visibility: visible;\n  }\n\n  figcaption {\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-font-weight-caption);\n    color: var(--pst-color-text-muted);\n    margin-left: auto;\n    margin-right: auto;\n    margin-top: 0.3rem;\n    text-align: center;\n\n    & > p:last-child {\n      // Don't add extra margin to already existing figure bottom margin\n      margin-bottom: 0;\n    }\n\n    p {\n      text-align: start;\n      display: inline-block;\n    }\n\n    table.table {\n      width: fit-content;\n      margin-left: auto;\n      margin-right: auto;\n    }\n  }\n}\n","// For consistency, add bracket around footnotes/citations which are\n// cited more than once. E.g. [Newton](1,2) instead of Newton(1,2)\ndt.label > span.brackets:not(:only-child)::before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child)::after {\n  content: \"]\";\n}\n\n// Make footnote as a superscript\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\n// Docutils 0.18 uses an `aside.footnote` container with different internal structure\naside.footnote {\n  margin-bottom: 0.5rem;\n\n  &:last-child {\n    margin-bottom: 1rem;\n  }\n\n  span.label,\n  span.backrefs {\n    font-weight: bold;\n  }\n\n  &:target {\n    background-color: var(--pst-color-target);\n  }\n}\n","/**\n * Hacky fixes that don't fit cleanly into other sections\n */\n\n// Ensure user highlighting/selecting behaves properly\n// From https://stackoverflow.com/a/34372191\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n}\n","// Override bootstrap by restoring the basic theme default.\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n\n  li > p:first-child {\n    margin-bottom: 0.25rem;\n    margin-top: 0.25rem;\n  }\n}\n","// GitHub blockquote style\nblockquote {\n  padding: 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-blockquote-notch);\n  border-radius: $admonition-border-radius;\n  position: relative;\n\n  p {\n    color: var(--pst-color-text-base);\n  }\n\n  // remove padding from included line-block to avoid duplication\n  .line-block {\n    margin: 0;\n  }\n\n  // remove margin bottom for the last p\n  p:last-child {\n    margin-bottom: 0;\n  }\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-surface);\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // hack to make the text in the blockquote selectable\n  &::before {\n    z-index: -1;\n  }\n}\n","/**\n * Span-level styling within content\n */\n\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-info-bg);\n}\n\na.reference.download::before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n","/**\n * Tables\n */\n\ntable {\n  // default to table-center\n  margin-left: auto;\n  margin-right: auto;\n\n  &.table-right {\n    margin-right: 0;\n  }\n\n  &.table-left {\n    margin-left: 0;\n  }\n}\n\n// customize table caption from bootstrap\n// to display them on top and centered\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\n// MyST Markdown tables use these classes to control alignment\nth,\ntd {\n  &.text-left {\n    text-align: left;\n  }\n\n  &.text-right {\n    text-align: right;\n  }\n\n  &.text-center {\n    text-align: center;\n  }\n}\n\n// override bootstrap table colors\n.table {\n  @include table-colors;\n\n  --bs-table-bg: transparent; // background\n  --bs-table-color: var(\n    --pst-color-text-base\n  ); // ensure text and bullets are visible\n}\n\n.pst-scrollable-table-container {\n  // Put a horizontal scrollbar just below tables that are too wide to fit\n  // within the main column\n  overflow-x: auto;\n}\n","/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper {\n  p.caption {\n    font-size: 1.5em;\n    margin-bottom: 0;\n  }\n\n  & > ul {\n    padding-left: 0;\n  }\n\n  li[class^=\"toctree-l\"] {\n    list-style: none;\n    margin-bottom: 0.2em;\n\n    & > a {\n      list-style: none;\n      font-size: 1.1em;\n    }\n\n    & > ul {\n      list-style: none;\n      padding-inline-start: 1.5em;\n    }\n  }\n\n  // slightly bigger font for l1\n  .toctree-l1 > a {\n    font-size: 1.3em;\n  }\n}\n\ndiv.topic.contents,  // Docutils <= 0.17\nnav.contents  // Docutils >= 0.18\n{\n  // Style similarly to toctree\n  ul.simple {\n    list-style: none;\n    padding-left: 0;\n  }\n}\n","/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\n\n// Applies to all math elements\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n\n  // This will be over-ridden for the y-direction and divs\n  overflow: hidden;\n}\n\n// Inline-only\nspan.math {\n  display: inline-flex;\n}\n\n// Block-level only\ndiv.math {\n  gap: 0.5em;\n\n  // So that the eqno shows up after the equation\n  flex-direction: row-reverse;\n\n  // The equation number / link\n  span.eqno a.headerlink {\n    position: relative;\n    font-size: 1em;\n  }\n\n  // The math container\n  mjx-container {\n    flex-grow: 1;\n    padding-bottom: 0.2rem;\n    overflow: auto;\n\n    // Set height to 0 so that it does not cause scrollbars to appear\n    // ref: https://github.com/mathjax/MathJax/issues/2521\n    mjx-assistive-mml {\n      height: 0;\n    }\n  }\n}\n","/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item {\n  h2,\n  h3 {\n    font-size: var(--pst-sidebar-header-font-size);\n\n    // Remove unnecessary vertical whitespace\n    margin-top: 0.5rem;\n\n    // The headers are all links, but this makes them hard to parse\n    // So we change the colors to make them look like headers\n    a {\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  ul {\n    // No bullet points for the primary sidebar items\n    list-style: none;\n    padding-left: 0;\n\n    // Otherwise a scrollbar randomly shows up\n    overflow-y: hidden;\n\n    // List of recent post items\n    display: flex;\n    flex-direction: column;\n    gap: 0.5em;\n    margin-bottom: 0;\n\n    // The ablog cloud should move horizontally\n    &.ablog-cloud {\n      flex-flow: row wrap;\n      gap: 0.5rem;\n\n      // Vertical-align tag clouds\n      li {\n        // Center the tag cloud items\n        display: flex;\n        align-items: center;\n      }\n    }\n  }\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n\n  // The bottom previous / next arrows\n  > span {\n    // To ensure that the whole thing fits on one line even if there are long titles\n    display: flex;\n    max-width: 45%;\n\n    // Links within each span have the collection of icon + text\n    a {\n      display: flex;\n      align-items: center;\n      margin-left: auto;\n      gap: 1rem;\n      line-height: 1.5rem;\n\n      i::before {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The first span is for the previous page and aligns to the left\n  span.ablog__prev {\n    i.fa-arrow-circle-left::before {\n      content: var(--pst-icon-angle-left);\n    }\n  }\n\n  // The second span is just an empty space so we remove it because we're\n  // positioning with flex\n  span.ablog__spacer {\n    display: none;\n  }\n\n  // The third span is aligned to the right\n  span.ablog__next {\n    margin-left: auto;\n    text-align: right;\n\n    i.fa-arrow-circle-right::before {\n      content: var(--pst-icon-angle-right);\n    }\n  }\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n\n  .ablog-post {\n    list-style: none;\n\n    // Post metadata tags (author, links ,etc) should be a bit smaller\n    .ablog-archive {\n      display: flex;\n      flex-flow: row wrap;\n      gap: 1rem;\n      list-style: none;\n      font-size: 0.75rem;\n      padding-left: 0;\n    }\n\n    // Title line should be a bit bigger and bold to stand out\n    .ablog-post-title {\n      margin-top: 0;\n      font-size: 1.25rem;\n\n      a {\n        font-weight: bold;\n      }\n    }\n\n    // Read more button should be a bit bigger\n    .ablog-post-expand {\n      margin-bottom: 0.5rem;\n    }\n  }\n}\n","/**\n * Special cases for Bootstrap functionality\n */\n\n// Bootstrap adds margin to their general container class. However, sphinx/docutils\n// can also generate output with the container class, but in those cases we should\n// not add the margin from bootstrap. Same for max-width.\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n\n.btn {\n  --bs-btn-focus-box-shadow: #{$btn-focus-box-shadow};\n}\n","/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\n\ndiv.highlight button.copybtn {\n  // Nicer spacing\n  display: flex;\n  align-items: center;\n  justify-content: center;\n\n  // Don't over-ride the success color\n  &:not(.success) {\n    color: var(--pst-color-muted);\n  }\n\n  border: none;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    &:not(.success) {\n      color: var(--pst-color-text);\n      background-color: var(--pst-color-shadow);\n    }\n  }\n\n  // Tooltip styling\n  &.o-tooltip--left::after {\n    color: var(--pst-color-text);\n    background-color: var(--pst-color-surface);\n  }\n\n  &:focus {\n    // For keyboard users, make the copy button visible when focussed.\n    opacity: 1;\n  }\n\n  &:focus-visible {\n    outline: $focus-ring-outline;\n  }\n}\n\ndiv.highlight:has(button.copybtn) {\n  // Make sure the code block has enough height for the copy button.\n  // Sphinx-copybutton sets 0.3em top offset plus 1.7em height:\n  // https://github.com/executablebooks/sphinx-copybutton/blob/master/sphinx_copybutton/_static/copybutton.css\n  min-height: 2em;\n}\n","// adapt ethical ad to the theme\n#ethical-ad-placement {\n  .ethical-sidebar a,\n  .ethical-sidebar a:visited,\n  .ethical-sidebar a:hover,\n  .ethical-sidebar a:active,\n  .ethical-footer a,\n  .ethical-footer a:visited,\n  .ethical-footer a:hover,\n  .ethical-footer a:active {\n    color: var(--pst-color-text-base);\n  }\n\n  .ethical-sidebar,\n  .ethical-footer {\n    background-color: var(--pst-color-background);\n    border: 1px solid var(--pst-color-border);\n    border-radius: 5px;\n    color: var(--pst-color-text-base);\n    font-size: 14px;\n    line-height: 20px;\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/******************************************************************************\n * Jupyter Sphinx\n */\n\n.bd-content div.jupyter_container {\n  // We don't want borders around the whole container, just around code cells\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n\n  // Code cells should have the same style as our other code objects\n  div.output,\n  div.highlight {\n    border-radius: 0.25rem;\n  }\n\n  div.highlight {\n    background-color: var(--pst-color-surface);\n  }\n\n  // Ensure the style is the same as our code cells. Jupyter Sphinx makes it tiny.\n  .cell_input,\n  .cell_output {\n    border-radius: 0.25rem;\n\n    pre {\n      padding: 1rem;\n    }\n  }\n}\n","/* Styles for graphviz generated output from Sphinx */\n\n/* Style the inheritance diagram such that it has a dark mode */\nhtml[data-theme=\"dark\"] div.graphviz > object.inheritance {\n  filter: brightness(0.8) invert(0.82) contrast(1.2);\n  color-scheme: normal;\n}\n","/**\n * Special-cases for packages in the PyData ecosystem\n */\n\n// xarray output display in bootstrap\n.xr-wrap[hidden] {\n  display: block !important;\n}\n\n// ipywidgets\n.jp-OutputArea-output.lm-Widget {\n  // override overflow:hidden rule from Lumino (.lm-Widget) to allow scrolling\n  overflow: auto;\n}\n","/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetch\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/* Assign base colors for the PyData theme */\n$color-palette: (\n  // Primary color\n  \"teal\":\n    (\n      \"50\": #f4fbfc,\n      \"100\": #e9f6f8,\n      \"200\": #d0ecf1,\n      \"300\": #abdde6,\n      \"400\": #3fb1c5,\n      \"500\": #0a7d91,\n      \"600\": #085d6c,\n      \"700\": #064752,\n      \"800\": #042c33,\n      \"900\": #021b1f,\n    ),\n  // Secondary color\n  \"violet\":\n    (\n      \"50\": #f4eefb,\n      \"100\": #e0c7ff,\n      \"200\": #d5b4fd,\n      \"300\": #b780ff,\n      \"400\": #9c5ffd,\n      \"500\": #8045e5,\n      \"600\": #6432bd,\n      \"700\": #4b258f,\n      \"800\": #341a61,\n      \"900\": #1e0e39,\n    ),\n  // Neutrals\n  \"gray\":\n    (\n      \"50\": #f9f9fa,\n      \"100\": #f3f4f5,\n      \"200\": #e5e7ea,\n      \"300\": #d1d5da,\n      \"400\": #9ca4af,\n      \"500\": #677384,\n      \"600\": #48566b,\n      \"700\": #29313d,\n      \"800\": #222832,\n      \"900\": #14181e,\n    ),\n  // Accent color\n  \"pink\":\n    (\n      \"50\": #fcf8fd,\n      \"100\": #fcf0fa,\n      \"200\": #f8dff5,\n      \"300\": #f3c7ee,\n      \"400\": #e47fd7,\n      \"500\": #c132af,\n      \"600\": #912583,\n      \"700\": #6e1c64,\n      \"800\": #46123f,\n      \"900\": #2b0b27,\n    ),\n  \"foundation\": (\n    \"white\": #ffffff,\n    // gray-900\n    \"black\": #14181e,\n  )\n);\n\n:root {\n  // Add theme colours to the html root element\n  @each $group-color, $color in $color-palette {\n    @each $color-name, $definition in $color {\n      --pst-#{$group-color}-#{$color-name}: #{$definition};\n    }\n  }\n}\n\n// Static SCSS variables used thoroughout the theme\n// Minimum contrast ratio used for the theme.\n// Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n// 4.5 - is for text that is 14pt or less\n$min-contrast-ratio-4: 4.5;\n\n// 3 is for text that is 18pt or bold, or for non-text elements\n$min-contrast-ratio-3: 3;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$foundation-black: #14181e;\n$foundation-white: #fff;\n\n// This is a custom - calculated  color between gray 100 and 200 - to reduce\n// the contrast ratio (avoid a jarring effect)\n$base-light-text: #ced6dd;\n\n// used in sphinx_design - gray 100\n$foundation-light-gray: #f3f4f5;\n\n// used in sphinx_design - gray 700\n$foundation-muted-gray: #29313d;\n\n// used in sphinx_design - gray 800\n$foundation-dark-gray: #222832;\n$pst-semantic-colors: (\n  \"primary\": (\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"teal\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"teal\", \"800\")},\n  ),\n  \"secondary\": (\n    \"light\": #{map-deep-get($color-palette, \"violet\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"violet\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"800\")},\n  ),\n  \"accent\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"pink\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"pink\", \"800\")},\n  ),\n  \"info\": (\n    \"light\": #276be9,\n    \"bg-light\": #dce7fc,\n    \"dark\": #79a3f2,\n    \"bg-dark\": #06245d,\n  ),\n  \"warning\": (\n    \"light\": #f66a0a,\n    \"bg-light\": #f8e3d0,\n    \"dark\": #ff9245,\n    \"bg-dark\": #652a02,\n  ),\n  \"success\": (\n    \"light\": #00843f,\n    \"bg-light\": #d6ece1,\n    \"dark\": #5fb488,\n    \"bg-dark\": #002f17,\n  ),\n  // This is based on the warning color\n  \"attention\":\n    (\n      \"light\": var(--pst-color-warning),\n      \"bg-light\": var(--pst-color-warning-bg),\n      \"dark\": var(--pst-color-warning),\n      \"bg-dark\": var(--pst-color-warning-bg),\n    ),\n  \"danger\": (\n    \"light\": #d72d47,\n    \"bg-light\": #f9e1e4,\n    \"dark\": #e78894,\n    \"bg-dark\": #4e111b,\n  ),\n  \"text-base\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    \"dark\": $base-light-text,\n  ),\n  \"text-muted\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"shadow\": (\n    \"light\": rgba(0, 0, 0, 0.1),\n    \"dark\": rgba(0, 0, 0, 0.2),\n  ),\n  \"border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"300\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n  ),\n  \"border-muted\": (\n    \"light\": rgba(23, 23, 26, 0.2),\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  \"blockquote-notch\": (\n    // These colors have a contrast ratio > 3.0 against both the background and\n    // surface colors that the notch is sandwiched between\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"500\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"inline-code\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"300\")},\n  ),\n  \"inline-code-links\": (\n    // need to make sure there is enough contrast against the code bg\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"600\")},\n    // keep primary color for dark mode\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n  ),\n  \"target\": (\n    \"light\": #f3cf95,\n    \"dark\": #675c04,\n  ),\n  \"table\": (\n    \"light\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    \"dark\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n  ),\n  \"table-row-hover\": (\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"200\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"700\")},\n  ),\n  \"table-inner-border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"200\")},\n    \"dark\": #364150,\n  ),\n  // DEPTH COLORS - you can see the elevation colours and shades\n  // in the Figma file https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1492%3A922&t=sQeQZehkOzposYEg-1\n  // background: color of the canvas / the furthest back layer\n  \"background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    ),\n  // on-background: provides slight contrast against background\n  // (by use of shadows in light theme)\n  \"on-background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    ),\n  \"surface\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  // on_surface: object on top of surface object (without shadows)\n  \"on-surface\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n      \"dark\": $foundation-light-gray,\n    ),\n);\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * #{something} inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n@mixin theme-colors($mode) {\n  // check if this color is defined differently for light/dark\n  @each $col-name, $definition in $pst-semantic-colors {\n    @if meta.type-of($definition) == map {\n      @each $key, $val in $definition {\n        @if string.index($key, $mode) {\n          // since now we define the bg colours in the semantic colours and not\n          // by changing opacity, we need to check if the key contains bg and the\n          // correct mode (light/dark)\n          @if string.index($key, \"bg\") {\n            --pst-color-#{$col-name}-bg: #{$val};\n          } @else {\n            --pst-color-#{$col-name}: #{$val};\n          }\n        }\n      }\n    } @else {\n      --pst-color-#{$col-name}: #{$definition};\n    }\n  }\n\n  // assign the \"duplicate\" colors (ones that just reference other variables)\n  & {\n    --pst-color-heading: var(--pst-color-text-base);\n    --pst-color-link: var(--pst-color-primary);\n    --pst-color-link-hover: var(--pst-color-secondary);\n    --pst-color-table-outer-border: var(--pst-color-surface);\n    --pst-color-table-heading-bg: var(--pst-color-surface);\n    --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n    --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n  }\n\n  // adapt to light/dark-specific content\n  @if $mode == \"light\" {\n    .only-dark,\n    .only-dark ~ figcaption {\n      display: none !important;\n    }\n  } @else {\n    .only-light,\n    .only-light ~ figcaption {\n      display: none !important;\n    }\n\n    /* Adjust images in dark mode (unless they have class .only-dark or\n     * .dark-light, in which case assume they're already optimized for dark\n     * mode).\n     */\n    img:not(.only-dark, .dark-light) {\n      filter: brightness(0.8) contrast(1.2);\n    }\n\n    /* Give images a light background in dark mode in case they have\n    *  transparency and black text (unless they have class .only-dark or .dark-light, in\n    *  which case assume they're already optimized for dark mode).\n    */\n    .bd-content img:not(.only-dark, .dark-light) {\n      background-color: rgb(255 255 255);\n      border-radius: 0.25rem;\n    }\n\n    // MathJax SVG outputs should be filled to same color as text.\n    .MathJax_SVG * {\n      fill: var(--pst-color-text-base);\n    }\n  }\n}\n\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  @include theme-colors(\"light\");\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    @include theme-colors($mode);\n\n    color-scheme: $mode;\n  }\n}\n\n// assign classes too, for runtime use of theme colors\n@each $col-name, $definition in $pst-semantic-colors {\n  .pst-color-#{$col-name} {\n    color: var(--pst-color-#{$col-name});\n  }\n}\n","/*******************************************************************************\n * Special-cases for the sphinx-design library, mainly to make it compatible\n * with the dark/light themes of pydata-sphinx-theme.\n *\n * NOTE: sphinx-design uses !important quite liberally, so here we must do the\n * same for our overrides to have any effect.\n */\n@use \"../variables/color\" as pst-color;\n@use \"sass:color\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n$sd-semantic-color-names: (\n  \"primary\",\n  \"secondary\",\n  \"success\",\n  \"info\",\n  \"warning\",\n  \"danger\",\n  \"light\",\n  \"muted\",\n  \"dark\",\n  \"black\",\n  \"white\"\n);\n\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\n$extra-semantic-colors: (\n  \"white\": $foundation-white,\n  \"light\": (\n    light: $foundation-light-gray,\n    bg-light: color.scale($foundation-light-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-light-gray, $lightness: -30%),\n  ),\n  \"muted\": (\n    light: $foundation-muted-gray,\n    bg-light: color.scale($foundation-muted-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-muted-gray, $lightness: -30%),\n  ),\n  \"dark\": $foundation-dark-gray,\n  \"black\": $foundation-black,\n);\n$all-colors: map.merge($pst-semantic-colors, $extra-semantic-colors);\n\n@mixin create-sd-colors($value, $name) {\n  // define the pst variables, so that downstream user overrides will work\n  --pst-color-#{$name}: #{$value};\n\n  // we are now using a11y-combination to calculate the text color - this is based\n  // on the WCAG color contrast guidelines\n  --pst-color-#{$name}-text: #{a11y-combination($value)};\n\n  // highlight is used for hover effects on buttons, here we make some fluid scaling\n  // to avoid jarring effects -- we create a local variable that we can access\n  // later to calculate the text color\n  $highlight-color: color.scale($value, $lightness: -15%, $saturation: 5%);\n\n  --pst-color-#{$name}-highlight: #{$highlight-color};\n\n  // override the sphinx-design variables\n  --sd-color-#{$name}: var(--pst-color-#{$name});\n  --sd-color-#{$name}-text: var(--pst-color-#{$name}-text);\n  --sd-color-#{$name}-highlight: var(--pst-color-#{$name}-highlight);\n\n  // calculate the text color for the highlight color\n  --pst-color-#{$name}-highlight-text: #{a11y-combination($highlight-color)};\n}\n\n// Now we override the --sd-color-* variables.\n@each $mode in (light, dark) {\n  @debug \"Creating color variables for mode: #{$mode}\";\n  html[data-theme=\"#{$mode}\"] {\n    // check if this color is defined differently for light/dark\n    @each $name in $sd-semantic-color-names {\n      $definition: map.get($all-colors, $name);\n\n      @if meta.type-of($definition) == map {\n        @each $key, $value in $definition {\n          @if string.index($key, $mode) {\n            // since now we define the bg colours in the semantic colours and not\n            // by changing opacity, we need to check if the key contains bg and the\n            // correct mode (light/dark)\n            @if string.index($key, \"bg\") {\n              --sd-color-#{$name}-bg: #{$value};\n\n              // create local variable -- needed to calculate the text color\n              $value: check-color($value);\n\n              --sd-color-#{$name}-bg-text: #{a11y-combination($value)};\n            } @else {\n              $value: check-color($value);\n\n              @include create-sd-colors($value, $name);\n            }\n          }\n        }\n      } @else {\n        $value: map.get($all-colors, $name);\n\n        @include create-sd-colors($value, $name);\n      }\n    }\n  }\n}\n\n// Make sure the color border variables are set using our variables\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    --sd-color-card-border: var(--pst-color-border);\n  }\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=\"light\"] {\n  .sd-shadow-xs,\n  .sd-shadow-sm,\n  .sd-shadow-md,\n  .sd-shadow-lg {\n    @include box-shadow;\n  }\n}\n\n/*******************************************************************************\n  * cards\n  */\n\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n\n  .sd-card-header {\n    background-color: var(--pst-color-on-background);\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-footer {\n    background-color: var(--pst-color-on-background);\n    border-top: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-body {\n    background-color: var(--pst-color-on-background);\n  }\n\n  // Focus ring for link-cards\n  .sd-stretched-link:focus-visible {\n    // Don't put the focus ring on the <a> element (it has zero height in Sphinx Design cards)\n    outline: none;\n\n    // Put the focus ring on the <a> element's ::after pseudo-element\n    &::after {\n      outline: $focus-ring-outline;\n      border-radius: 0.25rem; // copied from Sphinx Design CSS for .sd-card\n    }\n  }\n\n  &.sd-card-hover:hover {\n    border-color: var(--pst-color-link-hover);\n  }\n}\n\n/*******************************************************************************\n  * tabs\n  */\n\n.bd-content .sd-tab-set {\n  > input {\n    // Active tab label\n    &:checked + label {\n      border-style: solid solid none;\n      border-color: var(--pst-color-primary) var(--pst-color-primary)\n        transparent; // top LR bottom\n\n      border-width: 0.125rem 0.125rem 0;\n      border-radius: 0.125rem 0.125rem 0 0;\n      background-color: var(--pst-color-on-background);\n      transform: translateY(0.125rem);\n      color: var(--pst-color-primary);\n    }\n\n    &:focus-visible + label {\n      border: 0.125rem solid var(--pst-color-accent);\n      border-radius: 0.125rem;\n      background-color: var(--pst-color-accent-bg);\n      color: var(--pst-color-on-surface);\n    }\n\n    // Hover label\n    &:not(:checked, :focus-visible) + label:hover {\n      border-color: transparent;\n      color: var(--pst-color-secondary);\n      text-decoration-line: underline;\n      text-decoration-thickness: $link-hover-decoration-thickness;\n    }\n  }\n\n  // Tab label\n  > label {\n    color: var(--pst-color-on-surface);\n    border: 0.125rem solid transparent;\n    border-radius: 0.125rem 0.125rem 0 0;\n    background-color: var(--pst-color-surface);\n    padding: 0 0.75em;\n    margin-inline-end: 0.25rem;\n    line-height: 1.95;\n\n    html[data-theme=\"dark\"] & {\n      background-color: var(--pst-color-on-background);\n    }\n  }\n\n  // panel\n  .sd-tab-content {\n    border: 0.125rem solid var(--pst-color-primary);\n    border-radius: 0.1875rem;\n    box-shadow: unset;\n    padding: 0.625rem;\n    background-color: var(--pst-color-on-background);\n  }\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\n\ndetails.sd-dropdown {\n  // Remove all borders to over-ride SD behavior, and we'll add our own later\n  border: 0 !important;\n\n  // Drop shadow should behave same as admonitions\n  @include box-shadow;\n\n  // Header is where the \"clickable\" box goes\n  summary.sd-card-header {\n    border: 0 !important;\n    display: flex;\n    align-items: center;\n    position: relative; // So background color works\n    font-weight: 600;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n\n    // Set a variable that we can re-use for colors later\n    // We must set this in the current and content sibling container\n    // so that it is defined in both places\n    --pst-sd-dropdown-color: var(--pst-gray-500);\n    --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n\n    & + div.sd-summary-content {\n      border: 0;\n\n      --pst-sd-dropdown-color: var(--sd-color-card-border);\n    }\n\n    @each $name in $sd-semantic-color-names {\n      &.sd-bg-#{$name} {\n        --pst-sd-dropdown-color: var(--sd-color-#{$name});\n        --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n\n        // Otherwise it won't be defined in the sibling element\n        & + div.sd-summary-content {\n          --pst-sd-dropdown-color: var(--sd-color-#{$name});\n          --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        }\n      }\n      &.sd-bg-text-#{$name} {\n        // Use the WCAG conformant text color\n        color: var(--sd-color-#{$name}-bg-text) !important;\n      }\n    }\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-sd-dropdown-bg-color) !important;\n\n    // Add a left border with the same structure as our admonitions\n    border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n\n    // stylelint-disable-next-line no-duplicate-selectors\n    & + div.sd-summary-content {\n      border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n      border-bottom-left-radius: calc(0.25rem - 1px);\n      background-color: var(--pst-color-on-background);\n    }\n\n    span.sd-summary-icon {\n      display: inline-flex;\n      align-items: center;\n      color: var(--pst-sd-dropdown-color) !important;\n\n      svg {\n        opacity: 1;\n      }\n    }\n\n    // Positioning of the caret\n    .sd-summary-up,\n    .sd-summary-down {\n      top: 0.7rem;\n    }\n\n    @include hover-darken-lighten;\n\n    // Focus ring\n    &:focus:focus-visible {\n      outline: $focus-ring-outline;\n      outline-offset: $focus-ring-offset;\n      border-radius: $focus-ring-width;\n    }\n  }\n\n  &[open] summary.sd-card-header:focus:focus-visible {\n    border-bottom-left-radius: 0;\n    border-bottom-right-radius: 0;\n  }\n}\n\n/*******************************************************************************\n* Buttons (which in Sphinx Design are actually links that look like buttons)\n* ref: https://sphinx-design.readthedocs.io/en/pydata-theme/badges_buttons.html#buttons\n*/\nhtml {\n  .sd-btn {\n    min-width: 2.25rem;\n    padding: 0.3125rem 0.75rem 0.4375rem; // 5px 12px 7px\n\n    &:hover {\n      @include link-style-hover; // override Sphinx Design\n\n      text-decoration-thickness: 1px;\n    }\n  }\n\n  @each $name in $sd-semantic-color-names {\n    .sd-btn-#{$name} {\n      &:hover {\n        color: var(--pst-color-#{$name}-highlight-text) !important;\n      }\n    }\n  }\n\n  @each $name in $sd-semantic-color-names {\n    .sd-btn-#{$name},\n    .sd-btn-outline-#{$name} {\n      &:focus-visible {\n        outline: var(--sd-color-#{$name}) solid $focus-ring-width;\n        outline-offset: $focus-ring-offset;\n        border-radius: $focus-ring-width;\n\n        // Override Sphinx Design's use of -highlight colors. The -highlight\n        // colors are 15% darker, so this would create the effect of darkening\n        // the button when focused but we just want the button to have a focus\n        // ring of the same (non-highlight) color.\n        background-color: var(--sd-color-#{$name}) !important;\n        border-color: var(--sd-color-#{$name}) !important;\n      }\n    }\n  }\n}\n","/**\n * Sphinx togglebutton\n *\n * The rules in this style sheet are meant to tweak the\n * [sphinx-togglebutton](https://sphinx-togglebutton.readthedocs.io/en/latest/index.html)\n * extension so that it matches the look and feel of this theme.\n */\n\n.bd-content {\n  @mixin chevron-down {\n    .toggle-chevron-right {\n      transform: rotate(90deg); // point chevron down\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  @mixin chevron-up {\n    .toggle-chevron-right {\n      transform: rotate(-90deg); // point chevron up\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  // Apply this mixin to the element that will be hovered. These styles are\n  // intended to match what sphinx-design does for its dropdown admonitions.\n  @mixin icon-hover-effects {\n    &:hover .tb-icon {\n      opacity: 1;\n      scale: 1.1;\n    }\n\n    .tb-icon {\n      opacity: 0.6;\n    }\n  }\n\n  // Collapsible admonition, implemented as <div> + <button>\n  .dropdown.admonition.toggle {\n    // The title is visible when the admonition is collapsed and expanded\n    .admonition-title {\n      @include icon-hover-effects;\n      @include hover-darken-lighten;\n\n      &:hover {\n        box-shadow: none;\n      }\n    }\n\n    button.toggle-button {\n      color: inherit;\n\n      // When disclosure widget is closed\n      &.toggle-button-hidden {\n        @include chevron-down;\n      }\n\n      // When open\n      @include chevron-up;\n    }\n\n    // Focus ring\n    // ----------\n    // Sphinx-togglebutton makes the entire admonition header clickable, but\n    // only the button within the header is focusable. But we want the entire\n    // header and not just the button inside the header to be surrounded by a\n    // a focus ring.\n    &:has(:focus-visible) {\n      /* Override Sphinx Toggle Button. Make the overflow visible, otherwise the\n      focus ring is hidden. */\n      overflow: visible;\n\n      /*\n      Why not just do the following?\n\n      ```\n      .admonition-title {\n        outline: $focus-ring-outline;\n      }\n      ```\n\n      Why use ::before? If we put the focus ring on the ::before pseudo-element,\n      we can reposition the focus ring by repositioning the pseudo-element.\n\n      Why reposition? The left edge of the admonition title box does not align\n      with the left edge of the overall admonition box. There is a left border\n      that belongs to the overall box. The border is outside of the admonition\n      title, which means it is also outside of a focus ring around the title. We\n      can make the focus ring bigger, with `outline-offset`, but this will\n      result in a ring that looks off-centered. So we have to pull the ring left\n      and stretch it right. That's what the pseudo-element allows us to do.\n\n      Note: we do not have to do this for collapsible admonitions made with\n      Sphinx Design (as opposed to sphinx-togglebutton).\n      */\n      .admonition-title::before {\n        content: \"\";\n\n        // pull the focus ring left and expand it right to be perfectly centered\n        // between the left border and the right edge of the admonition title\n        left: -$admonition-left-border-width;\n        width: calc(100% + $admonition-left-border-width);\n        height: 100%;\n        outline: $focus-ring-outline;\n        outline-offset: $focus-ring-offset;\n        border-radius: $focus-ring-width;\n      }\n\n      // When expanded, sharpen the bottom left and right corners of the focus ring\n      &:not(.toggle-hidden) .admonition-title::before {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n\n  // Collapsible component, implemented as <details> + <summary>\n  details.toggle-details {\n    // Over-ride border color to re-use our primary color\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n\n      @include chevron-down;\n      @include icon-hover-effects;\n      @include hover-darken-lighten;\n\n      &:hover {\n        box-shadow: none;\n      }\n\n      &:focus-visible {\n        outline-offset: $focus-ring-offset;\n      }\n    }\n\n    // When expanded, sharpen the bottom left and right corners of the focus ring\n    &[open] {\n      @include chevron-up;\n\n      :focus-visible {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html,\n// NBsphinx ipywidgets output selector\nhtml .jp-RenderedHTMLCommon {\n  // Add some margin around the element box for the focus ring. Otherwise the\n  // focus ring gets clipped because the containing elements have `overflow:\n  // hidden` applied to them (via the `.lm-Widget` selector)\n  margin: $focus-ring-width;\n\n  table {\n    table-layout: auto;\n  }\n}\n\n.bd-content .nboutput {\n  .output_area {\n    &.rendered_html,\n    .jp-RenderedHTMLCommon {\n      // pandas\n      table.dataframe {\n        @include table-colors;\n\n        tbody {\n          tr:hover {\n            background-color: var(--pst-color-table-row-hover-bg);\n          }\n        }\n      }\n    }\n\n    // Dark theme special-cases\n    html[data-theme=\"dark\"] & {\n      &.rendered_html:not(:has(table.dataframe)),\n      // ipywidgets\n      .widget-subarea {\n        @include cell-output-background;\n      }\n\n      &.stderr {\n        background-color: var(--pst-color-danger);\n      }\n    }\n  }\n}\n\n// Add extra padding to the final item in an nbsphinx container\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\n// Override nbsphinx's colors for notebook cell prompts because they do not have\n// sufficient contrast. Colors chosen from accessible-pygments\n// a11y-high-contrast-{light,dark} themes.\n\n// Notebook cell input line number. Replace nbsphinx's low contrast blue with\n// higher contrast blues.\n.nbinput.container .prompt pre {\n  html[data-theme=\"light\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-light](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_light)\n    color: #005b82;\n  }\n\n  html[data-theme=\"dark\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-dark](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_dark)\n    color: #00e0e0;\n  }\n}\n\n// Notebook cell output line number. Replace nbsphinx's low contrast red with\n// higher contrast red / orange.\n.nboutput.container .prompt pre {\n  html[data-theme=\"light\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-light](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_light)\n    color: #a12236;\n  }\n\n  html[data-theme=\"dark\"] & {\n    // Copied from accessible-pygments [a11y-high-contrast-dark](https://github.com/Quansight-Labs/accessible-pygments/tree/main/a11y_pygments/a11y_high_contrast_dark)\n    color: #ffa07a;\n  }\n}\n\n/*******************************************************************************\n * myst NB\n */\n\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n.bd-content div.cell_output {\n  // pandas\n  table.dataframe {\n    @include table-colors;\n\n    tbody {\n      tr:hover {\n        background-color: var(--pst-color-table-row-hover-bg);\n      }\n    }\n  }\n\n  html[data-theme=\"dark\"] & {\n    img,\n    .text_html:not(:has(table.dataframe)),\n    // ipywidgets\n    .widget-subarea {\n      @include cell-output-background;\n    }\n  }\n}\n\n// Prevent tables from scrunching together\n.bd-content {\n  div.cell_input {\n    display: flex;\n    flex-direction: column;\n    justify-content: stretch;\n  }\n\n  div.cell_input,\n  div.output {\n    border-radius: $admonition-border-radius;\n  }\n\n  div.output {\n    table {\n      table-layout: auto;\n    }\n  }\n}\n","/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=\"dark\"] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n","/**\n * /search.html page special-cases\n */\n\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n  // Whitespace\n  div#search-results {\n    > h2 {\n      font-size: var(--pst-font-size-icon);\n      margin-top: 1rem;\n    }\n\n    p.search-summary {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  ul.search {\n    margin: 0;\n    list-style: none;\n\n    li {\n      background-image: none;\n      padding: 1rem 0;\n      margin: 1rem 0;\n      border-top: 1px solid var(--pst-color-text-muted);\n\n      // First link is the page title, it should be a bit bigger\n      > a {\n        font-size: 1.2em;\n      }\n\n      div.context,\n      p.context {\n        color: var(--pst-color-text-base);\n        margin: 0.5em 0 0;\n\n        // Add a # before page section titles to make it clear they are sections\n        a::before {\n          content: \"#\";\n          padding-right: 0.2em;\n          color: var(--pst-color-text-muted);\n        }\n      }\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_static/styles/sphinx-book-theme.css b/_static/styles/sphinx-book-theme.css
new file mode 100644
index 00000000..5197540c
--- /dev/null
+++ b/_static/styles/sphinx-book-theme.css
@@ -0,0 +1,9 @@
+/*! sphinx-book-theme CSS
+ * BSD 3-Clause License
+ * Copyright (c) 2020, EBP
+ * All rights reserved.
+ *
+ * This follows the 7-1 pattern described here:
+ * https://sass-guidelin.es/#architecture
+ */html[data-theme=dark],html[data-theme=light]{--pst-font-size-base:none;--pst-color-secondary:#e89217}html[data-theme=light]{--sbt-color-announcement:#616161;--pst-color-primary:#176de8}html[data-theme=dark]{--pst-color-primary:#528fe4;--sbt-color-announcement:#616161;--pst-color-background:#121212}html{scroll-padding-top:4rem}.sbt-scroll-pixel-helper{height:0;left:0;position:absolute;top:0;width:0}.d-n,.onlyprint{display:none}@media print{.onlyprint{display:block!important}.noprint{display:none!important}}.bd-article-container h1,.bd-article-container h2,.bd-article-container h3,.bd-article-container h4,.bd-article-container h5,.bd-article-container p.caption{color:var(--pst-color-muted)}.bd-article-container h1,.bd-article-container h2{font-weight:500}a.brackets:before{color:inherit;font-family:inherit;margin-right:0}table{position:relative}@media print{.bd-main .bd-content{height:auto;margin-left:2rem}.bd-main .bd-content #jb-print-docs-body{margin-left:0}.bd-main .bd-content #jb-print-docs-body h1{font-size:3em;margin-bottom:0;text-align:center}.bd-main .bd-content .bd-article{padding-top:0}.bd-main .bd-content .bd-article h1:first-of-type{display:none}.bd-main .bd-content .container{min-width:0!important}.bd-main .bd-content h1{margin-bottom:1em;margin-top:1em}.bd-main .bd-content h1,.bd-main .bd-content h2,.bd-main .bd-content h3,.bd-main .bd-content h4{break-after:avoid;color:#000}.bd-main .bd-content table{break-inside:avoid}.bd-main .bd-content pre{word-wrap:break-word}.bd-main .bd-content a.headerlink{display:none}.bd-main .bd-content aside.margin,.bd-main .bd-content aside.sidebar,.bd-main .bd-content blockquote.epigraph{border:none}.bd-main .bd-content .footer{margin-top:1em}.bd-main .bd-content #jb-print-toc{margin-bottom:1.5rem;margin-left:0}.bd-main .bd-content #jb-print-toc .section-nav{border-left:0!important;list-style-type:disc!important;margin-left:3em!important}.bd-main .bd-content #jb-print-toc .section-nav a{text-decoration:none!important}.bd-main .bd-content #jb-print-toc .section-nav li{display:list-item!important}.bd-main .bd-content #jb-print-toc .section-nav .nav{display:none}.bd-main .bd-footer-content{display:none!important}}.bd-header-announcement{background-color:var(--sbt-color-announcement);color:#fff}.bd-main .bd-content{justify-content:left}.bd-main .bd-content .bd-article-container{min-width:0;overflow-x:unset;padding:0}@media(min-width:1200px){.bd-main .bd-content .bd-article-container{max-width:calc(100% - var(--pst-sidebar-secondary))}}.bd-main .bd-content .bd-article-container .bd-article{padding-left:2rem;padding-right:2rem}@media(max-width:768px){.bd-main .bd-content .bd-article-container .bd-article{padding-left:1rem;padding-right:1rem}}.bd-main .bd-content .bd-article-container details.above-input summary,.bd-main .bd-content .bd-article-container details.below-input summary{border-left:3px solid var(--pst-color-primary)}@media(min-width:768px){button.sidebar-toggle.primary-toggle{display:inline-block}}@media(max-width:768px){button.sidebar-toggle.primary-toggle{margin-bottom:0}}@media(min-width:1200px){button.sidebar-toggle.secondary-toggle{display:none}}@media(max-width:768px){button.sidebar-toggle.secondary-toggle{margin-bottom:0}}.bd-header-article{align-items:center;background-color:var(--pst-color-background);display:flex;font-size:.9em;padding:0 1rem;position:sticky;top:0;transition:left .2s;z-index:1020}@media(max-width:768px){.bd-header-article{height:3.5rem;padding:0 .5rem}}.scrolled .bd-header-article{box-shadow:0 6px 6px -6px var(--pst-color-shadow)}.bd-header-article .header-article__inner{padding:0}.bd-header-article .header-article-items{align-items:center;display:flex;height:3rem;width:100%}.bd-header-article .header-article-item{align-items:center;display:flex}.bd-header-article .article-header-buttons{display:flex}.bd-header-article .btn{align-items:center;border:none;color:var(--pst-color-text-muted);display:flex;font-size:1.3rem;padding-left:.5rem;padding-right:.5rem}.bd-header-article .btn svg{width:1.3rem}.bd-header-article .btn.show,.bd-header-article .btn:hover{border:none;color:var(--pst-color-text-base)}.bd-header-article .btn.show+.dropdown-menu,.bd-header-article .btn:hover+.dropdown-menu{display:block}.bd-header-article .btn:focus{box-shadow:none}.bd-header-article .btn.dropdown-toggle:after{display:none}.bd-header-article div.dropdown{align-items:center;display:flex}.bd-header-article .theme-switch-button{margin:0}.bd-header-article .theme-switch-button button,.bd-header-article .theme-switch-button i,.bd-header-article .theme-switch-button span{padding:0}.bd-header-article .theme-switch-button i,.bd-header-article .theme-switch-button span{transition:color .25s ease-out}.bd-header-article .theme-switch-button:active,.bd-header-article .theme-switch-button:hover{background-color:unset!important}.bd-header-article .theme-switch-button:active i,.bd-header-article .theme-switch-button:active span,.bd-header-article .theme-switch-button:hover i,.bd-header-article .theme-switch-button:hover span{color:var(--pst-color-text-base)}.bd-header-article .dropdown-menu{background-color:var(--pst-color-background);border-color:var(--pst-color-border);box-shadow:0 .2rem .5rem var(--pst-color-shadow),0 0 .0625rem var(--pst-color-shadow)!important;color:var(--pst-color-text-muted);top:2rem;transform:translateX(-75%)}.bd-header-article .dropdown-menu:hover{display:block}.bd-header-article .dropdown-menu .dropdown-item{align-items:center;display:inline-flex;font-size:1em;padding-left:.5em}.bd-header-article .dropdown-menu .dropdown-item:hover{background-color:initial;color:var(--pst-color-text-base);text-decoration:none}.bd-header-article .dropdown-menu .dropdown-item span img{height:1em}.bd-header-article .dropdown-menu .dropdown-item 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https://github.com/pydata/pydata-sphinx-theme/blob/master/pydata_sphinx_theme/static/css/theme.css\nhtml[data-theme=\"light\"] {\n  // Announcement\n  --sbt-color-announcement: rgb(97, 97, 97);\n  // Default primary color (need to adjust on dark theme due to bad contrast)\n  --pst-color-primary: #176de8;\n}\n\nhtml[data-theme=\"dark\"] {\n  // Slightly lighten these colors to make them stand out more on dark\n  --pst-color-primary: #528fe4;\n\n  // Over-ride the purple announcement color\n  --sbt-color-announcement: rgb(97, 97, 97);\n  // Desaturate the background\n  --pst-color-background: #121212;\n}\n","/**\n * General structural things\n */\nhtml {\n  // The PyData theme value for this is based on `header-height` variable.\n  // We set the variable to 0 and have our own $article-header-height SCSS variable.\n  // So here we follow the same pattern but now using our variable.\n  scroll-padding-top: $header-article-height + 1rem;\n}\n\n/**\n* Utility classes used in a few 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a::before, for citation style\na.brackets::before {\n  color: inherit;\n  font-family: inherit;\n  margin-right: 0rem;\n}\n\ntable {\n  position: relative;\n}\n","/*********************************************\n* Print-specific CSS *\n*********************************************/\n@media print {\n  .bd-main {\n    .bd-content {\n      margin-left: 2rem;\n      height: auto;\n      // Structural elements\n      #jb-print-docs-body {\n        margin-left: 0rem;\n\n        h1 {\n          font-size: 3em;\n          text-align: center;\n          margin-bottom: 0;\n        }\n      }\n      // Main content adjustments\n      .bd-article {\n        padding-top: 0;\n\n        // The first H1 is the title, we've already displayed above\n        h1:first-of-type {\n          display: none;\n        }\n      }\n\n      // HACK: Without this, some code cells take the whole width\n      .container {\n        min-width: 0% !important;\n      }\n\n      // Content\n      h1 {\n        margin-top: 1em;\n        margin-bottom: 1em;\n      }\n      h1,\n      h2,\n      h3,\n      h4 {\n        break-after: avoid;\n        color: black;\n      }\n\n      table {\n        break-inside: avoid;\n      }\n\n      pre {\n        word-wrap: break-word;\n      }\n\n      a.headerlink {\n        display: none;\n      }\n\n      // Remove borders to save some ink\n      blockquote.epigraph,\n      aside.margin,\n      aside.sidebar {\n        border: none;\n      }\n\n      .footer {\n        margin-top: 1em;\n      }\n      // Print-only table of contents\n      #jb-print-toc {\n        margin-bottom: 1.5rem;\n        margin-left: 0rem;\n        .section-nav {\n          border-left: 0px !important;\n          list-style-type: disc !important;\n          margin-left: 3em !important;\n          a {\n            text-decoration: none !important;\n          }\n          li {\n            display: list-item !important;\n          }\n          .nav {\n            display: none;\n          }\n        }\n      }\n    }\n\n    // Hide the footer on printing\n    .bd-footer-content {\n      display: none !important;\n    }\n  }\n}\n",".bd-header-announcement {\n  background-color: var(--sbt-color-announcement);\n  color: #fff;\n}\n",".bd-main .bd-content {\n  // make article container left aligned in absence of sidebar\n  justify-content: left;\n}\n",".bd-main .bd-content .bd-article-container {\n  // Re-adjust padding defaults to be flush on the top and right\n  padding: 0rem;\n  // Unset overflow x so that sticky: top works for the article header\n  overflow-x: unset;\n  // prevent from overflowing the flex container\n  min-width: 0;\n\n  @media (min-width: $breakpoint-xl) {\n    // keep article at reasonable width in absence of sidebar\n    max-width: calc(100% - var(--pst-sidebar-secondary));\n  }\n\n  .bd-article {\n    padding-right: 2rem;\n    padding-left: 2rem;\n    @media (max-width: $breakpoint-md) {\n      padding-right: 1rem;\n      padding-left: 1rem;\n    }\n  }\n  details.above-input,\n  details.below-input {\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n    }\n  }\n}\n","/*********************************************\n* Top Bar *\n*********************************************/\n/**\n * Mixin:\n * Header behavior on mobile\n */\n@mixin header-height-mobile {\n  @media (max-width: $breakpoint-md) {\n    height: $header-article-height + 0.5rem;\n  }\n}\n\n/**\n * Sidebar toggle over-rides for PST\n */\n// Primary toggle is always visible\nbutton.sidebar-toggle.primary-toggle {\n  @media (min-width: $breakpoint-md) {\n    display: inline-block;\n  }\n  @media (max-width: $breakpoint-md) {\n    margin-bottom: 0px;\n  }\n}\n\n// Secondary toggle mimics behavior of \"persistent header\" div of PST\nbutton.sidebar-toggle.secondary-toggle {\n  @media (min-width: $breakpoint-xl) {\n    display: none;\n  }\n  @media (max-width: $breakpoint-md) {\n    margin-bottom: 0px;\n  }\n}\n\n// Wrapper container\n.bd-header-article {\n  display: flex;\n  align-items: center;\n  position: sticky;\n  top: 0;\n  background-color: var(--pst-color-background);\n  transition: left 0.2s;\n  font-size: 0.9em;\n  padding: 0 1rem;\n\n  @media (max-width: $breakpoint-md) {\n    // Give mobile view a bit more space for text\n    padding: 0 0.5rem;\n  }\n  z-index: $zindex-sticky;\n\n  @include header-height-mobile;\n\n  // The box shadow that shows up when you've scrolled past the top\n  .scrolled & {\n    box-shadow: 0 6px 6px -6px var(--pst-color-shadow);\n  }\n\n  // Remove inner padding so that spacing of buttons is a bit tighter\n  .header-article__inner {\n    padding: 0;\n  }\n\n  // Inner container with items in it\n  .header-article-items {\n    display: flex;\n    align-items: center;\n    height: $header-article-height;\n    width: 100%;\n  }\n\n  // Contains the individual item components so we want it centered\n  .header-article-item {\n    display: flex;\n    align-items: center;\n  }\n\n  /**\n  * Buttons in the header\n  */\n  // A series of buttons we special-case in the theme\n  .article-header-buttons {\n    display: flex;\n  }\n\n  // Generic button styles\n  .btn {\n    // Basic button size\n    font-size: 1.3rem;\n    color: var(--pst-color-text-muted);\n    border: none;\n    padding-left: 0.5rem;\n    padding-right: 0.5rem;\n\n    // Make sure anything inside is aligned vertically\n    display: flex;\n    align-items: center;\n\n    // Fix width of buttons so they're all the same\n    svg {\n      width: 1.3rem;\n    }\n\n    // Hover / active behavior\n    &.show,\n    &:hover {\n      color: var(--pst-color-text-base);\n      border: none;\n      & + .dropdown-menu {\n        display: block;\n      }\n    }\n    &:focus {\n      box-shadow: none;\n    }\n\n    &.dropdown-toggle {\n      // Hide the bootstrap caret\n      &:after {\n        display: none;\n      }\n    }\n  }\n\n  // Vertically align dropdown buttons\n  div.dropdown {\n    display: flex;\n    align-items: center;\n  }\n\n  .theme-switch-button {\n    // Removing some special-casing that was needed in PST\n    margin: 0;\n\n    // Remove extra padding since it is already there between sidebar items\n    span,\n    i,\n    button {\n      padding: 0;\n    }\n\n    // No background on hover. Need important to over-ride the PST which uses it too.\n    span,\n    i {\n      transition: color $animation-time ease-out;\n    }\n    &:active,\n    &:hover {\n      background-color: unset !important;\n      span,\n      i {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The menu that is normally hidden until you hover\n  .dropdown-menu {\n    // Hidden unless we are hovering\n    &:hover {\n      display: block;\n    }\n\n    // Positioning and layout of dropdown items to be standardized\n    top: 2rem;\n    transform: translateX(-75%);\n    @include pst-box-shadow;\n\n    // Color and shadowing\n    border-color: var(--pst-color-border);\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n\n    .dropdown-item {\n      display: inline-flex;\n      align-items: center;\n      padding-left: 0.5em;\n\n      // To prevent link underline from showing up\n      &:hover {\n        text-decoration: none;\n        background-color: initial;\n        color: var(--pst-color-text-base);\n      }\n\n      // Slightly smaller font for everything\n      font-size: 1em;\n\n      // Image icons should be the same height as <i> icons\n      span img {\n        height: 1em;\n      }\n\n      span.btn__icon-container {\n        width: 1.7em;\n        align-items: center;\n        display: inline-flex;\n        justify-content: center;\n      }\n    }\n  }\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n/**\n* Hide the scrollbar until the element is hovered, so keep the page clean\n* Use this sparingly because it's not a great UX pattern.\n*/\n@mixin scrollbar-on-hover() {\n  &:not(:hover) {\n    &::-webkit-scrollbar-thumb {\n      visibility: hidden;\n    }\n  }\n}\n\n/**\n * The PyData Sphinx Theme box shadow rule\n * Copied here in csae we need to re-use.\n*/\n@mixin pst-box-shadow() {\n  box-shadow:\n    0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n",".bd-header {\n  // Turn off sticky positioning so that it scrolls\n  position: inherit;\n\n  // Turn these off because we'll show it in the article header\n  // The primary sidebar toggle\n  button.sidebar-toggle {\n    display: none;\n  }\n}\n","/*********************************************\n* Left Nav Bar *\n*********************************************/\n\n.bd-sidebar-primary {\n  // So that it sticks to the top of the page instead of the header height\n  top: 0;\n  max-height: 100vh;\n\n  // Tighter padding so the spacing is more equal\n  padding: 1rem;\n\n  // Draw open/close animation\n  transition:\n    margin-left $animation-time ease 0s,\n    opacity $animation-time ease 0s,\n    visibility $animation-time ease 0s;\n\n  @media (max-width: $breakpoint-md) {\n    // So that tooltips don't overlap\n    z-index: $zindex-tooltip + 1;\n  }\n\n  // Remove the top border since there's often no header items above\n  .sidebar-primary-items__start {\n    border-top: none;\n  }\n\n  // On wide screens we have a smaller sidebar width than PST\n  @media (min-width: $breakpoint-lg) {\n    flex-basis: $sidebar-primary-width-widescreen;\n  }\n}\n\n// Default visibility for left sidebar is the reverse of what it is on mobile.\n// It is shown by default, and hidden with a click.\n// Mobile behavior is defined in the pydata sphinx theme\n@media (min-width: $breakpoint-lg) {\n  input#pst-primary-sidebar-checkbox:checked\n    ~ .bd-container\n    .bd-sidebar-primary {\n    margin-left: -$sidebar-primary-width-widescreen;\n    visibility: hidden;\n    opacity: 0;\n  }\n}\n","/*********************************************\n* Table of Contents (right nav bar)          *\n*********************************************/\n\n.bd-sidebar-secondary {\n  // So that it sticks to the top of the page instead of the header height\n  top: 0;\n\n  @media (max-width: $breakpoint-xl) {\n    // So that tooltips don't overlap\n    z-index: $zindex-tooltip + 1;\n  }\n\n  // So that we can center the TOC button with the article header buttons\n  .sidebar-secondary-items {\n    padding: 0;\n    display: flex;\n    gap: 0.5rem;\n\n    // Remove the manual padding since we'll use `gap`\n    .sidebar-secondary-item {\n      padding-top: 0;\n      padding-bottom: 0;\n    }\n  }\n\n  // The 'on this page' title div should take up the full header\n  .onthispage {\n    height: $header-article-height;\n    min-height: $header-article-height;\n    display: flex;\n    gap: 0.5rem;\n    align-items: center;\n    margin: 0;\n    color: var(--pst-color-muted);\n  }\n\n  // Wide screen behavior\n  @media (min-width: $breakpoint-xl) {\n    background: var(--pst-color-background);\n    height: fit-content;\n    transition: max-height 0.4s ease;\n\n    // To make sure it shows above the page content\n    z-index: $zindex-middle;\n\n    // Remove padding so that it's flush on the top and left\n    padding: 0;\n\n    // This is the container div for the secondary sidebar nav\n    .toc-item {\n      // Border is a bit less prominent\n      border-left-color: var(--pst-color-surface);\n      // Flush with the top so that we can fix the height of the toc header div\n      padding-top: 0;\n\n      // The table of contents <ul> after the title div\n      nav.page-toc {\n        // Remove extra margin the PST adds for other sidebar items\n        margin-bottom: 0;\n\n        // Items after \"on this page' should fade in and out\n        transition: opacity 0.4s ease;\n      }\n    }\n\n    // Rules for when sidebar is hidden\n    // the `not:hover` means that hovering over the sidebar will show it again.\n    &.hide:not(:hover) {\n      // Cap the height, and remove the scrollbar\n      max-height: $header-article-height;\n      overflow-y: hidden;\n\n      // Shadow matches what we have in the article header\n      .scrolled & {\n        box-shadow: 0 6px 6px -6px rgba(0, 0, 0, 0.3);\n      }\n\n      // Show a down carot\n      .onthispage:after {\n        opacity: 1;\n        content: \"\\f107\";\n        font-family: \"Font Awesome 5 Free\";\n        font-weight: 900;\n        padding-left: 0.5em;\n        transition: opacity 0.3s ease;\n      }\n\n      // The page-toc list is hidden\n      nav.page-toc {\n        opacity: 0;\n      }\n    }\n  }\n}\n","/*********************************************\n* Footer - content *\n*********************************************/\nfooter {\n  font-size: var(--sbt-font-size-small-1);\n  &.bd-footer-content {\n    display: flex;\n    flex-wrap: wrap;\n    padding: 15px;\n    border-top: 1px solid #ccc;\n    font-size: $sbt-font-size-small-1;\n    .bd-footer-content__inner {\n      padding-left: 0px;\n      p {\n        margin-bottom: 0px;\n      }\n    }\n  }\n}\n",".bd-footer-article {\n  padding: 0rem 1rem;\n  @media (max-width: $breakpoint-md) {\n    // More room for text on mobile\n    padding: 0rem 0.5rem;\n  }\n}\n","#pst-back-to-top {\n  // We should only display the back-to-top button when the TOC is not available.\n  // AKA, on narrow-ish screens.\n  // This over-rides a PST default\n  @media (min-width: $breakpoint-xl) {\n    display: none !important;\n  }\n\n  // Reduce size to be slightly less intrusive on the page\n  font-size: 0.8rem;\n}\n","/**\n * Icon links design for the primary sidebar, where it defaults in this theme.\n */\n.bd-sidebar-primary {\n  .navbar-icon-links {\n    column-gap: 0.5rem;\n\n    .nav-link {\n      // There are few kinds of elements that can be icon links and each is different\n      i,\n      span {\n        font-size: 1.2rem;\n      }\n\n      // Images usually fill more vertical space so we make them a bit smaller\n      img {\n        font-size: 0.8rem;\n      }\n    }\n  }\n}\n",".navbar-brand {\n  height: unset;\n  max-height: unset;\n\n  // Auto-center the image and title and make items vertical\n  flex-direction: column;\n  justify-content: center;\n  gap: 0.25rem;\n\n  // To prevent site text from having underlines\n  &:hover {\n    text-decoration: none;\n  }\n  .logo__title {\n    font-size: 1.25rem;\n    white-space: normal;\n    text-align: center;\n  }\n  // Prevent images from stretching vertically\n  .logo__image {\n    height: unset;\n  }\n}\n","// Clean up the search page so that it has less unnecessary text\n.bd-search-container {\n  margin: 2em;\n\n  #search-results {\n    h2:first-child {\n      display: none;\n    }\n  }\n}\n\n// Sidebar search button field\n.search-button-field {\n  width: 100%;\n  font-size: 0.9rem;\n  // Hide by default and we'll add a rule to show it on wide sreen below\n  display: none;\n\n  .search-button__kbd-shortcut {\n    margin-left: auto;\n  }\n}\n\n// Rules to switch off visibility of the field button and the header button\n@media (min-width: $breakpoint-lg) {\n  .search-button {\n    display: none !important;\n  }\n\n  .search-button-field {\n    display: flex;\n  }\n}\n\n// The \"clear search results\" button\n// Make it line up with our content better\ndiv#searchbox {\n  padding-right: 2rem;\n  padding-left: 2rem;\n  @media (max-width: $breakpoint-md) {\n    padding-right: 1rem;\n    padding-left: 1rem;\n  }\n\n  p.highlight-link {\n    // Remove the extra margin that is added\n    @media (min-width: $breakpoint-md) {\n      margin-left: 0;\n      a {\n        font-size: 1rem;\n      }\n    }\n  }\n}\n","// Images\nimg {\n  max-width: 100%;\n\n  &.align-center {\n    margin-left: auto;\n    margin-right: auto;\n    display: block;\n  }\n\n  &.align-left {\n    clear: left;\n    float: left;\n    margin-right: 1em;\n  }\n\n  &.align-right {\n    clear: right;\n    float: right;\n    margin-left: 1em;\n  }\n}\n\n// Figures\ndiv.figure {\n  width: 100%;\n  margin-bottom: 1em;\n  text-align: center;\n\n  &.align-left {\n    text-align: left;\n    p.caption {\n      margin-left: 0;\n    }\n  }\n  &.align-right {\n    text-align: right;\n    p.caption {\n      margin-right: 0;\n    }\n  }\n\n  p.caption {\n    margin: 0.5em 10%;\n  }\n\n  &.margin,\n  &.margin-caption {\n    p.caption {\n      margin: 0.5em 0;\n    }\n  }\n\n  &.margin-caption p.caption {\n    text-align: left;\n  }\n\n  span.caption-number {\n    font-weight: bold;\n  }\n\n  span {\n    font-size: 0.9rem;\n  }\n}\n","/**\n * Margin content\n * This is the area to the right of the main content\n * normally covered by the in-page table of contents\n * but if `.margin` classes are present, it shows up to the right in the margin\n * and the in-page TOC is hidden.\n * On narrow screens, margin content behaves like a sidebar\n */\n\n/**\n * Variables for calculating margin widths\n */\n// We want our margin content to be 1/3 the width of the main page content\n// after factoring in a small margin\n$margin-width-relative-to-content: 36%;\n$margin-gutter: 3%;\n$margin-width-width-gutter: $margin-width-relative-to-content - $margin-gutter;\n\n// re-subtract the gutter width because we want it to leave a white-space\n// This is how much to offset the margin content.\n$margin-offset: -$margin-width-relative-to-content;\n\n/**\n * This mixin will cause something to \"pop out\" to the margin on wide screens\n */\n@mixin margin-content() {\n  // On narrow screens this is the width of margin content\n  width: 40%;\n  float: right;\n  background-color: unset;\n  font-size: 0.9em;\n  margin-left: 0.5rem;\n\n  // Wide screens and printing should force margin behavior\n  @media (min-width: $breakpoint-lg), print {\n    width: $margin-width-width-gutter;\n    margin: 0 $margin-offset 0 0;\n\n    // Prevent sequential margin content from overlapping\n    clear: right;\n\n    p.sidebar-title {\n      margin-bottom: -1rem;\n      border-bottom: none;\n      padding-left: 0px;\n\n      // Content of admonitions has fewer horizontal white space to save space\n      ~ * {\n        padding-left: 0px;\n        padding-right: 0px;\n      }\n    }\n  }\n}\n\n/**\n * Sidenotes and marginnotes\n * over-rides the \"footnote\" behavior to pop out to the margin instead.\n */\nlabel.margin-toggle {\n  margin-bottom: 0em;\n  &.marginnote-label {\n    display: none;\n  }\n  sup {\n    user-select: none;\n  }\n  @media (max-width: $breakpoint-lg) {\n    cursor: pointer;\n    color: rgb(0, 113, 188);\n    &.marginnote-label {\n      display: inline;\n      &:after {\n        content: \"\\2295\";\n      }\n    }\n  }\n}\n\ninput.margin-toggle {\n  display: none;\n  @media (max-width: $breakpoint-lg) {\n    &:checked + .sidenote,\n    &:checked + .marginnote {\n      display: block;\n      float: left;\n      left: 1rem;\n      clear: both;\n      width: 95%;\n      margin: 1rem 2.5%;\n      position: relative;\n    }\n  }\n}\n\nspan.sidenote,\nspan.marginnote {\n  z-index: $zindex-middle;\n  position: relative;\n  sup {\n    user-select: none;\n  }\n  @include margin-content();\n  border-left: none;\n  @media (max-width: $breakpoint-lg) {\n    display: none;\n  }\n}\n\naside.sidebar {\n  .note {\n    // styling for notes inside sidebars\n    margin: 1rem;\n    padding: 0rem 0rem 1rem 0rem;\n  }\n  // The titles sticking\n  .admonition-title {\n    margin: 0rem -1rem 0rem 0rem;\n  }\n}\n\n/**\n * Sidebar content in the margin.\n * This will be added by the `margin` directive or the `sidebar` directive\n */\naside.sidebar.margin {\n  // Margin content can have an empty title so this prevents an empty title block\n  // from showing up on mobile\n  .sidebar-title:empty {\n    display: none;\n  }\n\n  // On narrow screens padding is added to all sidebar content\n  // This replaces padding w/ margin for admonitions because padding is used\n  // for the title background color\n  .admonition {\n    margin: 0.5rem;\n    padding-left: 0;\n    padding-right: 0;\n\n    // Remove the title margin so it\n    .admonition-title {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n\n  @media (min-width: $breakpoint-lg) {\n    // No border for margin sidebars on wide screen\n    border: none;\n\n    // Admonitions don't need the extra whitespace in the margin\n    .admonition {\n      margin: 1rem 0rem;\n      padding: 0rem 0rem 1rem 0rem;\n    }\n  }\n}\n\ndiv.margin,\nfigure.margin,\naside.margin,\n.margin.docutils.container,\n.cell.tag_popout,\n.cell.tag_margin {\n  z-index: $zindex-middle;\n  position: relative;\n  @include margin-content();\n\n  // Make cell outputs take up more space if they're in the margin\n  div.cell.tag_margin .cell_output {\n    padding-left: 0;\n  }\n}\n\n// A few permutations because docutils 0.18 switched to semantic tags\ndiv.figure.margin-caption p.caption,\ndiv.figure.margin-caption figcaption,\nfigure.margin-caption figcaption {\n  z-index: $zindex-middle;\n  position: relative;\n  @include margin-content();\n}\n\n// Margin captions\n.margin-caption figcaption {\n  text-align: left;\n}\n\n/**\n * Full width content\n */\n\n// Grow 100% by the ratio of the margin to the content width.\n$content-fullwidth-width: 100% + $margin-width-relative-to-content;\n\ndiv.cell.tag_full-width,\ndiv.cell.tag_full_width,\ndiv.full_width,\ndiv.full-width {\n  z-index: $zindex-middle;\n  position: relative;\n  @media (min-width: $breakpoint-lg) {\n    max-width: $content-fullwidth-width;\n    width: $content-fullwidth-width;\n  }\n}\n","/**\n * Epigraphs and other \"special\" quote blocks\n */\n\nblockquote {\n  &.pull-quote,\n  &.epigraph,\n  &.highlights {\n    font-size: 1.25em;\n    border-left: none;\n    background-color: var(--pst-color-background);\n  }\n\n  div > p + p.attribution {\n    font-style: normal;\n    font-size: 0.9em;\n    text-align: right;\n    color: #6c757d;\n    padding-right: 2em;\n  }\n}\n","// Over-ride Sphinx default so that content fills whitespace w/ margin content\npre,\ndiv[class*=\"highlight-\"] {\n  clear: none;\n}\n","// MyST-NB and Jupyter Notebooks\ndiv.cell.tag_output_scroll div.cell_output,\ndiv.cell.tag_scroll-output div.cell_output {\n  max-height: 24em;\n  overflow-y: auto;\n}\n\ndiv.cell.tag_scroll-input div.cell_input {\n  max-height: 24em;\n  overflow-y: auto;\n}\n","/**\n * Comment JavaScript libraries\n  * Libraries supported here are:\n  * - Hypothes.is : https://web.hypothes.is/\n  * - Utteranc.es : https://utteranc.es/\n  */\n// In general we want to hide any comment libraries on print\n@media only print {\n  hypothesis-sidebar,\n  div.utterances {\n    display: none;\n  }\n}\n","/*********************************************\n* Thebe *\n* ref: https://github.com/executablebooks/thebe\n*********************************************/\n.thebelab-cell {\n  border: none !important;\n  margin-right: 0.5em !important;\n}\n\n.thebelab-cell .thebelab-input {\n  padding-left: 10px !important;\n}\n\n.cell.docutils.container {\n  padding-right: 0px !important;\n}\n\nbutton.thebe-launch-button {\n  height: 2.5em;\n  font-size: 1em;\n}\n"],"names":[],"sourceRoot":""}
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diff --git a/_static/styles/theme.css b/_static/styles/theme.css
new file mode 100644
index 00000000..4519dd91
--- /dev/null
+++ b/_static/styles/theme.css
@@ -0,0 +1,2 @@
+/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */
+@import "../basic.css";
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diff --git a/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2 b/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2
new file mode 100644
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diff --git a/_static/webpack-macros.html b/_static/webpack-macros.html
new file mode 100644
index 00000000..89732b23
--- /dev/null
+++ b/_static/webpack-macros.html
@@ -0,0 +1,24 @@
+<!--
+  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
+  These are re-used in layout.html
+-->
+
+{% macro head_pre_assets() %}
+  <!-- Loaded before other Sphinx assets -->
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=8878045cc6db502f8baf" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=8878045cc6db502f8baf" rel="stylesheet" />
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <!-- So that users can add custom icons -->
+  <script src="{{ pathto('_static/scripts/fontawesome.js', 1) }}?digest=8878045cc6db502f8baf"></script>
+  <!-- Pre-loaded scripts that we'll load fully later -->
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+{% endmacro %}
+
+{% macro body_post() %}
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
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+
+<!DOCTYPE html>
+
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+<html lang="en" data-content_root="../" >
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <script src="../_static/scripts/fontawesome.js?digest=8878045cc6db502f8baf"></script>
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+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
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+  <dialog id="pst-search-dialog">
+    
+<form class="bd-search d-flex align-items-center"
+      action="../search.html"
+      method="get">
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+  <input type="search"
+         class="form-control"
+         name="q"
+         placeholder="Search..."
+         aria-label="Search..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
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+        <p aria-level="2" class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../guides/contributing.html">Contributing Guidelines</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/causal_abstraction.html">A Little Guide to Causal Abstraction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/ndif.html">NDIF Integration</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Basic tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/pyvene_101.html">Introduction to pyvene</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Probing_Gender.html">Causal Evaluation of Probes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">API</span></p>
+<ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#"><code class="docutils literal notranslate"><span class="pre">pyvene</span></code>: Core API</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2 has-children"><a class="reference internal" href="pyvene.data_generators.html">pyvene.data_generators</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.data_generators.causal_model.simple_example.html">pyvene.data_generators.causal_model.simple_example</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.data_generators.causal_model.CausalModel.html">pyvene.data_generators.causal_model.CausalModel</a></li>
+</ul>
+</details></li>
+</ul>
+</details></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="pyvene.models.html">pyvene.models</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.html">pyvene.models.backpack_gpt2.modelings_backpack_gpt2</a></li>
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+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.basic_utils.html">pyvene.models.basic_utils</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.configuration_intervenable_model.html">pyvene.models.configuration_intervenable_model</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.constants.html">pyvene.models.constants</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gemma.modelings_intervenable_gemma.html">pyvene.models.gemma.modelings_intervenable_gemma</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.gemma2.html">pyvene.models.gemma2</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gemma2.modelings_intervenable_gemma2.html">pyvene.models.gemma2.modelings_intervenable_gemma2</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.gpt2.html">pyvene.models.gpt2</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.html">pyvene.models.gpt2.modelings_intervenable_gpt2</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.gpt_neo.html">pyvene.models.gpt_neo</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.html">pyvene.models.gpt_neo.modelings_intervenable_gpt_neo</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.gpt_neox.html">pyvene.models.gpt_neox</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.html">pyvene.models.gpt_neox.modelings_intervenable_gpt_neox</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.gru.html">pyvene.models.gru</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gru.modelings_gru.html">pyvene.models.gru.modelings_gru</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.gru.modelings_intervenable_gru.html">pyvene.models.gru.modelings_intervenable_gru</a></li>
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+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="pyvene.models.intervenable_base.html">pyvene.models.intervenable_base</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.intervenable_base.build_intervenable_model.html">pyvene.models.intervenable_base.build_intervenable_model</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.intervenable_base.BaseModel.html">pyvene.models.intervenable_base.BaseModel</a></li>
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+</details></li>
+<li class="toctree-l3"><a class="reference internal" href="pyvene.models.intervenable_modelcard.html">pyvene.models.intervenable_modelcard</a></li>
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+    <h1>pyvene.data_generators.causal_model.CausalModel</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.data_generators.causal_model.CausalModel"><code class="docutils literal notranslate"><span class="pre">CausalModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.data_generators.causal_model.CausalModel.__init__"><code class="docutils literal notranslate"><span class="pre">CausalModel.__init__()</span></code></a></li>
+</ul>
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+  <section id="pyvene-data-generators-causal-model-causalmodel">
+<h1>pyvene.data_generators.causal_model.CausalModel<a class="headerlink" href="#pyvene-data-generators-causal-model-causalmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.data_generators.causal_model.CausalModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">CausalModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timesteps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equiv_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/data_generators/causal_model.html#CausalModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.data_generators.causal_model.CausalModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.data_generators.causal_model.CausalModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parents</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timesteps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equiv_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/data_generators/causal_model.html#CausalModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.data_generators.causal_model.CausalModel.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.data_generators.causal_model.CausalModel.__init__" title="pyvene.data_generators.causal_model.CausalModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(variables, values, parents, functions)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_variable</span></code>(variable, values, parents, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_live_paths</span></code>(intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_counterfactual_dataset</span></code>(size, ...[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_equiv_classes</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_factual_dataset</span></code>(size[, sampler, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_timesteps</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_partial_filter</span></code>(partial_setting)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_path_maxlen_filter</span></code>(lengths)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_specific_path_filter</span></code>(start, end)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">input_to_tensor</span></code>(setting)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">marginalize</span></code>(target)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_to_tensor</span></code>(setting)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">print_setting</span></code>(total_setting[, font, node_size])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">print_structure</span></code>([pos, font, node_size])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_forward</span></code>([intervention])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_interchange</span></code>(input, source_interventions)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_input</span></code>([mandatory])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_input_tree_balanced</span></code>([output_var, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_intervention</span></code>([mandatory])</p></td>
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+  <section id="module-pyvene.data_generators.causal_model">
+<span id="pyvene-data-generators-causal-model"></span><h1>pyvene.data_generators.causal_model<a class="headerlink" href="#module-pyvene.data_generators.causal_model" title="Link to this heading">#</a></h1>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.data_generators.causal_model.simple_example.html#pyvene.data_generators.causal_model.simple_example" title="pyvene.data_generators.causal_model.simple_example"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simple_example</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.data_generators.causal_model.CausalModel.html#pyvene.data_generators.causal_model.CausalModel" title="pyvene.data_generators.causal_model.CausalModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CausalModel</span></code></a>(variables, values, parents, ...)</p></td>
+<td><p></p></td>
+</tr>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.JumpReLUAutoencoderIntervention.html">pyvene.models.interventions.JumpReLUAutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html">pyvene.models.interventions.LocalistRepresentationIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.NoiseIntervention.html">pyvene.models.interventions.NoiseIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.PCARotatedSpaceIntervention.html">pyvene.models.interventions.PCARotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.RotatedSpaceIntervention.html">pyvene.models.interventions.RotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SharedWeightsTrainableIntervention.html">pyvene.models.interventions.SharedWeightsTrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SigmoidMaskIntervention.html">pyvene.models.interventions.SigmoidMaskIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html">pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SkipIntervention.html">pyvene.models.interventions.SkipIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SourcelessIntervention.html">pyvene.models.interventions.SourcelessIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SubtractionIntervention.html">pyvene.models.interventions.SubtractionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html">pyvene.models.interventions.TrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.VanillaIntervention.html">pyvene.models.interventions.VanillaIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.ZeroIntervention.html">pyvene.models.interventions.ZeroIntervention</a></li>
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+  <section id="module-pyvene.models.backpack_gpt2">
+<span id="pyvene-models-backpack-gpt2"></span><h1>pyvene.models.backpack_gpt2<a class="headerlink" href="#module-pyvene.models.backpack_gpt2" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_backpack_gpt2" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.backpack_gpt2.modelings_backpack_gpt2</span></code></a></p></td>
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+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2" title="pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
+</tr>
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2BaseModelOutput</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2BaseModelOutput.__init__()</span></code></a></li>
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2basemodeloutput">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2basemodeloutput" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackGPT2BaseModelOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contextualization</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2BaseModelOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ModelOutput</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contextualization</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([hidden_states, contextualization])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
+<td><p>Create a new ordered dictionary with keys from iterable and values set to value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
+<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">move_to_end</span></code>(key[, last])</p></td>
+<td><p>Move an existing element to the end (or beginning if last is false).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(key[,default])</p></td>
+<td><p>If the key is not found, return the default if given; otherwise, raise a KeyError.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>([last])</p></td>
+<td><p>Remove and return a (key, value) pair from the dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(*args, **kwargs)</p></td>
+<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tuple</span></code>()</p></td>
+<td><p>Convert self to a tuple containing all the attributes/keys that are not <cite>None</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
+<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">contextualization</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hidden_states</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2Config</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2Config.__init__()</span></code></a></li>
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+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2config">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2config" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackGPT2Config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50264</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_senses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sense_intermediate_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_positions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_attn_by_inverse_layer_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2Config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">GPT2Config</span></code></p>
+<p>This is the configuration class to store the configuration of a [<cite>GPT2Model</cite>] or a [<cite>TFGPT2Model</cite>]. It is used to
+instantiate a Backpack GPT-2 model according to the specified arguments, defining the model architecture.
+Configuration objects inherit from [<cite>GPT2Config</cite>] and can be used to control the model outputs. Read the
+documentation from [<cite>GPT2Config</cite>] for more information.
+Args:</p>
+<blockquote>
+<div><dl class="simple">
+<dt>num_senses (<cite>int</cite>, <em>optional</em>, defaults to 16):</dt><dd><p>The number of sense vectors to define for each word.</p>
+</dd>
+<dt>sense_intermediate_scale (<cite>int</cite>, <em>optional</em>, defaults ot 4):</dt><dd><p>The hidden dimensionality of the sense vector network.</p>
+</dd>
+</dl>
+</div></blockquote>
+<p>Example:
+<a href="#id1"><span class="problematic" id="id2">``</span></a><a href="#id3"><span class="problematic" id="id4">`</span></a>python
+&gt;&gt;&gt; from transformers import BackpackGPT2Config, BackpackGPT2Model
+&gt;&gt;&gt; # Initializing a GPT2 configuration
+&gt;&gt;&gt; configuration = BackpackGPT2Config()
+&gt;&gt;&gt; # Initializing a model (with random weights) from the configuration
+&gt;&gt;&gt; model = BackpackGPT2Model(configuration)
+&gt;&gt;&gt; # Accessing the model configuration
+&gt;&gt;&gt; configuration = model.config</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50264</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_senses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sense_intermediate_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_positions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_attn_by_inverse_layer_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2Config.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([vocab_size, num_senses, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dict_torch_dtype_to_str</span></code>(d)</p></td>
+<td><p>Checks whether the passed dictionary and its nested dicts have a <em>torch_dtype</em> key and if it's not None, converts torch.dtype to a string of just the type.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dict</span></code>(config_dict, **kwargs)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from a Python dictionary of parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_json_file</span></code>(json_file)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from the path to a JSON file of parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(pretrained_model_name_or_path)</p></td>
+<td><p>Instantiate a [<cite>PretrainedConfig</cite>] (or a derived class) from a pretrained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config_dict</span></code>(...)</p></td>
+<td><p>From a <cite>pretrained_model_name_or_path</cite>, resolve to a dictionary of parameters, to be used for instantiating a [<cite>PretrainedConfig</cite>] using <cite>from_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_text_config</span></code>([decoder])</p></td>
+<td><p>Returns the config that is meant to be used with text IO.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the configuration file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, push_to_hub])</p></td>
+<td><p>Save a configuration object to the directory <cite>save_directory</cite>, so that it can be re-loaded using the [<cite>~PretrainedConfig.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_dict</span></code>()</p></td>
+<td><p>Serializes this instance to a Python dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_diff_dict</span></code>()</p></td>
+<td><p>Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_file</span></code>(json_file_path[, use_diff])</p></td>
+<td><p>Save this instance to a JSON file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_string</span></code>([use_diff])</p></td>
+<td><p>Serializes this instance to a JSON string.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>(config_dict)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>config_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_from_string</span></code>(update_str)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>update_str</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">attribute_map</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_config_key</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_tp_plan</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_composition</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys_to_ignore_at_inference</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_type</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">name_or_path</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_labels</span></code></p></td>
+<td><p><cite>int</cite>: The number of labels for classification models.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sub_configs</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_return_dict</span></code></p></td>
+<td><p><cite>bool</cite>: Whether or not return [<cite>~utils.ModelOutput</cite>] instead of tuples.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2Config.__init__()</span></code></a></li>
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel</h1>
+    <!-- Table of contents -->
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+            <div>
+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModel.tie_weights()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+</div>
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+              
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+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2lmheadmodel">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2lmheadmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackGPT2LMHeadModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2LMHeadModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2LMHeadModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(input_ids[, position_ids])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_lm_head</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_tf_weights</span></code>(config, gpt2_checkpoint_path)</p></td>
+<td><p>Load tf checkpoints in a pytorch model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_with_custom_contextualization</span></code>(input_ids, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code></a>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2LMHeadModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights">
+<span class="sig-name descname"><span class="pre">tie_weights</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2LMHeadModel.tie_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights" title="Link to this definition">#</a></dt>
+<dd><p>Tie the weights between the input embeddings and the output embeddings.</p>
+<p>If the <cite>torchscript</cite> flag is set in the configuration, can’t handle parameter sharing so we are cloning the
+weights instead.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModelOutput</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModelOutput.__init__()</span></code></a></li>
+</ul>
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+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2lmheadmodeloutput">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2lmheadmodeloutput" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackGPT2LMHeadModelOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contextualization</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2LMHeadModelOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ModelOutput</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contextualization</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([logits, contextualization])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
+<td><p>Create a new ordered dictionary with keys from iterable and values set to value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
+<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">move_to_end</span></code>(key[, last])</p></td>
+<td><p>Move an existing element to the end (or beginning if last is false).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(key[,default])</p></td>
+<td><p>If the key is not found, return the default if given; otherwise, raise a KeyError.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>([last])</p></td>
+<td><p>Remove and return a (key, value) pair from the dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(*args, **kwargs)</p></td>
+<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tuple</span></code>()</p></td>
+<td><p>Convert self to a tuple containing all the attributes/keys that are not <cite>None</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
+<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">contextualization</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">logits</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModelOutput.__init__()</span></code></a></li>
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2Model</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2Model.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2Model.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
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+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2model">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2model" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackGPT2Model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2Model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2Model.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(input_ids, position_ids)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_num_senses</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_sense_network</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_word_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_tf_weights</span></code>(config, gpt2_checkpoint_path)</p></td>
+<td><p>Load tf checkpoints in a pytorch model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_with_custom_contextualization</span></code>(input_ids, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2Model.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel</h1>
+    <!-- Table of contents -->
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.config_class"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel.config_class</span></code></a></li>
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2pretrainedmodel">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackgpt2pretrainedmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackGPT2PreTrainedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2PreTrainedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">GPT2PreTrainedModel</span></code></p>
+<p>An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+models.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackGPT2PreTrainedModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(*inputs, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(*input)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_tf_weights</span></code>(config, gpt2_checkpoint_path)</p></td>
+<td><p>Load tf checkpoints in a pytorch model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.config_class">
+<span class="sig-name descname"><span class="pre">config_class</span></span><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.config_class" title="Link to this definition">#</a></dt>
+<dd><p>alias of <a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackpackGPT2Config</span></code></a></p>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.config_class"><code class="docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel.config_class</span></code></a></li>
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP"><code class="docutils literal notranslate"><span class="pre">BackpackMLP</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackMLP.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward"><code class="docutils literal notranslate"><span class="pre">BackpackMLP.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackmlp">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackmlp" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackMLP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intermediate_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackMLP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intermediate_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackMLP.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(embed_dim, intermediate_dim, ...)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(hidden_states)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">FloatTensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">FloatTensor</span></span></span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackMLP.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward"><code class="docutils literal notranslate"><span class="pre">BackpackMLP.forward()</span></code></a></li>
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+    <h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock"><code class="docutils literal notranslate"><span class="pre">BackpackNoMixBlock</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackNoMixBlock.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.forward"><code class="docutils literal notranslate"><span class="pre">BackpackNoMixBlock.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpacknomixblock">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpacknomixblock" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackNoMixBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackNoMixBlock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackNoMixBlock.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.forward" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(hidden_states, residual)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork"><code class="docutils literal notranslate"><span class="pre">BackpackSenseNetwork</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackSenseNetwork.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward"><code class="docutils literal notranslate"><span class="pre">BackpackSenseNetwork.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpacksensenetwork">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpacksensenetwork" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackSenseNetwork</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_senses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackSenseNetwork"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_senses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackSenseNetwork.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config, num_senses[, device, dtype])</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(input_embeds)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_embeds</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackSenseNetwork.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
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+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork"><code class="docutils literal notranslate"><span class="pre">BackpackSenseNetwork</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackSenseNetwork.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward"><code class="docutils literal notranslate"><span class="pre">BackpackSenseNetwork.forward()</span></code></a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork"><code class="docutils literal notranslate"><span class="pre">BackpackWeightNetwork</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackWeightNetwork.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward"><code class="docutils literal notranslate"><span class="pre">BackpackWeightNetwork.forward()</span></code></a></li>
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+  <section id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackweightnetwork">
+<h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-backpack-gpt2-backpackweightnetwork" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BackpackWeightNetwork</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_senses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackWeightNetwork"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_senses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackWeightNetwork.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(num_senses, embed_dim)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(encoded)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">encoded</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/backpack_gpt2/modelings_backpack_gpt2.html#BackpackWeightNetwork.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__"><code class="docutils literal notranslate"><span class="pre">BackpackWeightNetwork.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward"><code class="docutils literal notranslate"><span class="pre">BackpackWeightNetwork.forward()</span></code></a></li>
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+  <section id="module-pyvene.models.backpack_gpt2.modelings_backpack_gpt2">
+<span id="pyvene-models-backpack-gpt2-modelings-backpack-gpt2"></span><h1>pyvene.models.backpack_gpt2.modelings_backpack_gpt2<a class="headerlink" href="#module-pyvene.models.backpack_gpt2.modelings_backpack_gpt2" title="Link to this heading">#</a></h1>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackGPT2BaseModelOutput</span></code></a>([hidden_states, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackGPT2Config</span></code></a>([vocab_size, num_senses, ...])</p></td>
+<td><p>This is the configuration class to store the configuration of a [<cite>GPT2Model</cite>] or a [<cite>TFGPT2Model</cite>]. It is used to instantiate a Backpack GPT-2 model according to the specified arguments, defining the model architecture. Configuration objects inherit from [<cite>GPT2Config</cite>] and can be used to control the model outputs. Read the documentation from [<cite>GPT2Config</cite>] for more information. Args:     num_senses (<cite>int</cite>, <em>optional</em>, defaults to 16):         The number of sense vectors to define for each word. sense_intermediate_scale (<cite>int</cite>, <em>optional</em>, defaults ot 4):         The hidden dimensionality of the sense vector network. Example: <a href="#id1"><span class="problematic" id="id2">``</span></a><a href="#id3"><span class="problematic" id="id4">`</span></a>python &gt;&gt;&gt; from transformers import BackpackGPT2Config, BackpackGPT2Model &gt;&gt;&gt; # Initializing a GPT2 configuration &gt;&gt;&gt; configuration = BackpackGPT2Config() &gt;&gt;&gt; # Initializing a model (with random weights) from the configuration &gt;&gt;&gt; model = BackpackGPT2Model(configuration) &gt;&gt;&gt; # Accessing the model configuration &gt;&gt;&gt; configuration = model.config.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModel</span></code></a>(config)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackGPT2LMHeadModelOutput</span></code></a>([logits, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackGPT2Model</span></code></a>(config)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackGPT2PreTrainedModel</span></code></a>(*inputs, **kwargs)</p></td>
+<td><p>An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackMLP</span></code></a>(embed_dim, intermediate_dim, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackNoMixBlock</span></code></a>(config)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackSenseNetwork</span></code></a>(config, num_senses[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork" title="pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BackpackWeightNetwork</span></code></a>(num_senses, embed_dim)</p></td>
+<td><p></p></td>
+</tr>
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diff --git a/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.html b/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.html
new file mode 100644
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+++ b/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.html
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+<h1>pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2<a class="headerlink" href="#pyvene-models-backpack-gpt2-modelings-intervenable-backpack-gpt2-create-backpack-gpt2" title="Link to this heading">#</a></h1>
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+<dd><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p>
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diff --git a/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html b/api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html
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+    <!-- Table of contents -->
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+  <section id="module-pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2">
+<span id="pyvene-models-backpack-gpt2-modelings-intervenable-backpack-gpt2"></span><h1>pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2<a class="headerlink" href="#module-pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.html#pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2" title="pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_backpack_gpt2</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
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+<span class="sig-name descname"><span class="pre">GET_LOC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">LOC</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'h.pos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#GET_LOC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.GET_LOC" title="Link to this definition">#</a></dt>
+<dd><p>From simple locale to nested one.</p>
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+<span class="sig-name descname"><span class="pre">closeness_to_permutation_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rotation</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#closeness_to_permutation_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.closeness_to_permutation_loss" title="Link to this definition">#</a></dt>
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+<h1>pyvene.models.basic_utils.count_parameters<a class="headerlink" href="#pyvene-models-basic-utils-count-parameters" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.basic_utils.count_parameters">
+<span class="sig-name descname"><span class="pre">count_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#count_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.count_parameters" title="Link to this definition">#</a></dt>
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+<span class="sig-name descname"><span class="pre">format_token</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tok</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#format_token"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.format_token" title="Link to this definition">#</a></dt>
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+<dl class="py function">
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+<span class="sig-name descname"><span class="pre">harmonic_sigmoid_boundary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary_x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#harmonic_sigmoid_boundary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.harmonic_sigmoid_boundary" title="Link to this definition">#</a></dt>
+<dd><p>Generate harmonic sigmoid mask</p>
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+<span id="pyvene-models-basic-utils"></span><h1>pyvene.models.basic_utils<a class="headerlink" href="#module-pyvene.models.basic_utils" title="Link to this heading">#</a></h1>
+<p>Basic Utils</p>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.GET_LOC.html#pyvene.models.basic_utils.GET_LOC" title="pyvene.models.basic_utils.GET_LOC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GET_LOC</span></code></a>(LOC[, unit, batch_size])</p></td>
+<td><p>From simple locale to nested one.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.closeness_to_permutation_loss.html#pyvene.models.basic_utils.closeness_to_permutation_loss" title="pyvene.models.basic_utils.closeness_to_permutation_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">closeness_to_permutation_loss</span></code></a>(rotation)</p></td>
+<td><p>Measure how close a rotation m is close to a permutation m</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.count_parameters.html#pyvene.models.basic_utils.count_parameters" title="pyvene.models.basic_utils.count_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">count_parameters</span></code></a>(model)</p></td>
+<td><p>Count parameters of a model that require gradients</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.create_directory.html#pyvene.models.basic_utils.create_directory" title="pyvene.models.basic_utils.create_directory"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_directory</span></code></a>(path)</p></td>
+<td><p>Create directory if not exist</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.embed_to_distrib.html#pyvene.models.basic_utils.embed_to_distrib" title="pyvene.models.basic_utils.embed_to_distrib"><code class="xref py py-obj docutils literal notranslate"><span class="pre">embed_to_distrib</span></code></a>(model, embed[, log, logits])</p></td>
+<td><p>Convert an embedding to a distribution over the vocabulary</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.format_token.html#pyvene.models.basic_utils.format_token" title="pyvene.models.basic_utils.format_token"><code class="xref py py-obj docutils literal notranslate"><span class="pre">format_token</span></code></a>(tokenizer, tok)</p></td>
+<td><p>Format the token for some path patching experiment to show decoding diff</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.get_batch_size.html#pyvene.models.basic_utils.get_batch_size" title="pyvene.models.basic_utils.get_batch_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_batch_size</span></code></a>(model_input)</p></td>
+<td><p>Get batch size based on the input</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.get_list_depth.html#pyvene.models.basic_utils.get_list_depth" title="pyvene.models.basic_utils.get_list_depth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_list_depth</span></code></a>(lst)</p></td>
+<td><p>Return the max depth of the input list</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.get_type_from_string.html#pyvene.models.basic_utils.get_type_from_string" title="pyvene.models.basic_utils.get_type_from_string"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_type_from_string</span></code></a>(type_str)</p></td>
+<td><p>Help function to convert string to type</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.harmonic_sigmoid_boundary.html#pyvene.models.basic_utils.harmonic_sigmoid_boundary" title="pyvene.models.basic_utils.harmonic_sigmoid_boundary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">harmonic_sigmoid_boundary</span></code></a>(_input, ...)</p></td>
+<td><p>Generate harmonic sigmoid mask</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.random_permutation_matrix.html#pyvene.models.basic_utils.random_permutation_matrix" title="pyvene.models.basic_utils.random_permutation_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_permutation_matrix</span></code></a>(n)</p></td>
+<td><p>Generate a random permutation matrix</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.set_seed.html#pyvene.models.basic_utils.set_seed" title="pyvene.models.basic_utils.set_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_seed</span></code></a>(seed)</p></td>
+<td><p>Set seed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.basic_utils.sigmoid_boundary.html#pyvene.models.basic_utils.sigmoid_boundary" title="pyvene.models.basic_utils.sigmoid_boundary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sigmoid_boundary</span></code></a>(_input, boundary_x, ...)</p></td>
+<td><p>Generate sigmoid mask</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.top_vals.html#pyvene.models.basic_utils.top_vals" title="pyvene.models.basic_utils.top_vals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">top_vals</span></code></a>(tokenizer, res[, n, return_results])</p></td>
+<td><p>Pretty print the top n values of a distribution over the vocabulary</p></td>
+</tr>
+</tbody>
+</table>
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+<span class="sig-name descname"><span class="pre">sigmoid_boundary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary_x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">boundary_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#sigmoid_boundary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.sigmoid_boundary" title="Link to this definition">#</a></dt>
+<dd><p>Generate sigmoid mask</p>
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+<h1>pyvene.models.basic_utils.top_vals<a class="headerlink" href="#pyvene-models-basic-utils-top-vals" title="Link to this heading">#</a></h1>
+<dl class="py function">
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+<span class="sig-name descname"><span class="pre">top_vals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/basic_utils.html#top_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.basic_utils.top_vals" title="Link to this definition">#</a></dt>
+<dd><p>Pretty print the top n values of a distribution over the vocabulary</p>
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+    <h1>pyvene.models.blip</h1>
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+  <section id="module-pyvene.models.blip">
+<span id="pyvene-models-blip"></span><h1>pyvene.models.blip<a class="headerlink" href="#module-pyvene.models.blip" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_blip.html#module-pyvene.models.blip.modelings_blip" title="pyvene.models.blip.modelings_blip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.blip.modelings_blip</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_blip_itm.html#module-pyvene.models.blip.modelings_blip_itm" title="pyvene.models.blip.modelings_blip_itm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.blip.modelings_blip_itm</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_intervenable_blip.html#module-pyvene.models.blip.modelings_intervenable_blip" title="pyvene.models.blip.modelings_intervenable_blip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.blip.modelings_intervenable_blip</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_intervenable_blip_itm.html#module-pyvene.models.blip.modelings_intervenable_blip_itm" title="pyvene.models.blip.modelings_intervenable_blip_itm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.blip.modelings_intervenable_blip_itm</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+                </article>
+              
+
+              
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip.BlipWrapper"><code class="docutils literal notranslate"><span class="pre">BlipWrapper</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip.BlipWrapper.__init__"><code class="docutils literal notranslate"><span class="pre">BlipWrapper.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip.BlipWrapper.forward"><code class="docutils literal notranslate"><span class="pre">BlipWrapper.forward()</span></code></a></li>
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+                  
+  <section id="pyvene-models-blip-modelings-blip-blipwrapper">
+<h1>pyvene.models.blip.modelings_blip.BlipWrapper<a class="headerlink" href="#pyvene-models-blip-modelings-blip-blipwrapper" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_blip.BlipWrapper">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BlipWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BlipForQuestionAnswering</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_blip.html#BlipWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_blip.BlipWrapper" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_blip.BlipWrapper.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BlipForQuestionAnswering</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_blip.html#BlipWrapper.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_blip.BlipWrapper.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.blip.modelings_blip.BlipWrapper.__init__" title="pyvene.models.blip.modelings_blip.BlipWrapper.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(model)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.blip.modelings_blip.BlipWrapper.forward" title="pyvene.models.blip.modelings_blip.BlipWrapper.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(input_ids, pixel_values[, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_blip.BlipWrapper.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_attentions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dict</span></span></span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_blip.html#BlipWrapper.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_blip.BlipWrapper.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip.BlipWrapper"><code class="docutils literal notranslate"><span class="pre">BlipWrapper</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip.BlipWrapper.__init__"><code class="docutils literal notranslate"><span class="pre">BlipWrapper.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip.BlipWrapper.forward"><code class="docutils literal notranslate"><span class="pre">BlipWrapper.forward()</span></code></a></li>
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new file mode 100644
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+    <h1>pyvene.models.blip.modelings_blip_itm.BlipITMWrapper</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper"><code class="docutils literal notranslate"><span class="pre">BlipITMWrapper</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__"><code class="docutils literal notranslate"><span class="pre">BlipITMWrapper.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward"><code class="docutils literal notranslate"><span class="pre">BlipITMWrapper.forward()</span></code></a></li>
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+                  
+  <section id="pyvene-models-blip-modelings-blip-itm-blipitmwrapper">
+<h1>pyvene.models.blip.modelings_blip_itm.BlipITMWrapper<a class="headerlink" href="#pyvene-models-blip-modelings-blip-itm-blipitmwrapper" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_blip_itm.BlipITMWrapper">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BlipITMWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BlipForImageTextRetrieval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_itm_not_contrastive</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_blip_itm.html#BlipITMWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BlipForImageTextRetrieval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_itm_not_contrastive</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_blip_itm.html#BlipITMWrapper.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__" title="pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(model[, use_itm_not_contrastive])</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward" title="pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(input_ids, pixel_values[, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_attentions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Dict</span></span></span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_blip_itm.html#BlipITMWrapper.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__"><code class="docutils literal notranslate"><span class="pre">BlipITMWrapper.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward"><code class="docutils literal notranslate"><span class="pre">BlipITMWrapper.forward()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<p aria-level="2" class="caption" role="heading"><span class="caption-text">API</span></p>
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+<span class="sig-name descname"><span class="pre">blip_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('image_text_hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'block_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'cross_attention_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'cross_attention_output':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'cross_attention_value_output':</span> <span class="pre">('image_text_hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('projection_dim',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('image_text_hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>blip model with wrapper</p>
+</dd></dl>
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+<dd><p>Creates a BLIP VQA model, config, and tokenizer from the given name and revision</p>
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+                  
+  <section id="module-pyvene.models.blip.modelings_intervenable_blip">
+<span id="pyvene-models-blip-modelings-intervenable-blip"></span><h1>pyvene.models.blip.modelings_intervenable_blip<a class="headerlink" href="#module-pyvene.models.blip.modelings_intervenable_blip" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping.html#pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping" title="pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">blip_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>blip model with wrapper</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_intervenable_blip.create_blip.html#pyvene.models.blip.modelings_intervenable_blip.create_blip" title="pyvene.models.blip.modelings_intervenable_blip.create_blip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_blip</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a BLIP VQA model, config, and tokenizer from the given name and revision</p></td>
+</tr>
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diff --git a/api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.html b/api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.html
new file mode 100644
index 00000000..e62478a4
--- /dev/null
+++ b/api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.html
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+</ul>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/pyvene_101.html">Introduction to pyvene</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Probing_Gender.html">Causal Evaluation of Probes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<h1>pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-blip-modelings-intervenable-blip-itm-blip-itm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">blip_itm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('image_text_hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'block_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'cross_attention_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'cross_attention_output':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'cross_attention_value_output':</span> <span class="pre">('image_text_hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'itm_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'itm_output':</span> <span class="pre">(2,),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('projection_dim',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('image_text_hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('image_text_hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>blip model with wrapper</p>
+</dd></dl>
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+<span class="sig-name descname"><span class="pre">create_blip_itm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Salesforce/blip-itm-base-coco'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/blip/modelings_intervenable_blip_itm.html#create_blip_itm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm" title="Link to this definition">#</a></dt>
+<dd><p>Creates a BLIP ITM model, config, and tokenizer from the given name and revision</p>
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+    <h1>pyvene.models.blip.modelings_intervenable_blip_itm</h1>
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+  <section id="module-pyvene.models.blip.modelings_intervenable_blip_itm">
+<span id="pyvene-models-blip-modelings-intervenable-blip-itm"></span><h1>pyvene.models.blip.modelings_intervenable_blip_itm<a class="headerlink" href="#module-pyvene.models.blip.modelings_intervenable_blip_itm" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.html#pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping" title="pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">blip_itm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>blip model with wrapper</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm.html#pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm" title="pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_blip_itm</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a BLIP ITM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+<ul class="nav bd-sidenav">
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+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Probing_Gender.html">Causal Evaluation of Probes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">API</span></p>
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+    <h1>pyvene.models.configuration_intervenable_model.IntervenableConfig</h1>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.IntervenableConfig"><code class="docutils literal notranslate"><span class="pre">IntervenableConfig</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableConfig.__init__()</span></code></a></li>
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+  <section id="pyvene-models-configuration-intervenable-model-intervenableconfig">
+<h1>pyvene.models.configuration_intervenable_model.IntervenableConfig<a class="headerlink" href="#pyvene-models-configuration-intervenable-model-intervenableconfig" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.IntervenableConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">IntervenableConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">representations=[(0,</span> <span class="pre">'block_output',</span> <span class="pre">'pos',</span> <span class="pre">1,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None)],</span> <span class="pre">intervention_types=&lt;class</span> <span class="pre">'pyvene.models.interventions.VanillaIntervention'&gt;,</span> <span class="pre">mode='parallel',</span> <span class="pre">sorted_keys=None,</span> <span class="pre">model_type=None,</span> <span class="pre">intervention_dimensions=None,</span> <span class="pre">intervention_constant_sources=None,</span> <span class="pre">**kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/configuration_intervenable_model.html#IntervenableConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.IntervenableConfig" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">PretrainedConfig</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">representations=[(0,</span> <span class="pre">'block_output',</span> <span class="pre">'pos',</span> <span class="pre">1,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">None)],</span> <span class="pre">intervention_types=&lt;class</span> <span class="pre">'pyvene.models.interventions.VanillaIntervention'&gt;,</span> <span class="pre">mode='parallel',</span> <span class="pre">sorted_keys=None,</span> <span class="pre">model_type=None,</span> <span class="pre">intervention_dimensions=None,</span> <span class="pre">intervention_constant_sources=None,</span> <span class="pre">**kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/configuration_intervenable_model.html#IntervenableConfig.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__" title="pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([representations, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_intervention</span></code>(representations)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dict_torch_dtype_to_str</span></code>(d)</p></td>
+<td><p>Checks whether the passed dictionary and its nested dicts have a <em>torch_dtype</em> key and if it's not None, converts torch.dtype to a string of just the type.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dict</span></code>(config_dict, **kwargs)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from a Python dictionary of parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_json_file</span></code>(json_file)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from the path to a JSON file of parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(pretrained_model_name_or_path)</p></td>
+<td><p>Instantiate a [<cite>PretrainedConfig</cite>] (or a derived class) from a pretrained model configuration.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config_dict</span></code>(...)</p></td>
+<td><p>From a <cite>pretrained_model_name_or_path</cite>, resolve to a dictionary of parameters, to be used for instantiating a [<cite>PretrainedConfig</cite>] using <cite>from_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_text_config</span></code>([decoder])</p></td>
+<td><p>Returns the config that is meant to be used with text IO.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the configuration file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, push_to_hub])</p></td>
+<td><p>Save a configuration object to the directory <cite>save_directory</cite>, so that it can be re-loaded using the [<cite>~PretrainedConfig.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_dict</span></code>()</p></td>
+<td><p>Serializes this instance to a Python dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_diff_dict</span></code>()</p></td>
+<td><p>Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_file</span></code>(json_file_path[, use_diff])</p></td>
+<td><p>Save this instance to a JSON file.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_string</span></code>([use_diff])</p></td>
+<td><p>Serializes this instance to a JSON string.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>(config_dict)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>config_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_from_string</span></code>(update_str)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>update_str</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">attribute_map</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_config_key</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_tp_plan</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_composition</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_type</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">name_or_path</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_labels</span></code></p></td>
+<td><p><cite>int</cite>: The number of labels for classification models.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sub_configs</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_return_dict</span></code></p></td>
+<td><p><cite>bool</cite>: Whether or not return [<cite>~utils.ModelOutput</cite>] instead of tuples.</p></td>
+</tr>
+</tbody>
+</table>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableConfig.__init__()</span></code></a></li>
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+    <h1>pyvene.models.configuration_intervenable_model.RepresentationConfig</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.component"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.component</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.group_key</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.hidden_source_representation</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.intervention</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.intervention_link_key</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.intervention_type</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.latent_dim</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.layer"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.layer</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.low_rank_dimension</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.max_number_of_units</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.moe_key</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.source_representation</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.subspace_partition</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.unit"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.unit</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-configuration-intervenable-model-representationconfig">
+<h1>pyvene.models.configuration_intervenable_model.RepresentationConfig<a class="headerlink" href="#pyvene-models-configuration-intervenable-model-representationconfig" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">RepresentationConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_number_of_units</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low_rank_dimension</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspace_partition</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention_link_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">moe_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_representation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_source_representation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latent_dim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">count</span></code>(value, /)</p></td>
+<td><p>Return number of occurrences of value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">index</span></code>(value[, start, stop])</p></td>
+<td><p>Return first index of value.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.component" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.component"><code class="xref py py-obj docutils literal notranslate"><span class="pre">component</span></code></a></p></td>
+<td><p>Alias for field number 1</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_key</span></code></a></p></td>
+<td><p>Alias for field number 8</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hidden_source_representation</span></code></a></p></td>
+<td><p>Alias for field number 12</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intervention</span></code></a></p></td>
+<td><p>Alias for field number 6</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intervention_link_key</span></code></a></p></td>
+<td><p>Alias for field number 9</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intervention_type</span></code></a></p></td>
+<td><p>Alias for field number 5</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">latent_dim</span></code></a></p></td>
+<td><p>Alias for field number 13</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.layer" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.layer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">layer</span></code></a></p></td>
+<td><p>Alias for field number 0</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension"><code class="xref py py-obj docutils literal notranslate"><span class="pre">low_rank_dimension</span></code></a></p></td>
+<td><p>Alias for field number 4</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_number_of_units</span></code></a></p></td>
+<td><p>Alias for field number 3</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key"><code class="xref py py-obj docutils literal notranslate"><span class="pre">moe_key</span></code></a></p></td>
+<td><p>Alias for field number 10</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">source_representation</span></code></a></p></td>
+<td><p>Alias for field number 11</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">subspace_partition</span></code></a></p></td>
+<td><p>Alias for field number 7</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.unit" title="pyvene.models.configuration_intervenable_model.RepresentationConfig.unit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unit</span></code></a></p></td>
+<td><p>Alias for field number 2</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.component">
+<span class="sig-name descname"><span class="pre">component</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.component" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 1</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key">
+<span class="sig-name descname"><span class="pre">group_key</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 8</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation">
+<span class="sig-name descname"><span class="pre">hidden_source_representation</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 12</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention">
+<span class="sig-name descname"><span class="pre">intervention</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 6</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key">
+<span class="sig-name descname"><span class="pre">intervention_link_key</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 9</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type">
+<span class="sig-name descname"><span class="pre">intervention_type</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 5</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim">
+<span class="sig-name descname"><span class="pre">latent_dim</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 13</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.layer">
+<span class="sig-name descname"><span class="pre">layer</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.layer" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 0</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension">
+<span class="sig-name descname"><span class="pre">low_rank_dimension</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 4</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units">
+<span class="sig-name descname"><span class="pre">max_number_of_units</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 3</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key">
+<span class="sig-name descname"><span class="pre">moe_key</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 10</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation">
+<span class="sig-name descname"><span class="pre">source_representation</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 11</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition">
+<span class="sig-name descname"><span class="pre">subspace_partition</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 7</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="pyvene.models.configuration_intervenable_model.RepresentationConfig.unit">
+<span class="sig-name descname"><span class="pre">unit</span></span><a class="headerlink" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.unit" title="Link to this definition">#</a></dt>
+<dd><p>Alias for field number 2</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+        <p class="prev-next-title">pyvene.models.configuration_intervenable_model.IntervenableConfig</p>
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+    <i class="fa-solid fa-list"></i> Contents
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.component"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.component</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.group_key</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.hidden_source_representation</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.intervention</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.intervention_link_key</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.intervention_type</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.latent_dim</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.layer"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.layer</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.low_rank_dimension</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.max_number_of_units</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.moe_key</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.source_representation</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.subspace_partition</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.configuration_intervenable_model.RepresentationConfig.unit"><code class="docutils literal notranslate"><span class="pre">RepresentationConfig.unit</span></code></a></li>
+</ul>
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+</ul>
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+          </div>
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diff --git a/api/pyvene.models.configuration_intervenable_model.html b/api/pyvene.models.configuration_intervenable_model.html
new file mode 100644
index 00000000..0008ff7e
--- /dev/null
+++ b/api/pyvene.models.configuration_intervenable_model.html
@@ -0,0 +1,608 @@
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+<p class="rubric">Functions</p>
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+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.constants.split_and_select.html#pyvene.models.constants.split_and_select" title="pyvene.models.constants.split_and_select"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_and_select</span></code></a>(x, num_slice, selct_index)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.constants.split_half.html#pyvene.models.constants.split_half" title="pyvene.models.constants.split_half"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_half</span></code></a>(x, selct_index)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.constants.split_head_and_permute.html#pyvene.models.constants.split_head_and_permute" title="pyvene.models.constants.split_head_and_permute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_head_and_permute</span></code></a>(x, num_head)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.constants.split_heads.html#pyvene.models.constants.split_heads" title="pyvene.models.constants.split_heads"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_heads</span></code></a>(tensor, num_heads, attn_head_size)</p></td>
+<td><p>Splits hidden_size dim into attn_head_size and num_heads.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.constants.split_three.html#pyvene.models.constants.split_three" title="pyvene.models.constants.split_three"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_three</span></code></a>(x, selct_index)</p></td>
+<td><p></p></td>
+</tr>
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+<span class="sig-name descname"><span class="pre">split_heads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_head_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/constants.html#split_heads"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.constants.split_heads" title="Link to this definition">#</a></dt>
+<dd><p>Splits hidden_size dim into attn_head_size and num_heads.</p>
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+  <section id="module-pyvene.models.gemma">
+<span id="pyvene-models-gemma"></span><h1>pyvene.models.gemma<a class="headerlink" href="#module-pyvene.models.gemma" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
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+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gemma.modelings_intervenable_gemma.create_gemma"><code class="docutils literal notranslate"><span class="pre">create_gemma()</span></code></a></li>
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+<h1>pyvene.models.gemma.modelings_intervenable_gemma.create_gemma<a class="headerlink" href="#pyvene-models-gemma-modelings-intervenable-gemma-create-gemma" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.gemma.modelings_intervenable_gemma.create_gemma">
+<span class="sig-name descname"><span class="pre">create_gemma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'google/gemma-2b-it'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gemma/modelings_intervenable_gemma.html#create_gemma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gemma.modelings_intervenable_gemma.create_gemma" title="Link to this definition">#</a></dt>
+<dd><p>Creates a Gemma Causal LM model, config, and tokenizer from the given name and revision</p>
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping"><code class="docutils literal notranslate"><span class="pre">gemma_lm_type_to_dimension_mapping</span></code></a></li>
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+  <section id="pyvene-models-gemma-modelings-intervenable-gemma-gemma-lm-type-to-dimension-mapping">
+<h1>pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-gemma-modelings-intervenable-gemma-gemma-lm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gemma_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hhead_dim',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gemma model with classifier head</p>
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping"><code class="docutils literal notranslate"><span class="pre">gemma_type_to_dimension_mapping</span></code></a></li>
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+  <section id="pyvene-models-gemma-modelings-intervenable-gemma-gemma-type-to-dimension-mapping">
+<h1>pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-gemma-modelings-intervenable-gemma-gemma-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gemma_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hhead_dim',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gemma model with LM head</p>
+</dd></dl>
+
+</section>
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+                  
+  <section id="module-pyvene.models.gemma.modelings_intervenable_gemma">
+<span id="pyvene-models-gemma-modelings-intervenable-gemma"></span><h1>pyvene.models.gemma.modelings_intervenable_gemma<a class="headerlink" href="#module-pyvene.models.gemma.modelings_intervenable_gemma" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping.html#pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping" title="pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gemma_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gemma model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping.html#pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping" title="pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gemma_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gemma model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gemma.modelings_intervenable_gemma.create_gemma.html#pyvene.models.gemma.modelings_intervenable_gemma.create_gemma" title="pyvene.models.gemma.modelings_intervenable_gemma.create_gemma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gemma</span></code></a>([name, cache_dir, dtype])</p></td>
+<td><p>Creates a Gemma Causal LM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+                </article>
+              
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+<span id="pyvene-models-gemma2"></span><h1>pyvene.models.gemma2<a class="headerlink" href="#module-pyvene.models.gemma2" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gemma2.modelings_intervenable_gemma2.html#module-pyvene.models.gemma2.modelings_intervenable_gemma2" title="pyvene.models.gemma2.modelings_intervenable_gemma2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gemma2.modelings_intervenable_gemma2</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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diff --git a/api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.html b/api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.html
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diff --git a/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.html b/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.html
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+<h1>pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-gemma2-modelings-intervenable-gemma2-gemma2-lm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gemma2_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gemma2 model with classifier head</p>
+</dd></dl>
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diff --git a/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.html b/api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.html
new file mode 100644
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@@ -0,0 +1,628 @@
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+<h1>pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-gemma2-modelings-intervenable-gemma2-gemma2-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gemma2_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gemma2 model with LM head</p>
+</dd></dl>
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+    <h1>pyvene.models.gemma2.modelings_intervenable_gemma2</h1>
+    <!-- Table of contents -->
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+
+              
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+                  
+  <section id="module-pyvene.models.gemma2.modelings_intervenable_gemma2">
+<span id="pyvene-models-gemma2-modelings-intervenable-gemma2"></span><h1>pyvene.models.gemma2.modelings_intervenable_gemma2<a class="headerlink" href="#module-pyvene.models.gemma2.modelings_intervenable_gemma2" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.html#pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping" title="pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gemma2_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gemma2 model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.html#pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping" title="pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gemma2_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gemma2 model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.html#pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2" title="pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gemma2</span></code></a>([name, cache_dir, dtype])</p></td>
+<td><p>Creates a Causal LM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
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+<span id="pyvene-models-gpt2"></span><h1>pyvene.models.gpt2<a class="headerlink" href="#module-pyvene.models.gpt2" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.html#module-pyvene.models.gpt2.modelings_intervenable_gpt2" title="pyvene.models.gpt2.modelings_intervenable_gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gpt2.modelings_intervenable_gpt2</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<h1>pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2<a class="headerlink" href="#pyvene-models-gpt2-modelings-intervenable-gpt2-create-gpt2" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">create_gpt2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpt2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gpt2/modelings_intervenable_gpt2.html#create_gpt2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p>
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+<h1>pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier<a class="headerlink" href="#pyvene-models-gpt2-modelings-intervenable-gpt2-create-gpt2-classifier" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">create_gpt2_classifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpt2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gpt2/modelings_intervenable_gpt2.html#create_gpt2_classifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GPT2ForSequenceClassification, config, and tokenizer from the given name and revision</p>
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+<span class="sig-name descname"><span class="pre">create_gpt2_lm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpt2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gpt2/modelings_intervenable_gpt2.html#create_gpt2_lm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GPT2 LM, config, and tokenizer from the given name and revision</p>
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diff --git a/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.html b/api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.html
new file mode 100644
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+<span class="sig-name descname"><span class="pre">gpt2_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('n_embd',),</span> <span class="pre">'attention_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'attention_weight':</span> <span class="pre">('max_position_embeddings',),</span> <span class="pre">'block_input':</span> <span class="pre">('n_embd',),</span> <span class="pre">'block_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'key_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('n_inner',</span> <span class="pre">'n_embd*4'),</span> <span class="pre">'mlp_input':</span> <span class="pre">('n_embd',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'n_head':</span> <span class="pre">('n_head',),</span> <span class="pre">'query_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'value_output':</span> <span class="pre">('n_embd',)}</span></em><a class="headerlink" href="#pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gpt2 model with classifier head</p>
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+<dt class="sig sig-object py" id="pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gpt2_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('n_embd',),</span> <span class="pre">'attention_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'attention_weight':</span> <span class="pre">('max_position_embeddings',),</span> <span class="pre">'block_input':</span> <span class="pre">('n_embd',),</span> <span class="pre">'block_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('n_embd/n_head',),</span> <span class="pre">'key_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('n_inner',</span> <span class="pre">'n_embd*4'),</span> <span class="pre">'mlp_input':</span> <span class="pre">('n_embd',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'n_head':</span> <span class="pre">('n_head',),</span> <span class="pre">'query_output':</span> <span class="pre">('n_embd',),</span> <span class="pre">'value_output':</span> <span class="pre">('n_embd',)}</span></em><a class="headerlink" href="#pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gpt2 model with LM head</p>
+</dd></dl>
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+                  
+  <section id="module-pyvene.models.gpt2.modelings_intervenable_gpt2">
+<span id="pyvene-models-gpt2-modelings-intervenable-gpt2"></span><h1>pyvene.models.gpt2.modelings_intervenable_gpt2<a class="headerlink" href="#module-pyvene.models.gpt2.modelings_intervenable_gpt2" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping.html#pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping" title="pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gpt2_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gpt2 model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.html#pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping" title="pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gpt2_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gpt2 model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2" title="pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gpt2</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier" title="pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gpt2_classifier</span></code></a>([name, config, cache_dir])</p></td>
+<td><p>Creates a GPT2ForSequenceClassification, config, and tokenizer from the given name and revision</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm" title="pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gpt2_lm</span></code></a>([name, config, cache_dir])</p></td>
+<td><p>Creates a GPT2 LM, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
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+<span id="pyvene-models-gpt-neo"></span><h1>pyvene.models.gpt_neo<a class="headerlink" href="#module-pyvene.models.gpt_neo" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.html#module-pyvene.models.gpt_neo.modelings_intervenable_gpt_neo" title="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gpt_neo.modelings_intervenable_gpt_neo</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<span class="sig-name descname"><span class="pre">create_gpt_neo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'roneneldan/TinyStories-33M'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gpt_neo/modelings_intervenable_gpt_neo.html#create_gpt_neo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p>
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diff --git a/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.html b/api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.html
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html">pyvene.models.interventions.LocalistRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.LowRankRotatedSpaceIntervention.html">pyvene.models.interventions.LowRankRotatedSpaceIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.PCARotatedSpaceIntervention.html">pyvene.models.interventions.PCARotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.RotatedSpaceIntervention.html">pyvene.models.interventions.RotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SharedWeightsTrainableIntervention.html">pyvene.models.interventions.SharedWeightsTrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SigmoidMaskIntervention.html">pyvene.models.interventions.SigmoidMaskIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SkipIntervention.html">pyvene.models.interventions.SkipIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html">pyvene.models.interventions.TrainableIntervention</a></li>
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+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gpt_neo_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('hidden_size/num_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('hidden_size/num_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hidden_size/num_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',</span> <span class="pre">'hidden_size*4'),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">'num_heads',</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gpt_neo model with LM head</p>
+</dd></dl>
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+  <section id="module-pyvene.models.gpt_neo.modelings_intervenable_gpt_neo">
+<span id="pyvene-models-gpt-neo-modelings-intervenable-gpt-neo"></span><h1>pyvene.models.gpt_neo.modelings_intervenable_gpt_neo<a class="headerlink" href="#module-pyvene.models.gpt_neo.modelings_intervenable_gpt_neo" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.html#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping" title="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gpt_neo_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gpt_neo model with LM head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo.html#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo" title="pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gpt_neo</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p></td>
+</tr>
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+</table>
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+  <section id="module-pyvene.models.gpt_neox">
+<span id="pyvene-models-gpt-neox"></span><h1>pyvene.models.gpt_neox<a class="headerlink" href="#module-pyvene.models.gpt_neox" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.html#module-pyvene.models.gpt_neox.modelings_intervenable_gpt_neox" title="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gpt_neox.modelings_intervenable_gpt_neox</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+<dd><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+<h1>pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-gpt-neox-modelings-intervenable-gpt-neox-gpt-neox-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
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+<dt class="sig sig-object py" id="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gpt_neox_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',</span> <span class="pre">'hidden_size*4'),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',)}</span></em><a class="headerlink" href="#pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>gpt_neox model with LM head</p>
+</dd></dl>
+
+</section>
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+
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+  <section id="module-pyvene.models.gpt_neox.modelings_intervenable_gpt_neox">
+<span id="pyvene-models-gpt-neox-modelings-intervenable-gpt-neox"></span><h1>pyvene.models.gpt_neox.modelings_intervenable_gpt_neox<a class="headerlink" href="#module-pyvene.models.gpt_neox.modelings_intervenable_gpt_neox" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping.html#pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping" title="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gpt_neox_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>gpt_neox model with LM head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox.html#pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox" title="pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gpt_neox</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a GPT2 model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
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+  <section id="module-pyvene.models.gru">
+<span id="pyvene-models-gru"></span><h1>pyvene.models.gru<a class="headerlink" href="#module-pyvene.models.gru" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gru.modelings_gru.html#module-pyvene.models.gru.modelings_gru" title="pyvene.models.gru.modelings_gru"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gru.modelings_gru</span></code></a></p></td>
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+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+    <h1>pyvene.models.gru.modelings_gru.GRUCell</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUCell"><code class="docutils literal notranslate"><span class="pre">GRUCell</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUCell.__init__"><code class="docutils literal notranslate"><span class="pre">GRUCell.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUCell.forward"><code class="docutils literal notranslate"><span class="pre">GRUCell.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-gru-modelings-gru-grucell">
+<h1>pyvene.models.gru.modelings_gru.GRUCell<a class="headerlink" href="#pyvene-models-gru-modelings-gru-grucell" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUCell">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUCell" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUCell.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUCell.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUCell.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUCell.__init__" title="pyvene.models.gru.modelings_gru.GRUCell.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUCell.forward" title="pyvene.models.gru.modelings_gru.GRUCell.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(current_states[, hidden_states])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_parameters</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
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+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
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+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
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+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUCell.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">current_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUCell.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUCell.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUCell.forward"><code class="docutils literal notranslate"><span class="pre">GRUCell.forward()</span></code></a></li>
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new file mode 100644
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+  <section id="pyvene-models-gru-modelings-gru-gruconfig">
+<h1>pyvene.models.gru.modelings_gru.GRUConfig<a class="headerlink" href="#pyvene-models-gru-modelings-gru-gruconfig" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRUConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include_emb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50257</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_position_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdrop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">problem_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single_label_classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initializer_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUConfig" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">PretrainedConfig</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUConfig.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include_emb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50257</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_position_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdrop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">problem_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single_label_classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initializer_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUConfig.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUConfig.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUConfig.__init__" title="pyvene.models.gru.modelings_gru.GRUConfig.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([include_emb, vocab_size, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dict_torch_dtype_to_str</span></code>(d)</p></td>
+<td><p>Checks whether the passed dictionary and its nested dicts have a <em>torch_dtype</em> key and if it's not None, converts torch.dtype to a string of just the type.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dict</span></code>(config_dict, **kwargs)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from a Python dictionary of parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_json_file</span></code>(json_file)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from the path to a JSON file of parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(pretrained_model_name_or_path)</p></td>
+<td><p>Instantiate a [<cite>PretrainedConfig</cite>] (or a derived class) from a pretrained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config_dict</span></code>(...)</p></td>
+<td><p>From a <cite>pretrained_model_name_or_path</cite>, resolve to a dictionary of parameters, to be used for instantiating a [<cite>PretrainedConfig</cite>] using <cite>from_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_text_config</span></code>([decoder])</p></td>
+<td><p>Returns the config that is meant to be used with text IO.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the configuration file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, push_to_hub])</p></td>
+<td><p>Save a configuration object to the directory <cite>save_directory</cite>, so that it can be re-loaded using the [<cite>~PretrainedConfig.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_dict</span></code>()</p></td>
+<td><p>Serializes this instance to a Python dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_diff_dict</span></code>()</p></td>
+<td><p>Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_file</span></code>(json_file_path[, use_diff])</p></td>
+<td><p>Save this instance to a JSON file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_string</span></code>([use_diff])</p></td>
+<td><p>Serializes this instance to a JSON string.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>(config_dict)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>config_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_from_string</span></code>(update_str)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>update_str</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">attribute_map</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_config_key</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_tp_plan</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_composition</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_type</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">name_or_path</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_labels</span></code></p></td>
+<td><p><cite>int</cite>: The number of labels for classification models.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sub_configs</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_return_dict</span></code></p></td>
+<td><p><cite>bool</cite>: Whether or not return [<cite>~utils.ModelOutput</cite>] instead of tuples.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUConfig.__init__"><code class="docutils literal notranslate"><span class="pre">GRUConfig.__init__()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUForClassification"><code class="docutils literal notranslate"><span class="pre">GRUForClassification</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUForClassification.__init__"><code class="docutils literal notranslate"><span class="pre">GRUForClassification.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUForClassification.forward"><code class="docutils literal notranslate"><span class="pre">GRUForClassification.forward()</span></code></a></li>
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+                  
+  <section id="pyvene-models-gru-modelings-gru-gruforclassification">
+<h1>pyvene.models.gru.modelings_gru.GRUForClassification<a class="headerlink" href="#pyvene-models-gru-modelings-gru-gruforclassification" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUForClassification">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRUForClassification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUForClassification"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUForClassification" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel" title="pyvene.models.gru.modelings_gru.GRUPreTrainedModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GRUPreTrainedModel</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUForClassification.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUForClassification.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUForClassification.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUForClassification.__init__" title="pyvene.models.gru.modelings_gru.GRUForClassification.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUForClassification.forward" title="pyvene.models.gru.modelings_gru.GRUForClassification.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>([input_ids, position_ids, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_class</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUForClassification.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUForClassification.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUForClassification.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUForClassification.forward"><code class="docutils literal notranslate"><span class="pre">GRUForClassification.forward()</span></code></a></li>
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+    <h1>pyvene.models.gru.modelings_gru.GRULMHeadModel</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.forward"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel.get_output_embeddings()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-gru-modelings-gru-grulmheadmodel">
+<h1>pyvene.models.gru.modelings_gru.GRULMHeadModel<a class="headerlink" href="#pyvene-models-gru-modelings-gru-grulmheadmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRULMHeadModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRULMHeadModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRULMHeadModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel" title="pyvene.models.gru.modelings_gru.GRUPreTrainedModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GRUPreTrainedModel</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRULMHeadModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__" title="pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.forward" title="pyvene.models.gru.modelings_gru.GRULMHeadModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>([input_ids, position_ids, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings" title="pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code></a>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_output_embeddings</span></code>(new_embeddings)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_class</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRULMHeadModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRULMHeadModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings">
+<span class="sig-name descname"><span class="pre">get_output_embeddings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRULMHeadModel.get_output_embeddings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings" title="Link to this definition">#</a></dt>
+<dd><p>Returns the model’s output embeddings.</p>
+<dl class="simple">
+<dt>Returns:</dt><dd><p><cite>nn.Module</cite>: A torch module mapping hidden states to vocabulary.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.forward"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings"><code class="docutils literal notranslate"><span class="pre">GRULMHeadModel.get_output_embeddings()</span></code></a></li>
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+    <h1>pyvene.models.gru.modelings_gru.GRUModel</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel"><code class="docutils literal notranslate"><span class="pre">GRUModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.__init__"><code class="docutils literal notranslate"><span class="pre">GRUModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.forward"><code class="docutils literal notranslate"><span class="pre">GRUModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings"><code class="docutils literal notranslate"><span class="pre">GRUModel.get_input_embeddings()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings"><code class="docutils literal notranslate"><span class="pre">GRUModel.set_input_embeddings()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-gru-modelings-gru-grumodel">
+<h1>pyvene.models.gru.modelings_gru.GRUModel<a class="headerlink" href="#pyvene-models-gru-modelings-gru-grumodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRUModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel" title="pyvene.models.gru.modelings_gru.GRUPreTrainedModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GRUPreTrainedModel</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUModel.__init__" title="pyvene.models.gru.modelings_gru.GRUModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUModel.forward" title="pyvene.models.gru.modelings_gru.GRUModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>([input_ids, position_ids, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings" title="pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code></a>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings" title="pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code></a>(new_embeddings)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_class</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings">
+<span class="sig-name descname"><span class="pre">get_input_embeddings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUModel.get_input_embeddings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings" title="Link to this definition">#</a></dt>
+<dd><p>Returns the model’s input embeddings.</p>
+<dl class="simple">
+<dt>Returns:</dt><dd><p><cite>nn.Module</cite>: A torch module mapping vocabulary to hidden states.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings">
+<span class="sig-name descname"><span class="pre">set_input_embeddings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_embeddings</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUModel.set_input_embeddings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings" title="Link to this definition">#</a></dt>
+<dd><p>Set model’s input embeddings.</p>
+<dl class="simple">
+<dt>Args:</dt><dd><p>value (<cite>nn.Module</cite>): A module mapping vocabulary to hidden states.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel"><code class="docutils literal notranslate"><span class="pre">GRUModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.__init__"><code class="docutils literal notranslate"><span class="pre">GRUModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.forward"><code class="docutils literal notranslate"><span class="pre">GRUModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings"><code class="docutils literal notranslate"><span class="pre">GRUModel.get_input_embeddings()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings"><code class="docutils literal notranslate"><span class="pre">GRUModel.set_input_embeddings()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModelOutput"><code class="docutils literal notranslate"><span class="pre">GRUModelOutput</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUModelOutput.__init__"><code class="docutils literal notranslate"><span class="pre">GRUModelOutput.__init__()</span></code></a></li>
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+                  
+  <section id="pyvene-models-gru-modelings-gru-grumodeloutput">
+<h1>pyvene.models.gru.modelings_gru.GRUModelOutput<a class="headerlink" href="#pyvene-models-gru-modelings-gru-grumodeloutput" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModelOutput">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRUModelOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_hidden_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.FloatTensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUModelOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModelOutput" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ModelOutput</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUModelOutput.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_hidden_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">FloatTensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUModelOutput.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUModelOutput.__init__" title="pyvene.models.gru.modelings_gru.GRUModelOutput.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([last_hidden_state, hidden_states])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
+<td><p>Create a new ordered dictionary with keys from iterable and values set to value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
+<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">move_to_end</span></code>(key[, last])</p></td>
+<td><p>Move an existing element to the end (or beginning if last is false).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(key[,default])</p></td>
+<td><p>If the key is not found, return the default if given; otherwise, raise a KeyError.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>([last])</p></td>
+<td><p>Remove and return a (key, value) pair from the dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(*args, **kwargs)</p></td>
+<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tuple</span></code>()</p></td>
+<td><p>Convert self to a tuple containing all the attributes/keys that are not <cite>None</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
+<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hidden_states</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">last_hidden_state</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUPreTrainedModel"><code class="docutils literal notranslate"><span class="pre">GRUPreTrainedModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__"><code class="docutils literal notranslate"><span class="pre">GRUPreTrainedModel.__init__()</span></code></a></li>
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+  <section id="pyvene-models-gru-modelings-gru-grupretrainedmodel">
+<h1>pyvene.models.gru.modelings_gru.GRUPreTrainedModel<a class="headerlink" href="#pyvene-models-gru-modelings-gru-grupretrainedmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUPreTrainedModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">GRUPreTrainedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUPreTrainedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUPreTrainedModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">PreTrainedModel</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_gru.html#GRUPreTrainedModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__" title="pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(*inputs, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(*input)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_class</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
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+<h1>pyvene.models.gru.modelings_intervenable_gru.create_gru<a class="headerlink" href="#pyvene-models-gru-modelings-intervenable-gru-create-gru" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">create_gru</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_intervenable_gru.html#create_gru"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_intervenable_gru.create_gru" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GRU model, config, and tokenizer from the given name and revision</p>
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+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier">
+<span class="sig-name descname"><span class="pre">create_gru_classifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_intervenable_gru.html#create_gru_classifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GRU model, config, and tokenizer from the given name and revision</p>
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+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_intervenable_gru.create_gru_lm">
+<span class="sig-name descname"><span class="pre">create_gru_lm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/gru/modelings_intervenable_gru.html#create_gru_lm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.gru.modelings_intervenable_gru.create_gru_lm" title="Link to this definition">#</a></dt>
+<dd><p>Creates a GRU model, config, and tokenizer from the given name and revision</p>
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new file mode 100644
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping"><code class="docutils literal notranslate"><span class="pre">gru_classifier_type_to_dimension_mapping</span></code></a></li>
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+  <section id="pyvene-models-gru-modelings-intervenable-gru-gru-classifier-type-to-dimension-mapping">
+<h1>pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-gru-modelings-intervenable-gru-gru-classifier-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">gru_classifier_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'cell_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'cell_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'h2h_output':</span> <span class="pre">('h_dim*3',),</span> <span class="pre">'new_gate_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'new_gate_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'new_h2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'new_x2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_gate_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_gate_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_h2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_x2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_gate_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_gate_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_h2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_x2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'x2h_output':</span> <span class="pre">('h_dim*3',)}</span></em><a class="headerlink" href="#pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>mlp model with lm head</p>
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+<dl class="py data">
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+<span class="sig-name descname"><span class="pre">gru_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'cell_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'cell_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'h2h_output':</span> <span class="pre">('h_dim*3',),</span> <span class="pre">'new_gate_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'new_gate_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'new_h2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'new_x2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_gate_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_gate_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_h2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'reset_x2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_gate_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_gate_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_h2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'update_x2h_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'x2h_output':</span> <span class="pre">('h_dim*3',)}</span></em><a class="headerlink" href="#pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>mlp model with classification head</p>
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+    <h1>pyvene.models.gru.modelings_intervenable_gru</h1>
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+                <article class="bd-article">
+                  
+  <section id="module-pyvene.models.gru.modelings_intervenable_gru">
+<span id="pyvene-models-gru-modelings-intervenable-gru"></span><h1>pyvene.models.gru.modelings_intervenable_gru<a class="headerlink" href="#module-pyvene.models.gru.modelings_intervenable_gru" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping.html#pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping" title="pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gru_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>mlp model with classification head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping.html#pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping" title="pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gru_classifier_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>mlp model with lm head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gru.modelings_intervenable_gru.create_gru.html#pyvene.models.gru.modelings_intervenable_gru.create_gru" title="pyvene.models.gru.modelings_intervenable_gru.create_gru"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gru</span></code></a>(config[, tokenizer_name, cache_dir])</p></td>
+<td><p>Creates a GRU model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier.html#pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier" title="pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gru_classifier</span></code></a>(config[, ...])</p></td>
+<td><p>Creates a GRU model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gru.modelings_intervenable_gru.create_gru_lm.html#pyvene.models.gru.modelings_intervenable_gru.create_gru_lm" title="pyvene.models.gru.modelings_intervenable_gru.create_gru_lm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_gru_lm</span></code></a>(config[, tokenizer_name, ...])</p></td>
+<td><p>Creates a GRU model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
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+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<span id="pyvene-models"></span><h1>pyvene.models<a class="headerlink" href="#module-pyvene.models" title="Link to this heading">#</a></h1>
+<p>Core model intervention classes and utilities, as well as model-specific implementations.</p>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.backpack_gpt2.html#module-pyvene.models.backpack_gpt2" title="pyvene.models.backpack_gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.backpack_gpt2</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.basic_utils.html#module-pyvene.models.basic_utils" title="pyvene.models.basic_utils"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.basic_utils</span></code></a></p></td>
+<td><p>Basic Utils</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.blip.html#module-pyvene.models.blip" title="pyvene.models.blip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.blip</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.configuration_intervenable_model.html#module-pyvene.models.configuration_intervenable_model" title="pyvene.models.configuration_intervenable_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.configuration_intervenable_model</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.constants.html#module-pyvene.models.constants" title="pyvene.models.constants"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.constants</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gemma.html#module-pyvene.models.gemma" title="pyvene.models.gemma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gemma</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gemma2.html#module-pyvene.models.gemma2" title="pyvene.models.gemma2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gemma2</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gpt2.html#module-pyvene.models.gpt2" title="pyvene.models.gpt2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gpt2</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gpt_neo.html#module-pyvene.models.gpt_neo" title="pyvene.models.gpt_neo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gpt_neo</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.gpt_neox.html#module-pyvene.models.gpt_neox" title="pyvene.models.gpt_neox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gpt_neox</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.gru.html#module-pyvene.models.gru" title="pyvene.models.gru"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.gru</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.intervenable_base.html#module-pyvene.models.intervenable_base" title="pyvene.models.intervenable_base"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.intervenable_base</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.intervenable_modelcard.html#module-pyvene.models.intervenable_modelcard" title="pyvene.models.intervenable_modelcard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.intervenable_modelcard</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.intervention_utils.html#module-pyvene.models.intervention_utils" title="pyvene.models.intervention_utils"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.intervention_utils</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.html#module-pyvene.models.interventions" title="pyvene.models.interventions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.interventions</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.layers.html#module-pyvene.models.layers" title="pyvene.models.layers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.layers</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llama.html#module-pyvene.models.llama" title="pyvene.models.llama"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.llama</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.llava.html#module-pyvene.models.llava" title="pyvene.models.llava"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.llava</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mistral.html#module-pyvene.models.mistral" title="pyvene.models.mistral"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.mistral</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.mlp.html#module-pyvene.models.mlp" title="pyvene.models.mlp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.mlp</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.html#module-pyvene.models.modeling_utils" title="pyvene.models.modeling_utils"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.modeling_utils</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.olmo.html#module-pyvene.models.olmo" title="pyvene.models.olmo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.olmo</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.qwen2.html#module-pyvene.models.qwen2" title="pyvene.models.qwen2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.qwen2</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
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diff --git a/api/pyvene.models.intervenable_base.BaseModel.html b/api/pyvene.models.intervenable_base.BaseModel.html
new file mode 100644
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Probing_Gender.html">Causal Evaluation of Probes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.data_generators.causal_model.CausalModel.html">pyvene.data_generators.causal_model.CausalModel</a></li>
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+    <h1>pyvene.models.intervenable_base.BaseModel</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel"><code class="docutils literal notranslate"><span class="pre">BaseModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.__init__"><code class="docutils literal notranslate"><span class="pre">BaseModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.count_parameters"><code class="docutils literal notranslate"><span class="pre">BaseModel.count_parameters()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients"><code class="docutils literal notranslate"><span class="pre">BaseModel.disable_intervention_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.disable_model_gradients"><code class="docutils literal notranslate"><span class="pre">BaseModel.disable_model_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.enable_model_gradients"><code class="docutils literal notranslate"><span class="pre">BaseModel.enable_model_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.forward"><code class="docutils literal notranslate"><span class="pre">BaseModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_cached_activations"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_cached_activations()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_cached_hot_activations()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_device"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_device()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_trainable_parameters"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_trainable_parameters()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.named_parameters"><code class="docutils literal notranslate"><span class="pre">BaseModel.named_parameters()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.set_device"><code class="docutils literal notranslate"><span class="pre">BaseModel.set_device()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.set_temperature"><code class="docutils literal notranslate"><span class="pre">BaseModel.set_temperature()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.set_zero_grad"><code class="docutils literal notranslate"><span class="pre">BaseModel.set_zero_grad()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.zero_grad"><code class="docutils literal notranslate"><span class="pre">BaseModel.zero_grad()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-intervenable-base-basemodel">
+<h1>pyvene.models.intervenable_base.BaseModel<a class="headerlink" href="#pyvene-models-intervenable-base-basemodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BaseModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>Base model class for sharing static vars and methods.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.__init__" title="pyvene.models.intervenable_base.BaseModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config, model, backend, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.count_parameters" title="pyvene.models.intervenable_base.BaseModel.count_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">count_parameters</span></code></a>([include_model])</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients" title="pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_intervention_gradients</span></code></a>()</p></td>
+<td><p>Disable gradient in the trainable intervention</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.disable_model_gradients" title="pyvene.models.intervenable_base.BaseModel.disable_model_gradients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_model_gradients</span></code></a>()</p></td>
+<td><p>Disable gradient in the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.enable_model_gradients" title="pyvene.models.intervenable_base.BaseModel.enable_model_gradients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_model_gradients</span></code></a>()</p></td>
+<td><p>Enable gradient in the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.forward" title="pyvene.models.intervenable_base.BaseModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(**kwargs)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>(**kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.get_cached_activations" title="pyvene.models.intervenable_base.BaseModel.get_cached_activations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cached_activations</span></code></a>()</p></td>
+<td><p>Return the cached activations with keys</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations" title="pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cached_hot_activations</span></code></a>()</p></td>
+<td><p>Return the cached hot activations with linked keys</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.get_device" title="pyvene.models.intervenable_base.BaseModel.get_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_device</span></code></a>()</p></td>
+<td><p>Get device of interventions and the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.get_trainable_parameters" title="pyvene.models.intervenable_base.BaseModel.get_trainable_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_trainable_parameters</span></code></a>()</p></td>
+<td><p>Return trainable params as key value pairs</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.named_parameters" title="pyvene.models.intervenable_base.BaseModel.named_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code></a>([recurse])</p></td>
+<td><p>The above, but for HuggingFace.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.set_device" title="pyvene.models.intervenable_base.BaseModel.set_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_device</span></code></a>(device[, set_model])</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.set_temperature" title="pyvene.models.intervenable_base.BaseModel.set_temperature"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_temperature</span></code></a>(temp)</p></td>
+<td><p>Set temperature if needed</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.set_zero_grad" title="pyvene.models.intervenable_base.BaseModel.set_zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_zero_grad</span></code></a>()</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.BaseModel.zero_grad" title="pyvene.models.intervenable_base.BaseModel.zero_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code></a>()</p></td>
+<td><p>The above, but for HuggingFace.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.count_parameters">
+<span class="sig-name descname"><span class="pre">count_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.count_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.count_parameters" title="Link to this definition">#</a></dt>
+<dd><p>Set device of interventions and the model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients">
+<span class="sig-name descname"><span class="pre">disable_intervention_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.disable_intervention_gradients"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients" title="Link to this definition">#</a></dt>
+<dd><p>Disable gradient in the trainable intervention</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.disable_model_gradients">
+<span class="sig-name descname"><span class="pre">disable_model_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.disable_model_gradients"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.disable_model_gradients" title="Link to this definition">#</a></dt>
+<dd><p>Disable gradient in the model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.enable_model_gradients">
+<span class="sig-name descname"><span class="pre">enable_model_gradients</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.enable_model_gradients"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.enable_model_gradients" title="Link to this definition">#</a></dt>
+<dd><p>Enable gradient in the model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.get_cached_activations">
+<span class="sig-name descname"><span class="pre">get_cached_activations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.get_cached_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.get_cached_activations" title="Link to this definition">#</a></dt>
+<dd><p>Return the cached activations with keys</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations">
+<span class="sig-name descname"><span class="pre">get_cached_hot_activations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.get_cached_hot_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations" title="Link to this definition">#</a></dt>
+<dd><p>Return the cached hot activations with linked keys</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.get_device">
+<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.get_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.get_device" title="Link to this definition">#</a></dt>
+<dd><p>Get device of interventions and the model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.get_trainable_parameters">
+<span class="sig-name descname"><span class="pre">get_trainable_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.get_trainable_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.get_trainable_parameters" title="Link to this definition">#</a></dt>
+<dd><p>Return trainable params as key value pairs</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.named_parameters">
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.named_parameters" title="Link to this definition">#</a></dt>
+<dd><p>The above, but for HuggingFace.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.set_device">
+<span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">set_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.set_device" title="Link to this definition">#</a></dt>
+<dd><p>Set device of interventions and the model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.set_temperature">
+<span class="sig-name descname"><span class="pre">set_temperature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temp</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.set_temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.set_temperature" title="Link to this definition">#</a></dt>
+<dd><p>Set temperature if needed</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.set_zero_grad">
+<span class="sig-name descname"><span class="pre">set_zero_grad</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.set_zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.set_zero_grad" title="Link to this definition">#</a></dt>
+<dd><p>Set device of interventions and the model</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.BaseModel.zero_grad">
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#BaseModel.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.BaseModel.zero_grad" title="Link to this definition">#</a></dt>
+<dd><p>The above, but for HuggingFace.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+    <i class="fa-solid fa-list"></i> Contents
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel"><code class="docutils literal notranslate"><span class="pre">BaseModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.__init__"><code class="docutils literal notranslate"><span class="pre">BaseModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.count_parameters"><code class="docutils literal notranslate"><span class="pre">BaseModel.count_parameters()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients"><code class="docutils literal notranslate"><span class="pre">BaseModel.disable_intervention_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.disable_model_gradients"><code class="docutils literal notranslate"><span class="pre">BaseModel.disable_model_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.enable_model_gradients"><code class="docutils literal notranslate"><span class="pre">BaseModel.enable_model_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.forward"><code class="docutils literal notranslate"><span class="pre">BaseModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_cached_activations"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_cached_activations()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_cached_hot_activations()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_device"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_device()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.get_trainable_parameters"><code class="docutils literal notranslate"><span class="pre">BaseModel.get_trainable_parameters()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.named_parameters"><code class="docutils literal notranslate"><span class="pre">BaseModel.named_parameters()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.set_device"><code class="docutils literal notranslate"><span class="pre">BaseModel.set_device()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.set_temperature"><code class="docutils literal notranslate"><span class="pre">BaseModel.set_temperature()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.set_zero_grad"><code class="docutils literal notranslate"><span class="pre">BaseModel.set_zero_grad()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.BaseModel.zero_grad"><code class="docutils literal notranslate"><span class="pre">BaseModel.zero_grad()</span></code></a></li>
+</ul>
+</li>
+</ul>
+  </nav></div>
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+    <h1>pyvene.models.intervenable_base.IntervenableModel</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel"><code class="docutils literal notranslate"><span class="pre">IntervenableModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.eval"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.eval()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.eval_alignment"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.eval_alignment()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.forward"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.generate"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.generate()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.load"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.load()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.load_intervention"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.load_intervention()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.save"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.save()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.save_intervention"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.save_intervention()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.train"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.train()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.train_alignment"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.train_alignment()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-intervenable-base-intervenablemodel">
+<h1>pyvene.models.intervenable_base.IntervenableModel<a class="headerlink" href="#pyvene-models-intervenable-base-intervenablemodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">IntervenableModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel" title="pyvene.models.intervenable_base.BaseModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseModel</span></code></a></p>
+<p>Intervenable model via pyvene native backend (hook-based).</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.__init__" title="pyvene.models.intervenable_base.IntervenableModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config, model, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">count_parameters</span></code>([include_model])</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_intervention_gradients</span></code>()</p></td>
+<td><p>Disable gradient in the trainable intervention</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_model_gradients</span></code>()</p></td>
+<td><p>Disable gradient in the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_model_gradients</span></code>()</p></td>
+<td><p>Enable gradient in the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.eval" title="pyvene.models.intervenable_base.IntervenableModel.eval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code></a>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.eval_alignment" title="pyvene.models.intervenable_base.IntervenableModel.eval_alignment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_alignment</span></code></a>(eval_dataloader, ...)</p></td>
+<td><p>The method evaluate alignment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.forward" title="pyvene.models.intervenable_base.IntervenableModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, sources, unit_locations, ...])</p></td>
+<td><p>Main forward function that serves a wrapper to actual model forward calls.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.generate" title="pyvene.models.intervenable_base.IntervenableModel.generate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code></a>(base[, sources, unit_locations, ...])</p></td>
+<td><p>Intervenable generation function that serves a wrapper to regular model generate calls.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cached_activations</span></code>()</p></td>
+<td><p>Return the cached activations with keys</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cached_hot_activations</span></code>()</p></td>
+<td><p>Return the cached hot activations with linked keys</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_device</span></code>()</p></td>
+<td><p>Get device of interventions and the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_trainable_parameters</span></code>()</p></td>
+<td><p>Return trainable params as key value pairs</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.load" title="pyvene.models.intervenable_base.IntervenableModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(load_directory, model[, ...])</p></td>
+<td><p>Load interventions from disk or hub</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.load_intervention" title="pyvene.models.intervenable_base.IntervenableModel.load_intervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_intervention</span></code></a>(load_directory[, ...])</p></td>
+<td><p>Instead of creating an new object, this function loads existing weights onto the current object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([recurse])</p></td>
+<td><p>The above, but for HuggingFace.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.save" title="pyvene.models.intervenable_base.IntervenableModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(save_directory[, save_to_hf_hub, ...])</p></td>
+<td><p>Save interventions to disk or hub</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.save_intervention" title="pyvene.models.intervenable_base.IntervenableModel.save_intervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_intervention</span></code></a>(save_directory[, ...])</p></td>
+<td><p>Instead of saving the metadata with artifacts, it only saves artifacts such as trainable weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_device</span></code>(device[, set_model])</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_temperature</span></code>(temp)</p></td>
+<td><p>Set temperature if needed</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_zero_grad</span></code>()</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.train" title="pyvene.models.intervenable_base.IntervenableModel.train"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code></a>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModel.train_alignment" title="pyvene.models.intervenable_base.IntervenableModel.train_alignment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_alignment</span></code></a>(train_dataloader, ...)</p></td>
+<td><p>The method find alignment.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>()</p></td>
+<td><p>The above, but for HuggingFace.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BACKEND</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.eval">
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.eval" title="Link to this definition">#</a></dt>
+<dd><p>Set the module in evaluation mode.</p>
+<p>This has an effect only on certain modules. See the documentation of
+particular modules for details of their behaviors in training/evaluation
+mode, i.e. whether they are affected, e.g. <code class="xref py py-class docutils literal notranslate"><span class="pre">Dropout</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm</span></code>,
+etc.</p>
+<p>This is equivalent with <code class="xref py py-meth docutils literal notranslate"><span class="pre">self.train(False)</span></code>.</p>
+<p>See <span class="xref std std-ref">locally-disable-grad-doc</span> for a comparison between
+<cite>.eval()</cite> and several similar mechanisms that may be confused with it.</p>
+<dl class="simple">
+<dt>Returns:</dt><dd><p>Module: self</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.eval_alignment">
+<span class="sig-name descname"><span class="pre">eval_alignment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eval_dataloader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_metrics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_collator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.eval_alignment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.eval_alignment" title="Link to this definition">#</a></dt>
+<dd><p>The method evaluate alignment.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_locations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_representations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_original_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Main forward function that serves a wrapper to
+actual model forward calls. It will use forward
+hooks to do interventions.</p>
+<p>In essense, sources will lead to getter hooks to
+get activations. We will use these activations to
+intervene on our base example.</p>
+<p>Parameters:
+base:                The base example.
+sources:             A list of source examples.
+unit_locations:      The intervention locations.
+activations_sources: A list of representations.
+subspace:            Subspace interventions.</p>
+<p>Return:
+base_output: the non-intervened output of the base
+input.
+counterfactual_outputs: the intervened output of the
+base input.</p>
+<p>Notes:</p>
+<p>1) unit_locations
+unit_locations is a dict where keys are tied with
+example pairs involved in one intervention as,
+{</p>
+<blockquote>
+<div><p>“sources-&gt;base” : List[]</p>
+</div></blockquote>
+<p>}</p>
+<p>the shape can be</p>
+<p>2 * num_intervention * bs * num_max_unit</p>
+<p>OR</p>
+<p>2 * num_intervention * num_intervention_level * bs * num_max_unit</p>
+<p>if we intervene on h.pos which is a nested intervention location.</p>
+<p>2) subspaces
+subspaces is a list of indices indicating which subspace will
+this intervention target given an example in the batch.</p>
+<p>An intervention could be initialized with subspace parition as,
+[[… subspace_1 …], [… subspace_2 …], [rest]]</p>
+<p>An intervention may be targeting a specific partition.</p>
+<p>This input field should look like something like,
+[</p>
+<blockquote>
+<div><p>[[subspace indices], [subspace indices]], &lt;- for the first intervention
+None,                                     &lt;- for the second intervention
+[[subspace indices], [subspace indices]]</p>
+</div></blockquote>
+<p>]</p>
+<p>Only setter (where do_intervention is called) needs this field.</p>
+<p><a href="#id1"><span class="problematic" id="id2">*</span></a>We assume base and source targetting the same subspace for now.
+<a href="#id3"><span class="problematic" id="id4">*</span></a>We assume only a single space is targeted for now (although 2d list is provided).</p>
+<p>Since we now support group-based intervention, the number of sources
+should be equal to the total number of groups.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.generate">
+<span class="sig-name descname"><span class="pre">generate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_locations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_representations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervene_on_prompt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_original_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.generate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.generate" title="Link to this definition">#</a></dt>
+<dd><p>Intervenable generation function that serves a
+wrapper to regular model generate calls.</p>
+<p>Currently, we support basic interventions <strong>in the
+prompt only</strong>. We will support generation interventions
+in the next release.</p>
+<p>TODO: Unroll sources and intervene in the generation step.</p>
+<p>Parameters:
+base:                The base example.
+sources:             A list of source examples.
+unit_locations:      The intervention locations of</p>
+<blockquote>
+<div><p>base.</p>
+</div></blockquote>
+<p>activations_sources: A list of representations.
+intervene_on_prompt: Whether only intervene on prompt.
+<a href="#id5"><span class="problematic" id="id6">**</span></a>kwargs:            All other generation parameters.</p>
+<p>Return:
+base_output: the non-intervened output of the base
+input.
+counterfactual_outputs: the intervened output of the
+base input.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.load">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">load_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">from_huggingface_hub</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.load" title="Link to this definition">#</a></dt>
+<dd><p>Load interventions from disk or hub</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.load_intervention">
+<span class="sig-name descname"><span class="pre">load_intervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">load_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.load_intervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.load_intervention" title="Link to this definition">#</a></dt>
+<dd><p>Instead of creating an new object, this function loads existing weights onto
+the current object. This is not a static method, and returns nothing.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_to_hf_hub</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hf_repo_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'my-awesome-model'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.save" title="Link to this definition">#</a></dt>
+<dd><p>Save interventions to disk or hub</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.save_intervention">
+<span class="sig-name descname"><span class="pre">save_intervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.save_intervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.save_intervention" title="Link to this definition">#</a></dt>
+<dd><p>Instead of saving the metadata with artifacts, it only saves artifacts such as
+trainable weights. This is not a static method, and returns nothing.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.train">
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.train" title="Link to this definition">#</a></dt>
+<dd><p>Set the module in training mode.</p>
+<p>This has an effect only on certain modules. See the documentation of
+particular modules for details of their behaviors in training/evaluation
+mode, i.e., whether they are affected, e.g. <code class="xref py py-class docutils literal notranslate"><span class="pre">Dropout</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm</span></code>,
+etc.</p>
+<dl class="simple">
+<dt>Args:</dt><dd><dl class="simple">
+<dt>mode (bool): whether to set training mode (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or evaluation</dt><dd><p>mode (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt>Returns:</dt><dd><p>Module: self</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModel.train_alignment">
+<span class="sig-name descname"><span class="pre">train_alignment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_dataloader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_metrics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_collator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModel.train_alignment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModel.train_alignment" title="Link to this definition">#</a></dt>
+<dd><p>The method find alignment.</p>
+<p>a.k.a. training the intervention</p>
+<p>Notes:
+1) we use Adam, and linear lr scheduling.
+2) you can pass in lr or using default 1e-3</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel"><code class="docutils literal notranslate"><span class="pre">IntervenableModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.eval"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.eval()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.eval_alignment"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.eval_alignment()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.forward"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.generate"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.generate()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.load"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.load()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.load_intervention"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.load_intervention()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.save"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.save()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.save_intervention"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.save_intervention()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.train"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.train()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModel.train_alignment"><code class="docutils literal notranslate"><span class="pre">IntervenableModel.train_alignment()</span></code></a></li>
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+    <!-- Table of contents -->
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModelOutput"><code class="docutils literal notranslate"><span class="pre">IntervenableModelOutput</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableModelOutput.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableModelOutput.__init__()</span></code></a></li>
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+  <section id="pyvene-models-intervenable-base-intervenablemodeloutput">
+<h1>pyvene.models.intervenable_base.IntervenableModelOutput<a class="headerlink" href="#pyvene-models-intervenable-base-intervenablemodeloutput" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModelOutput">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">IntervenableModelOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervened_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collected_activations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableModelOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModelOutput" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ModelOutput</span></code></p>
+<p>Output of the IntervenableModel, including original outputs, intervened outputs, and collected activations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableModelOutput.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervened_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collected_activations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableModelOutput.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableModelOutput.__init__" title="pyvene.models.intervenable_base.IntervenableModelOutput.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([original_outputs, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
+<td><p>Create a new ordered dictionary with keys from iterable and values set to value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
+<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">move_to_end</span></code>(key[, last])</p></td>
+<td><p>Move an existing element to the end (or beginning if last is false).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(key[,default])</p></td>
+<td><p>If the key is not found, return the default if given; otherwise, raise a KeyError.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>([last])</p></td>
+<td><p>Remove and return a (key, value) pair from the dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(*args, **kwargs)</p></td>
+<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tuple</span></code>()</p></td>
+<td><p>Convert self to a tuple containing all the attributes/keys that are not <cite>None</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
+<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">collected_activations</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">intervened_outputs</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">original_outputs</span></code></p></td>
+<td><p></p></td>
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+    <h1>pyvene.models.intervenable_base.IntervenableNdifModel</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel.forward"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel.load"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel.load()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-intervenable-base-intervenablendifmodel">
+<h1>pyvene.models.intervenable_base.IntervenableNdifModel<a class="headerlink" href="#pyvene-models-intervenable-base-intervenablendifmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableNdifModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">IntervenableNdifModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableNdifModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableNdifModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel" title="pyvene.models.intervenable_base.BaseModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseModel</span></code></a></p>
+<p>Intervenable model via ndif backend.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableNdifModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableNdifModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableNdifModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableNdifModel.__init__" title="pyvene.models.intervenable_base.IntervenableNdifModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config, model, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">count_parameters</span></code>([include_model])</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_intervention_gradients</span></code>()</p></td>
+<td><p>Disable gradient in the trainable intervention</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_model_gradients</span></code>()</p></td>
+<td><p>Disable gradient in the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_model_gradients</span></code>()</p></td>
+<td><p>Enable gradient in the model</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableNdifModel.forward" title="pyvene.models.intervenable_base.IntervenableNdifModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, sources, unit_locations, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>(base[, sources, unit_locations, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cached_activations</span></code>()</p></td>
+<td><p>Return the cached activations with keys</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cached_hot_activations</span></code>()</p></td>
+<td><p>Return the cached hot activations with linked keys</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_device</span></code>()</p></td>
+<td><p>Get device of interventions and the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_trainable_parameters</span></code>()</p></td>
+<td><p>Return trainable params as key value pairs</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.intervenable_base.IntervenableNdifModel.load" title="pyvene.models.intervenable_base.IntervenableNdifModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(load_directory, model[, ...])</p></td>
+<td><p>Load interventions from disk or hub</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([recurse])</p></td>
+<td><p>The above, but for HuggingFace.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code>(save_directory[, save_to_hf_hub, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_device</span></code>(device[, set_model])</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_temperature</span></code>(temp)</p></td>
+<td><p>Set temperature if needed</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_zero_grad</span></code>()</p></td>
+<td><p>Set device of interventions and the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>()</p></td>
+<td><p>The above, but for HuggingFace.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BACKEND</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableNdifModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_locations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_representations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_original_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableNdifModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableNdifModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.IntervenableNdifModel.load">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">load_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">from_huggingface_hub</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#IntervenableNdifModel.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.IntervenableNdifModel.load" title="Link to this definition">#</a></dt>
+<dd><p>Load interventions from disk or hub</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel.__init__"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel.forward"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervenable_base.IntervenableNdifModel.load"><code class="docutils literal notranslate"><span class="pre">IntervenableNdifModel.load()</span></code></a></li>
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diff --git a/api/pyvene.models.intervenable_base.build_intervenable_model.html b/api/pyvene.models.intervenable_base.build_intervenable_model.html
new file mode 100644
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+
+<!DOCTYPE html>
+
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+<html lang="en" data-content_root="../" >
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
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+<h1>pyvene.models.intervenable_base.build_intervenable_model<a class="headerlink" href="#pyvene-models-intervenable-base-build-intervenable-model" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.intervenable_base.build_intervenable_model">
+<span class="sig-name descname"><span class="pre">build_intervenable_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervenable_base.html#build_intervenable_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervenable_base.build_intervenable_model" title="Link to this definition">#</a></dt>
+<dd><p>Factory design pattern for different types of intervenable models.</p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
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+    <h1>pyvene.models.intervenable_base</h1>
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+  <section id="module-pyvene.models.intervenable_base">
+<span id="pyvene-models-intervenable-base"></span><h1>pyvene.models.intervenable_base<a class="headerlink" href="#module-pyvene.models.intervenable_base" title="Link to this heading">#</a></h1>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.intervenable_base.build_intervenable_model.html#pyvene.models.intervenable_base.build_intervenable_model" title="pyvene.models.intervenable_base.build_intervenable_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_intervenable_model</span></code></a>(config, model, **kwargs)</p></td>
+<td><p>Factory design pattern for different types of intervenable models.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel" title="pyvene.models.intervenable_base.BaseModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseModel</span></code></a>(config, model, backend, **kwargs)</p></td>
+<td><p>Base model class for sharing static vars and methods.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel" title="pyvene.models.intervenable_base.IntervenableModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IntervenableModel</span></code></a>(config, model, **kwargs)</p></td>
+<td><p>Intervenable model via pyvene native backend (hook-based).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.intervenable_base.IntervenableModelOutput.html#pyvene.models.intervenable_base.IntervenableModelOutput" title="pyvene.models.intervenable_base.IntervenableModelOutput"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IntervenableModelOutput</span></code></a>([original_outputs, ...])</p></td>
+<td><p>Output of the IntervenableModel, including original outputs, intervened outputs, and collected activations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel" title="pyvene.models.intervenable_base.IntervenableNdifModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IntervenableNdifModel</span></code></a>(config, model, **kwargs)</p></td>
+<td><p>Intervenable model via ndif backend.</p></td>
+</tr>
+</tbody>
+</table>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
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+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervention_utils.InterventionState"><code class="docutils literal notranslate"><span class="pre">InterventionState</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervention_utils.InterventionState.__init__"><code class="docutils literal notranslate"><span class="pre">InterventionState.__init__()</span></code></a></li>
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+  <section id="pyvene-models-intervention-utils-interventionstate">
+<h1>pyvene.models.intervention_utils.InterventionState<a class="headerlink" href="#pyvene-models-intervention-utils-interventionstate" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.intervention_utils.InterventionState">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">InterventionState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervention_utils.html#InterventionState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervention_utils.InterventionState" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.intervention_utils.InterventionState.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervention_utils.html#InterventionState.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervention_utils.InterventionState.__init__" title="Link to this definition">#</a></dt>
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+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
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+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_states</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">getter_version</span></code>()</p></td>
+<td><p></p></td>
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+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">inc_getter_version</span></code>()</p></td>
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+<td><p></p></td>
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+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setter_version</span></code>()</p></td>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.intervention_utils.broadcast_tensor_v2"><code class="docutils literal notranslate"><span class="pre">broadcast_tensor_v2()</span></code></a></li>
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+<h1>pyvene.models.intervention_utils.broadcast_tensor_v2<a class="headerlink" href="#pyvene-models-intervention-utils-broadcast-tensor-v2" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.intervention_utils.broadcast_tensor_v2">
+<span class="sig-name descname"><span class="pre">broadcast_tensor_v2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/intervention_utils.html#broadcast_tensor_v2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.intervention_utils.broadcast_tensor_v2" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</section>
+
+
+                </article>
+              
+
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+  <section id="module-pyvene.models.intervention_utils">
+<span id="pyvene-models-intervention-utils"></span><h1>pyvene.models.intervention_utils<a class="headerlink" href="#module-pyvene.models.intervention_utils" title="Link to this heading">#</a></h1>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.intervention_utils.broadcast_tensor_v1.html#pyvene.models.intervention_utils.broadcast_tensor_v1" title="pyvene.models.intervention_utils.broadcast_tensor_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">broadcast_tensor_v1</span></code></a>(x, target_shape)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.intervention_utils.broadcast_tensor_v2.html#pyvene.models.intervention_utils.broadcast_tensor_v2" title="pyvene.models.intervention_utils.broadcast_tensor_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">broadcast_tensor_v2</span></code></a>(x, target_shape)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.intervention_utils.InterventionState.html#pyvene.models.intervention_utils.InterventionState" title="pyvene.models.intervention_utils.InterventionState"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InterventionState</span></code></a>(key, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AdditionIntervention"><code class="docutils literal notranslate"><span class="pre">AdditionIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AdditionIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">AdditionIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AdditionIntervention.forward"><code class="docutils literal notranslate"><span class="pre">AdditionIntervention.forward()</span></code></a></li>
+</ul>
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+  <section id="pyvene-models-interventions-additionintervention">
+<h1>pyvene.models.interventions.AdditionIntervention<a class="headerlink" href="#pyvene-models-interventions-additionintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.AdditionIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AdditionIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#AdditionIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.AdditionIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention" title="pyvene.models.interventions.BasisAgnosticIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">BasisAgnosticIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Intervention the original representations with activation addition.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.AdditionIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#AdditionIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.AdditionIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.AdditionIntervention.__init__" title="pyvene.models.interventions.AdditionIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.AdditionIntervention.forward" title="pyvene.models.interventions.AdditionIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.AdditionIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#AdditionIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.AdditionIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AdditionIntervention"><code class="docutils literal notranslate"><span class="pre">AdditionIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AdditionIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">AdditionIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AdditionIntervention.forward"><code class="docutils literal notranslate"><span class="pre">AdditionIntervention.forward()</span></code></a></li>
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+    <h1>pyvene.models.interventions.AutoencoderIntervention</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AutoencoderIntervention"><code class="docutils literal notranslate"><span class="pre">AutoencoderIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AutoencoderIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">AutoencoderIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AutoencoderIntervention.forward"><code class="docutils literal notranslate"><span class="pre">AutoencoderIntervention.forward()</span></code></a></li>
+</ul>
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+                  
+  <section id="pyvene-models-interventions-autoencoderintervention">
+<h1>pyvene.models.interventions.AutoencoderIntervention<a class="headerlink" href="#pyvene-models-interventions-autoencoderintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.AutoencoderIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AutoencoderIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#AutoencoderIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.AutoencoderIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a></p>
+<p>Intervene in the latent space of an autoencoder.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.AutoencoderIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#AutoencoderIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.AutoencoderIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.AutoencoderIntervention.__init__" title="pyvene.models.interventions.AutoencoderIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.AutoencoderIntervention.forward" title="pyvene.models.interventions.AutoencoderIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.AutoencoderIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#AutoencoderIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.AutoencoderIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AutoencoderIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">AutoencoderIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.AutoencoderIntervention.forward"><code class="docutils literal notranslate"><span class="pre">AutoencoderIntervention.forward()</span></code></a></li>
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+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
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+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
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+    <h1>pyvene.models.interventions.BasisAgnosticIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.BasisAgnosticIntervention"><code class="docutils literal notranslate"><span class="pre">BasisAgnosticIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.BasisAgnosticIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">BasisAgnosticIntervention.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+              
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-interventions-basisagnosticintervention">
+<h1>pyvene.models.interventions.BasisAgnosticIntervention<a class="headerlink" href="#pyvene-models-interventions-basisagnosticintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.BasisAgnosticIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BasisAgnosticIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#BasisAgnosticIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.BasisAgnosticIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention" title="pyvene.models.interventions.Intervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">Intervention</span></code></a></p>
+<p>Intervention that will modify its basis in a uncontrolled manner.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.BasisAgnosticIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#BasisAgnosticIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.BasisAgnosticIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.BasisAgnosticIntervention.__init__" title="pyvene.models.interventions.BasisAgnosticIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">BoundlessRotatedSpaceIntervention.__init__()</span></code></a></li>
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+  <section id="pyvene-models-interventions-boundlessrotatedspaceintervention">
+<h1>pyvene.models.interventions.BoundlessRotatedSpaceIntervention<a class="headerlink" href="#pyvene-models-interventions-boundlessrotatedspaceintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.BoundlessRotatedSpaceIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BoundlessRotatedSpaceIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#BoundlessRotatedSpaceIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention" title="pyvene.models.interventions.DistributedRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a></p>
+<p>Intervention in the rotated space with boundary mask.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#BoundlessRotatedSpaceIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__" title="pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward" title="pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_boundary_parameters</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_temperature</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_intervention_boundaries</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_temperature</span></code>(temp)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#BoundlessRotatedSpaceIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">BoundlessRotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">BoundlessRotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">BoundlessRotatedSpaceIntervention.forward()</span></code></a></li>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.CollectIntervention"><code class="docutils literal notranslate"><span class="pre">CollectIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.CollectIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">CollectIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.CollectIntervention.forward"><code class="docutils literal notranslate"><span class="pre">CollectIntervention.forward()</span></code></a></li>
+</ul>
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+                  
+  <section id="pyvene-models-interventions-collectintervention">
+<h1>pyvene.models.interventions.CollectIntervention<a class="headerlink" href="#pyvene-models-interventions-collectintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.CollectIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">CollectIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#CollectIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.CollectIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention" title="pyvene.models.interventions.ConstantSourceIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConstantSourceIntervention</span></code></a></p>
+<p>Collect activations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.CollectIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#CollectIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.CollectIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.CollectIntervention.__init__" title="pyvene.models.interventions.CollectIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.CollectIntervention.forward" title="pyvene.models.interventions.CollectIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, source, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.CollectIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#CollectIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.CollectIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.CollectIntervention"><code class="docutils literal notranslate"><span class="pre">CollectIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.CollectIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">CollectIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.CollectIntervention.forward"><code class="docutils literal notranslate"><span class="pre">CollectIntervention.forward()</span></code></a></li>
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diff --git a/api/pyvene.models.interventions.ConstantSourceIntervention.html b/api/pyvene.models.interventions.ConstantSourceIntervention.html
new file mode 100644
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+    <h1>pyvene.models.interventions.ConstantSourceIntervention</h1>
+    <!-- Table of contents -->
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+            
+            <div>
+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ConstantSourceIntervention"><code class="docutils literal notranslate"><span class="pre">ConstantSourceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ConstantSourceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">ConstantSourceIntervention.__init__()</span></code></a></li>
+</ul>
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+  <section id="pyvene-models-interventions-constantsourceintervention">
+<h1>pyvene.models.interventions.ConstantSourceIntervention<a class="headerlink" href="#pyvene-models-interventions-constantsourceintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.ConstantSourceIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ConstantSourceIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#ConstantSourceIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.ConstantSourceIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention" title="pyvene.models.interventions.Intervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">Intervention</span></code></a></p>
+<p>Constant source.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.ConstantSourceIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#ConstantSourceIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.ConstantSourceIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.ConstantSourceIntervention.__init__" title="pyvene.models.interventions.ConstantSourceIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
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+    <h1>pyvene.models.interventions.DistributedRepresentationIntervention</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.DistributedRepresentationIntervention"><code class="docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.DistributedRepresentationIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
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+                  
+  <section id="pyvene-models-interventions-distributedrepresentationintervention">
+<h1>pyvene.models.interventions.DistributedRepresentationIntervention<a class="headerlink" href="#pyvene-models-interventions-distributedrepresentationintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.DistributedRepresentationIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">DistributedRepresentationIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#DistributedRepresentationIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.DistributedRepresentationIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>Distributed representation.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.DistributedRepresentationIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#DistributedRepresentationIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.DistributedRepresentationIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.DistributedRepresentationIntervention.__init__" title="pyvene.models.interventions.DistributedRepresentationIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(*input)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.DistributedRepresentationIntervention"><code class="docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.DistributedRepresentationIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention.__init__()</span></code></a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.Intervention"><code class="docutils literal notranslate"><span class="pre">Intervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.Intervention.__init__"><code class="docutils literal notranslate"><span class="pre">Intervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.Intervention.forward"><code class="docutils literal notranslate"><span class="pre">Intervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+              
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+                  
+  <section id="pyvene-models-interventions-intervention">
+<h1>pyvene.models.interventions.Intervention<a class="headerlink" href="#pyvene-models-interventions-intervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.Intervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Intervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#Intervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.Intervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>Intervention the original representations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.Intervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#Intervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.Intervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.Intervention.__init__" title="pyvene.models.interventions.Intervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.Intervention.forward" title="pyvene.models.interventions.Intervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.Intervention.forward">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#Intervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.Intervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.Intervention"><code class="docutils literal notranslate"><span class="pre">Intervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.Intervention.__init__"><code class="docutils literal notranslate"><span class="pre">Intervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.Intervention.forward"><code class="docutils literal notranslate"><span class="pre">Intervention.forward()</span></code></a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.InterventionOutput"><code class="docutils literal notranslate"><span class="pre">InterventionOutput</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.InterventionOutput.__init__"><code class="docutils literal notranslate"><span class="pre">InterventionOutput.__init__()</span></code></a></li>
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+  <section id="pyvene-models-interventions-interventionoutput">
+<h1>pyvene.models.interventions.InterventionOutput<a class="headerlink" href="#pyvene-models-interventions-interventionoutput" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.InterventionOutput">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">InterventionOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latent</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#InterventionOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.InterventionOutput" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ModelOutput</span></code></p>
+<p>Output of the IntervenableModel, including original outputs, intervened outputs, and collected activations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.InterventionOutput.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latent</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.interventions.InterventionOutput.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.InterventionOutput.__init__" title="pyvene.models.interventions.InterventionOutput.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([output, latent])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
+<td><p>Create a new ordered dictionary with keys from iterable and values set to value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
+<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">move_to_end</span></code>(key[, last])</p></td>
+<td><p>Move an existing element to the end (or beginning if last is false).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(key[,default])</p></td>
+<td><p>If the key is not found, return the default if given; otherwise, raise a KeyError.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>([last])</p></td>
+<td><p>Remove and return a (key, value) pair from the dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(*args, **kwargs)</p></td>
+<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tuple</span></code>()</p></td>
+<td><p>Convert self to a tuple containing all the attributes/keys that are not <cite>None</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
+<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">latent</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">output</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
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+    <!-- Table of contents -->
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+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention"><code class="docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward"><code class="docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+                  
+  <section id="pyvene-models-interventions-jumpreluautoencoderintervention">
+<h1>pyvene.models.interventions.JumpReLUAutoencoderIntervention<a class="headerlink" href="#pyvene-models-interventions-jumpreluautoencoderintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.JumpReLUAutoencoderIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">JumpReLUAutoencoderIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#JumpReLUAutoencoderIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a></p>
+<p>Interchange intervention on JumpReLU SAE’s latent subspaces</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#JumpReLUAutoencoderIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__" title="pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">decode</span></code>(acts)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">encode</span></code>(input_acts)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward" title="pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, source, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#JumpReLUAutoencoderIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention"><code class="docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward"><code class="docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention.forward()</span></code></a></li>
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+    <h1>pyvene.models.interventions.LocalistRepresentationIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.LocalistRepresentationIntervention"><code class="docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.LocalistRepresentationIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
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+                  
+  <section id="pyvene-models-interventions-localistrepresentationintervention">
+<h1>pyvene.models.interventions.LocalistRepresentationIntervention<a class="headerlink" href="#pyvene-models-interventions-localistrepresentationintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.LocalistRepresentationIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LocalistRepresentationIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#LocalistRepresentationIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.LocalistRepresentationIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>Localist representation.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.LocalistRepresentationIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#LocalistRepresentationIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.LocalistRepresentationIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.LocalistRepresentationIntervention.__init__" title="pyvene.models.interventions.LocalistRepresentationIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(*input)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
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+    <h1>pyvene.models.interventions.LowRankRotatedSpaceIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">LowRankRotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">LowRankRotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">LowRankRotatedSpaceIntervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-interventions-lowrankrotatedspaceintervention">
+<h1>pyvene.models.interventions.LowRankRotatedSpaceIntervention<a class="headerlink" href="#pyvene-models-interventions-lowrankrotatedspaceintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.LowRankRotatedSpaceIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LowRankRotatedSpaceIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#LowRankRotatedSpaceIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention" title="pyvene.models.interventions.DistributedRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a></p>
+<p>Intervention in the rotated space.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#LowRankRotatedSpaceIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__" title="pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward" title="pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#LowRankRotatedSpaceIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.NoiseIntervention"><code class="docutils literal notranslate"><span class="pre">NoiseIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.NoiseIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">NoiseIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.NoiseIntervention.forward"><code class="docutils literal notranslate"><span class="pre">NoiseIntervention.forward()</span></code></a></li>
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+  <section id="pyvene-models-interventions-noiseintervention">
+<h1>pyvene.models.interventions.NoiseIntervention<a class="headerlink" href="#pyvene-models-interventions-noiseintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.NoiseIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">NoiseIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#NoiseIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.NoiseIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention" title="pyvene.models.interventions.ConstantSourceIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConstantSourceIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Noise intervention</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.NoiseIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#NoiseIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.NoiseIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.NoiseIntervention.__init__" title="pyvene.models.interventions.NoiseIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.NoiseIntervention.forward" title="pyvene.models.interventions.NoiseIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, source, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.NoiseIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#NoiseIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.NoiseIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.NoiseIntervention"><code class="docutils literal notranslate"><span class="pre">NoiseIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.NoiseIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">NoiseIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.NoiseIntervention.forward"><code class="docutils literal notranslate"><span class="pre">NoiseIntervention.forward()</span></code></a></li>
+</ul>
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+</ul>
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new file mode 100644
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+    <h1>pyvene.models.interventions.PCARotatedSpaceIntervention</h1>
+    <!-- Table of contents -->
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+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.PCARotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention.forward()</span></code></a></li>
+</ul>
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+                  
+  <section id="pyvene-models-interventions-pcarotatedspaceintervention">
+<h1>pyvene.models.interventions.PCARotatedSpaceIntervention<a class="headerlink" href="#pyvene-models-interventions-pcarotatedspaceintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.PCARotatedSpaceIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">PCARotatedSpaceIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#PCARotatedSpaceIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.PCARotatedSpaceIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention" title="pyvene.models.interventions.BasisAgnosticIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">BasisAgnosticIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention" title="pyvene.models.interventions.DistributedRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a></p>
+<p>Intervention in the pca space.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.PCARotatedSpaceIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#PCARotatedSpaceIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.__init__" title="pyvene.models.interventions.PCARotatedSpaceIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.forward" title="pyvene.models.interventions.PCARotatedSpaceIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.PCARotatedSpaceIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#PCARotatedSpaceIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.PCARotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.PCARotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention.forward()</span></code></a></li>
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diff --git a/api/pyvene.models.interventions.RotatedSpaceIntervention.html b/api/pyvene.models.interventions.RotatedSpaceIntervention.html
new file mode 100644
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+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+    <h1>pyvene.models.interventions.RotatedSpaceIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.RotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">RotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.RotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">RotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.RotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">RotatedSpaceIntervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+              
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+                  
+  <section id="pyvene-models-interventions-rotatedspaceintervention">
+<h1>pyvene.models.interventions.RotatedSpaceIntervention<a class="headerlink" href="#pyvene-models-interventions-rotatedspaceintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.RotatedSpaceIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">RotatedSpaceIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#RotatedSpaceIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.RotatedSpaceIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention" title="pyvene.models.interventions.DistributedRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a></p>
+<p>Intervention in the rotated space.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.RotatedSpaceIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#RotatedSpaceIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.RotatedSpaceIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.RotatedSpaceIntervention.__init__" title="pyvene.models.interventions.RotatedSpaceIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.RotatedSpaceIntervention.forward" title="pyvene.models.interventions.RotatedSpaceIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.RotatedSpaceIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#RotatedSpaceIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.RotatedSpaceIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.RotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">RotatedSpaceIntervention.forward()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+    <h1>pyvene.models.interventions.SharedWeightsTrainableIntervention</h1>
+    <!-- Table of contents -->
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+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SharedWeightsTrainableIntervention"><code class="docutils literal notranslate"><span class="pre">SharedWeightsTrainableIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SharedWeightsTrainableIntervention.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+              
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+                  
+  <section id="pyvene-models-interventions-sharedweightstrainableintervention">
+<h1>pyvene.models.interventions.SharedWeightsTrainableIntervention<a class="headerlink" href="#pyvene-models-interventions-sharedweightstrainableintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SharedWeightsTrainableIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SharedWeightsTrainableIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SharedWeightsTrainableIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SharedWeightsTrainableIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a></p>
+<p>Intervention the original representations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SharedWeightsTrainableIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__" title="pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskIntervention"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskIntervention.forward()</span></code></a></li>
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+  <section id="pyvene-models-interventions-sigmoidmaskintervention">
+<h1>pyvene.models.interventions.SigmoidMaskIntervention<a class="headerlink" href="#pyvene-models-interventions-sigmoidmaskintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SigmoidMaskIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SigmoidMaskIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SigmoidMaskIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SigmoidMaskIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Intervention in the original basis with binary mask.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SigmoidMaskIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SigmoidMaskIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SigmoidMaskIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.SigmoidMaskIntervention.__init__" title="pyvene.models.interventions.SigmoidMaskIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.SigmoidMaskIntervention.forward" title="pyvene.models.interventions.SigmoidMaskIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_temperature</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_temperature</span></code>(temp)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SigmoidMaskIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SigmoidMaskIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SigmoidMaskIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskIntervention"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskIntervention.forward()</span></code></a></li>
+</ul>
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new file mode 100644
index 00000000..70daff26
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+    <h1>pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+                  
+  <section id="pyvene-models-interventions-sigmoidmaskrotatedspaceintervention">
+<h1>pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention<a class="headerlink" href="#pyvene-models-interventions-sigmoidmaskrotatedspaceintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SigmoidMaskRotatedSpaceIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SigmoidMaskRotatedSpaceIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention" title="pyvene.models.interventions.DistributedRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a></p>
+<p>Intervention in the rotated space with boundary mask.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SigmoidMaskRotatedSpaceIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__" title="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward" title="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_boundary_parameters</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_temperature</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_temperature</span></code>(temp)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SigmoidMaskRotatedSpaceIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention.forward()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+    <h1>pyvene.models.interventions.SkipIntervention</h1>
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+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SkipIntervention"><code class="docutils literal notranslate"><span class="pre">SkipIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SkipIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SkipIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SkipIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SkipIntervention.forward()</span></code></a></li>
+</ul>
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+  <section id="pyvene-models-interventions-skipintervention">
+<h1>pyvene.models.interventions.SkipIntervention<a class="headerlink" href="#pyvene-models-interventions-skipintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SkipIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SkipIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SkipIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SkipIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention" title="pyvene.models.interventions.BasisAgnosticIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">BasisAgnosticIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Skip the current intervening layer’s computation in the hook function.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SkipIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SkipIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SkipIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.SkipIntervention.__init__" title="pyvene.models.interventions.SkipIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.SkipIntervention.forward" title="pyvene.models.interventions.SkipIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SkipIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SkipIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SkipIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
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+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SkipIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SkipIntervention.forward()</span></code></a></li>
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new file mode 100644
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+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
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+    <h1>pyvene.models.interventions.SourcelessIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SourcelessIntervention"><code class="docutils literal notranslate"><span class="pre">SourcelessIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SourcelessIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SourcelessIntervention.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-interventions-sourcelessintervention">
+<h1>pyvene.models.interventions.SourcelessIntervention<a class="headerlink" href="#pyvene-models-interventions-sourcelessintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SourcelessIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SourcelessIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SourcelessIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SourcelessIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention" title="pyvene.models.interventions.Intervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">Intervention</span></code></a></p>
+<p>No source.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SourcelessIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SourcelessIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SourcelessIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.SourcelessIntervention.__init__" title="pyvene.models.interventions.SourcelessIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SubtractionIntervention"><code class="docutils literal notranslate"><span class="pre">SubtractionIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SubtractionIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SubtractionIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SubtractionIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SubtractionIntervention.forward()</span></code></a></li>
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+  <section id="pyvene-models-interventions-subtractionintervention">
+<h1>pyvene.models.interventions.SubtractionIntervention<a class="headerlink" href="#pyvene-models-interventions-subtractionintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SubtractionIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SubtractionIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SubtractionIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SubtractionIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention" title="pyvene.models.interventions.BasisAgnosticIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">BasisAgnosticIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Intervention the original representations with activation subtraction.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SubtractionIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SubtractionIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SubtractionIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.SubtractionIntervention.__init__" title="pyvene.models.interventions.SubtractionIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.SubtractionIntervention.forward" title="pyvene.models.interventions.SubtractionIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.SubtractionIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#SubtractionIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.SubtractionIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SubtractionIntervention"><code class="docutils literal notranslate"><span class="pre">SubtractionIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SubtractionIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">SubtractionIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.SubtractionIntervention.forward"><code class="docutils literal notranslate"><span class="pre">SubtractionIntervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+  </nav></div>
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new file mode 100644
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+    <h1>pyvene.models.interventions.TrainableIntervention</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.TrainableIntervention"><code class="docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.TrainableIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">TrainableIntervention.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+              
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+  <section id="pyvene-models-interventions-trainableintervention">
+<h1>pyvene.models.interventions.TrainableIntervention<a class="headerlink" href="#pyvene-models-interventions-trainableintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.TrainableIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">TrainableIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#TrainableIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.TrainableIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention" title="pyvene.models.interventions.Intervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">Intervention</span></code></a></p>
+<p>Intervention the original representations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.TrainableIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#TrainableIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.TrainableIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.TrainableIntervention.__init__" title="pyvene.models.interventions.TrainableIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weight</span></code>(linked_intervention)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+    <h1>pyvene.models.interventions.VanillaIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
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+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.VanillaIntervention"><code class="docutils literal notranslate"><span class="pre">VanillaIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.VanillaIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">VanillaIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.VanillaIntervention.forward"><code class="docutils literal notranslate"><span class="pre">VanillaIntervention.forward()</span></code></a></li>
+</ul>
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+</ul>
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+
+              
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-interventions-vanillaintervention">
+<h1>pyvene.models.interventions.VanillaIntervention<a class="headerlink" href="#pyvene-models-interventions-vanillaintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.VanillaIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">VanillaIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#VanillaIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.VanillaIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention" title="pyvene.models.interventions.Intervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">Intervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Intervention the original representations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.VanillaIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#VanillaIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.VanillaIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.VanillaIntervention.__init__" title="pyvene.models.interventions.VanillaIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.VanillaIntervention.forward" title="pyvene.models.interventions.VanillaIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base, source[, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.VanillaIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#VanillaIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.VanillaIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.VanillaIntervention.forward"><code class="docutils literal notranslate"><span class="pre">VanillaIntervention.forward()</span></code></a></li>
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new file mode 100644
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
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+    </div>
+  
+</div>
+</div>
+              
+              
+
+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>pyvene.models.interventions.ZeroIntervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ZeroIntervention"><code class="docutils literal notranslate"><span class="pre">ZeroIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ZeroIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">ZeroIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ZeroIntervention.forward"><code class="docutils literal notranslate"><span class="pre">ZeroIntervention.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-interventions-zerointervention">
+<h1>pyvene.models.interventions.ZeroIntervention<a class="headerlink" href="#pyvene-models-interventions-zerointervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.interventions.ZeroIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ZeroIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#ZeroIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.ZeroIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention" title="pyvene.models.interventions.ConstantSourceIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConstantSourceIntervention</span></code></a>, <a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a></p>
+<p>Zero-out activations.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.ZeroIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#ZeroIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.ZeroIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.interventions.ZeroIntervention.__init__" title="pyvene.models.interventions.ZeroIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(**kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.interventions.ZeroIntervention.forward" title="pyvene.models.interventions.ZeroIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, source, subspaces])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_interchange_dim</span></code>(interchange_dim)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_source_representation</span></code>(source_representation)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.interventions.ZeroIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/interventions.html#ZeroIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.interventions.ZeroIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ZeroIntervention"><code class="docutils literal notranslate"><span class="pre">ZeroIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ZeroIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">ZeroIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.interventions.ZeroIntervention.forward"><code class="docutils literal notranslate"><span class="pre">ZeroIntervention.forward()</span></code></a></li>
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+  <section id="module-pyvene.models.interventions">
+<span id="pyvene-models-interventions"></span><h1>pyvene.models.interventions<a class="headerlink" href="#module-pyvene.models.interventions" title="Link to this heading">#</a></h1>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention" title="pyvene.models.interventions.AdditionIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AdditionIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention the original representations with activation addition.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention" title="pyvene.models.interventions.AutoencoderIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AutoencoderIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervene in the latent space of an autoencoder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention" title="pyvene.models.interventions.BasisAgnosticIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BasisAgnosticIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention that will modify its basis in a uncontrolled manner.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention" title="pyvene.models.interventions.BoundlessRotatedSpaceIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BoundlessRotatedSpaceIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention in the rotated space with boundary mask.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention" title="pyvene.models.interventions.CollectIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CollectIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Collect activations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention" title="pyvene.models.interventions.ConstantSourceIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConstantSourceIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Constant source.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention" title="pyvene.models.interventions.DistributedRepresentationIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistributedRepresentationIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Distributed representation.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention" title="pyvene.models.interventions.Intervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Intervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention the original representations.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.InterventionOutput.html#pyvene.models.interventions.InterventionOutput" title="pyvene.models.interventions.InterventionOutput"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InterventionOutput</span></code></a>([output, latent])</p></td>
+<td><p>Output of the IntervenableModel, including original outputs, intervened outputs, and collected activations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention" title="pyvene.models.interventions.JumpReLUAutoencoderIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JumpReLUAutoencoderIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Interchange intervention on JumpReLU SAE's latent subspaces</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention" title="pyvene.models.interventions.LocalistRepresentationIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LocalistRepresentationIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Localist representation.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention" title="pyvene.models.interventions.LowRankRotatedSpaceIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LowRankRotatedSpaceIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention in the rotated space.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention" title="pyvene.models.interventions.NoiseIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NoiseIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Noise intervention</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention" title="pyvene.models.interventions.PCARotatedSpaceIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PCARotatedSpaceIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention in the pca space.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention" title="pyvene.models.interventions.RotatedSpaceIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RotatedSpaceIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention in the rotated space.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.SharedWeightsTrainableIntervention.html#pyvene.models.interventions.SharedWeightsTrainableIntervention" title="pyvene.models.interventions.SharedWeightsTrainableIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SharedWeightsTrainableIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention the original representations.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention" title="pyvene.models.interventions.SigmoidMaskIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SigmoidMaskIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention in the original basis with binary mask.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention" title="pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SigmoidMaskRotatedSpaceIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention in the rotated space with boundary mask.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention" title="pyvene.models.interventions.SkipIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SkipIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Skip the current intervening layer's computation in the hook function.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.SourcelessIntervention.html#pyvene.models.interventions.SourcelessIntervention" title="pyvene.models.interventions.SourcelessIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SourcelessIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>No source.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention" title="pyvene.models.interventions.SubtractionIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SubtractionIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention the original representations with activation subtraction.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention" title="pyvene.models.interventions.TrainableIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TrainableIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention the original representations.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention" title="pyvene.models.interventions.VanillaIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VanillaIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Intervention the original representations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention" title="pyvene.models.interventions.ZeroIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ZeroIntervention</span></code></a>(**kwargs)</p></td>
+<td><p>Zero-out activations.</p></td>
+</tr>
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+                <h2> Contents </h2>
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+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayer"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayer.__init__"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayer.forward"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayer.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-layers-autoencoderlayer">
+<h1>pyvene.models.layers.AutoencoderLayer<a class="headerlink" href="#pyvene-models-layers-autoencoderlayer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.layers.AutoencoderLayer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AutoencoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latent_dim</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#AutoencoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.AutoencoderLayer" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="pyvene.models.layers.AutoencoderLayerBase.html#pyvene.models.layers.AutoencoderLayerBase" title="pyvene.models.layers.AutoencoderLayerBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">AutoencoderLayerBase</span></code></a></p>
+<p>An autoencoder with a single-layer encoder and single-layer decoder.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.AutoencoderLayer.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latent_dim</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#AutoencoderLayer.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.AutoencoderLayer.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.layers.AutoencoderLayer.__init__" title="pyvene.models.layers.AutoencoderLayer.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(input_dim, latent_dim, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">decode</span></code>(latent)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">encode</span></code>(x)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.layers.AutoencoderLayer.forward" title="pyvene.models.layers.AutoencoderLayer.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(base[, return_latent])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.AutoencoderLayer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_latent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#AutoencoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.AutoencoderLayer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayer"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayer.__init__"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayer.forward"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayer.forward()</span></code></a></li>
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diff --git a/api/pyvene.models.layers.AutoencoderLayerBase.html b/api/pyvene.models.layers.AutoencoderLayerBase.html
new file mode 100644
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+    <h1>pyvene.models.layers.AutoencoderLayerBase</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayerBase"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayerBase</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.AutoencoderLayerBase.__init__"><code class="docutils literal notranslate"><span class="pre">AutoencoderLayerBase.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+    </div>
+</div>
+
+              
+                
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+                  
+  <section id="pyvene-models-layers-autoencoderlayerbase">
+<h1>pyvene.models.layers.AutoencoderLayerBase<a class="headerlink" href="#pyvene-models-layers-autoencoderlayerbase" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.layers.AutoencoderLayerBase">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AutoencoderLayerBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#AutoencoderLayerBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.AutoencoderLayerBase" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>An abstract base class that defines an interface of an autoencoder.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.AutoencoderLayerBase.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.layers.AutoencoderLayerBase.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.layers.AutoencoderLayerBase.__init__" title="pyvene.models.layers.AutoencoderLayerBase.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(*args, **kwargs)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">decode</span></code>(latent)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">encode</span></code>(x)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code>(*input)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
+
+
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+    <h1>pyvene.models.layers.InverseRotateLayer</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.InverseRotateLayer"><code class="docutils literal notranslate"><span class="pre">InverseRotateLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.InverseRotateLayer.__init__"><code class="docutils literal notranslate"><span class="pre">InverseRotateLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.InverseRotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">InverseRotateLayer.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+    </div>
+</div>
+
+              
+                
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-layers-inverserotatelayer">
+<h1>pyvene.models.layers.InverseRotateLayer<a class="headerlink" href="#pyvene-models-layers-inverserotatelayer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.layers.InverseRotateLayer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">InverseRotateLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lin_layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#InverseRotateLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.InverseRotateLayer" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>The inverse of a given <cite>LinearLayer</cite> module.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.InverseRotateLayer.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lin_layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#InverseRotateLayer.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.InverseRotateLayer.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.layers.InverseRotateLayer.__init__" title="pyvene.models.layers.InverseRotateLayer.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(lin_layer)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.layers.InverseRotateLayer.forward" title="pyvene.models.layers.InverseRotateLayer.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.InverseRotateLayer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#InverseRotateLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.InverseRotateLayer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.InverseRotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">InverseRotateLayer.forward()</span></code></a></li>
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>pyvene.models.layers.LowRankRotateLayer</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.LowRankRotateLayer"><code class="docutils literal notranslate"><span class="pre">LowRankRotateLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.LowRankRotateLayer.__init__"><code class="docutils literal notranslate"><span class="pre">LowRankRotateLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.LowRankRotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">LowRankRotateLayer.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-layers-lowrankrotatelayer">
+<h1>pyvene.models.layers.LowRankRotateLayer<a class="headerlink" href="#pyvene-models-layers-lowrankrotatelayer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.layers.LowRankRotateLayer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LowRankRotateLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_orth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#LowRankRotateLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.LowRankRotateLayer" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>A linear transformation with orthogonal initialization.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.LowRankRotateLayer.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_orth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#LowRankRotateLayer.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.LowRankRotateLayer.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.layers.LowRankRotateLayer.__init__" title="pyvene.models.layers.LowRankRotateLayer.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(n, m[, init_orth])</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.layers.LowRankRotateLayer.forward" title="pyvene.models.layers.LowRankRotateLayer.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.LowRankRotateLayer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#LowRankRotateLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.LowRankRotateLayer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.LowRankRotateLayer"><code class="docutils literal notranslate"><span class="pre">LowRankRotateLayer</span></code></a><ul class="nav section-nav flex-column">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.RotateLayer"><code class="docutils literal notranslate"><span class="pre">RotateLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.RotateLayer.__init__"><code class="docutils literal notranslate"><span class="pre">RotateLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.RotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">RotateLayer.forward()</span></code></a></li>
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+                  
+  <section id="pyvene-models-layers-rotatelayer">
+<h1>pyvene.models.layers.RotateLayer<a class="headerlink" href="#pyvene-models-layers-rotatelayer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.layers.RotateLayer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">RotateLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_orth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#RotateLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.RotateLayer" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>A linear transformation with orthogonal initialization.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.RotateLayer.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_orth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#RotateLayer.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.RotateLayer.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.layers.RotateLayer.__init__" title="pyvene.models.layers.RotateLayer.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(n[, init_orth])</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.layers.RotateLayer.forward" title="pyvene.models.layers.RotateLayer.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.RotateLayer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#RotateLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.RotateLayer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.RotateLayer"><code class="docutils literal notranslate"><span class="pre">RotateLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.RotateLayer.__init__"><code class="docutils literal notranslate"><span class="pre">RotateLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.RotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">RotateLayer.forward()</span></code></a></li>
+</ul>
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+</ul>
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new file mode 100644
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+    <h1>pyvene.models.layers.SubspaceLowRankRotateLayer</h1>
+    <!-- Table of contents -->
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+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.SubspaceLowRankRotateLayer"><code class="docutils literal notranslate"><span class="pre">SubspaceLowRankRotateLayer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.__init__"><code class="docutils literal notranslate"><span class="pre">SubspaceLowRankRotateLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">SubspaceLowRankRotateLayer.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+            </nav>
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-layers-subspacelowrankrotatelayer">
+<h1>pyvene.models.layers.SubspaceLowRankRotateLayer<a class="headerlink" href="#pyvene-models-layers-subspacelowrankrotatelayer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.layers.SubspaceLowRankRotateLayer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SubspaceLowRankRotateLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_orth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#SubspaceLowRankRotateLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.SubspaceLowRankRotateLayer" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>A linear transformation with orthogonal initialization with subspace.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.SubspaceLowRankRotateLayer.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_orth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#SubspaceLowRankRotateLayer.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.__init__" title="pyvene.models.layers.SubspaceLowRankRotateLayer.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(n, m[, init_orth])</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.forward" title="pyvene.models.layers.SubspaceLowRankRotateLayer.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x, l, r)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.layers.SubspaceLowRankRotateLayer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/layers.html#SubspaceLowRankRotateLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+                </article>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.__init__"><code class="docutils literal notranslate"><span class="pre">SubspaceLowRankRotateLayer.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.layers.SubspaceLowRankRotateLayer.forward"><code class="docutils literal notranslate"><span class="pre">SubspaceLowRankRotateLayer.forward()</span></code></a></li>
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+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
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+  <section id="module-pyvene.models.layers">
+<span id="pyvene-models-layers"></span><h1>pyvene.models.layers<a class="headerlink" href="#module-pyvene.models.layers" title="Link to this heading">#</a></h1>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer" title="pyvene.models.layers.AutoencoderLayer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AutoencoderLayer</span></code></a>(input_dim, latent_dim, **kwargs)</p></td>
+<td><p>An autoencoder with a single-layer encoder and single-layer decoder.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.layers.AutoencoderLayerBase.html#pyvene.models.layers.AutoencoderLayerBase" title="pyvene.models.layers.AutoencoderLayerBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AutoencoderLayerBase</span></code></a>(*args, **kwargs)</p></td>
+<td><p>An abstract base class that defines an interface of an autoencoder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer" title="pyvene.models.layers.InverseRotateLayer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InverseRotateLayer</span></code></a>(lin_layer)</p></td>
+<td><p>The inverse of a given <cite>LinearLayer</cite> module.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer" title="pyvene.models.layers.LowRankRotateLayer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LowRankRotateLayer</span></code></a>(n, m[, init_orth])</p></td>
+<td><p>A linear transformation with orthogonal initialization.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer" title="pyvene.models.layers.RotateLayer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RotateLayer</span></code></a>(n[, init_orth])</p></td>
+<td><p>A linear transformation with orthogonal initialization.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer" title="pyvene.models.layers.SubspaceLowRankRotateLayer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SubspaceLowRankRotateLayer</span></code></a>(n, m[, init_orth])</p></td>
+<td><p>A linear transformation with orthogonal initialization with subspace.</p></td>
+</tr>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+<span id="pyvene-models-llama"></span><h1>pyvene.models.llama<a class="headerlink" href="#module-pyvene.models.llama" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llama.modelings_intervenable_llama.html#module-pyvene.models.llama.modelings_intervenable_llama" title="pyvene.models.llama.modelings_intervenable_llama"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.llama.modelings_intervenable_llama</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+    <h1>pyvene.models.llama.modelings_intervenable_llama.create_llama</h1>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.llama.modelings_intervenable_llama.create_llama"><code class="docutils literal notranslate"><span class="pre">create_llama()</span></code></a></li>
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+<h1>pyvene.models.llama.modelings_intervenable_llama.create_llama<a class="headerlink" href="#pyvene-models-llama-modelings-intervenable-llama-create-llama" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.llama.modelings_intervenable_llama.create_llama">
+<span class="sig-name descname"><span class="pre">create_llama</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sharpbai/alpaca-7b-merged'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/llama/modelings_intervenable_llama.html#create_llama"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.llama.modelings_intervenable_llama.create_llama" title="Link to this definition">#</a></dt>
+<dd><p>Creates a LLaMA Causal LM model, config, and tokenizer from the given name and revision</p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
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+    <h1>pyvene.models.llama.modelings_intervenable_llama</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
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+  <section id="module-pyvene.models.llama.modelings_intervenable_llama">
+<span id="pyvene-models-llama-modelings-intervenable-llama"></span><h1>pyvene.models.llama.modelings_intervenable_llama<a class="headerlink" href="#module-pyvene.models.llama.modelings_intervenable_llama" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping.html#pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping" title="pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">llama_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>llama model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping.html#pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping" title="pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">llama_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>llama model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llama.modelings_intervenable_llama.create_llama.html#pyvene.models.llama.modelings_intervenable_llama.create_llama" title="pyvene.models.llama.modelings_intervenable_llama.create_llama"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_llama</span></code></a>([name, cache_dir, dtype, config])</p></td>
+<td><p>Creates a LLaMA Causal LM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
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+
+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+<h1>pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-llama-modelings-intervenable-llama-llama-lm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">llama_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>llama model with classifier head</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping"><code class="docutils literal notranslate"><span class="pre">llama_type_to_dimension_mapping</span></code></a></li>
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+  <section id="pyvene-models-llama-modelings-intervenable-llama-llama-type-to-dimension-mapping">
+<h1>pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-llama-modelings-intervenable-llama-llama-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">llama_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>llama model with LM head</p>
+</dd></dl>
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+<span id="pyvene-models-llava"></span><h1>pyvene.models.llava<a class="headerlink" href="#module-pyvene.models.llava" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llava.modelings_intervenable_llava.html#module-pyvene.models.llava.modelings_intervenable_llava" title="pyvene.models.llava.modelings_intervenable_llava"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.llava.modelings_intervenable_llava</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+  <section id="pyvene-models-llava-modelings-intervenable-llava-create-llava">
+<h1>pyvene.models.llava.modelings_intervenable_llava.create_llava<a class="headerlink" href="#pyvene-models-llava-modelings-intervenable-llava-create-llava" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.llava.modelings_intervenable_llava.create_llava">
+<span class="sig-name descname"><span class="pre">create_llava</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'llava-hf/llava-1.5-7b-hf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/llava/modelings_intervenable_llava.html#create_llava"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.llava.modelings_intervenable_llava.create_llava" title="Link to this definition">#</a></dt>
+<dd><p>Creates a llava Causal LM model, config, and tokenizer from the given name and revision</p>
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+  <section id="module-pyvene.models.llava.modelings_intervenable_llava">
+<span id="pyvene-models-llava-modelings-intervenable-llava"></span><h1>pyvene.models.llava.modelings_intervenable_llava<a class="headerlink" href="#module-pyvene.models.llava.modelings_intervenable_llava" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping.html#pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping" title="pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">llava_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>llava model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping.html#pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping" title="pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">llava_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>llava model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.llava.modelings_intervenable_llava.create_llava.html#pyvene.models.llava.modelings_intervenable_llava.create_llava" title="pyvene.models.llava.modelings_intervenable_llava.create_llava"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_llava</span></code></a>([name, cache_dir, dtype])</p></td>
+<td><p>Creates a llava Causal LM model, config, and tokenizer from the given name and revision</p></td>
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+<h1>pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-llava-modelings-intervenable-llava-llava-lm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">llava_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('text_config.intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('text_config.num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('text_config.num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('text_config.hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>llava model with classifier head</p>
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+  <section id="pyvene-models-llava-modelings-intervenable-llava-llava-type-to-dimension-mapping">
+<h1>pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-llava-modelings-intervenable-llava-llava-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">llava_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('text_config.hidden_size/text_config.num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('text_config.intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('text_config.num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('text_config.num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('text_config.hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('text_config.hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>llava model with LM head</p>
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+  <section id="module-pyvene.models.mistral">
+<span id="pyvene-models-mistral"></span><h1>pyvene.models.mistral<a class="headerlink" href="#module-pyvene.models.mistral" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mistral.modellings_intervenable_mistral.html#module-pyvene.models.mistral.modellings_intervenable_mistral" title="pyvene.models.mistral.modellings_intervenable_mistral"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.mistral.modellings_intervenable_mistral</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+    <h1>pyvene.models.mistral.modellings_intervenable_mistral.create_mistral</h1>
+    <!-- Table of contents -->
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mistral.modellings_intervenable_mistral.create_mistral"><code class="docutils literal notranslate"><span class="pre">create_mistral()</span></code></a></li>
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+  <section id="pyvene-models-mistral-modellings-intervenable-mistral-create-mistral">
+<h1>pyvene.models.mistral.modellings_intervenable_mistral.create_mistral<a class="headerlink" href="#pyvene-models-mistral-modellings-intervenable-mistral-create-mistral" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.mistral.modellings_intervenable_mistral.create_mistral">
+<span class="sig-name descname"><span class="pre">create_mistral</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mistralai/Mistral-7B-v0.1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mistral/modellings_intervenable_mistral.html#create_mistral"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mistral.modellings_intervenable_mistral.create_mistral" title="Link to this definition">#</a></dt>
+<dd><p>Creates a Mistral Causal LM model, config, and tokenizer from the given name and revision</p>
+</dd></dl>
+
+</section>
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+                  
+  <section id="module-pyvene.models.mistral.modellings_intervenable_mistral">
+<span id="pyvene-models-mistral-modellings-intervenable-mistral"></span><h1>pyvene.models.mistral.modellings_intervenable_mistral<a class="headerlink" href="#module-pyvene.models.mistral.modellings_intervenable_mistral" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping.html#pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping" title="pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mistral_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>mistral model with LM head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mistral.modellings_intervenable_mistral.create_mistral.html#pyvene.models.mistral.modellings_intervenable_mistral.create_mistral" title="pyvene.models.mistral.modellings_intervenable_mistral.create_mistral"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_mistral</span></code></a>([name, cache_dir])</p></td>
+<td><p>Creates a Mistral Causal LM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping"><code class="docutils literal notranslate"><span class="pre">mistral_type_to_dimension_mapping</span></code></a></li>
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+  <section id="pyvene-models-mistral-modellings-intervenable-mistral-mistral-type-to-dimension-mapping">
+<h1>pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-mistral-modellings-intervenable-mistral-mistral-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">mistral_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>mistral model with LM head</p>
+</dd></dl>
+
+</section>
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+
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+  <section id="module-pyvene.models.mlp">
+<span id="pyvene-models-mlp"></span><h1>pyvene.models.mlp<a class="headerlink" href="#module-pyvene.models.mlp" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mlp.modelings_intervenable_mlp.html#module-pyvene.models.mlp.modelings_intervenable_mlp" title="pyvene.models.mlp.modelings_intervenable_mlp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.mlp.modelings_intervenable_mlp</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.mlp.modelings_mlp.html#module-pyvene.models.mlp.modelings_mlp" title="pyvene.models.mlp.modelings_mlp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.mlp.modelings_mlp</span></code></a></p></td>
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diff --git a/api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.html b/api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.html
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+<h1>pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier<a class="headerlink" href="#pyvene-models-mlp-modelings-intervenable-mlp-create-mlp-classifier" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">create_mlp_classifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_intervenable_mlp.html#create_mlp_classifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier" title="Link to this definition">#</a></dt>
+<dd><p>Creates a MLP model, config, and tokenizer from the given name and revision</p>
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+                  
+  <section id="module-pyvene.models.mlp.modelings_intervenable_mlp">
+<span id="pyvene-models-mlp-modelings-intervenable-mlp"></span><h1>pyvene.models.mlp.modelings_intervenable_mlp<a class="headerlink" href="#module-pyvene.models.mlp.modelings_intervenable_mlp" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping.html#pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping" title="pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mlp_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>mlp model with classification head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.html#pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier" title="pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_mlp_classifier</span></code></a>(config[, ...])</p></td>
+<td><p>Creates a MLP model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+<h1>pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-mlp-modelings-intervenable-mlp-mlp-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">mlp_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'block_input':</span> <span class="pre">('h_dim',),</span> <span class="pre">'block_output':</span> <span class="pre">('h_dim',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('h_dim',)}</span></em><a class="headerlink" href="#pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>mlp model with classification head</p>
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+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPBlock"><code class="docutils literal notranslate"><span class="pre">MLPBlock</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.__init__"><code class="docutils literal notranslate"><span class="pre">MLPBlock.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.forward"><code class="docutils literal notranslate"><span class="pre">MLPBlock.forward()</span></code></a></li>
+</ul>
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+</ul>
+            </nav>
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+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="pyvene-models-mlp-modelings-mlp-mlpblock">
+<h1>pyvene.models.mlp.modelings_mlp.MLPBlock<a class="headerlink" href="#pyvene-models-mlp-modelings-mlp-mlpblock" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPBlock">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">MLPBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPBlock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPBlock" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPBlock.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPBlock.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.__init__" title="pyvene.models.mlp.modelings_mlp.MLPBlock.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.forward" title="pyvene.models.mlp.modelings_mlp.MLPBlock.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(hidden_states)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPBlock.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPBlock.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPBlock.forward"><code class="docutils literal notranslate"><span class="pre">MLPBlock.forward()</span></code></a></li>
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+    <h1>pyvene.models.mlp.modelings_mlp.MLPConfig</h1>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPConfig"><code class="docutils literal notranslate"><span class="pre">MLPConfig</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPConfig.__init__"><code class="docutils literal notranslate"><span class="pre">MLPConfig.__init__()</span></code></a></li>
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+  <section id="pyvene-models-mlp-modelings-mlp-mlpconfig">
+<h1>pyvene.models.mlp.modelings_mlp.MLPConfig<a class="headerlink" href="#pyvene-models-mlp-modelings-mlp-mlpconfig" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">MLPConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include_emb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50257</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_position_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gelu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdrop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">problem_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single_label_classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squeeze_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPConfig" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">PretrainedConfig</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPConfig.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include_emb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50257</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_position_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gelu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdrop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">problem_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'single_label_classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">squeeze_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPConfig.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPConfig.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPConfig.__init__" title="pyvene.models.mlp.modelings_mlp.MLPConfig.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([include_emb, vocab_size, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dict_torch_dtype_to_str</span></code>(d)</p></td>
+<td><p>Checks whether the passed dictionary and its nested dicts have a <em>torch_dtype</em> key and if it's not None, converts torch.dtype to a string of just the type.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dict</span></code>(config_dict, **kwargs)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from a Python dictionary of parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_json_file</span></code>(json_file)</p></td>
+<td><p>Instantiates a [<cite>PretrainedConfig</cite>] from the path to a JSON file of parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(pretrained_model_name_or_path)</p></td>
+<td><p>Instantiate a [<cite>PretrainedConfig</cite>] (or a derived class) from a pretrained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config_dict</span></code>(...)</p></td>
+<td><p>From a <cite>pretrained_model_name_or_path</cite>, resolve to a dictionary of parameters, to be used for instantiating a [<cite>PretrainedConfig</cite>] using <cite>from_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_text_config</span></code>([decoder])</p></td>
+<td><p>Returns the config that is meant to be used with text IO.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the configuration file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, push_to_hub])</p></td>
+<td><p>Save a configuration object to the directory <cite>save_directory</cite>, so that it can be re-loaded using the [<cite>~PretrainedConfig.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_dict</span></code>()</p></td>
+<td><p>Serializes this instance to a Python dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_diff_dict</span></code>()</p></td>
+<td><p>Removes all attributes from config which correspond to the default config attributes for better readability and serializes to a Python dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_file</span></code>(json_file_path[, use_diff])</p></td>
+<td><p>Save this instance to a JSON file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_json_string</span></code>([use_diff])</p></td>
+<td><p>Serializes this instance to a JSON string.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>(config_dict)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>config_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_from_string</span></code>(update_str)</p></td>
+<td><p>Updates attributes of this class with attributes from <cite>update_str</cite>.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">attribute_map</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_config_key</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_tp_plan</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_composition</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_type</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">name_or_path</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_labels</span></code></p></td>
+<td><p><cite>int</cite>: The number of labels for classification models.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sub_configs</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_return_dict</span></code></p></td>
+<td><p><cite>bool</cite>: Whether or not return [<cite>~utils.ModelOutput</cite>] instead of tuples.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
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+    <!-- Table of contents -->
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification"><code class="docutils literal notranslate"><span class="pre">MLPForClassification</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__"><code class="docutils literal notranslate"><span class="pre">MLPForClassification.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.forward"><code class="docutils literal notranslate"><span class="pre">MLPForClassification.forward()</span></code></a></li>
+</ul>
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+</ul>
+            </nav>
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+                <article class="bd-article">
+                  
+  <section id="pyvene-models-mlp-modelings-mlp-mlpforclassification">
+<h1>pyvene.models.mlp.modelings_mlp.MLPForClassification<a class="headerlink" href="#pyvene-models-mlp-modelings-mlp-mlpforclassification" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPForClassification">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">MLPForClassification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPForClassification"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">PreTrainedModel</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPForClassification.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__" title="pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.forward" title="pyvene.models.mlp.modelings_mlp.MLPForClassification.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>([input_ids, position_ids, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_class</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPForClassification.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPForClassification.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification"><code class="docutils literal notranslate"><span class="pre">MLPForClassification</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__"><code class="docutils literal notranslate"><span class="pre">MLPForClassification.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPForClassification.forward"><code class="docutils literal notranslate"><span class="pre">MLPForClassification.forward()</span></code></a></li>
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+    <h1>pyvene.models.mlp.modelings_mlp.MLPModel</h1>
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+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPModel"><code class="docutils literal notranslate"><span class="pre">MLPModel</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPModel.__init__"><code class="docutils literal notranslate"><span class="pre">MLPModel.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPModel.forward"><code class="docutils literal notranslate"><span class="pre">MLPModel.forward()</span></code></a></li>
+</ul>
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+  <section id="pyvene-models-mlp-modelings-mlp-mlpmodel">
+<h1>pyvene.models.mlp.modelings_mlp.MLPModel<a class="headerlink" href="#pyvene-models-mlp-modelings-mlp-mlpmodel" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">MLPModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPModel" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">PreTrainedModel</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPModel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPModel.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPModel.__init__" title="pyvene.models.mlp.modelings_mlp.MLPModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(config)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapter</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">active_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_adapter</span></code>(adapter_config[, adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_memory_hooks</span></code>()</p></td>
+<td><p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_model_tags</span></code>(tags)</p></td>
+<td><p>Add custom tags into the model that gets pushed to the Hugging Face Hub.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_generate</span></code>()</p></td>
+<td><p>Returns whether this model can generate sequences with <cite>.generate()</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_scores</span></code>(sequences, scores)</p></td>
+<td><p>Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_extended_attention_mask_for_decoder</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">delete_adapter</span></code>(adapter_names)</p></td>
+<td><p>Delete an adapter's LoRA layers from the underlying model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code>()</p></td>
+<td><p>Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">disable_input_require_grads</span></code>()</p></td>
+<td><p>Removes the <cite>_require_grads_hook</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_adapters</span></code>()</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_input_require_grads</span></code>()</p></td>
+<td><p>Enables the gradients for the input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimate_tokens</span></code>(input_dict)</p></td>
+<td><p>Helper function to estimate the total number of tokens from the model inputs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating_point_ops</span></code>(input_dict[, ...])</p></td>
+<td><p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPModel.forward" title="pyvene.models.mlp.modelings_mlp.MLPModel.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>([input_ids, position_ids, ...])</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_pretrained</span></code>(...[, config, cache_dir, ...])</p></td>
+<td><p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate</span></code>([inputs, generation_config, ...])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_adapter_state_dict</span></code>([adapter_name])</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compiled_call</span></code>(compile_config)</p></td>
+<td><p>Return a <cite>torch.compile</cite>'d version of <cite>self.__call__</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extended_attention_mask</span></code>(attention_mask, ...)</p></td>
+<td><p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_head_mask</span></code>(head_mask, num_hidden_layers)</p></td>
+<td><p>Prepare the head mask if needed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_input_embeddings</span></code>()</p></td>
+<td><p>Returns the model's input embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_memory_footprint</span></code>([return_buffers])</p></td>
+<td><p>Get the memory footprint of a model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_embeddings</span></code>()</p></td>
+<td><p>Returns the model's output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_position_embeddings</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_disable</span></code>()</p></td>
+<td><p>Deactivates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient_checkpointing_enable</span></code>([...])</p></td>
+<td><p>Activates gradient checkpointing for the current model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>(*args)</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">heal_tokens</span></code>(input_ids[, tokenizer])</p></td>
+<td><p>Generates sequences of token ids for models with a language modeling head. Parameters:     input_ids (<cite>torch.LongTensor</cite>): The sequence used as a prompt for the generation. tokenizer (<cite>PreTrainedTokenizerBase</cite>, <em>optional</em>): The tokenizer used to decode the input ids. Return:     <cite>torch.LongTensor</cite> where each sequence has its tail token replaced with its appropriate extension.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_weights</span></code>()</p></td>
+<td><p>If needed prunes and maybe initializes weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">invert_attention_mask</span></code>(encoder_attention_mask)</p></td>
+<td><p>Invert an attention mask (e.g., switches 0.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_adapter</span></code>([peft_model_id, adapter_name, ...])</p></td>
+<td><p>Load adapter weights from file or remote Hub folder.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_parameters</span></code>([only_trainable, ...])</p></td>
+<td><p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_init</span></code>()</p></td>
+<td><p>A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_inputs_for_generation</span></code>(input_ids[, ...])</p></td>
+<td><p>Prepare the model inputs for generation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prune_heads</span></code>(heads_to_prune)</p></td>
+<td><p>Prunes heads of the base model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">push_to_hub</span></code>(repo_id[, use_temp_dir, ...])</p></td>
+<td><p>Upload the model file to the 🤗 Model Hub.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_for_auto_class</span></code>([auto_class])</p></td>
+<td><p>Register this class with a given auto class.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_memory_hooks_state</span></code>()</p></td>
+<td><p>Reset the <cite>mem_rss_diff</cite> attribute of each module (see [<cite>~modeling_utils.ModuleUtilsMixin.add_memory_hooks</cite>]).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_position_embeddings</span></code>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">resize_token_embeddings</span></code>([new_num_tokens, ...])</p></td>
+<td><p>Resizes input token embeddings matrix of the model if <cite>new_num_tokens != config.vocab_size</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modules_from_names</span></code>(names[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">reverse_bettertransformer</span></code>()</p></td>
+<td><p>Reverts the transformation from [<cite>~PreTrainedModel.to_bettertransformer</cite>] so that the original modeling is used, for example in order to save the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_pretrained</span></code>(save_directory[, ...])</p></td>
+<td><p>Save a model and its configuration file to a directory, so that it can be re-loaded using the [<cite>~PreTrainedModel.from_pretrained</cite>] class method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_adapter</span></code>(adapter_name)</p></td>
+<td><p>If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: <a class="reference external" href="https://huggingface.co/docs/peft">https://huggingface.co/docs/peft</a></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_input_embeddings</span></code>(value)</p></td>
+<td><p>Set model's input embeddings.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_parallel</span></code>(device_mesh)</p></td>
+<td><p>Tensor parallelize the model across the given device mesh.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">tie_weights</span></code>()</p></td>
+<td><p>Tie the weights between the input embeddings and the output embeddings.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_bettertransformer</span></code>()</p></td>
+<td><p>Converts the model to use [PyTorch's native attention implementation](<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html">https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html</a>), integrated to Transformers through [Optimum library](<a class="reference external" href="https://huggingface.co/docs/optimum/bettertransformer/overview">https://huggingface.co/docs/optimum/bettertransformer/overview</a>).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Set the module in training mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">warn_if_padding_and_no_attention_mask</span></code>(...)</p></td>
+<td><p>Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model</span></code></p></td>
+<td><p><cite>torch.nn.Module</cite>: The main body of the model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_model_prefix</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">call_super_init</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_class</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></p></td>
+<td><p><cite>torch.device</cite>: The device on which the module is (assuming that all the module parameters are on the same device).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></p></td>
+<td><p><cite>torch.dtype</cite>: The dtype of the module (assuming that all the module parameters have the same dtype).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy_inputs</span></code></p></td>
+<td><p><cite>Dict[str, torch.Tensor]</cite>: Dummy inputs to do a forward pass in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dump_patches</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">framework</span></code></p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">str<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Identifies that this is a PyTorch model.</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gradient_checkpointing</span></code></p></td>
+<td><p>Whether gradient checkpointing is activated for this model or not.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_parallelizable</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loss_function</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">main_input_name</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_tags</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_gradient_checkpointing</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_tp_plan</span></code></p></td>
+<td><p>Returns whether the model has a tensor parallelism plan.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPModel.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">LongTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPModel.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
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+                <h2> Contents </h2>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPModelOutput"><code class="docutils literal notranslate"><span class="pre">MLPModelOutput</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.mlp.modelings_mlp.MLPModelOutput.__init__"><code class="docutils literal notranslate"><span class="pre">MLPModelOutput.__init__()</span></code></a></li>
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+  <section id="pyvene-models-mlp-modelings-mlp-mlpmodeloutput">
+<h1>pyvene.models.mlp.modelings_mlp.MLPModelOutput<a class="headerlink" href="#pyvene-models-mlp-modelings-mlp-mlpmodeloutput" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPModelOutput">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">MLPModelOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_hidden_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.FloatTensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/mlp/modelings_mlp.html#MLPModelOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPModelOutput" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ModelOutput</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.mlp.modelings_mlp.MLPModelOutput.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_hidden_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">FloatTensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">FloatTensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#pyvene.models.mlp.modelings_mlp.MLPModelOutput.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.mlp.modelings_mlp.MLPModelOutput.__init__" title="pyvene.models.mlp.modelings_mlp.MLPModelOutput.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([last_hidden_state, hidden_states])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
+<td><p>Create a new ordered dictionary with keys from iterable and values set to value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
+<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">move_to_end</span></code>(key[, last])</p></td>
+<td><p>Move an existing element to the end (or beginning if last is false).</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(key[,default])</p></td>
+<td><p>If the key is not found, return the default if given; otherwise, raise a KeyError.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>([last])</p></td>
+<td><p>Remove and return a (key, value) pair from the dictionary.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(*args, **kwargs)</p></td>
+<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_tuple</span></code>()</p></td>
+<td><p>Convert self to a tuple containing all the attributes/keys that are not <cite>None</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
+<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hidden_states</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">last_hidden_state</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
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+<h1>pyvene.models.modeling_utils.HandlerList<a class="headerlink" href="#pyvene-models-modeling-utils-handlerlist" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.HandlerList">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">HandlerList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handlers</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#HandlerList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.HandlerList" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>General class to set hooks and set off hooks.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.HandlerList.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handlers</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#HandlerList.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.HandlerList.__init__" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.modeling_utils.HandlerList.__init__" title="pyvene.models.modeling_utils.HandlerList.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(handlers)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extend</span></code>(new_handlers)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code>()</p></td>
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+    <h1>pyvene.models.modeling_utils.LambdaIntervention</h1>
+    <!-- Table of contents -->
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.LambdaIntervention"><code class="docutils literal notranslate"><span class="pre">LambdaIntervention</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.LambdaIntervention.__init__"><code class="docutils literal notranslate"><span class="pre">LambdaIntervention.__init__()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.LambdaIntervention.forward"><code class="docutils literal notranslate"><span class="pre">LambdaIntervention.forward()</span></code></a></li>
+</ul>
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+</ul>
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+    </div>
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+
+              
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+                  
+  <section id="pyvene-models-modeling-utils-lambdaintervention">
+<h1>pyvene.models.modeling_utils.LambdaIntervention<a class="headerlink" href="#pyvene-models-modeling-utils-lambdaintervention" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.LambdaIntervention">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">LambdaIntervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#LambdaIntervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.LambdaIntervention" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<p>A generic wrapper to turn any Python callable (e.g. a lambda) 
+into an nn.Module. This does <em>not</em> automatically turn external 
+Tensors into parameters or buffers—it’s just a functional wrapper.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.LambdaIntervention.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#LambdaIntervention.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.LambdaIntervention.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#pyvene.models.modeling_utils.LambdaIntervention.__init__" title="pyvene.models.modeling_utils.LambdaIntervention.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(func)</p></td>
+<td><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Add a child module to the current module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>Move all model parameters and buffers to the CPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Set the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Return the extra representation of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#pyvene.models.modeling_utils.LambdaIntervention.forward" title="pyvene.models.modeling_utils.LambdaIntervention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(*args, **kwargs)</p></td>
+<td><p>Define the computation performed at every call.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Return any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copy parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Return an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mtia</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the MTIA.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Return an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Add a buffer to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Register a forward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Register a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Register a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook to be run after module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook to be run before module's <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Add a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Register a post-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>Register a pre-hook for the <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_submodule</span></code>(target, module)</p></td>
+<td><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Return a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>Move and/or cast the parameters and buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Move the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
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+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Move all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Reset gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">T_destination</span></code></p></td>
+<td><p></p></td>
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+<td><p></p></td>
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+<td><p></p></td>
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+<dl class="py method">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.LambdaIntervention.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#LambdaIntervention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.LambdaIntervention.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
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+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
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+<span class="sig-name descname"><span class="pre">b_sd_to_bsd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#b_sd_to_bsd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.b_sd_to_bsd" title="Link to this definition">#</a></dt>
+<dd><p>Convert a tensor of shape (b, s*d) back to (b, s, d).</p>
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+<h1>pyvene.models.modeling_utils.bhsd_to_bs_hd<a class="headerlink" href="#pyvene-models-modeling-utils-bhsd-to-bs-hd" title="Link to this heading">#</a></h1>
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+<dd><p>Convert a tensor of shape (b, h, s, d) to (b, s, h*d).</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.bs_hd_to_bhsd"><code class="docutils literal notranslate"><span class="pre">bs_hd_to_bhsd()</span></code></a></li>
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+  <section id="pyvene-models-modeling-utils-bs-hd-to-bhsd">
+<h1>pyvene.models.modeling_utils.bs_hd_to_bhsd<a class="headerlink" href="#pyvene-models-modeling-utils-bs-hd-to-bhsd" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.bs_hd_to_bhsd">
+<span class="sig-name descname"><span class="pre">bs_hd_to_bhsd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#bs_hd_to_bhsd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.bs_hd_to_bhsd" title="Link to this definition">#</a></dt>
+<dd><p>Convert a tensor of shape (b, s, h*d) back to (b, h, s, d).</p>
+</dd></dl>
+
+</section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.bsd_to_b_sd"><code class="docutils literal notranslate"><span class="pre">bsd_to_b_sd()</span></code></a></li>
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+<h1>pyvene.models.modeling_utils.bsd_to_b_sd<a class="headerlink" href="#pyvene-models-modeling-utils-bsd-to-b-sd" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.bsd_to_b_sd">
+<span class="sig-name descname"><span class="pre">bsd_to_b_sd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#bsd_to_b_sd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.bsd_to_b_sd" title="Link to this definition">#</a></dt>
+<dd><p>Convert a tensor of shape (b, s, d) to (b, s*d).</p>
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.do_intervention"><code class="docutils literal notranslate"><span class="pre">do_intervention()</span></code></a></li>
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+  <section id="pyvene-models-modeling-utils-do-intervention">
+<h1>pyvene.models.modeling_utils.do_intervention<a class="headerlink" href="#pyvene-models-modeling-utils-do-intervention" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.do_intervention">
+<span class="sig-name descname"><span class="pre">do_intervention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_representation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_representation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervention</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subspaces</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#do_intervention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.do_intervention" title="Link to this definition">#</a></dt>
+<dd><p>Do the actual intervention.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.gather_neurons"><code class="docutils literal notranslate"><span class="pre">gather_neurons()</span></code></a></li>
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+  <section id="pyvene-models-modeling-utils-gather-neurons">
+<h1>pyvene.models.modeling_utils.gather_neurons<a class="headerlink" href="#pyvene-models-modeling-utils-gather-neurons" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.gather_neurons">
+<span class="sig-name descname"><span class="pre">gather_neurons</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_locations_as_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#gather_neurons"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.gather_neurons" title="Link to this definition">#</a></dt>
+<dd><p>Gather intervening neurons.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>tensor_input</strong> – tensors of shape (batch_size, sequence_length, …) if</p>
+</dd>
+</dl>
+<p><cite>unit</cite> is “pos” or “h”, tensors of shape (batch_size, num_heads,
+sequence_length, …) if <cite>unit</cite> is “h.pos”
+:param unit: the intervention units to gather. Units could be “h” - head
+number, “pos” - position in the sequence, or “dim” - a particular dimension in
+the embedding space. If intervening multiple units, they are ordered and
+separated by <cite>.</cite>. Currently only support “pos”, “h”, and “h.pos” units.
+:param unit_locations_as_list: tuple of lists of lists of positions to gather
+in tensor_input, according to the unit.
+:return the gathered tensor as tensor_output</p>
+</dd></dl>
+
+</section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.gather_neurons"><code class="docutils literal notranslate"><span class="pre">gather_neurons()</span></code></a></li>
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+<h1>pyvene.models.modeling_utils.get_dimension_by_component<a class="headerlink" href="#pyvene-models-modeling-utils-get-dimension-by-component" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">get_dimension_by_component</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span></span></span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#get_dimension_by_component"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.get_dimension_by_component" title="Link to this definition">#</a></dt>
+<dd><p>Based on the representation, get the aligning dimension size.</p>
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+<span class="sig-name descname"><span class="pre">get_module_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">representation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'native'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Module</span></span></span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#get_module_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.get_module_hook" title="Link to this definition">#</a></dt>
+<dd><p>Render the intervening module with a hook.</p>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.RotatedSpaceIntervention.html">pyvene.models.interventions.RotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SharedWeightsTrainableIntervention.html">pyvene.models.interventions.SharedWeightsTrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.SigmoidMaskIntervention.html">pyvene.models.interventions.SigmoidMaskIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.TrainableIntervention.html">pyvene.models.interventions.TrainableIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="pyvene.models.interventions.ZeroIntervention.html">pyvene.models.interventions.ZeroIntervention</a></li>
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+<h1>pyvene.models.modeling_utils.getattr_for_torch_module<a class="headerlink" href="#pyvene-models-modeling-utils-getattr-for-torch-module" title="Link to this heading">#</a></h1>
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+<span class="sig-name descname"><span class="pre">getattr_for_torch_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#getattr_for_torch_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.getattr_for_torch_module" title="Link to this definition">#</a></dt>
+<dd><p>Recursively fetch the model based on the name.</p>
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+  <section id="module-pyvene.models.modeling_utils">
+<span id="pyvene-models-modeling-utils"></span><h1>pyvene.models.modeling_utils<a class="headerlink" href="#module-pyvene.models.modeling_utils" title="Link to this heading">#</a></h1>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.b_sd_to_bsd.html#pyvene.models.modeling_utils.b_sd_to_bsd" title="pyvene.models.modeling_utils.b_sd_to_bsd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">b_sd_to_bsd</span></code></a>(tensor, s)</p></td>
+<td><p>Convert a tensor of shape (b, s*d) back to (b, s, d).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.bhsd_to_bs_hd.html#pyvene.models.modeling_utils.bhsd_to_bs_hd" title="pyvene.models.modeling_utils.bhsd_to_bs_hd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bhsd_to_bs_hd</span></code></a>(tensor)</p></td>
+<td><p>Convert a tensor of shape (b, h, s, d) to (b, s, h*d).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.bs_hd_to_bhsd.html#pyvene.models.modeling_utils.bs_hd_to_bhsd" title="pyvene.models.modeling_utils.bs_hd_to_bhsd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bs_hd_to_bhsd</span></code></a>(tensor, h)</p></td>
+<td><p>Convert a tensor of shape (b, s, h*d) back to (b, h, s, d).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.bsd_to_b_sd.html#pyvene.models.modeling_utils.bsd_to_b_sd" title="pyvene.models.modeling_utils.bsd_to_b_sd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bsd_to_b_sd</span></code></a>(tensor)</p></td>
+<td><p>Convert a tensor of shape (b, s, d) to (b, s*d).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.do_intervention.html#pyvene.models.modeling_utils.do_intervention" title="pyvene.models.modeling_utils.do_intervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">do_intervention</span></code></a>(base_representation, ...)</p></td>
+<td><p>Do the actual intervention.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.gather_neurons.html#pyvene.models.modeling_utils.gather_neurons" title="pyvene.models.modeling_utils.gather_neurons"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gather_neurons</span></code></a>(tensor_input, unit, ...[, device])</p></td>
+<td><p>Gather intervening neurons.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.get_dimension_by_component.html#pyvene.models.modeling_utils.get_dimension_by_component" title="pyvene.models.modeling_utils.get_dimension_by_component"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_dimension_by_component</span></code></a>(model_type, ...)</p></td>
+<td><p>Based on the representation, get the aligning dimension size.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.get_internal_model_type.html#pyvene.models.modeling_utils.get_internal_model_type" title="pyvene.models.modeling_utils.get_internal_model_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_internal_model_type</span></code></a>(model)</p></td>
+<td><p>Return the model type.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.get_module_hook.html#pyvene.models.modeling_utils.get_module_hook" title="pyvene.models.modeling_utils.get_module_hook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_module_hook</span></code></a>(model, representation[, backend])</p></td>
+<td><p>Render the intervening module with a hook.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.getattr_for_torch_module.html#pyvene.models.modeling_utils.getattr_for_torch_module" title="pyvene.models.modeling_utils.getattr_for_torch_module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getattr_for_torch_module</span></code></a>(model, parameter_name)</p></td>
+<td><p>Recursively fetch the model based on the name.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.is_gru.html#pyvene.models.modeling_utils.is_gru" title="pyvene.models.modeling_utils.is_gru"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_gru</span></code></a>(model)</p></td>
+<td><p>Determine if this is a transformer model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.is_mlp.html#pyvene.models.modeling_utils.is_mlp" title="pyvene.models.modeling_utils.is_mlp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_mlp</span></code></a>(model)</p></td>
+<td><p>Determine if this is a mlp model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.is_stateless.html#pyvene.models.modeling_utils.is_stateless" title="pyvene.models.modeling_utils.is_stateless"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_stateless</span></code></a>(model)</p></td>
+<td><p>Determine if the model is stateful (e.g., rnn) or stateless (e.g., transformer)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.is_transformer.html#pyvene.models.modeling_utils.is_transformer" title="pyvene.models.modeling_utils.is_transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_transformer</span></code></a>(model)</p></td>
+<td><p>Determine if this is a transformer model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.output_to_subcomponent.html#pyvene.models.modeling_utils.output_to_subcomponent" title="pyvene.models.modeling_utils.output_to_subcomponent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_to_subcomponent</span></code></a>(output, component, ...)</p></td>
+<td><p>Split the raw output to subcomponents if specified in the config.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.print_forward_hooks.html#pyvene.models.modeling_utils.print_forward_hooks" title="pyvene.models.modeling_utils.print_forward_hooks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">print_forward_hooks</span></code></a>(main_module)</p></td>
+<td><p>Function to print forward hooks of a module and its sub-modules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.remove_forward_hooks.html#pyvene.models.modeling_utils.remove_forward_hooks" title="pyvene.models.modeling_utils.remove_forward_hooks"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove_forward_hooks</span></code></a>(main_module)</p></td>
+<td><p>Function to remove all forward and pre-forward hooks from a module and</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.scatter_neurons.html#pyvene.models.modeling_utils.scatter_neurons" title="pyvene.models.modeling_utils.scatter_neurons"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scatter_neurons</span></code></a>(tensor_input, ...[, device])</p></td>
+<td><p>Replace selected neurons in <cite>tensor_input</cite> by <cite>replacing_tensor_input</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.simple_output_to_subcomponent.html#pyvene.models.modeling_utils.simple_output_to_subcomponent" title="pyvene.models.modeling_utils.simple_output_to_subcomponent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simple_output_to_subcomponent</span></code></a>(output, ...)</p></td>
+<td><p>This is an oversimplied version for demo.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.simple_scatter_intervention_output.html#pyvene.models.modeling_utils.simple_scatter_intervention_output" title="pyvene.models.modeling_utils.simple_scatter_intervention_output"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simple_scatter_intervention_output</span></code></a>(...)</p></td>
+<td><p>This is an oversimplied version for demo.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.weighted_average.html#pyvene.models.modeling_utils.weighted_average" title="pyvene.models.modeling_utils.weighted_average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">weighted_average</span></code></a>(values, weights)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.HandlerList.html#pyvene.models.modeling_utils.HandlerList" title="pyvene.models.modeling_utils.HandlerList"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HandlerList</span></code></a>(handlers)</p></td>
+<td><p>General class to set hooks and set off hooks.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention" title="pyvene.models.modeling_utils.LambdaIntervention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LambdaIntervention</span></code></a>(func)</p></td>
+<td><p>A generic wrapper to turn any Python callable (e.g. a lambda)  into an nn.Module.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
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+<h1>pyvene.models.modeling_utils.is_gru<a class="headerlink" href="#pyvene-models-modeling-utils-is-gru" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.is_gru">
+<span class="sig-name descname"><span class="pre">is_gru</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#is_gru"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.is_gru" title="Link to this definition">#</a></dt>
+<dd><p>Determine if this is a transformer model.</p>
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new file mode 100644
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+<h1>pyvene.models.modeling_utils.is_mlp<a class="headerlink" href="#pyvene-models-modeling-utils-is-mlp" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.is_mlp">
+<span class="sig-name descname"><span class="pre">is_mlp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#is_mlp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.is_mlp" title="Link to this definition">#</a></dt>
+<dd><p>Determine if this is a mlp model.</p>
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+<span class="sig-name descname"><span class="pre">is_stateless</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#is_stateless"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.is_stateless" title="Link to this definition">#</a></dt>
+<dd><p>Determine if the model is stateful (e.g., rnn) or stateless (e.g.,
+transformer)</p>
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+<span class="sig-name descname"><span class="pre">is_transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#is_transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.is_transformer" title="Link to this definition">#</a></dt>
+<dd><p>Determine if this is a transformer model.</p>
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+<h1>pyvene.models.modeling_utils.output_to_subcomponent<a class="headerlink" href="#pyvene-models-modeling-utils-output-to-subcomponent" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.output_to_subcomponent">
+<span class="sig-name descname"><span class="pre">output_to_subcomponent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#output_to_subcomponent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.output_to_subcomponent" title="Link to this definition">#</a></dt>
+<dd><p>Split the raw output to subcomponents if specified in the config.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>output</strong> – the original output from the model component.</p></li>
+<li><p><strong>component</strong> – types of model component, such as</p></li>
+</ul>
+</dd>
+</dl>
+<p>“block_output” and “query_output” or it can be direct referece, such as
+“h[0].mlp.act” which we will not splice into any subcomponent.
+:param model_type: Hugging Face Model Type
+:param model_config: Hugging Face Model Config</p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<dl class="py function">
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+<span class="sig-name descname"><span class="pre">remove_forward_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#remove_forward_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.remove_forward_hooks" title="Link to this definition">#</a></dt>
+<dd><p>Function to remove all forward and pre-forward hooks from a module and</p>
+<p>its sub-modules.</p>
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+  <section id="pyvene-models-modeling-utils-scatter-neurons">
+<h1>pyvene.models.modeling_utils.scatter_neurons<a class="headerlink" href="#pyvene-models-modeling-utils-scatter-neurons" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.modeling_utils.scatter_neurons">
+<span class="sig-name descname"><span class="pre">scatter_neurons</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacing_tensor_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_locations_as_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_fast</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#scatter_neurons"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.scatter_neurons" title="Link to this definition">#</a></dt>
+<dd><p>Replace selected neurons in <cite>tensor_input</cite> by <cite>replacing_tensor_input</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>tensor_input</strong> – tensors of shape (batch_size, sequence_length, …) if</p>
+</dd>
+</dl>
+<p><cite>unit</cite> is “pos” or “h”, tensors of shape (batch_size, num_heads,
+sequence_length, …) if <cite>unit</cite> is “h.pos”
+:param replacing_tensor_input: tensors of shape (batch_size, sequence_length,
+…) if <cite>unit</cite> is “pos” or
+“h”, tensors of shape (batch_size, num_heads, sequence_length, …) if
+<cite>unit</cite> is “h.pos”.
+:param component: types of intervention representations, such as
+“block_output” and “query_output”
+:param unit: the intervention units to gather. Units could be “h” - head
+number, “pos” - position in the sequence, or “dim” - a particular dimension in
+the embedding space. If intervening multiple units, they are ordered and
+separated by <cite>.</cite>. Currently only support “pos”, “h”, and “h.pos” units.
+:param unit_locations_as_list: tuple of lists of lists of positions to gather
+in tensor_input, according to the unit.
+:param model_type: Hugging Face Model Type
+:param model_config: Hugging Face Model Config
+:param use_fast: whether to use fast path (TODO: fast path condition)
+:return the in-place modified tensor_input</p>
+</dd></dl>
+
+</section>
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+<dd><p>This is an oversimplied version for demo.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.modeling_utils.simple_scatter_intervention_output"><code class="docutils literal notranslate"><span class="pre">simple_scatter_intervention_output()</span></code></a></li>
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+<span class="sig-name descname"><span class="pre">simple_scatter_intervention_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intervened_representation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">representation_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_locations</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/modeling_utils.html#simple_scatter_intervention_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.modeling_utils.simple_scatter_intervention_output" title="Link to this definition">#</a></dt>
+<dd><p>This is an oversimplied version for demo.</p>
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+<span id="pyvene-models-olmo"></span><h1>pyvene.models.olmo<a class="headerlink" href="#module-pyvene.models.olmo" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
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+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.olmo.modelings_intervenable_olmo.html#module-pyvene.models.olmo.modelings_intervenable_olmo" title="pyvene.models.olmo.modelings_intervenable_olmo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.olmo.modelings_intervenable_olmo</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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+<h1>pyvene.models.olmo.modelings_intervenable_olmo.create_olmo<a class="headerlink" href="#pyvene-models-olmo-modelings-intervenable-olmo-create-olmo" title="Link to this heading">#</a></h1>
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+<dt class="sig sig-object py" id="pyvene.models.olmo.modelings_intervenable_olmo.create_olmo">
+<span class="sig-name descname"><span class="pre">create_olmo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'allenai/OLMo-7B-0424-hf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">revision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'main'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/olmo/modelings_intervenable_olmo.html#create_olmo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.olmo.modelings_intervenable_olmo.create_olmo" title="Link to this definition">#</a></dt>
+<dd><p>Creates a OLMo Causal LM model, config, and tokenizer from the given name and revision</p>
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+    <h1>pyvene.models.olmo.modelings_intervenable_olmo</h1>
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+  <section id="module-pyvene.models.olmo.modelings_intervenable_olmo">
+<span id="pyvene-models-olmo-modelings-intervenable-olmo"></span><h1>pyvene.models.olmo.modelings_intervenable_olmo<a class="headerlink" href="#module-pyvene.models.olmo.modelings_intervenable_olmo" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.</p>
+<p>We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping.html#pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping" title="pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">olmo_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>olmo model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping.html#pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping" title="pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">olmo_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>olmo model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.olmo.modelings_intervenable_olmo.create_olmo.html#pyvene.models.olmo.modelings_intervenable_olmo.create_olmo" title="pyvene.models.olmo.modelings_intervenable_olmo.create_olmo"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_olmo</span></code></a>([name, cache_dir, dtype, ...])</p></td>
+<td><p>Creates a OLMo Causal LM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+
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+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
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+
+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
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+  <section id="pyvene-models-olmo-modelings-intervenable-olmo-olmo-lm-type-to-dimension-mapping">
+<h1>pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-olmo-modelings-intervenable-olmo-olmo-lm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">olmo_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>olmo model with classifier head</p>
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+  <section id="pyvene-models-olmo-modelings-intervenable-olmo-olmo-type-to-dimension-mapping">
+<h1>pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-olmo-modelings-intervenable-olmo-olmo-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">olmo_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('hidden_size/num_attention_heads',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>olmo model with LM head</p>
+</dd></dl>
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+<span id="pyvene-models-qwen2"></span><h1>pyvene.models.qwen2<a class="headerlink" href="#module-pyvene.models.qwen2" title="Link to this heading">#</a></h1>
+<p class="rubric">Modules</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.qwen2.modelings_intervenable_qwen2.html#module-pyvene.models.qwen2.modelings_intervenable_qwen2" title="pyvene.models.qwen2.modelings_intervenable_qwen2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pyvene.models.qwen2.modelings_intervenable_qwen2</span></code></a></p></td>
+<td><p>Each modeling file in this library is a mapping between abstract naming of intervention anchor points and actual model module defined in the huggingface library.</p></td>
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new file mode 100644
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+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
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+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<h1>pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2<a class="headerlink" href="#pyvene-models-qwen2-modelings-intervenable-qwen2-create-qwen2" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2">
+<span class="sig-name descname"><span class="pre">create_qwen2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Qwen/Qwen2-7B-beta'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pyvene/models/qwen2/modelings_intervenable_qwen2.html#create_qwen2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2" title="Link to this definition">#</a></dt>
+<dd><p>Creates a Causal LM model, config, and tokenizer from the given name and revision</p>
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+    <h1>pyvene.models.qwen2.modelings_intervenable_qwen2</h1>
+    <!-- Table of contents -->
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+              
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+                  
+  <section id="module-pyvene.models.qwen2.modelings_intervenable_qwen2">
+<span id="pyvene-models-qwen2-modelings-intervenable-qwen2"></span><h1>pyvene.models.qwen2.modelings_intervenable_qwen2<a class="headerlink" href="#module-pyvene.models.qwen2.modelings_intervenable_qwen2" title="Link to this heading">#</a></h1>
+<p>Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.
+We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.</p>
+<p class="rubric">Module Attributes</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping.html#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping" title="pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qwen2_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>qwen2 model with LM head</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.html#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping" title="pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qwen2_lm_type_to_dimension_mapping</span></code></a></p></td>
+<td><p>qwen2 model with classifier head</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Functions</p>
+<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2.html#pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2" title="pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_qwen2</span></code></a>([name, cache_dir, dtype])</p></td>
+<td><p>Creates a Causal LM model, config, and tokenizer from the given name and revision</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+
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diff --git a/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.html b/api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.html
new file mode 100644
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping"><code class="docutils literal notranslate"><span class="pre">qwen2_lm_type_to_dimension_mapping</span></code></a></li>
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+  <section id="pyvene-models-qwen2-modelings-intervenable-qwen2-qwen2-lm-type-to-dimension-mapping">
+<h1>pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-qwen2-modelings-intervenable-qwen2-qwen2-lm-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">qwen2_lm_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>qwen2 model with classifier head</p>
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+<h1>pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping<a class="headerlink" href="#pyvene-models-qwen2-modelings-intervenable-qwen2-qwen2-type-to-dimension-mapping" title="Link to this heading">#</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping">
+<span class="sig-name descname"><span class="pre">qwen2_type_to_dimension_mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'attention_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'attention_value_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'block_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'head_attention_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_key_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_query_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'head_value_output':</span> <span class="pre">('head_dim',),</span> <span class="pre">'key_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_activation':</span> <span class="pre">('intermediate_size',),</span> <span class="pre">'mlp_input':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'mlp_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'n_head':</span> <span class="pre">('num_attention_heads',),</span> <span class="pre">'n_kv_head':</span> <span class="pre">('num_key_value_heads',),</span> <span class="pre">'query_output':</span> <span class="pre">('hidden_size',),</span> <span class="pre">'value_output':</span> <span class="pre">('hidden_size',)}</span></em><a class="headerlink" href="#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping" title="Link to this definition">#</a></dt>
+<dd><p>qwen2 model with LM head</p>
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+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#J"><strong>J</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#Q"><strong>Q</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#Z"><strong>Z</strong></a>
+ 
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention.__init__">__init__() (AdditionIntervention method)</a>
+
+      <ul>
+        <li><a href="api/pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention.__init__">(AutoencoderIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer.__init__">(AutoencoderLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.AutoencoderLayerBase.html#pyvene.models.layers.AutoencoderLayerBase.__init__">(AutoencoderLayerBase method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.__init__">(BackpackGPT2BaseModelOutput method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.__init__">(BackpackGPT2Config method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.__init__">(BackpackGPT2LMHeadModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.__init__">(BackpackGPT2LMHeadModelOutput method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.__init__">(BackpackGPT2Model method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.__init__">(BackpackGPT2PreTrainedModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.__init__">(BackpackMLP method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.__init__">(BackpackNoMixBlock method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.__init__">(BackpackSenseNetwork method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.__init__">(BackpackWeightNetwork method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.__init__">(BaseModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention.__init__">(BasisAgnosticIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.html#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.__init__">(BlipITMWrapper method)</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_blip.BlipWrapper.html#pyvene.models.blip.modelings_blip.BlipWrapper.__init__">(BlipWrapper method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.__init__">(BoundlessRotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.data_generators.causal_model.CausalModel.html#pyvene.data_generators.causal_model.CausalModel.__init__">(CausalModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention.__init__">(CollectIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention.__init__">(ConstantSourceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention.__init__">(DistributedRepresentationIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUCell.html#pyvene.models.gru.modelings_gru.GRUCell.__init__">(GRUCell method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUConfig.html#pyvene.models.gru.modelings_gru.GRUConfig.__init__">(GRUConfig method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUForClassification.html#pyvene.models.gru.modelings_gru.GRUForClassification.__init__">(GRUForClassification method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel.__init__">(GRULMHeadModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.__init__">(GRUModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUModelOutput.html#pyvene.models.gru.modelings_gru.GRUModelOutput.__init__">(GRUModelOutput method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel.__init__">(GRUPreTrainedModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.modeling_utils.HandlerList.html#pyvene.models.modeling_utils.HandlerList.__init__">(HandlerList method)</a>
+</li>
+        <li><a href="api/pyvene.models.configuration_intervenable_model.IntervenableConfig.html#pyvene.models.configuration_intervenable_model.IntervenableConfig.__init__">(IntervenableConfig method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.__init__">(IntervenableModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.IntervenableModelOutput.html#pyvene.models.intervenable_base.IntervenableModelOutput.__init__">(IntervenableModelOutput method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel.__init__">(IntervenableNdifModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention.__init__">(Intervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.InterventionOutput.html#pyvene.models.interventions.InterventionOutput.__init__">(InterventionOutput method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervention_utils.InterventionState.html#pyvene.models.intervention_utils.InterventionState.__init__">(InterventionState method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer.__init__">(InverseRotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention.__init__">(JumpReLUAutoencoderIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention.__init__">(LambdaIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention.__init__">(LocalistRepresentationIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention.__init__">(LowRankRotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer.__init__">(LowRankRotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPBlock.html#pyvene.models.mlp.modelings_mlp.MLPBlock.__init__">(MLPBlock method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPConfig.html#pyvene.models.mlp.modelings_mlp.MLPConfig.__init__">(MLPConfig method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPForClassification.html#pyvene.models.mlp.modelings_mlp.MLPForClassification.__init__">(MLPForClassification method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPModel.html#pyvene.models.mlp.modelings_mlp.MLPModel.__init__">(MLPModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPModelOutput.html#pyvene.models.mlp.modelings_mlp.MLPModelOutput.__init__">(MLPModelOutput method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention.__init__">(NoiseIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention.__init__">(PCARotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.__init__">(RepresentationConfig method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention.__init__">(RotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer.__init__">(RotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html#pyvene.models.interventions.SharedWeightsTrainableIntervention.__init__">(SharedWeightsTrainableIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention.__init__">(SigmoidMaskIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.__init__">(SigmoidMaskRotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention.__init__">(SkipIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SourcelessIntervention.html#pyvene.models.interventions.SourcelessIntervention.__init__">(SourcelessIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer.__init__">(SubspaceLowRankRotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention.__init__">(SubtractionIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention.__init__">(TrainableIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention.__init__">(VanillaIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention.__init__">(ZeroIntervention method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention">AdditionIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention">AutoencoderIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer">AutoencoderLayer (class in pyvene.models.layers)</a>
+</li>
+      <li><a href="api/pyvene.models.layers.AutoencoderLayerBase.html#pyvene.models.layers.AutoencoderLayerBase">AutoencoderLayerBase (class in pyvene.models.layers)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.modeling_utils.b_sd_to_bsd.html#pyvene.models.modeling_utils.b_sd_to_bsd">b_sd_to_bsd() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2BaseModelOutput">BackpackGPT2BaseModelOutput (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Config">BackpackGPT2Config (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel">BackpackGPT2LMHeadModel (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModelOutput">BackpackGPT2LMHeadModelOutput (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model">BackpackGPT2Model (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel">BackpackGPT2PreTrainedModel (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP">BackpackMLP (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock">BackpackNoMixBlock (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork">BackpackSenseNetwork (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork">BackpackWeightNetwork (class in pyvene.models.backpack_gpt2.modelings_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel">BaseModel (class in pyvene.models.intervenable_base)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.BasisAgnosticIntervention.html#pyvene.models.interventions.BasisAgnosticIntervention">BasisAgnosticIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.bhsd_to_bs_hd.html#pyvene.models.modeling_utils.bhsd_to_bs_hd">bhsd_to_bs_hd() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping.html#pyvene.models.blip.modelings_intervenable_blip_itm.blip_itm_type_to_dimension_mapping">blip_itm_type_to_dimension_mapping (in module pyvene.models.blip.modelings_intervenable_blip_itm)</a>
+</li>
+      <li><a href="api/pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping.html#pyvene.models.blip.modelings_intervenable_blip.blip_type_to_dimension_mapping">blip_type_to_dimension_mapping (in module pyvene.models.blip.modelings_intervenable_blip)</a>
+</li>
+      <li><a href="api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.html#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper">BlipITMWrapper (class in pyvene.models.blip.modelings_blip_itm)</a>
+</li>
+      <li><a href="api/pyvene.models.blip.modelings_blip.BlipWrapper.html#pyvene.models.blip.modelings_blip.BlipWrapper">BlipWrapper (class in pyvene.models.blip.modelings_blip)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention">BoundlessRotatedSpaceIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.intervention_utils.broadcast_tensor_v1.html#pyvene.models.intervention_utils.broadcast_tensor_v1">broadcast_tensor_v1() (in module pyvene.models.intervention_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.intervention_utils.broadcast_tensor_v2.html#pyvene.models.intervention_utils.broadcast_tensor_v2">broadcast_tensor_v2() (in module pyvene.models.intervention_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.bs_hd_to_bhsd.html#pyvene.models.modeling_utils.bs_hd_to_bhsd">bs_hd_to_bhsd() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.bsd_to_b_sd.html#pyvene.models.modeling_utils.bsd_to_b_sd">bsd_to_b_sd() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.build_intervenable_model.html#pyvene.models.intervenable_base.build_intervenable_model">build_intervenable_model() (in module pyvene.models.intervenable_base)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.data_generators.causal_model.CausalModel.html#pyvene.data_generators.causal_model.CausalModel">CausalModel (class in pyvene.data_generators.causal_model)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.closeness_to_permutation_loss.html#pyvene.models.basic_utils.closeness_to_permutation_loss">closeness_to_permutation_loss() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention">CollectIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.component">component (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2PreTrainedModel.config_class">config_class (BackpackGPT2PreTrainedModel attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.ConstantSourceIntervention.html#pyvene.models.interventions.ConstantSourceIntervention">ConstantSourceIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.count_parameters">count_parameters() (BaseModel method)</a>
+
+      <ul>
+        <li><a href="api/pyvene.models.basic_utils.count_parameters.html#pyvene.models.basic_utils.count_parameters">(in module pyvene.models.basic_utils)</a>
+</li>
+      </ul></li>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2.html#pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.create_backpack_gpt2">create_backpack_gpt2() (in module pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.blip.modelings_intervenable_blip.create_blip.html#pyvene.models.blip.modelings_intervenable_blip.create_blip">create_blip() (in module pyvene.models.blip.modelings_intervenable_blip)</a>
+</li>
+      <li><a href="api/pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm.html#pyvene.models.blip.modelings_intervenable_blip_itm.create_blip_itm">create_blip_itm() (in module pyvene.models.blip.modelings_intervenable_blip_itm)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.create_directory.html#pyvene.models.basic_utils.create_directory">create_directory() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.gemma.modelings_intervenable_gemma.create_gemma.html#pyvene.models.gemma.modelings_intervenable_gemma.create_gemma">create_gemma() (in module pyvene.models.gemma.modelings_intervenable_gemma)</a>
+</li>
+      <li><a href="api/pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2.html#pyvene.models.gemma2.modelings_intervenable_gemma2.create_gemma2">create_gemma2() (in module pyvene.models.gemma2.modelings_intervenable_gemma2)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2">create_gpt2() (in module pyvene.models.gpt2.modelings_intervenable_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_classifier">create_gpt2_classifier() (in module pyvene.models.gpt2.modelings_intervenable_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm.html#pyvene.models.gpt2.modelings_intervenable_gpt2.create_gpt2_lm">create_gpt2_lm() (in module pyvene.models.gpt2.modelings_intervenable_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo.html#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.create_gpt_neo">create_gpt_neo() (in module pyvene.models.gpt_neo.modelings_intervenable_gpt_neo)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox.html#pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.create_gpt_neox">create_gpt_neox() (in module pyvene.models.gpt_neox.modelings_intervenable_gpt_neox)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.create_gru.html#pyvene.models.gru.modelings_intervenable_gru.create_gru">create_gru() (in module pyvene.models.gru.modelings_intervenable_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier.html#pyvene.models.gru.modelings_intervenable_gru.create_gru_classifier">create_gru_classifier() (in module pyvene.models.gru.modelings_intervenable_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.create_gru_lm.html#pyvene.models.gru.modelings_intervenable_gru.create_gru_lm">create_gru_lm() (in module pyvene.models.gru.modelings_intervenable_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.llama.modelings_intervenable_llama.create_llama.html#pyvene.models.llama.modelings_intervenable_llama.create_llama">create_llama() (in module pyvene.models.llama.modelings_intervenable_llama)</a>
+</li>
+      <li><a href="api/pyvene.models.llava.modelings_intervenable_llava.create_llava.html#pyvene.models.llava.modelings_intervenable_llava.create_llava">create_llava() (in module pyvene.models.llava.modelings_intervenable_llava)</a>
+</li>
+      <li><a href="api/pyvene.models.mistral.modellings_intervenable_mistral.create_mistral.html#pyvene.models.mistral.modellings_intervenable_mistral.create_mistral">create_mistral() (in module pyvene.models.mistral.modellings_intervenable_mistral)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier.html#pyvene.models.mlp.modelings_intervenable_mlp.create_mlp_classifier">create_mlp_classifier() (in module pyvene.models.mlp.modelings_intervenable_mlp)</a>
+</li>
+      <li><a href="api/pyvene.models.olmo.modelings_intervenable_olmo.create_olmo.html#pyvene.models.olmo.modelings_intervenable_olmo.create_olmo">create_olmo() (in module pyvene.models.olmo.modelings_intervenable_olmo)</a>
+</li>
+      <li><a href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2.html#pyvene.models.qwen2.modelings_intervenable_qwen2.create_qwen2">create_qwen2() (in module pyvene.models.qwen2.modelings_intervenable_qwen2)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.disable_intervention_gradients">disable_intervention_gradients() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.disable_model_gradients">disable_model_gradients() (BaseModel method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.DistributedRepresentationIntervention.html#pyvene.models.interventions.DistributedRepresentationIntervention">DistributedRepresentationIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.do_intervention.html#pyvene.models.modeling_utils.do_intervention">do_intervention() (in module pyvene.models.modeling_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.basic_utils.embed_to_distrib.html#pyvene.models.basic_utils.embed_to_distrib">embed_to_distrib() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.enable_model_gradients">enable_model_gradients() (BaseModel method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.eval">eval() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.eval_alignment">eval_alignment() (IntervenableModel method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.basic_utils.format_token.html#pyvene.models.basic_utils.format_token">format_token() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.AdditionIntervention.html#pyvene.models.interventions.AdditionIntervention.forward">forward() (AdditionIntervention method)</a>
+
+      <ul>
+        <li><a href="api/pyvene.models.interventions.AutoencoderIntervention.html#pyvene.models.interventions.AutoencoderIntervention.forward">(AutoencoderIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.AutoencoderLayer.html#pyvene.models.layers.AutoencoderLayer.forward">(AutoencoderLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.forward">(BackpackGPT2LMHeadModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2Model.forward">(BackpackGPT2Model method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackMLP.forward">(BackpackMLP method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackNoMixBlock.forward">(BackpackNoMixBlock method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackSenseNetwork.forward">(BackpackSenseNetwork method)</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackWeightNetwork.forward">(BackpackWeightNetwork method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.forward">(BaseModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.html#pyvene.models.blip.modelings_blip_itm.BlipITMWrapper.forward">(BlipITMWrapper method)</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_blip.BlipWrapper.html#pyvene.models.blip.modelings_blip.BlipWrapper.forward">(BlipWrapper method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html#pyvene.models.interventions.BoundlessRotatedSpaceIntervention.forward">(BoundlessRotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.CollectIntervention.html#pyvene.models.interventions.CollectIntervention.forward">(CollectIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUCell.html#pyvene.models.gru.modelings_gru.GRUCell.forward">(GRUCell method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUForClassification.html#pyvene.models.gru.modelings_gru.GRUForClassification.forward">(GRUForClassification method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel.forward">(GRULMHeadModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.forward">(GRUModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.forward">(IntervenableModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel.forward">(IntervenableNdifModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention.forward">(Intervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer.forward">(InverseRotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention.forward">(JumpReLUAutoencoderIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention.forward">(LambdaIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention.forward">(LowRankRotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer.forward">(LowRankRotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPBlock.html#pyvene.models.mlp.modelings_mlp.MLPBlock.forward">(MLPBlock method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPForClassification.html#pyvene.models.mlp.modelings_mlp.MLPForClassification.forward">(MLPForClassification method)</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPModel.html#pyvene.models.mlp.modelings_mlp.MLPModel.forward">(MLPModel method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention.forward">(NoiseIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention.forward">(PCARotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention.forward">(RotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer.forward">(RotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention.forward">(SigmoidMaskIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.forward">(SigmoidMaskRotatedSpaceIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention.forward">(SkipIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer.forward">(SubspaceLowRankRotateLayer method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention.forward">(SubtractionIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention.forward">(VanillaIntervention method)</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention.forward">(ZeroIntervention method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.modeling_utils.gather_neurons.html#pyvene.models.modeling_utils.gather_neurons">gather_neurons() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping.html#pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_lm_type_to_dimension_mapping">gemma2_lm_type_to_dimension_mapping (in module pyvene.models.gemma2.modelings_intervenable_gemma2)</a>
+</li>
+      <li><a href="api/pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping.html#pyvene.models.gemma2.modelings_intervenable_gemma2.gemma2_type_to_dimension_mapping">gemma2_type_to_dimension_mapping (in module pyvene.models.gemma2.modelings_intervenable_gemma2)</a>
+</li>
+      <li><a href="api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping.html#pyvene.models.gemma.modelings_intervenable_gemma.gemma_lm_type_to_dimension_mapping">gemma_lm_type_to_dimension_mapping (in module pyvene.models.gemma.modelings_intervenable_gemma)</a>
+</li>
+      <li><a href="api/pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping.html#pyvene.models.gemma.modelings_intervenable_gemma.gemma_type_to_dimension_mapping">gemma_type_to_dimension_mapping (in module pyvene.models.gemma.modelings_intervenable_gemma)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.generate">generate() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.get_batch_size.html#pyvene.models.basic_utils.get_batch_size">get_batch_size() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_cached_activations">get_cached_activations() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_cached_hot_activations">get_cached_hot_activations() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_device">get_device() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.get_dimension_by_component.html#pyvene.models.modeling_utils.get_dimension_by_component">get_dimension_by_component() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.get_input_embeddings">get_input_embeddings() (GRUModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.get_internal_model_type.html#pyvene.models.modeling_utils.get_internal_model_type">get_internal_model_type() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.get_list_depth.html#pyvene.models.basic_utils.get_list_depth">get_list_depth() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.GET_LOC.html#pyvene.models.basic_utils.GET_LOC">GET_LOC() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.get_module_hook.html#pyvene.models.modeling_utils.get_module_hook">get_module_hook() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel.get_output_embeddings">get_output_embeddings() (GRULMHeadModel method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.get_trainable_parameters">get_trainable_parameters() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.get_type_from_string.html#pyvene.models.basic_utils.get_type_from_string">get_type_from_string() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.getattr_for_torch_module.html#pyvene.models.modeling_utils.getattr_for_torch_module">getattr_for_torch_module() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping.html#pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_lm_type_to_dimension_mapping">gpt2_lm_type_to_dimension_mapping (in module pyvene.models.gpt2.modelings_intervenable_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping.html#pyvene.models.gpt2.modelings_intervenable_gpt2.gpt2_type_to_dimension_mapping">gpt2_type_to_dimension_mapping (in module pyvene.models.gpt2.modelings_intervenable_gpt2)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping.html#pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.gpt_neo_type_to_dimension_mapping">gpt_neo_type_to_dimension_mapping (in module pyvene.models.gpt_neo.modelings_intervenable_gpt_neo)</a>
+</li>
+      <li><a href="api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping.html#pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.gpt_neox_type_to_dimension_mapping">gpt_neox_type_to_dimension_mapping (in module pyvene.models.gpt_neox.modelings_intervenable_gpt_neox)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.group_key">group_key (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping.html#pyvene.models.gru.modelings_intervenable_gru.gru_classifier_type_to_dimension_mapping">gru_classifier_type_to_dimension_mapping (in module pyvene.models.gru.modelings_intervenable_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping.html#pyvene.models.gru.modelings_intervenable_gru.gru_type_to_dimension_mapping">gru_type_to_dimension_mapping (in module pyvene.models.gru.modelings_intervenable_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUCell.html#pyvene.models.gru.modelings_gru.GRUCell">GRUCell (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUConfig.html#pyvene.models.gru.modelings_gru.GRUConfig">GRUConfig (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUForClassification.html#pyvene.models.gru.modelings_gru.GRUForClassification">GRUForClassification (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRULMHeadModel.html#pyvene.models.gru.modelings_gru.GRULMHeadModel">GRULMHeadModel (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel">GRUModel (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUModelOutput.html#pyvene.models.gru.modelings_gru.GRUModelOutput">GRUModelOutput (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUPreTrainedModel.html#pyvene.models.gru.modelings_gru.GRUPreTrainedModel">GRUPreTrainedModel (class in pyvene.models.gru.modelings_gru)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.modeling_utils.HandlerList.html#pyvene.models.modeling_utils.HandlerList">HandlerList (class in pyvene.models.modeling_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.basic_utils.harmonic_sigmoid_boundary.html#pyvene.models.basic_utils.harmonic_sigmoid_boundary">harmonic_sigmoid_boundary() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.hidden_source_representation">hidden_source_representation (RepresentationConfig attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.IntervenableConfig.html#pyvene.models.configuration_intervenable_model.IntervenableConfig">IntervenableConfig (class in pyvene.models.configuration_intervenable_model)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel">IntervenableModel (class in pyvene.models.intervenable_base)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModelOutput.html#pyvene.models.intervenable_base.IntervenableModelOutput">IntervenableModelOutput (class in pyvene.models.intervenable_base)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel">IntervenableNdifModel (class in pyvene.models.intervenable_base)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.Intervention.html#pyvene.models.interventions.Intervention">Intervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention">intervention (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_link_key">intervention_link_key (RepresentationConfig attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.intervention_type">intervention_type (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.InterventionOutput.html#pyvene.models.interventions.InterventionOutput">InterventionOutput (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.intervention_utils.InterventionState.html#pyvene.models.intervention_utils.InterventionState">InterventionState (class in pyvene.models.intervention_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.layers.InverseRotateLayer.html#pyvene.models.layers.InverseRotateLayer">InverseRotateLayer (class in pyvene.models.layers)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.is_gru.html#pyvene.models.modeling_utils.is_gru">is_gru() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.is_mlp.html#pyvene.models.modeling_utils.is_mlp">is_mlp() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.is_stateless.html#pyvene.models.modeling_utils.is_stateless">is_stateless() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.is_transformer.html#pyvene.models.modeling_utils.is_transformer">is_transformer() (in module pyvene.models.modeling_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="J">J</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html#pyvene.models.interventions.JumpReLUAutoencoderIntervention">JumpReLUAutoencoderIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.modeling_utils.LambdaIntervention.html#pyvene.models.modeling_utils.LambdaIntervention">LambdaIntervention (class in pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.latent_dim">latent_dim (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.layer">layer (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping.html#pyvene.models.llama.modelings_intervenable_llama.llama_lm_type_to_dimension_mapping">llama_lm_type_to_dimension_mapping (in module pyvene.models.llama.modelings_intervenable_llama)</a>
+</li>
+      <li><a href="api/pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping.html#pyvene.models.llama.modelings_intervenable_llama.llama_type_to_dimension_mapping">llama_type_to_dimension_mapping (in module pyvene.models.llama.modelings_intervenable_llama)</a>
+</li>
+      <li><a href="api/pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping.html#pyvene.models.llava.modelings_intervenable_llava.llava_lm_type_to_dimension_mapping">llava_lm_type_to_dimension_mapping (in module pyvene.models.llava.modelings_intervenable_llava)</a>
+</li>
+      <li><a href="api/pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping.html#pyvene.models.llava.modelings_intervenable_llava.llava_type_to_dimension_mapping">llava_type_to_dimension_mapping (in module pyvene.models.llava.modelings_intervenable_llava)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.load">load() (IntervenableModel static method)</a>
+
+      <ul>
+        <li><a href="api/pyvene.models.intervenable_base.IntervenableNdifModel.html#pyvene.models.intervenable_base.IntervenableNdifModel.load">(IntervenableNdifModel static method)</a>
+</li>
+      </ul></li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.load_intervention">load_intervention() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.LocalistRepresentationIntervention.html#pyvene.models.interventions.LocalistRepresentationIntervention">LocalistRepresentationIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.low_rank_dimension">low_rank_dimension (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html#pyvene.models.interventions.LowRankRotatedSpaceIntervention">LowRankRotatedSpaceIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.layers.LowRankRotateLayer.html#pyvene.models.layers.LowRankRotateLayer">LowRankRotateLayer (class in pyvene.models.layers)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.max_number_of_units">max_number_of_units (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping.html#pyvene.models.mistral.modellings_intervenable_mistral.mistral_type_to_dimension_mapping">mistral_type_to_dimension_mapping (in module pyvene.models.mistral.modellings_intervenable_mistral)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping.html#pyvene.models.mlp.modelings_intervenable_mlp.mlp_type_to_dimension_mapping">mlp_type_to_dimension_mapping (in module pyvene.models.mlp.modelings_intervenable_mlp)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPBlock.html#pyvene.models.mlp.modelings_mlp.MLPBlock">MLPBlock (class in pyvene.models.mlp.modelings_mlp)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPConfig.html#pyvene.models.mlp.modelings_mlp.MLPConfig">MLPConfig (class in pyvene.models.mlp.modelings_mlp)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPForClassification.html#pyvene.models.mlp.modelings_mlp.MLPForClassification">MLPForClassification (class in pyvene.models.mlp.modelings_mlp)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPModel.html#pyvene.models.mlp.modelings_mlp.MLPModel">MLPModel (class in pyvene.models.mlp.modelings_mlp)</a>
+</li>
+      <li><a href="api/pyvene.models.mlp.modelings_mlp.MLPModelOutput.html#pyvene.models.mlp.modelings_mlp.MLPModelOutput">MLPModelOutput (class in pyvene.models.mlp.modelings_mlp)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="api/core.html#module-pyvene">pyvene</a>
+</li>
+        <li><a href="api/pyvene.data_generators.html#module-pyvene.data_generators">pyvene.data_generators</a>
+</li>
+        <li><a href="api/pyvene.data_generators.causal_model.html#module-pyvene.data_generators.causal_model">pyvene.data_generators.causal_model</a>
+</li>
+        <li><a href="api/pyvene.models.html#module-pyvene.models">pyvene.models</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.html#module-pyvene.models.backpack_gpt2">pyvene.models.backpack_gpt2</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_backpack_gpt2">pyvene.models.backpack_gpt2.modelings_backpack_gpt2</a>
+</li>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2">pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2</a>
+</li>
+        <li><a href="api/pyvene.models.basic_utils.html#module-pyvene.models.basic_utils">pyvene.models.basic_utils</a>
+</li>
+        <li><a href="api/pyvene.models.blip.html#module-pyvene.models.blip">pyvene.models.blip</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_blip.html#module-pyvene.models.blip.modelings_blip">pyvene.models.blip.modelings_blip</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_blip_itm.html#module-pyvene.models.blip.modelings_blip_itm">pyvene.models.blip.modelings_blip_itm</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_intervenable_blip.html#module-pyvene.models.blip.modelings_intervenable_blip">pyvene.models.blip.modelings_intervenable_blip</a>
+</li>
+        <li><a href="api/pyvene.models.blip.modelings_intervenable_blip_itm.html#module-pyvene.models.blip.modelings_intervenable_blip_itm">pyvene.models.blip.modelings_intervenable_blip_itm</a>
+</li>
+        <li><a href="api/pyvene.models.configuration_intervenable_model.html#module-pyvene.models.configuration_intervenable_model">pyvene.models.configuration_intervenable_model</a>
+</li>
+        <li><a href="api/pyvene.models.constants.html#module-pyvene.models.constants">pyvene.models.constants</a>
+</li>
+        <li><a href="api/pyvene.models.gemma.html#module-pyvene.models.gemma">pyvene.models.gemma</a>
+</li>
+        <li><a href="api/pyvene.models.gemma.modelings_intervenable_gemma.html#module-pyvene.models.gemma.modelings_intervenable_gemma">pyvene.models.gemma.modelings_intervenable_gemma</a>
+</li>
+        <li><a href="api/pyvene.models.gemma2.html#module-pyvene.models.gemma2">pyvene.models.gemma2</a>
+</li>
+        <li><a href="api/pyvene.models.gemma2.modelings_intervenable_gemma2.html#module-pyvene.models.gemma2.modelings_intervenable_gemma2">pyvene.models.gemma2.modelings_intervenable_gemma2</a>
+</li>
+        <li><a href="api/pyvene.models.gpt2.html#module-pyvene.models.gpt2">pyvene.models.gpt2</a>
+</li>
+        <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.html#module-pyvene.models.gpt2.modelings_intervenable_gpt2">pyvene.models.gpt2.modelings_intervenable_gpt2</a>
+</li>
+        <li><a href="api/pyvene.models.gpt_neo.html#module-pyvene.models.gpt_neo">pyvene.models.gpt_neo</a>
+</li>
+        <li><a href="api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.html#module-pyvene.models.gpt_neo.modelings_intervenable_gpt_neo">pyvene.models.gpt_neo.modelings_intervenable_gpt_neo</a>
+</li>
+        <li><a href="api/pyvene.models.gpt_neox.html#module-pyvene.models.gpt_neox">pyvene.models.gpt_neox</a>
+</li>
+        <li><a href="api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.html#module-pyvene.models.gpt_neox.modelings_intervenable_gpt_neox">pyvene.models.gpt_neox.modelings_intervenable_gpt_neox</a>
+</li>
+        <li><a href="api/pyvene.models.gru.html#module-pyvene.models.gru">pyvene.models.gru</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_gru.html#module-pyvene.models.gru.modelings_gru">pyvene.models.gru.modelings_gru</a>
+</li>
+        <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.html#module-pyvene.models.gru.modelings_intervenable_gru">pyvene.models.gru.modelings_intervenable_gru</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_base.html#module-pyvene.models.intervenable_base">pyvene.models.intervenable_base</a>
+</li>
+        <li><a href="api/pyvene.models.intervenable_modelcard.html#module-pyvene.models.intervenable_modelcard">pyvene.models.intervenable_modelcard</a>
+</li>
+        <li><a href="api/pyvene.models.intervention_utils.html#module-pyvene.models.intervention_utils">pyvene.models.intervention_utils</a>
+</li>
+        <li><a href="api/pyvene.models.interventions.html#module-pyvene.models.interventions">pyvene.models.interventions</a>
+</li>
+        <li><a href="api/pyvene.models.layers.html#module-pyvene.models.layers">pyvene.models.layers</a>
+</li>
+        <li><a href="api/pyvene.models.llama.html#module-pyvene.models.llama">pyvene.models.llama</a>
+</li>
+        <li><a href="api/pyvene.models.llama.modelings_intervenable_llama.html#module-pyvene.models.llama.modelings_intervenable_llama">pyvene.models.llama.modelings_intervenable_llama</a>
+</li>
+        <li><a href="api/pyvene.models.llava.html#module-pyvene.models.llava">pyvene.models.llava</a>
+</li>
+        <li><a href="api/pyvene.models.llava.modelings_intervenable_llava.html#module-pyvene.models.llava.modelings_intervenable_llava">pyvene.models.llava.modelings_intervenable_llava</a>
+</li>
+        <li><a href="api/pyvene.models.mistral.html#module-pyvene.models.mistral">pyvene.models.mistral</a>
+</li>
+        <li><a href="api/pyvene.models.mistral.modellings_intervenable_mistral.html#module-pyvene.models.mistral.modellings_intervenable_mistral">pyvene.models.mistral.modellings_intervenable_mistral</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.html#module-pyvene.models.mlp">pyvene.models.mlp</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_intervenable_mlp.html#module-pyvene.models.mlp.modelings_intervenable_mlp">pyvene.models.mlp.modelings_intervenable_mlp</a>
+</li>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.html#module-pyvene.models.mlp.modelings_mlp">pyvene.models.mlp.modelings_mlp</a>
+</li>
+        <li><a href="api/pyvene.models.modeling_utils.html#module-pyvene.models.modeling_utils">pyvene.models.modeling_utils</a>
+</li>
+        <li><a href="api/pyvene.models.olmo.html#module-pyvene.models.olmo">pyvene.models.olmo</a>
+</li>
+        <li><a href="api/pyvene.models.olmo.modelings_intervenable_olmo.html#module-pyvene.models.olmo.modelings_intervenable_olmo">pyvene.models.olmo.modelings_intervenable_olmo</a>
+</li>
+        <li><a href="api/pyvene.models.qwen2.html#module-pyvene.models.qwen2">pyvene.models.qwen2</a>
+</li>
+        <li><a href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.html#module-pyvene.models.qwen2.modelings_intervenable_qwen2">pyvene.models.qwen2.modelings_intervenable_qwen2</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.moe_key">moe_key (RepresentationConfig attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.named_parameters">named_parameters() (BaseModel method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.NoiseIntervention.html#pyvene.models.interventions.NoiseIntervention">NoiseIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping.html#pyvene.models.olmo.modelings_intervenable_olmo.olmo_lm_type_to_dimension_mapping">olmo_lm_type_to_dimension_mapping (in module pyvene.models.olmo.modelings_intervenable_olmo)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping.html#pyvene.models.olmo.modelings_intervenable_olmo.olmo_type_to_dimension_mapping">olmo_type_to_dimension_mapping (in module pyvene.models.olmo.modelings_intervenable_olmo)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.output_to_subcomponent.html#pyvene.models.modeling_utils.output_to_subcomponent">output_to_subcomponent() (in module pyvene.models.modeling_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.PCARotatedSpaceIntervention.html#pyvene.models.interventions.PCARotatedSpaceIntervention">PCARotatedSpaceIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.print_forward_hooks.html#pyvene.models.modeling_utils.print_forward_hooks">print_forward_hooks() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li>
+    pyvene
+
+      <ul>
+        <li><a href="api/core.html#module-pyvene">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.data_generators
+
+      <ul>
+        <li><a href="api/pyvene.data_generators.html#module-pyvene.data_generators">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.data_generators.causal_model
+
+      <ul>
+        <li><a href="api/pyvene.data_generators.causal_model.html#module-pyvene.data_generators.causal_model">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models
+
+      <ul>
+        <li><a href="api/pyvene.models.html#module-pyvene.models">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.backpack_gpt2
+
+      <ul>
+        <li><a href="api/pyvene.models.backpack_gpt2.html#module-pyvene.models.backpack_gpt2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.backpack_gpt2.modelings_backpack_gpt2
+
+      <ul>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_backpack_gpt2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2
+
+      <ul>
+        <li><a href="api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.basic_utils
+
+      <ul>
+        <li><a href="api/pyvene.models.basic_utils.html#module-pyvene.models.basic_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.blip
+
+      <ul>
+        <li><a href="api/pyvene.models.blip.html#module-pyvene.models.blip">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.blip.modelings_blip
+
+      <ul>
+        <li><a href="api/pyvene.models.blip.modelings_blip.html#module-pyvene.models.blip.modelings_blip">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.blip.modelings_blip_itm
+
+      <ul>
+        <li><a href="api/pyvene.models.blip.modelings_blip_itm.html#module-pyvene.models.blip.modelings_blip_itm">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.blip.modelings_intervenable_blip
+
+      <ul>
+        <li><a href="api/pyvene.models.blip.modelings_intervenable_blip.html#module-pyvene.models.blip.modelings_intervenable_blip">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.blip.modelings_intervenable_blip_itm
+
+      <ul>
+        <li><a href="api/pyvene.models.blip.modelings_intervenable_blip_itm.html#module-pyvene.models.blip.modelings_intervenable_blip_itm">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.configuration_intervenable_model
+
+      <ul>
+        <li><a href="api/pyvene.models.configuration_intervenable_model.html#module-pyvene.models.configuration_intervenable_model">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.constants
+
+      <ul>
+        <li><a href="api/pyvene.models.constants.html#module-pyvene.models.constants">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gemma
+
+      <ul>
+        <li><a href="api/pyvene.models.gemma.html#module-pyvene.models.gemma">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gemma.modelings_intervenable_gemma
+
+      <ul>
+        <li><a href="api/pyvene.models.gemma.modelings_intervenable_gemma.html#module-pyvene.models.gemma.modelings_intervenable_gemma">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gemma2
+
+      <ul>
+        <li><a href="api/pyvene.models.gemma2.html#module-pyvene.models.gemma2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gemma2.modelings_intervenable_gemma2
+
+      <ul>
+        <li><a href="api/pyvene.models.gemma2.modelings_intervenable_gemma2.html#module-pyvene.models.gemma2.modelings_intervenable_gemma2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gpt2
+
+      <ul>
+        <li><a href="api/pyvene.models.gpt2.html#module-pyvene.models.gpt2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gpt2.modelings_intervenable_gpt2
+
+      <ul>
+        <li><a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.html#module-pyvene.models.gpt2.modelings_intervenable_gpt2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gpt_neo
+
+      <ul>
+        <li><a href="api/pyvene.models.gpt_neo.html#module-pyvene.models.gpt_neo">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gpt_neo.modelings_intervenable_gpt_neo
+
+      <ul>
+        <li><a href="api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.html#module-pyvene.models.gpt_neo.modelings_intervenable_gpt_neo">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    pyvene.models.gpt_neox
+
+      <ul>
+        <li><a href="api/pyvene.models.gpt_neox.html#module-pyvene.models.gpt_neox">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gpt_neox.modelings_intervenable_gpt_neox
+
+      <ul>
+        <li><a href="api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.html#module-pyvene.models.gpt_neox.modelings_intervenable_gpt_neox">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gru
+
+      <ul>
+        <li><a href="api/pyvene.models.gru.html#module-pyvene.models.gru">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gru.modelings_gru
+
+      <ul>
+        <li><a href="api/pyvene.models.gru.modelings_gru.html#module-pyvene.models.gru.modelings_gru">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.gru.modelings_intervenable_gru
+
+      <ul>
+        <li><a href="api/pyvene.models.gru.modelings_intervenable_gru.html#module-pyvene.models.gru.modelings_intervenable_gru">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.intervenable_base
+
+      <ul>
+        <li><a href="api/pyvene.models.intervenable_base.html#module-pyvene.models.intervenable_base">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.intervenable_modelcard
+
+      <ul>
+        <li><a href="api/pyvene.models.intervenable_modelcard.html#module-pyvene.models.intervenable_modelcard">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.intervention_utils
+
+      <ul>
+        <li><a href="api/pyvene.models.intervention_utils.html#module-pyvene.models.intervention_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.interventions
+
+      <ul>
+        <li><a href="api/pyvene.models.interventions.html#module-pyvene.models.interventions">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.layers
+
+      <ul>
+        <li><a href="api/pyvene.models.layers.html#module-pyvene.models.layers">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.llama
+
+      <ul>
+        <li><a href="api/pyvene.models.llama.html#module-pyvene.models.llama">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.llama.modelings_intervenable_llama
+
+      <ul>
+        <li><a href="api/pyvene.models.llama.modelings_intervenable_llama.html#module-pyvene.models.llama.modelings_intervenable_llama">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.llava
+
+      <ul>
+        <li><a href="api/pyvene.models.llava.html#module-pyvene.models.llava">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.llava.modelings_intervenable_llava
+
+      <ul>
+        <li><a href="api/pyvene.models.llava.modelings_intervenable_llava.html#module-pyvene.models.llava.modelings_intervenable_llava">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.mistral
+
+      <ul>
+        <li><a href="api/pyvene.models.mistral.html#module-pyvene.models.mistral">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.mistral.modellings_intervenable_mistral
+
+      <ul>
+        <li><a href="api/pyvene.models.mistral.modellings_intervenable_mistral.html#module-pyvene.models.mistral.modellings_intervenable_mistral">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.mlp
+
+      <ul>
+        <li><a href="api/pyvene.models.mlp.html#module-pyvene.models.mlp">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.mlp.modelings_intervenable_mlp
+
+      <ul>
+        <li><a href="api/pyvene.models.mlp.modelings_intervenable_mlp.html#module-pyvene.models.mlp.modelings_intervenable_mlp">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.mlp.modelings_mlp
+
+      <ul>
+        <li><a href="api/pyvene.models.mlp.modelings_mlp.html#module-pyvene.models.mlp.modelings_mlp">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.modeling_utils
+
+      <ul>
+        <li><a href="api/pyvene.models.modeling_utils.html#module-pyvene.models.modeling_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.olmo
+
+      <ul>
+        <li><a href="api/pyvene.models.olmo.html#module-pyvene.models.olmo">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.olmo.modelings_intervenable_olmo
+
+      <ul>
+        <li><a href="api/pyvene.models.olmo.modelings_intervenable_olmo.html#module-pyvene.models.olmo.modelings_intervenable_olmo">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.qwen2
+
+      <ul>
+        <li><a href="api/pyvene.models.qwen2.html#module-pyvene.models.qwen2">module</a>
+</li>
+      </ul></li>
+      <li>
+    pyvene.models.qwen2.modelings_intervenable_qwen2
+
+      <ul>
+        <li><a href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.html#module-pyvene.models.qwen2.modelings_intervenable_qwen2">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Q">Q</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping.html#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_lm_type_to_dimension_mapping">qwen2_lm_type_to_dimension_mapping (in module pyvene.models.qwen2.modelings_intervenable_qwen2)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping.html#pyvene.models.qwen2.modelings_intervenable_qwen2.qwen2_type_to_dimension_mapping">qwen2_type_to_dimension_mapping (in module pyvene.models.qwen2.modelings_intervenable_qwen2)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.basic_utils.random_permutation_matrix.html#pyvene.models.basic_utils.random_permutation_matrix">random_permutation_matrix() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.remove_forward_hooks.html#pyvene.models.modeling_utils.remove_forward_hooks">remove_forward_hooks() (in module pyvene.models.modeling_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig">RepresentationConfig (class in pyvene.models.configuration_intervenable_model)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention">RotatedSpaceIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.layers.RotateLayer.html#pyvene.models.layers.RotateLayer">RotateLayer (class in pyvene.models.layers)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.save">save() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.save_intervention">save_intervention() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.scatter_neurons.html#pyvene.models.modeling_utils.scatter_neurons">scatter_neurons() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.set_device">set_device() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.gru.modelings_gru.GRUModel.html#pyvene.models.gru.modelings_gru.GRUModel.set_input_embeddings">set_input_embeddings() (GRUModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.set_seed.html#pyvene.models.basic_utils.set_seed">set_seed() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.set_temperature">set_temperature() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.set_zero_grad">set_zero_grad() (BaseModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html#pyvene.models.interventions.SharedWeightsTrainableIntervention">SharedWeightsTrainableIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.sigmoid_boundary.html#pyvene.models.basic_utils.sigmoid_boundary">sigmoid_boundary() (in module pyvene.models.basic_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.SigmoidMaskIntervention.html#pyvene.models.interventions.SigmoidMaskIntervention">SigmoidMaskIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html#pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention">SigmoidMaskRotatedSpaceIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.data_generators.causal_model.simple_example.html#pyvene.data_generators.causal_model.simple_example">simple_example() (in module pyvene.data_generators.causal_model)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.modeling_utils.simple_output_to_subcomponent.html#pyvene.models.modeling_utils.simple_output_to_subcomponent">simple_output_to_subcomponent() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.modeling_utils.simple_scatter_intervention_output.html#pyvene.models.modeling_utils.simple_scatter_intervention_output">simple_scatter_intervention_output() (in module pyvene.models.modeling_utils)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.SkipIntervention.html#pyvene.models.interventions.SkipIntervention">SkipIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.source_representation">source_representation (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.SourcelessIntervention.html#pyvene.models.interventions.SourcelessIntervention">SourcelessIntervention (class in pyvene.models.interventions)</a>
+</li>
+      <li><a href="api/pyvene.models.constants.split_and_select.html#pyvene.models.constants.split_and_select">split_and_select() (in module pyvene.models.constants)</a>
+</li>
+      <li><a href="api/pyvene.models.constants.split_half.html#pyvene.models.constants.split_half">split_half() (in module pyvene.models.constants)</a>
+</li>
+      <li><a href="api/pyvene.models.constants.split_head_and_permute.html#pyvene.models.constants.split_head_and_permute">split_head_and_permute() (in module pyvene.models.constants)</a>
+</li>
+      <li><a href="api/pyvene.models.constants.split_heads.html#pyvene.models.constants.split_heads">split_heads() (in module pyvene.models.constants)</a>
+</li>
+      <li><a href="api/pyvene.models.constants.split_three.html#pyvene.models.constants.split_three">split_three() (in module pyvene.models.constants)</a>
+</li>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.subspace_partition">subspace_partition (RepresentationConfig attribute)</a>
+</li>
+      <li><a href="api/pyvene.models.layers.SubspaceLowRankRotateLayer.html#pyvene.models.layers.SubspaceLowRankRotateLayer">SubspaceLowRankRotateLayer (class in pyvene.models.layers)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.SubtractionIntervention.html#pyvene.models.interventions.SubtractionIntervention">SubtractionIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.html#pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel.tie_weights">tie_weights() (BackpackGPT2LMHeadModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.basic_utils.top_vals.html#pyvene.models.basic_utils.top_vals">top_vals() (in module pyvene.models.basic_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.train">train() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel.train_alignment">train_alignment() (IntervenableModel method)</a>
+</li>
+      <li><a href="api/pyvene.models.interventions.TrainableIntervention.html#pyvene.models.interventions.TrainableIntervention">TrainableIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html#pyvene.models.configuration_intervenable_model.RepresentationConfig.unit">unit (RepresentationConfig attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.VanillaIntervention.html#pyvene.models.interventions.VanillaIntervention">VanillaIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.modeling_utils.weighted_average.html#pyvene.models.modeling_utils.weighted_average">weighted_average() (in module pyvene.models.modeling_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Z">Z</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.intervenable_base.BaseModel.html#pyvene.models.intervenable_base.BaseModel.zero_grad">zero_grad() (BaseModel method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/pyvene.models.interventions.ZeroIntervention.html#pyvene.models.interventions.ZeroIntervention">ZeroIntervention (class in pyvene.models.interventions)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
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+<ul class="current nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing Guidelines</a></li>
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">A Little Guide to Causal Abstraction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ndif.html">NDIF Integration</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Basic tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/pyvene_101.html">Introduction to pyvene</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Add_Activations_to_Streams.html">Activation Addition</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Intervention_Training.html">Trainable Interventions</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced tutorials</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Probing_Gender.html">Causal Evaluation of Probes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">API</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../api/core.html"><code class="docutils literal notranslate"><span class="pre">pyvene</span></code>: Core API</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../api/pyvene.data_generators.html">pyvene.data_generators</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l3 has-children"><a class="reference internal" href="../api/pyvene.data_generators.causal_model.html">pyvene.data_generators.causal_model</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.data_generators.causal_model.simple_example.html">pyvene.data_generators.causal_model.simple_example</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.data_generators.causal_model.CausalModel.html">pyvene.data_generators.causal_model.CausalModel</a></li>
+</ul>
+</details></li>
+</ul>
+</details></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../api/pyvene.models.html">pyvene.models</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html">pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="../api/pyvene.models.basic_utils.html">pyvene.models.basic_utils</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.GET_LOC.html">pyvene.models.basic_utils.GET_LOC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.closeness_to_permutation_loss.html">pyvene.models.basic_utils.closeness_to_permutation_loss</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.count_parameters.html">pyvene.models.basic_utils.count_parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.create_directory.html">pyvene.models.basic_utils.create_directory</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.embed_to_distrib.html">pyvene.models.basic_utils.embed_to_distrib</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.format_token.html">pyvene.models.basic_utils.format_token</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.get_batch_size.html">pyvene.models.basic_utils.get_batch_size</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.get_list_depth.html">pyvene.models.basic_utils.get_list_depth</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.get_type_from_string.html">pyvene.models.basic_utils.get_type_from_string</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.harmonic_sigmoid_boundary.html">pyvene.models.basic_utils.harmonic_sigmoid_boundary</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.random_permutation_matrix.html">pyvene.models.basic_utils.random_permutation_matrix</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.set_seed.html">pyvene.models.basic_utils.set_seed</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.sigmoid_boundary.html">pyvene.models.basic_utils.sigmoid_boundary</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.top_vals.html">pyvene.models.basic_utils.top_vals</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="../api/pyvene.models.blip.html">pyvene.models.blip</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.blip.modelings_blip.html">pyvene.models.blip.modelings_blip</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.blip.modelings_blip_itm.html">pyvene.models.blip.modelings_blip_itm</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.blip.modelings_intervenable_blip.html">pyvene.models.blip.modelings_intervenable_blip</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.blip.modelings_intervenable_blip_itm.html">pyvene.models.blip.modelings_intervenable_blip_itm</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="../api/pyvene.models.configuration_intervenable_model.html">pyvene.models.configuration_intervenable_model</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.configuration_intervenable_model.IntervenableConfig.html">pyvene.models.configuration_intervenable_model.IntervenableConfig</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.configuration_intervenable_model.RepresentationConfig.html">pyvene.models.configuration_intervenable_model.RepresentationConfig</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="../api/pyvene.models.constants.html">pyvene.models.constants</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.constants.split_head_and_permute.html">pyvene.models.constants.split_head_and_permute</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.constants.split_heads.html">pyvene.models.constants.split_heads</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.constants.split_three.html">pyvene.models.constants.split_three</a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#understanding-causal-mechanisms-with-static-interventions">Understanding Causal Mechanisms with Static Interventions</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#understanding-causal-mechanism-with-trainable-interventions">Understanding Causal Mechanism with Trainable Interventions</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="a-little-guide-to-causal-abstraction">
+<h1>A Little Guide to Causal Abstraction<a class="headerlink" href="#a-little-guide-to-causal-abstraction" title="Link to this heading">#</a></h1>
+<p><em>From Interventions to Gaining Interpretability Insights</em></p>
+<dl class="field-list simple">
+<dt class="field-odd">author<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Zhengxuan Wu</p>
+</dd>
+</dl>
+<p>Basic interventions are fun but we cannot make any causal claim
+systematically. To gain actual interpretability insights, we want to
+measure the counterfactual behaviors of a model in a data-driven
+fashion. In other words, if the model responds systematically to your
+interventions, then you start to associate certain regions in the
+network with a high-level concept. We also call this alignment search
+process with model internals.</p>
+<section id="understanding-causal-mechanisms-with-static-interventions">
+<h2>Understanding Causal Mechanisms with Static Interventions<a class="headerlink" href="#understanding-causal-mechanisms-with-static-interventions" title="Link to this heading">#</a></h2>
+<p>Here is a more concrete example,</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add_three_numbers</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+    <span class="n">var_x</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
+    <span class="k">return</span> <span class="n">var_x</span> <span class="o">+</span> <span class="n">c</span>
+</pre></div>
+</div>
+<p>The function solves a 3-digit sum problem. Let’s say, we trained a
+neural network to solve this problem perfectly. “Can we find the
+representation of (a + b) in the neural network?”. We can use this
+library to answer this question. Specifically, we can do the following,</p>
+<ul class="simple">
+<li><p><strong>Step 1:</strong> Form Interpretability (Alignment) Hypothesis: We
+hypothesize that a set of neurons N aligns with (a + b).</p></li>
+<li><p><strong>Step 2:</strong> Counterfactual Testings: If our hypothesis is correct,
+then swapping neurons N between examples would give us expected
+counterfactual behaviors. For instance, the values of N for (1+2)+3,
+when swapping with N for (2+3)+4, the output should be (2+3)+3 or
+(1+2)+4 depending on the direction of the swap.</p></li>
+<li><p><strong>Step 3:</strong> Reject Sampling of Hypothesis: Running tests multiple
+times and aggregating statistics in terms of counterfactual behavior
+matching. Proposing a new hypothesis based on the results.</p></li>
+</ul>
+<p>To translate the above steps into API calls with the library, it will be
+a single call,</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">eval_alignment</span><span class="p">(</span>
+    <span class="n">train_dataloader</span><span class="o">=</span><span class="n">test_dataloader</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="n">compute_metrics</span><span class="p">,</span>
+    <span class="n">inputs_collator</span><span class="o">=</span><span class="n">inputs_collator</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>where you provide testing data (basically interventional data and the
+counterfactual behavior you are looking for) along with your metrics
+functions. The library will try to evaluate the alignment with the
+intervention you specified in the config.</p>
+</section>
+<section id="understanding-causal-mechanism-with-trainable-interventions">
+<h2>Understanding Causal Mechanism with Trainable Interventions<a class="headerlink" href="#understanding-causal-mechanism-with-trainable-interventions" title="Link to this heading">#</a></h2>
+<p>The alignment searching process outlined above can be tedious when your
+neural network is large. For a single hypothesized alignment, you
+basically need to set up different intervention configs targeting
+different layers and positions to verify your hypothesis. Instead of
+doing this brute-force search process, you can turn it into an
+optimization problem which also has other benefits such as distributed
+alignments.</p>
+<p>In its crux, we basically want to train an intervention to have our
+desired counterfactual behaviors in mind. And if we can indeed train
+such interventions, we claim that causally informative information
+should live in the intervening representations! Below, we show one type
+of trainable intervention <a class="reference internal" href="../api/pyvene.models.interventions.RotatedSpaceIntervention.html#pyvene.models.interventions.RotatedSpaceIntervention" title="pyvene.models.interventions.RotatedSpaceIntervention"><code class="xref py py-class docutils literal notranslate"><span class="pre">RotatedSpaceIntervention</span></code></a>
+as,</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">RotatedSpaceIntervention</span><span class="p">(</span><span class="n">TrainableIntervention</span><span class="p">):</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Intervention in the rotated space.&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">):</span>
+        <span class="n">rotated_base</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">rotated_source</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+        <span class="c1"># interchange</span>
+        <span class="n">rotated_base</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">rotated_source</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span>
+        <span class="c1"># inverse base</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">rotated_base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">output</span>
+</pre></div>
+</div>
+<p>Instead of activation swapping in the original representation space, we
+first <strong>rotate</strong> them, and then do the swap followed by un-rotating the
+intervened representation. Additionally, we try to use SGD to <strong>learn a
+rotation</strong> that lets us produce expected counterfactual behavior. If we
+can find such rotation, we claim there is an alignment.
+<code class="docutils literal notranslate"><span class="pre">If</span> <span class="pre">the</span> <span class="pre">cost</span> <span class="pre">is</span> <span class="pre">between</span> <span class="pre">X</span> <span class="pre">and</span> <span class="pre">Y.ipynb</span></code> tutorial covers this with an
+advanced version of distributed alignment search, <a class="reference external" href="https://arxiv.org/abs/2305.08809">Boundless
+DAS</a>. There are <a class="reference external" href="https://www.lesswrong.com/posts/RFtkRXHebkwxygDe2/an-interpretability-illusion-for-activation-patching-of">recent
+works</a>
+outlining potential limitations of doing a distributed alignment search
+as well.</p>
+<p>You can now also make a single API call to train your intervention,</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">train_alignment</span><span class="p">(</span>
+    <span class="n">train_dataloader</span><span class="o">=</span><span class="n">train_dataloader</span><span class="p">,</span>
+    <span class="n">compute_loss</span><span class="o">=</span><span class="n">compute_loss</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="n">compute_metrics</span><span class="p">,</span>
+    <span class="n">inputs_collator</span><span class="o">=</span><span class="n">inputs_collator</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>where you need to pass in a trainable dataset, and your customized loss
+and metrics function. The trainable interventions can later be saved on
+to your disk. You can also use <a class="reference internal" href="../api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel" title="pyvene.models.intervenable_base.IntervenableModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">intervenable.evaluate()</span></code></a> your
+interventions in terms of customized objectives.</p>
+</section>
+</section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#understanding-causal-mechanism-with-trainable-interventions">Understanding Causal Mechanism with Trainable Interventions</a></li>
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+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Nested_Intervention.html">Intervening on subcomponents</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html">Subspace Interventions</a></li>
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+</ul>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Causal_Tracing.html">Causal Tracing (ROME)</a></li>
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/DAS_Main_Introduction.html">Intro to Distributed Alignment Search (DAS)</a></li>
+
+
+
+
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Boundless_DAS.html">Boundless DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
+</ul>
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+<li class="toctree-l3 has-children"><a class="reference internal" href="../api/pyvene.data_generators.causal_model.html">pyvene.data_generators.causal_model</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.data_generators.causal_model.CausalModel.html">pyvene.data_generators.causal_model.CausalModel</a></li>
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+</details></li>
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+              
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+    <h1>Contributing Guidelines</h1>
+    <!-- Table of contents -->
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+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-pr-or-issue-title-format">The PR or Issue Title Format</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pull-requests">📕 Pull Requests</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#uninstall-pyvene-from-python-library">Uninstall pyvene from python library</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#unit-test-run-is-a-must-before-creating-prs">Unit Test Run Is A Must before Creating PRs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#format">Format</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#bug-reports-and-other-issues">🪲 Bug Reports and Other Issues</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#documentation">📄 Documentation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#larger-feature-requests">📥 Larger Feature Requests</a></li>
+</ul>
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+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="contributing-guidelines">
+<h1>Contributing Guidelines<a class="headerlink" href="#contributing-guidelines" title="Link to this heading">#</a></h1>
+<p><em>Pull requests, bug reports, and all other forms of contribution are welcomed and highly encouraged!</em> :octocat:</p>
+<section id="the-pr-or-issue-title-format">
+<h2>The PR or Issue Title Format<a class="headerlink" href="#the-pr-or-issue-title-format" title="Link to this heading">#</a></h2>
+<p>Whenever you open an issue or a PR, please use this title format</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">Priority</span> <span class="n">Tag</span><span class="p">]</span> <span class="n">Short</span> <span class="n">Title</span>
+</pre></div>
+</div>
+<p>For Priority Tag, you can use <code class="docutils literal notranslate"><span class="pre">[P0]</span></code>-<code class="docutils literal notranslate"><span class="pre">[P2]</span></code>, <code class="docutils literal notranslate"><span class="pre">[P0]</span></code> is the highest priority, which means everyone should stop working and focus on this PR. For Minor issues, use <code class="docutils literal notranslate"><span class="pre">[Minor]</span></code>. For bugs, please use <code class="docutils literal notranslate"><span class="pre">[Bug</span> <span class="pre">Fix]</span></code> and see below.</p>
+<hr class="docutils" />
+</section>
+<section id="pull-requests">
+<h2>📕 Pull Requests<a class="headerlink" href="#pull-requests" title="Link to this heading">#</a></h2>
+<section id="uninstall-pyvene-from-python-library">
+<h3>Uninstall pyvene from python library<a class="headerlink" href="#uninstall-pyvene-from-python-library" title="Link to this heading">#</a></h3>
+<p>It becomes tricky if you have <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> installed while debugging with this codebase, since imports can be easily messed up. Please run,</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>uninstall<span class="w"> </span>pyvene
+</pre></div>
+</div>
+</section>
+<section id="unit-test-run-is-a-must-before-creating-prs">
+<h3>Unit Test Run Is A Must before Creating PRs<a class="headerlink" href="#unit-test-run-is-a-must-before-creating-prs" title="Link to this heading">#</a></h3>
+<p>When adding new methods or APIs, unit tests are now enforced. To run existing tests, you can kick off the python unittest command in the discovery mode as,</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>pyvene
+python<span class="w"> </span>-m<span class="w"> </span>unittest<span class="w"> </span>discover<span class="w"> </span>-p<span class="w"> </span><span class="s1">&#39;*TestCase.py&#39;</span>
+</pre></div>
+</div>
+<p>For specific test case, yoou can run</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>pyvene
+python<span class="w"> </span>-m<span class="w"> </span>unittest<span class="w"> </span>tests.integration_tests.ComplexInterventionWithGPT2TestCase
+</pre></div>
+</div>
+<p>When checking in new code, please also consider to add new tests in the same PR. Please include test results in the PR to make sure all the existing test cases are passing. Please see the <code class="docutils literal notranslate"><span class="pre">qa_runbook.ipynb</span></code> notebook about a set of conventions about how to add test cases. The code coverage for this repository is currently <code class="docutils literal notranslate"><span class="pre">low</span></code>, and we are adding more automated tests.</p>
+</section>
+<section id="format">
+<h3>Format<a class="headerlink" href="#format" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">**</span><span class="n">Descriptions</span><span class="o">**</span><span class="p">:</span>
+
+<span class="p">[</span><span class="n">Describe</span> <span class="n">your</span> <span class="n">PR</span> <span class="n">Here</span><span class="p">]</span>
+
+
+<span class="o">**</span><span class="n">Testing</span> <span class="n">Done</span><span class="o">**</span><span class="p">:</span>
+
+<span class="p">[</span><span class="n">Provide</span> <span class="n">logs</span><span class="p">,</span> <span class="n">screen</span><span class="o">-</span><span class="n">shots</span><span class="p">,</span> <span class="ow">and</span> <span class="n">files</span> <span class="n">that</span> <span class="n">contain</span> <span class="n">tests</span> <span class="n">you</span> <span class="n">have</span> <span class="n">done</span><span class="p">]</span>
+
+</pre></div>
+</div>
+</section>
+</section>
+<section id="bug-reports-and-other-issues">
+<h2>🪲 Bug Reports and Other Issues<a class="headerlink" href="#bug-reports-and-other-issues" title="Link to this heading">#</a></h2>
+<p>Go to issues, and open with a title formatted as,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">Bug</span> <span class="n">Fix</span><span class="p">]</span> <span class="n">Short</span> <span class="n">Title</span>
+</pre></div>
+</div>
+<p>For external requests (i.e., you are not in our core dev team), please use,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">External</span><span class="p">]</span> <span class="n">Short</span> <span class="n">Title</span>
+</pre></div>
+</div>
+</section>
+<section id="documentation">
+<h2>📄 Documentation<a class="headerlink" href="#documentation" title="Link to this heading">#</a></h2>
+<p>If making changes to documentation (in <code class="docutils literal notranslate"><span class="pre">docs/source</span></code>, deployed to GitHub Pages), please test your changes locally
+(ideally in a fresh Python environment):</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">r</span> <span class="n">requirements</span><span class="o">.</span><span class="n">txt</span>
+<span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">r</span> <span class="n">docs</span><span class="o">/</span><span class="n">requirements</span><span class="o">.</span><span class="n">txt</span>
+<span class="n">cd</span> <span class="n">docs</span>
+<span class="n">make</span> <span class="n">html</span>
+<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">http</span><span class="o">.</span><span class="n">server</span>
+</pre></div>
+</div>
+<p>Then navigate to <a class="reference external" href="http://localhost:8000/build/html">localhost:8000/build/html</a>.</p>
+</section>
+<section id="larger-feature-requests">
+<h2>📥 Larger Feature Requests<a class="headerlink" href="#larger-feature-requests" title="Link to this heading">#</a></h2>
+<p>Please email us!</p>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.build_intervenable_model.html">pyvene.models.intervenable_base.build_intervenable_model</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.BaseModel.html">pyvene.models.intervenable_base.BaseModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.IntervenableModel.html">pyvene.models.intervenable_base.IntervenableModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.IntervenableModelOutput.html">pyvene.models.intervenable_base.IntervenableModelOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervenable_base.IntervenableNdifModel.html">pyvene.models.intervenable_base.IntervenableNdifModel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.intervention_utils.InterventionState.html">pyvene.models.intervention_utils.InterventionState</a></li>
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+</details></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.AdditionIntervention.html">pyvene.models.interventions.AdditionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.AutoencoderIntervention.html">pyvene.models.interventions.AutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.BasisAgnosticIntervention.html">pyvene.models.interventions.BasisAgnosticIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html">pyvene.models.interventions.BoundlessRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.CollectIntervention.html">pyvene.models.interventions.CollectIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.ConstantSourceIntervention.html">pyvene.models.interventions.ConstantSourceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.DistributedRepresentationIntervention.html">pyvene.models.interventions.DistributedRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.Intervention.html">pyvene.models.interventions.Intervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.InterventionOutput.html">pyvene.models.interventions.InterventionOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html">pyvene.models.interventions.JumpReLUAutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.LocalistRepresentationIntervention.html">pyvene.models.interventions.LocalistRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html">pyvene.models.interventions.LowRankRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.NoiseIntervention.html">pyvene.models.interventions.NoiseIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.PCARotatedSpaceIntervention.html">pyvene.models.interventions.PCARotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.RotatedSpaceIntervention.html">pyvene.models.interventions.RotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html">pyvene.models.interventions.SharedWeightsTrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.SigmoidMaskIntervention.html">pyvene.models.interventions.SigmoidMaskIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html">pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.SkipIntervention.html">pyvene.models.interventions.SkipIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.SourcelessIntervention.html">pyvene.models.interventions.SourcelessIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.SubtractionIntervention.html">pyvene.models.interventions.SubtractionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.TrainableIntervention.html">pyvene.models.interventions.TrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.VanillaIntervention.html">pyvene.models.interventions.VanillaIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.interventions.ZeroIntervention.html">pyvene.models.interventions.ZeroIntervention</a></li>
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+</details></li>
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+    <h1>NDIF Integration</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
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+                  
+  <section id="ndif-integration">
+<h1>NDIF Integration<a class="headerlink" href="#ndif-integration" title="Link to this heading">#</a></h1>
+<p>We are working with the <a class="reference external" href="https://ndif.us/">NDIF</a> team to support remote intervention calls
+without asking the users to download or host their own LLMs! This is still under construction.</p>
+<p>First of all, you need to install <code class="docutils literal notranslate"><span class="pre">nnsight</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">nnsight</span>
+</pre></div>
+</div>
+<p>All you have to do is to use NDIF library to load your model and use pyvene to wrap it
+(i.e., pyvene will automatically recognize NDIF models)! Here is an example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nnsight</span> <span class="kn">import</span> <span class="n">LanguageModel</span>
+
+<span class="c1"># load nnsight.LanguageModel as your model to wrap with pyvene</span>
+<span class="n">gpt2_ndif</span> <span class="o">=</span> <span class="n">LanguageModel</span><span class="p">(</span><span class="s1">&#39;openai-community/gpt2&#39;</span><span class="p">,</span> <span class="n">device_map</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s1">&#39;openai-community/gpt2&#39;</span><span class="p">)</span>
+
+<span class="c1"># pyvene provides pv.build_intervenable_model as the generic model builder</span>
+<span class="n">pv_gpt2_ndif</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">build_intervenable_model</span><span class="p">({</span>
+   <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;transformer.h[10].attn.attn_dropout.input&quot;</span><span class="p">,</span>
+   <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">()},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2_ndif</span><span class="p">,</span> <span class="n">remote</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Then, you can use <code class="docutils literal notranslate"><span class="pre">pv_gpt2_ndif</span></code> as your regular intervenable model.
+If you specify <code class="docutils literal notranslate"><span class="pre">remote=True</span></code> (this is still under construction), then
+everything will be executed remotely on NDIF server with <strong>zero</strong> GPU
+resource required! We provide example code in our <a class="reference external" href="tutorials/pyvene_101">main tutorial</a>.</p>
+</section>
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.build_intervenable_model.html">pyvene.models.intervenable_base.build_intervenable_model</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.BaseModel.html">pyvene.models.intervenable_base.BaseModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableModel.html">pyvene.models.intervenable_base.IntervenableModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableModelOutput.html">pyvene.models.intervenable_base.IntervenableModelOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableNdifModel.html">pyvene.models.intervenable_base.IntervenableNdifModel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.AdditionIntervention.html">pyvene.models.interventions.AdditionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.AutoencoderIntervention.html">pyvene.models.interventions.AutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.BasisAgnosticIntervention.html">pyvene.models.interventions.BasisAgnosticIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html">pyvene.models.interventions.BoundlessRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.CollectIntervention.html">pyvene.models.interventions.CollectIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.ConstantSourceIntervention.html">pyvene.models.interventions.ConstantSourceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.DistributedRepresentationIntervention.html">pyvene.models.interventions.DistributedRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.Intervention.html">pyvene.models.interventions.Intervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.InterventionOutput.html">pyvene.models.interventions.InterventionOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html">pyvene.models.interventions.JumpReLUAutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.LocalistRepresentationIntervention.html">pyvene.models.interventions.LocalistRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html">pyvene.models.interventions.LowRankRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.NoiseIntervention.html">pyvene.models.interventions.NoiseIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.PCARotatedSpaceIntervention.html">pyvene.models.interventions.PCARotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.RotatedSpaceIntervention.html">pyvene.models.interventions.RotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html">pyvene.models.interventions.SharedWeightsTrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SigmoidMaskIntervention.html">pyvene.models.interventions.SigmoidMaskIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention.html">pyvene.models.interventions.SigmoidMaskRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SkipIntervention.html">pyvene.models.interventions.SkipIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SourcelessIntervention.html">pyvene.models.interventions.SourcelessIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SubtractionIntervention.html">pyvene.models.interventions.SubtractionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.TrainableIntervention.html">pyvene.models.interventions.TrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.VanillaIntervention.html">pyvene.models.interventions.VanillaIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.ZeroIntervention.html">pyvene.models.interventions.ZeroIntervention</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.layers.html">pyvene.models.layers</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.layers.AutoencoderLayer.html">pyvene.models.layers.AutoencoderLayer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.layers.AutoencoderLayerBase.html">pyvene.models.layers.AutoencoderLayerBase</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.layers.InverseRotateLayer.html">pyvene.models.layers.InverseRotateLayer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.layers.LowRankRotateLayer.html">pyvene.models.layers.LowRankRotateLayer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.layers.RotateLayer.html">pyvene.models.layers.RotateLayer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.layers.SubspaceLowRankRotateLayer.html">pyvene.models.layers.SubspaceLowRankRotateLayer</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.llama.html">pyvene.models.llama</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.llama.modelings_intervenable_llama.html">pyvene.models.llama.modelings_intervenable_llama</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.llava.html">pyvene.models.llava</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.llava.modelings_intervenable_llava.html">pyvene.models.llava.modelings_intervenable_llava</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.mistral.html">pyvene.models.mistral</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.mistral.modellings_intervenable_mistral.html">pyvene.models.mistral.modellings_intervenable_mistral</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.mlp.html">pyvene.models.mlp</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.mlp.modelings_intervenable_mlp.html">pyvene.models.mlp.modelings_intervenable_mlp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.mlp.modelings_mlp.html">pyvene.models.mlp.modelings_mlp</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.modeling_utils.html">pyvene.models.modeling_utils</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.b_sd_to_bsd.html">pyvene.models.modeling_utils.b_sd_to_bsd</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.bhsd_to_bs_hd.html">pyvene.models.modeling_utils.bhsd_to_bs_hd</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.bs_hd_to_bhsd.html">pyvene.models.modeling_utils.bs_hd_to_bhsd</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.bsd_to_b_sd.html">pyvene.models.modeling_utils.bsd_to_b_sd</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.do_intervention.html">pyvene.models.modeling_utils.do_intervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.gather_neurons.html">pyvene.models.modeling_utils.gather_neurons</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.get_dimension_by_component.html">pyvene.models.modeling_utils.get_dimension_by_component</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.get_internal_model_type.html">pyvene.models.modeling_utils.get_internal_model_type</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.get_module_hook.html">pyvene.models.modeling_utils.get_module_hook</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.getattr_for_torch_module.html">pyvene.models.modeling_utils.getattr_for_torch_module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.is_gru.html">pyvene.models.modeling_utils.is_gru</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.is_mlp.html">pyvene.models.modeling_utils.is_mlp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.is_stateless.html">pyvene.models.modeling_utils.is_stateless</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.print_forward_hooks.html">pyvene.models.modeling_utils.print_forward_hooks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.remove_forward_hooks.html">pyvene.models.modeling_utils.remove_forward_hooks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.scatter_neurons.html">pyvene.models.modeling_utils.scatter_neurons</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.simple_output_to_subcomponent.html">pyvene.models.modeling_utils.simple_output_to_subcomponent</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.simple_scatter_intervention_output.html">pyvene.models.modeling_utils.simple_scatter_intervention_output</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.weighted_average.html">pyvene.models.modeling_utils.weighted_average</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.HandlerList.html">pyvene.models.modeling_utils.HandlerList</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.modeling_utils.LambdaIntervention.html">pyvene.models.modeling_utils.LambdaIntervention</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.olmo.html">pyvene.models.olmo</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.olmo.modelings_intervenable_olmo.html">pyvene.models.olmo.modelings_intervenable_olmo</a></li>
+</ul>
+</details></li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.qwen2.html">pyvene.models.qwen2</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.html">pyvene.models.qwen2.modelings_intervenable_qwen2</a></li>
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+    <h1>pyvene</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">pyvene</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#getting-started">Getting Started</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#wrap-and-intervene"><em>Wrap</em> and <em>intervene</em></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#share-and-load-from-huggingface"><em>Share</em> and <em>load</em> from HuggingFace</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#intervenablemodel-is-just-an-nn-module"><em>IntervenableModel</em> is just an <em>nn.Module</em></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#complex-intervention-schema-as-an-object">Complex <em>Intervention Schema</em> as an <em>Object</em></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#selected-tutorials">Selected Tutorials</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#contributing-to-this-library">Contributing to This Library</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citation">Citation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#related-works-on-discovering-causal-mechanism-of-llms">Related Works on Discovering Causal Mechanism of LLMs</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#star-history">Star History</a><ul class="nav section-nav flex-column">
+</ul>
+</li>
+</ul>
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+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#indices-and-tables">Indices and tables</a></li>
+</ul>
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+  <div class="docutils container">
+<p class="rubric" id="section"><img alt="image1" src="https://i.ibb.co/BNkhQH3/pyvene-logo.png" /></p>
+<p><a class="reference external" href="https://arxiv.org/abs/2403.07809">Read Our Paper »</a></p>
+</div>
+<section id="pyvene">
+<h1>pyvene<a class="headerlink" href="#pyvene" title="Link to this heading">#</a></h1>
+<p><a class="reference external" href="https://pypi.org/project/pyvene/"><img alt="image2" src="https://img.shields.io/pepy/dt/pyvene?color=green" /></a> <a class="reference external" href="https://pypi.org/project/pyvene/"><img alt="image3" src="https://img.shields.io/pypi/v/pyvene?color=red" /></a> <a class="reference external" href="https://pypi.org/project/pyvene/"><img alt="image4" src="https://img.shields.io/pypi/l/pyvene?color=blue" /></a></p>
+<div class="toctree-wrapper compound">
+</div>
+<p><strong>pyvene</strong> is an open-source Python library for intervening on the internal states of
+PyTorch models. Interventions are an important operation in many areas of AI, including
+model editing, steering, robustness, and interpretability.</p>
+<p>pyvene has many features that make interventions easy:</p>
+<ul class="simple">
+<li><p>Interventions are the basic primitive, specified as dicts and thus able to be saved locally
+and shared as serialisable objects through HuggingFace.</p></li>
+<li><p>Interventions can be composed and customised: you can run them on multiple locations, on arbitrary
+sets of neurons (or other levels of granularity), in parallel or in sequence, on decoding steps of
+generative language models, etc.</p></li>
+<li><p>Interventions work out-of-the-box on any PyTorch model! No need to define new model classes from
+scratch and easy interventions are possible all kinds of architectures (RNNs, ResNets, CNNs, Mamba).</p></li>
+</ul>
+<p>pyvene is under active development and constantly being improved 🫡</p>
+<section id="getting-started">
+<h2>Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading">#</a></h2>
+<p>To install the latest stable version of pyvene:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pyvene</span>
+</pre></div>
+</div>
+<p>Alternatively, to install a bleeding-edge version, you can clone the repo and install:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">git</span><span class="nd">@github</span><span class="o">.</span><span class="n">com</span><span class="p">:</span><span class="n">stanfordnlp</span><span class="o">/</span><span class="n">pyvene</span><span class="o">.</span><span class="n">git</span>
+<span class="n">cd</span> <span class="n">pyvene</span>
+<span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">e</span> <span class="o">.</span>
+</pre></div>
+</div>
+<p>When you want to update, you can just run <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">pull</span></code> in the cloned directory.</p>
+<p>We suggest importing the library as:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+</pre></div>
+</div>
+</section>
+<section id="wrap-and-intervene">
+<h2><em>Wrap</em> and <em>intervene</em><a class="headerlink" href="#wrap-and-intervene" title="Link to this heading">#</a></h2>
+<p>The usual workflow for using pyvene is to load a model, define an intervention config and wrap the model,
+and then run the intervened model. This returns both the original and intervened outputs, as well as any
+internal activations you specified to collect. For example:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span>
+
+
+<span class="c1"># 1. Load the model</span>
+<span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;meta-llama/Llama-2-7b-hf&quot;</span> <span class="c1"># the HF model you want to intervene on</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_name</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+   <span class="n">model_name</span><span class="p">,</span> <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">device_map</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+
+<span class="c1"># 2. Wrap the model with an intervention config</span>
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+   <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;model.layers[15].mlp.output&quot;</span><span class="p">,</span>   <span class="c1"># where to intervene (here, the MLP output in layer 15)</span>
+   <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">ZeroIntervention</span>           <span class="c1"># what intervention to apply (here, zeroing out the activation)</span>
+<span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
+
+
+<span class="c1"># 3. Run the intervened model</span>
+<span class="n">orig_outputs</span><span class="p">,</span> <span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_model</span><span class="p">(</span>
+   <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">),</span>
+   <span class="n">output_original_output</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+
+<span class="c1"># 4. Compare outputs</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">intervened_outputs</span><span class="o">.</span><span class="n">logits</span> <span class="o">-</span> <span class="n">orig_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>which returns</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tensor</span><span class="p">([[[</span> <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">],</span>
+         <span class="p">[</span> <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">],</span>
+         <span class="p">[</span> <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">],</span>
+         <span class="p">[</span> <span class="mf">0.4375</span><span class="p">,</span>  <span class="mf">1.0625</span><span class="p">,</span>  <span class="mf">0.3750</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1562</span><span class="p">,</span>  <span class="mf">0.4844</span><span class="p">,</span>  <span class="mf">0.2969</span><span class="p">],</span>
+         <span class="p">[</span> <span class="mf">0.0938</span><span class="p">,</span>  <span class="mf">0.1250</span><span class="p">,</span>  <span class="mf">0.1875</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">0.2031</span><span class="p">,</span>  <span class="mf">0.0625</span><span class="p">,</span>  <span class="mf">0.2188</span><span class="p">],</span>
+         <span class="p">[</span> <span class="mf">0.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0625</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0312</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span>  <span class="mf">0.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0156</span><span class="p">]]],</span>
+      <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda:0&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="share-and-load-from-huggingface">
+<h2><em>Share</em> and <em>load</em> from HuggingFace<a class="headerlink" href="#share-and-load-from-huggingface" title="Link to this heading">#</a></h2>
+<p>pyvene has support for sharing and loading intervention schemata via HuggingFace.</p>
+<p>The following codeblock can reproduce <a class="reference external" href="https://github.com/likenneth/honest_llama/tree/master">honest_llama-2
+chat</a> from the
+paper <a class="reference external" href="https://arxiv.org/abs/2306.03341">Inference-Time Intervention: Eliciting Truthful Answers from a
+Language Model</a>. The added
+activations are only <strong>~0.14MB</strong> on disk!</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+
+<span class="c1"># 1. Load base model</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;meta-llama/Llama-2-7b-chat-hf&quot;</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="s2">&quot;meta-llama/Llama-2-7b-chat-hf&quot;</span><span class="p">,</span>
+    <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+
+<span class="c1"># 2. Load intervention from HF and wrap model</span>
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="s2">&quot;zhengxuanzenwu/intervenable_honest_llama2_chat_7B&quot;</span><span class="p">,</span> <span class="c1"># the activation diff ~0.14MB</span>
+    <span class="n">model</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<span class="c1"># 3. Let&#39;s run it!</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;llama-2-chat loaded with interventions:&quot;</span><span class="p">)</span>
+<span class="n">q</span> <span class="o">=</span> <span class="s2">&quot;What&#39;s a cure for insomnia that always works?&quot;</span>
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">iti_response_shared</span> <span class="o">=</span> <span class="n">pv_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompt</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">iti_response_shared</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>With this, once you discover some clever intervention schemes, you can
+share with others quickly without sharing the actual base LMs or the
+intervention code!</p>
+</section>
+<section id="intervenablemodel-is-just-an-nn-module">
+<span id="intervenablemodel-as-regular-nnmodule"></span><h2><em>IntervenableModel</em> is just an <em>nn.Module</em><a class="headerlink" href="#intervenablemodel-is-just-an-nn-module" title="Link to this heading">#</a></h2>
+<p>pyvene wraps PyTorch models in the <a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableModel.html#pyvene.models.intervenable_base.IntervenableModel" title="pyvene.models.intervenable_base.IntervenableModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">IntervenableModel</span></code></a> class. This is just a subclass of
+<code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, so you can use it just like any other PyTorch model! For example:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Dict</span>
+
+<span class="k">class</span> <span class="nc">ModelWithIntervenables</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ModelWithIntervenables</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv_gpt2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">768</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="c1"># Your other downstream components go here</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">activations_sources</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">subspaces</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pv_gpt2</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="p">,</span>
+            <span class="n">activations_sources</span><span class="p">,</span>
+            <span class="n">subspaces</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">counterfactual_x</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">))</span>
+</pre></div>
+</div>
+</section>
+<section id="complex-intervention-schema-as-an-object">
+<h2>Complex <em>Intervention Schema</em> as an <em>Object</em><a class="headerlink" href="#complex-intervention-schema-as-an-object" title="Link to this heading">#</a></h2>
+<p>One key abstraction that pyvene provides is the encapsulation of the
+intervention schema. While abstraction provides good user-interfaces,
+pyvene can support relatively complex intervention schema. The
+following helper function generates the schema for <em>path
+patching</em> for individual attention heads, for replicating experiments
+from the paper <a class="reference external" href="https://arxiv.org/abs/2211.00593">Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small</a>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="k">def</span> <span class="nf">path_patching_config</span><span class="p">(</span>
+    <span class="n">layer</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">,</span>
+    <span class="n">component</span><span class="o">=</span><span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s2">&quot;h.pos&quot;</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="n">intervening_component</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">component</span><span class="p">,</span> <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="n">unit</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}]</span>
+    <span class="n">restoring_components</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">stream</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;mlp_&quot;</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layer</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
+        <span class="p">]</span>
+    <span class="n">intervenable_config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span><span class="n">intervening_component</span> <span class="o">+</span> <span class="n">restoring_components</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">intervenable_config</span>
+</pre></div>
+</div>
+<p>You can now use the config generated by this function to wrap a model. And
+after you have done your intervention, you can share your path patching
+config with others:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+    <span class="n">path_patching_config</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">),</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span>
+<span class="p">)</span>
+<span class="c1"># saving the path</span>
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
+    <span class="n">save_directory</span><span class="o">=</span><span class="s2">&quot;./your_gpt2_path/&quot;</span>
+<span class="p">)</span>
+<span class="c1"># loading the path</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="s2">&quot;./tmp/&quot;</span><span class="p">,</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="selected-tutorials">
+<h2>Selected Tutorials<a class="headerlink" href="#selected-tutorials" title="Link to this heading">#</a></h2>
+<div class="pst-scrollable-table-container"><table class="table" id="id1">
+<caption><span class="caption-text">Tutorials</span><a class="headerlink" href="#id1" title="Link to this table">#</a></caption>
+<colgroup>
+<col style="width: 10.0%" />
+<col style="width: 20.0%" />
+<col style="width: 20.0%" />
+<col style="width: 50.0%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Level</p></th>
+<th class="head"><p>Tutorial</p></th>
+<th class="head"><p>Run in Colab</p></th>
+<th class="head"><p>Description</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Beginner</p></td>
+<td><p><a class="reference external" href="tutorials/pyvene_101.html">pyvene 101</a></p></td>
+<td><a class="reference external image-reference" href="https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/pyvene_101.ipynb"><img alt="https://colab.research.google.com/assets/colab-badge.svg" class="align-center" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+</td>
+<td><p>Introduce you to the basics of pyvene</p></td>
+</tr>
+<tr class="row-odd"><td><p>Intermediate</p></td>
+<td><p><a class="reference external" href="tutorials/advanced_tutorials/Causal_Tracing.html">ROME Causal Tracing</a></p></td>
+<td><a class="reference external image-reference" href="https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/advanced_tutorials/Causal_Tracing.ipynb"><img alt="https://colab.research.google.com/assets/colab-badge.svg" class="align-center" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+</td>
+<td><p>Reproduce ROME’s Results on Factual Associations with GPT2-XL</p></td>
+</tr>
+<tr class="row-even"><td><p>Intermediate</p></td>
+<td><p><a class="reference external" href="tutorials/advanced_tutorials/Probing_Gender.html">Intervention vs. Probing</a></p></td>
+<td><a class="reference external image-reference" href="https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/advanced_tutorials/Probing_Gender.ipynb"><img alt="https://colab.research.google.com/assets/colab-badge.svg" class="align-center" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+</td>
+<td><p>Illustrates how to run trainable interventions and probing with pythia-6.9B</p></td>
+</tr>
+<tr class="row-odd"><td><p>Advanced</p></td>
+<td><p><a class="reference external" href="tutorials/advanced_tutorials/DAS_Main_Introduction.html">Trainable Interventions for Causal Abstraction</a></p></td>
+<td><a class="reference external image-reference" href="https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/advanced_tutorials/DAS_Main_Introduction.ipynb"><img alt="https://colab.research.google.com/assets/colab-badge.svg" class="align-center" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+</td>
+<td><p>Illustrates how to train an intervention to discover causal mechanisms of a neural model</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+<section id="contributing-to-this-library">
+<h2>Contributing to This Library<a class="headerlink" href="#contributing-to-this-library" title="Link to this heading">#</a></h2>
+<p>Please see <a class="reference external" href="guides/contributing.html">our guidelines</a> about how to contribute
+to this repository.</p>
+<p><em>Pull requests, bug reports, and all other forms of contribution are
+welcomed and highly encouraged!</em></p>
+</section>
+<section id="citation">
+<h2>Citation<a class="headerlink" href="#citation" title="Link to this heading">#</a></h2>
+<p>If you use this repository, please cite our library paper:</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">wu-etal-2024-pyvene</span><span class="p">,</span>
+<span class="w">   </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;pyvene: A Library for Understanding and Improving {P}y{T}orch Models via Interventions&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;Wu, Zhengxuan and Geiger, Atticus and Arora, Aryaman and Huang, Jing and Wang, Zheng and Goodman, Noah and Manning, Christopher and Potts, Christopher&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">editor</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;Chang, Kai-Wei and Lee, Annie and Rajani, Nazneen&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">booktitle</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">month</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="nv">jun</span><span class="p">,</span>
+<span class="w">   </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;2024&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">address</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;Mexico City, Mexico&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">publisher</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;Association for Computational Linguistics&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;https://aclanthology.org/2024.naacl-demo.16&quot;</span><span class="p">,</span>
+<span class="w">   </span><span class="na">pages</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">&quot;158--165&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+<section id="related-works-on-discovering-causal-mechanism-of-llms">
+<h2>Related Works on Discovering Causal Mechanism of LLMs<a class="headerlink" href="#related-works-on-discovering-causal-mechanism-of-llms" title="Link to this heading">#</a></h2>
+<p>If you would like to read more works on this area, here is a list of
+papers that try to align or discover the causal mechanisms of LLMs.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://arxiv.org/abs/2106.02997">Causal Abstractions of Neural
+Networks</a>: This paper introduces
+interchange intervention (a.k.a. activation patching or causal
+scrubbing). It tries to align a causal model with the model’s
+representations.</p></li>
+<li><p><a class="reference external" href="https://arxiv.org/abs/2112.00826">Inducing Causal Structure for Interpretable Neural
+Networks</a>: Interchange
+intervention training (IIT) induces causal structures into the
+model’s representations.</p></li>
+<li><p><a class="reference external" href="https://arxiv.org/abs/2304.05969">Localizing Model Behavior with Path
+Patching</a>: Path patching (or
+causal scrubbing) to uncover causal paths in neural model.</p></li>
+<li><p><a class="reference external" href="https://arxiv.org/abs/2304.14997">Towards Automated Circuit Discovery for Mechanistic
+Interpretability</a>: Scalable
+method to prune out a small set of connections in a neural network
+that can still complete a task.</p></li>
+<li><p><a class="reference external" href="https://arxiv.org/abs/2211.00593">Interpretability in the Wild: a Circuit for Indirect Object
+Identification in GPT-2 small</a>:
+Path patching plus posthoc representation study to uncover a circuit
+that solves the indirect object identification (IOI) task.</p></li>
+<li><p><a class="reference external" href="https://arxiv.org/abs/2309.10312">Rigorously Assessing Natural Language Explanations of
+Neurons</a>: Using causal
+abstraction to validate <a class="reference external" href="https://openai.com/research/language-models-can-explain-neurons-in-language-models">neuron explanations released by
+OpenAI</a>.</p></li>
+</ul>
+</section>
+<section id="star-history">
+<h2>Star History<a class="headerlink" href="#star-history" title="Link to this heading">#</a></h2>
+<p><a class="reference external" href="https://star-history.com/#stanfordnlp/pyvene&amp;stanfordnlp/pyreft&amp;Date"><img alt="Star History Chart" src="https://api.star-history.com/svg?repos=stanfordnlp/pyvene,stanfordnlp/pyreft&amp;type=Date" /></a></p>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+</div>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+</section>
+<section id="indices-and-tables">
+<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">#</a></h1>
+<ul class="simple">
+<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
+<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
+<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
+</ul>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+    <a class="right-next"
+       href="guides/contributing.html"
+       title="next page">
+      <div class="prev-next-info">
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+            
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+                <div id="pst-secondary-sidebar" class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
+
+
+  <div class="sidebar-secondary-item">
+  <div class="page-toc tocsection onthispage">
+    <i class="fa-solid fa-list"></i> Contents
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">pyvene</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#getting-started">Getting Started</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#wrap-and-intervene"><em>Wrap</em> and <em>intervene</em></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#share-and-load-from-huggingface"><em>Share</em> and <em>load</em> from HuggingFace</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#intervenablemodel-is-just-an-nn-module"><em>IntervenableModel</em> is just an <em>nn.Module</em></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#complex-intervention-schema-as-an-object">Complex <em>Intervention Schema</em> as an <em>Object</em></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#selected-tutorials">Selected Tutorials</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#contributing-to-this-library">Contributing to This Library</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citation">Citation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#related-works-on-discovering-causal-mechanism-of-llms">Related Works on Discovering Causal Mechanism of LLMs</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#star-history">Star History</a><ul class="nav section-nav flex-column">
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#indices-and-tables">Indices and tables</a></li>
+</ul>
+
+  </nav></div>
+
+</div></div>
+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
+<div class="bd-footer-content__inner container">
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+By Stanford NLP
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+    
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+  <p class="copyright">
+    
+      © Copyright 2024, Stanford NLP.
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+  
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\ No newline at end of file
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+    <!-- Table of contents -->
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+                <article class="bd-article">
+                  
+
+   <h1>Python Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-p"><strong>p</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-p"><td></td><td>
+       <strong>p</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <a href="api/core.html#module-pyvene"><code class="xref">pyvene</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.data_generators.html#module-pyvene.data_generators"><code class="xref">pyvene.data_generators</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.data_generators.causal_model.html#module-pyvene.data_generators.causal_model"><code class="xref">pyvene.data_generators.causal_model</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.html#module-pyvene.models"><code class="xref">pyvene.models</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.backpack_gpt2.html#module-pyvene.models.backpack_gpt2"><code class="xref">pyvene.models.backpack_gpt2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.backpack_gpt2.modelings_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_backpack_gpt2"><code class="xref">pyvene.models.backpack_gpt2.modelings_backpack_gpt2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2.html#module-pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2"><code class="xref">pyvene.models.backpack_gpt2.modelings_intervenable_backpack_gpt2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.basic_utils.html#module-pyvene.models.basic_utils"><code class="xref">pyvene.models.basic_utils</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.blip.html#module-pyvene.models.blip"><code class="xref">pyvene.models.blip</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.blip.modelings_blip.html#module-pyvene.models.blip.modelings_blip"><code class="xref">pyvene.models.blip.modelings_blip</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.blip.modelings_blip_itm.html#module-pyvene.models.blip.modelings_blip_itm"><code class="xref">pyvene.models.blip.modelings_blip_itm</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.blip.modelings_intervenable_blip.html#module-pyvene.models.blip.modelings_intervenable_blip"><code class="xref">pyvene.models.blip.modelings_intervenable_blip</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.blip.modelings_intervenable_blip_itm.html#module-pyvene.models.blip.modelings_intervenable_blip_itm"><code class="xref">pyvene.models.blip.modelings_intervenable_blip_itm</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.configuration_intervenable_model.html#module-pyvene.models.configuration_intervenable_model"><code class="xref">pyvene.models.configuration_intervenable_model</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.constants.html#module-pyvene.models.constants"><code class="xref">pyvene.models.constants</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gemma.html#module-pyvene.models.gemma"><code class="xref">pyvene.models.gemma</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gemma.modelings_intervenable_gemma.html#module-pyvene.models.gemma.modelings_intervenable_gemma"><code class="xref">pyvene.models.gemma.modelings_intervenable_gemma</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gemma2.html#module-pyvene.models.gemma2"><code class="xref">pyvene.models.gemma2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gemma2.modelings_intervenable_gemma2.html#module-pyvene.models.gemma2.modelings_intervenable_gemma2"><code class="xref">pyvene.models.gemma2.modelings_intervenable_gemma2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gpt2.html#module-pyvene.models.gpt2"><code class="xref">pyvene.models.gpt2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gpt2.modelings_intervenable_gpt2.html#module-pyvene.models.gpt2.modelings_intervenable_gpt2"><code class="xref">pyvene.models.gpt2.modelings_intervenable_gpt2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gpt_neo.html#module-pyvene.models.gpt_neo"><code class="xref">pyvene.models.gpt_neo</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gpt_neo.modelings_intervenable_gpt_neo.html#module-pyvene.models.gpt_neo.modelings_intervenable_gpt_neo"><code class="xref">pyvene.models.gpt_neo.modelings_intervenable_gpt_neo</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gpt_neox.html#module-pyvene.models.gpt_neox"><code class="xref">pyvene.models.gpt_neox</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gpt_neox.modelings_intervenable_gpt_neox.html#module-pyvene.models.gpt_neox.modelings_intervenable_gpt_neox"><code class="xref">pyvene.models.gpt_neox.modelings_intervenable_gpt_neox</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gru.html#module-pyvene.models.gru"><code class="xref">pyvene.models.gru</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gru.modelings_gru.html#module-pyvene.models.gru.modelings_gru"><code class="xref">pyvene.models.gru.modelings_gru</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.gru.modelings_intervenable_gru.html#module-pyvene.models.gru.modelings_intervenable_gru"><code class="xref">pyvene.models.gru.modelings_intervenable_gru</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.intervenable_base.html#module-pyvene.models.intervenable_base"><code class="xref">pyvene.models.intervenable_base</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.intervenable_modelcard.html#module-pyvene.models.intervenable_modelcard"><code class="xref">pyvene.models.intervenable_modelcard</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.intervention_utils.html#module-pyvene.models.intervention_utils"><code class="xref">pyvene.models.intervention_utils</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.interventions.html#module-pyvene.models.interventions"><code class="xref">pyvene.models.interventions</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.layers.html#module-pyvene.models.layers"><code class="xref">pyvene.models.layers</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.llama.html#module-pyvene.models.llama"><code class="xref">pyvene.models.llama</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.llama.modelings_intervenable_llama.html#module-pyvene.models.llama.modelings_intervenable_llama"><code class="xref">pyvene.models.llama.modelings_intervenable_llama</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.llava.html#module-pyvene.models.llava"><code class="xref">pyvene.models.llava</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.llava.modelings_intervenable_llava.html#module-pyvene.models.llava.modelings_intervenable_llava"><code class="xref">pyvene.models.llava.modelings_intervenable_llava</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.mistral.html#module-pyvene.models.mistral"><code class="xref">pyvene.models.mistral</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.mistral.modellings_intervenable_mistral.html#module-pyvene.models.mistral.modellings_intervenable_mistral"><code class="xref">pyvene.models.mistral.modellings_intervenable_mistral</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.mlp.html#module-pyvene.models.mlp"><code class="xref">pyvene.models.mlp</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.mlp.modelings_intervenable_mlp.html#module-pyvene.models.mlp.modelings_intervenable_mlp"><code class="xref">pyvene.models.mlp.modelings_intervenable_mlp</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.mlp.modelings_mlp.html#module-pyvene.models.mlp.modelings_mlp"><code class="xref">pyvene.models.mlp.modelings_mlp</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.modeling_utils.html#module-pyvene.models.modeling_utils"><code class="xref">pyvene.models.modeling_utils</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.olmo.html#module-pyvene.models.olmo"><code class="xref">pyvene.models.olmo</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.olmo.modelings_intervenable_olmo.html#module-pyvene.models.olmo.modelings_intervenable_olmo"><code class="xref">pyvene.models.olmo.modelings_intervenable_olmo</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.qwen2.html#module-pyvene.models.qwen2"><code class="xref">pyvene.models.qwen2</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="api/pyvene.models.qwen2.modelings_intervenable_qwen2.html#module-pyvene.models.qwen2.modelings_intervenable_qwen2"><code class="xref">pyvene.models.qwen2.modelings_intervenable_qwen2</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
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+++ b/search.html
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+
+<!DOCTYPE html>
+
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+<html lang="en" data-content_root="./" >
+
+  <head>
+    <meta charset="utf-8" />
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+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
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+<form class="bd-search d-flex align-items-center"
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+         class="form-control"
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+         placeholder="Search..."
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.gru.modelings_intervenable_gru.html">pyvene.models.gru.modelings_intervenable_gru</a></li>
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+<li class="toctree-l3 has-children"><a class="reference internal" href="api/pyvene.models.intervenable_base.html">pyvene.models.intervenable_base</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.build_intervenable_model.html">pyvene.models.intervenable_base.build_intervenable_model</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.BaseModel.html">pyvene.models.intervenable_base.BaseModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableModel.html">pyvene.models.intervenable_base.IntervenableModel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableModelOutput.html">pyvene.models.intervenable_base.IntervenableModelOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervenable_base.IntervenableNdifModel.html">pyvene.models.intervenable_base.IntervenableNdifModel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.intervention_utils.InterventionState.html">pyvene.models.intervention_utils.InterventionState</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.AdditionIntervention.html">pyvene.models.interventions.AdditionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.AutoencoderIntervention.html">pyvene.models.interventions.AutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.BasisAgnosticIntervention.html">pyvene.models.interventions.BasisAgnosticIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.BoundlessRotatedSpaceIntervention.html">pyvene.models.interventions.BoundlessRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.CollectIntervention.html">pyvene.models.interventions.CollectIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.ConstantSourceIntervention.html">pyvene.models.interventions.ConstantSourceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.DistributedRepresentationIntervention.html">pyvene.models.interventions.DistributedRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.Intervention.html">pyvene.models.interventions.Intervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.InterventionOutput.html">pyvene.models.interventions.InterventionOutput</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.JumpReLUAutoencoderIntervention.html">pyvene.models.interventions.JumpReLUAutoencoderIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.LocalistRepresentationIntervention.html">pyvene.models.interventions.LocalistRepresentationIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.LowRankRotatedSpaceIntervention.html">pyvene.models.interventions.LowRankRotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.NoiseIntervention.html">pyvene.models.interventions.NoiseIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.PCARotatedSpaceIntervention.html">pyvene.models.interventions.PCARotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.RotatedSpaceIntervention.html">pyvene.models.interventions.RotatedSpaceIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SharedWeightsTrainableIntervention.html">pyvene.models.interventions.SharedWeightsTrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SigmoidMaskIntervention.html">pyvene.models.interventions.SigmoidMaskIntervention</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SkipIntervention.html">pyvene.models.interventions.SkipIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SourcelessIntervention.html">pyvene.models.interventions.SourcelessIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.SubtractionIntervention.html">pyvene.models.interventions.SubtractionIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.TrainableIntervention.html">pyvene.models.interventions.TrainableIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.VanillaIntervention.html">pyvene.models.interventions.VanillaIntervention</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api/pyvene.models.interventions.ZeroIntervention.html">pyvene.models.interventions.ZeroIntervention</a></li>
+</ul>
+</details></li>
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216], "argument": [9, 201, 216], "defin": [9, 10, 12, 14, 15, 16, 17, 18, 37, 39, 40, 43, 56, 61, 66, 73, 77, 82, 84, 85, 86, 89, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 133, 134, 135, 136, 137, 139, 144, 149, 153, 157, 159, 160, 164, 187, 192, 199, 200, 202, 208, 209, 214, 216], "architectur": [9, 199], "inherit": 9, "from": [9, 19, 21, 42, 45, 57, 62, 67, 68, 69, 74, 78, 90, 91, 92, 97, 99, 140, 145, 150, 154, 179, 181, 188, 193, 196, 198, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215], "can": [9, 10, 97, 179, 196, 197, 198, 199, 200, 202, 204, 206, 207, 208, 209, 210, 212, 213, 214, 215, 216], "control": [9, 216], "output": [9, 10, 85, 97, 98, 115, 179, 183, 196, 199, 200, 201, 202, 204, 206, 207, 211, 213, 214, 215, 216], "read": [9, 199, 200, 203, 207, 209, 210, 216], "document": [9, 97, 203, 204], "more": [9, 196, 197, 199, 202, 203, 204, 209, 216], "inform": [9, 196], "arg": [9, 86, 97, 133, 164, 202, 203, 206, 207], "int": [9, 32, 171, 200, 201, 202, 204, 205, 206, 207, 208, 209], "option": [9, 199, 207, 216], "default": [9, 97, 200, 209, 216], "The": [9, 96, 97, 134, 196, 199, 201, 204, 208, 210, 211, 212, 213, 214, 215], "number": [9, 48, 97, 170, 182, 202, 204, 206, 207, 210, 211, 212, 213, 215, 216], "sens": [9, 207, 216], "vector": [9, 202, 210, 216], "each": [9, 18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 202, 207, 208, 210, 211, 213, 214, 215, 216], "word": [9, 196, 200, 215, 216], "ot": 9, "hidden": [9, 85, 86, 201, 202, 207], "dimension": [9, 202, 216], "network": [9, 196, 199, 202, 204, 209, 216], "exampl": [9, 97, 196, 198, 199, 201, 202, 208, 209, 210, 211, 212, 213, 214, 215, 216], "transform": [9, 135, 136, 137, 175, 177, 178, 198, 199, 200, 201, 202, 203, 204, 206, 207, 208, 209, 214, 216], "import": [9, 197, 198, 199, 200, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216], "backpackgpt2model": 9, "initi": [9, 10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164, 216], "gpt2": [9, 19, 74, 78, 198, 199, 201, 203, 204, 205, 207, 210, 212, 216], "random": [9, 31, 201, 202, 203, 204, 205, 207, 208, 209], "weight": [9, 10, 13, 97, 185, 196, 202, 205, 207, 214], "access": [9, 203, 206, 207, 209], "config": [9, 10, 12, 14, 15, 16, 18, 19, 40, 42, 43, 45, 56, 57, 61, 62, 66, 67, 68, 69, 73, 74, 77, 78, 82, 84, 85, 86, 89, 90, 91, 92, 96, 97, 99, 100, 139, 140, 144, 145, 149, 150, 153, 154, 157, 159, 160, 179, 182, 187, 188, 192, 193, 196, 199, 200, 201, 202, 204, 205, 206, 207, 208, 209, 210, 211, 213, 214, 216], "backpackgpt2pretrainedmodel": [10, 12], "intern": [10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164, 196, 199, 202, 212], "state": [10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164, 177, 199, 201, 216], "share": [10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164, 204, 216], "both": [10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164, 199, 202, 204, 207], "nn": [10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164, 200, 202, 207, 208, 209, 212, 214], "scriptmodul": [10, 12, 13, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 88, 96, 97, 99, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 157, 159, 160, 164], "forward": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 180, 181, 196, 199, 200, 201, 202, 206, 208, 214, 216], "input_id": [10, 12, 37, 39, 84, 85, 86, 159, 160, 200, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216], "position_id": [10, 12, 84, 85, 86, 159, 160], "comput": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 199, 201, 202, 206, 207, 214, 216], "perform": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 201, 202, 203, 204, 207, 216], "everi": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 201, 207, 216], "call": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 198, 203, 204, 216], "should": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 197, 200, 201, 207, 209, 210, 211, 212, 213, 214, 215, 216], "overridden": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "all": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 181, 197, 198, 199, 201, 202, 203, 204, 205, 207, 208, 209, 216], "subclass": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 199], "although": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "recip": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "pass": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 197, 202, 203, 207, 208, 210, 214, 216], "need": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216], "within": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 201, 202, 203, 204, 207, 210, 211, 213, 215], "one": [10, 12, 14, 15, 16, 17, 21, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 200, 202, 203, 207, 208, 209, 212, 216], "instanc": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 207, 213, 216], "afterward": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "instead": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 204, 206, 207, 209, 210, 214, 216], "sinc": [10, 12, 14, 15, 16, 17, 32, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 197, 201, 202, 204, 207, 208, 209, 210, 212, 214, 216], "former": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "take": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 202, 204, 207, 216], "care": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "run": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 199, 204, 207, 209], "regist": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "hook": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 97, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 163, 164, 173, 180, 181, 203, 216], "while": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 197, 199, 202, 207, 208, 212], "latter": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "silent": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164], "ignor": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 201, 203, 204, 205, 216], "them": [10, 12, 14, 15, 16, 17, 37, 39, 82, 84, 85, 86, 96, 99, 107, 108, 110, 111, 114, 116, 118, 119, 120, 121, 123, 124, 125, 127, 129, 130, 132, 134, 135, 136, 137, 157, 159, 160, 164, 196, 199, 201, 202, 207, 216], "tie_weight": 10, "tie": 10, "between": [10, 18, 40, 43, 56, 61, 66, 73, 77, 89, 97, 139, 144, 149, 153, 187, 192, 196, 202, 203, 207, 208, 209, 214, 216], "input": [10, 13, 27, 28, 86, 88, 97, 198, 200, 201, 202, 203, 204, 206, 209, 210, 211, 212, 213, 214, 215, 216], "embed": [10, 25, 85, 86, 170, 182, 200, 201, 202, 206, 216], "If": [10, 170, 182, 196, 197, 198, 199, 200, 203, 207, 209, 210, 214, 216], "torchscript": 10, "flag": [10, 216], "set": [10, 32, 86, 96, 97, 163, 196, 197, 199, 203, 208], "t": [10, 196, 199, 202, 203, 206, 207, 214, 216], "handl": [10, 13], "paramet": [10, 23, 97, 164, 170, 179, 182, 200, 202, 207, 208, 209, 214, 216], "so": [10, 199, 200, 202, 204, 206, 207, 209, 214, 216], "we": [10, 18, 40, 43, 56, 61, 66, 73, 77, 89, 97, 139, 144, 149, 153, 179, 187, 192, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 211, 212, 213, 214, 215, 216], "ar": [10, 97, 170, 182, 196, 197, 198, 199, 200, 201, 202, 203, 204, 207, 208, 210, 212, 214, 215, 216], "clone": [10, 199, 207, 210], "logit": [11, 25, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216], "gpt2pretrainedmodel": 13, "an": [13, 25, 97, 108, 132, 133, 164, 183, 184, 196, 197, 198, 204, 206, 214, 216], "abstract": [13, 18, 40, 43, 56, 61, 66, 73, 77, 89, 114, 133, 139, 144, 149, 153, 187, 192, 199, 212, 216], "simpl": [13, 21, 202, 206, 208, 210, 216], "interfac": [13, 133, 199, 216], "download": [13, 198, 216], "load": [13, 97, 99, 198, 200, 201, 202, 203, 204, 205, 207, 210, 211, 212, 213, 215], "pretrain": [13, 207, 214, 216], "config_class": 13, "alia": [13, 48], "backpackgpt2config": 13, "embed_dim": [14, 17, 201, 204, 206, 214, 216], "intermediate_dim": 14, "out_dim": 14, "tupl": [14, 37, 39, 48, 87, 161, 170, 182, 199, 216], "residu": [15, 207], "devic": [16, 96, 170, 182, 199, 200, 201, 202, 203, 204, 205, 206, 208, 209, 212, 214], "dtype": [16, 57, 62, 140, 145, 188, 193], "input_emb": 16, "encod": [17, 132, 201, 202, 203, 206, 208, 210, 211, 213, 214, 215, 216], "file": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 197, 203, 204, 207, 216], "map": [18, 40, 43, 56, 61, 66, 73, 77, 85, 86, 89, 139, 144, 149, 153, 187, 192, 204, 207, 212, 214, 216], "name": [18, 19, 40, 42, 43, 45, 56, 57, 61, 62, 66, 67, 68, 69, 73, 74, 77, 78, 89, 90, 91, 92, 139, 140, 144, 145, 149, 150, 153, 154, 174, 187, 188, 192, 193, 201, 202, 205, 207, 208, 216], "anchor": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192], "point": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 202, 208, 214], "actual": [18, 40, 43, 56, 61, 66, 73, 77, 89, 97, 139, 144, 149, 153, 169, 187, 192, 196, 199, 216], "huggingfac": [18, 32, 40, 43, 56, 61, 66, 73, 77, 89, 96, 139, 144, 149, 153, 187, 192, 200, 203, 206, 208, 209], "also": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 196, 197, 202, 203, 204, 206, 207, 210, 214, 215, 216], "want": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 196, 199, 200, 201, 202, 203, 207, 209, 213, 214, 215, 216], "let": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 196, 199, 201, 202, 203, 204, 206, 214, 216], "know": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 203, 209, 212, 216], "how": [18, 22, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 187, 192, 197, 199, 201, 202, 204, 207, 208, 209, 210, 212, 213, 215, 216], "dimens": [18, 40, 43, 56, 61, 66, 73, 77, 89, 139, 144, 149, 153, 170, 171, 182, 187, 192, 202, 205, 207, 214, 216], "stanfordnlp": [19, 199, 200, 201, 202, 204, 205, 206, 208, 209, 210, 211, 213, 214, 215, 216], "cache_dir": [19, 42, 45, 57, 62, 67, 68, 69, 74, 78, 90, 91, 92, 140, 145, 150, 154, 188, 193, 203, 206], "creat": 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[[195, "pyvene-models-qwen2-modelings-intervenable-qwen2-qwen2-type-to-dimension-mapping"]], "A Little Guide to Causal Abstraction": [[196, "a-little-guide-to-causal-abstraction"]], "Understanding Causal Mechanisms with Static Interventions": [[196, "understanding-causal-mechanisms-with-static-interventions"]], "Understanding Causal Mechanism with Trainable Interventions": [[196, "understanding-causal-mechanism-with-trainable-interventions"]], "Contributing Guidelines": [[197, "contributing-guidelines"]], "The PR or Issue Title Format": [[197, "the-pr-or-issue-title-format"]], "\ud83d\udcd5 Pull Requests": [[197, "pull-requests"]], "Uninstall pyvene from python library": [[197, "uninstall-pyvene-from-python-library"]], "Unit Test Run Is A Must before Creating PRs": [[197, "unit-test-run-is-a-must-before-creating-prs"]], "Format": [[197, "format"]], "\ud83e\udeb2 Bug Reports and Other Issues": [[197, "bug-reports-and-other-issues"]], "\ud83d\udcc4 Documentation": [[197, "documentation"]], "\ud83d\udce5 Larger Feature Requests": [[197, "larger-feature-requests"]], "NDIF Integration": [[198, "ndif-integration"]], "pyvene": [[199, "pyvene"]], "Getting Started": [[199, "getting-started"]], "Wrap and intervene": [[199, "wrap-and-intervene"]], "Share and load from HuggingFace": [[199, "share-and-load-from-huggingface"]], "IntervenableModel is just an nn.Module": [[199, "intervenablemodel-is-just-an-nn-module"]], "Complex Intervention Schema as an Object": [[199, "complex-intervention-schema-as-an-object"]], "Selected Tutorials": [[199, "selected-tutorials"]], "Tutorials": [[199, "id1"]], "Contributing to This Library": [[199, "contributing-to-this-library"]], "Citation": [[199, "citation"]], "Related Works on Discovering Causal Mechanism of LLMs": [[199, "related-works-on-discovering-causal-mechanism-of-llms"]], "Star History": [[199, "star-history"]], "Indices and tables": [[199, "indices-and-tables"]], "Boundless DAS": [[200, "boundless-das"]], "Overview": [[200, "overview"], [203, "overview"], [204, "overview"], [205, "overview"], [209, "overview"], [210, "overview"], [211, "overview"], [212, "overview"], [213, "overview"], [214, "overview"], [215, "overview"]], "Set-up": [[200, "set-up"], [201, "set-up"], [202, "set-up"], [204, "set-up"], [205, "set-up"], [209, "set-up"], [210, "set-up"], [211, "set-up"], [212, "set-up"], [213, "set-up"], [214, "set-up"], [215, "set-up"], [216, "set-up"]], "Factual performance of instruct-tuned LLaMA-7B": [[200, "factual-performance-of-instruct-tuned-llama-7b"]], "Create training dataset for our trainable intervention (Boundless DAS)": [[200, "create-training-dataset-for-our-trainable-intervention-boundless-das"]], "Boundless DAS on Position-aligned Tokens": [[200, "boundless-das-on-position-aligned-tokens"]], "Causal Tracing (ROME)": [[201, "causal-tracing-rome"]], "Factual recall": [[201, "factual-recall"], [204, "factual-recall"], [211, "factual-recall"]], "Corrupted run": [[201, "corrupted-run"]], "Restored run": [[201, "restored-run"]], "Intro to Distributed Alignment Search (DAS)": [[202, "intro-to-distributed-alignment-search-das"]], "Contents": [[202, "contents"]], "The hierarchical equality task": [[202, "the-hierarchical-equality-task"]], "An Algorithm that Solves the Equality Task": [[202, "an-algorithm-that-solves-the-equality-task"]], "The algorithm with no intervention": [[202, "the-algorithm-with-no-intervention"]], "The algorithm with an intervention": [[202, "the-algorithm-with-an-intervention"]], "The algorithm with an interchange intervention": [[202, "the-algorithm-with-an-interchange-intervention"]], "Hand Crafting an MLP to Solve Hierarchical Equality": [[202, "hand-crafting-an-mlp-to-solve-hierarchical-equality"]], "Causal abstraction": [[202, "causal-abstraction"]], "Training an MLP to Solve Hierarchical Equality": [[202, "training-an-mlp-to-solve-hierarchical-equality"]], "Distributed Alignment Search": [[202, "distributed-alignment-search"]], "Replicating the IOI paper": [[203, "replicating-the-ioi-paper"]], "Setup": [[203, "setup"], [206, "setup"], [208, "setup"]], "Path patching config": [[203, "path-patching-config"]], "Dataset + Utils": [[203, "dataset-utils"]], "Name mover heads": [[203, "name-mover-heads"]], "IOI with DAS": [[204, "ioi-with-das"]], "Localizing the name position information in the main residual stream and MLP activations": [[204, "localizing-the-name-position-information-in-the-main-residual-stream-and-mlp-activations"]], "Localizing in across multiple tokens at layer 7, one layer earlier than layer 8": [[204, "localizing-in-across-multiple-tokens-at-layer-7-one-layer-earlier-than-layer-8"]], "Localizing in different streams": [[204, "localizing-in-different-streams"]], "Localizing in each individual head at layer 7 and 8": [[204, "localizing-in-each-individual-head-at-layer-7-and-8"]], "Localizing with group of heads using rankings of missing out head IIA": [[204, "localizing-with-group-of-heads-using-rankings-of-missing-out-head-iia"]], "Localizing the correct IO name with vanilla causal abstraction (no training)": [[204, "localizing-the-correct-io-name-with-vanilla-causal-abstraction-no-training"]], "Localizing the correct IO name with DAS (20 names only with 20 dimension DAS)": [[204, "localizing-the-correct-io-name-with-das-20-names-only-with-20-dimension-das"]], "Head\u2019s Query Representations to Trace Name Mover Head": [[204, "head-s-query-representations-to-trace-name-mover-head"]], "Use Boundless DAS to find information": [[204, "use-boundless-das-to-find-information"]], "DAS and Boundless DAS weight matrix and head importance": [[204, "das-and-boundless-das-weight-matrix-and-head-importance"]], "IOI with Masked Interventions": [[205, "ioi-with-masked-interventions"]], "Intervening on Vision-Language Models": [[206, "intervening-on-vision-language-models"]], "Load model and test inference": [[206, "load-model-and-test-inference"]], "Vanilla intervention": [[206, "vanilla-intervention"]], "Causal tracing": [[206, "causal-tracing"]], "Varying levels of noise": [[206, "varying-levels-of-noise"]], "Sampling noise and running repeatedly": [[206, "sampling-noise-and-running-repeatedly"]], "Nested Hierarchical Structure with MQNLI": [[207, "nested-hierarchical-structure-with-mqnli"]], "Set-Up": [[207, "set-up"]], "The MQNLI Task": [[207, "the-mqnli-task"]], "Example MQNLI Input": [[207, "example-mqnli-input"]], "MQNLI with an Intervention": [[207, "mqnli-with-an-intervention"]], "Training a Model on MQNLI": [[207, "training-a-model-on-mqnli"]], "Interpreting Our Model": [[207, "interpreting-our-model"]], "Evaluate DAS Alignment": [[207, "evaluate-das-alignment"]], "Causal Evaluation of Probes": [[208, "causal-evaluation-of-probes"]], "Load model and data": [[208, "load-model-and-data"]], "DAS": [[208, "das"]], "Probing": [[208, "probing"]], "An Exploration on Voting Mechanisms": [[209, "an-exploration-on-voting-mechanisms"]], "Display some training task instances": [[209, "display-some-training-task-instances"]], "creating alignment datasets": [[209, "creating-alignment-datasets"]], "where are we aligning in LLaMA?": [[209, "where-are-we-aligning-in-llama"]], "Loading back saved DAS params and intervene on single or multiple sites": [[209, "loading-back-saved-das-params-and-intervene-on-single-or-multiple-sites"]], "Now, what if we set two aligning intervention sites with contradicting values?": [[209, "now-what-if-we-set-two-aligning-intervention-sites-with-contradicting-values"]], "The results above show if two intervention sites take on contradicting values, it will trust the earlier site.": [[209, "the-results-above-show-if-two-intervention-sites-take-on-contradicting-values-it-will-trust-the-earlier-site"]], "Minor: make sure other control models cannot find good alignments": [[209, "minor-make-sure-other-control-models-cannot-find-good-alignments"]], "Activation Addition": [[210, "activation-addition"]], "Factual recall with our intervenable module directly": [[210, "factual-recall-with-our-intervenable-module-directly"]], "We add a word embedding to all MLP streams at the last position": [[210, "we-add-a-word-embedding-to-all-mlp-streams-at-the-last-position"]], "Let\u2019s have a more systematic analysis of the addition effect of MLP and MHA streams": [[210, "let-s-have-a-more-systematic-analysis-of-the-addition-effect-of-mlp-and-mha-streams"]], "Interchange Intervention": [[211, "interchange-intervention"]], "Patch Patching on Position-aligned Tokens": [[211, "patch-patching-on-position-aligned-tokens"]], "Trainable Interventions": [[212, "trainable-interventions"]], "Aligning the output variable with a single dimension in the rotated basis": [[212, "aligning-the-output-variable-with-a-single-dimension-in-the-rotated-basis"]], "A 1-d DAS direction learning config": [[212, "a-1-d-das-direction-learning-config"]], "Your own loss and metrics function": [[212, "your-own-loss-and-metrics-function"]], "Learning": [[212, "learning"]], "Evaluation": [[212, "evaluation"]], "Intervening on subcomponents": [[213, "intervening-on-subcomponents"]], "Factual Recall": [[213, "factual-recall"]], "Patch Patching on Position-aligned Tokens with in Specific Head": [[213, "patch-patching-on-position-aligned-tokens-with-in-specific-head"]], "Analyzing Sparse Autoencoders (SAEs) from Gemma Scope": [[214, "analyzing-sparse-autoencoders-saes-from-gemma-scope"]], "Loading the model and its tokenizer": [[214, "loading-the-model-and-its-tokenizer"]], "Loading a SAE, and create SAE interventions": [[214, "loading-a-sae-and-create-sae-interventions"]], "Implementing SAEs as pyvene-native Interventions": [[214, "implementing-saes-as-pyvene-native-interventions"]], "Running the model with SAE to collect activations with pyvene APIs": [[214, "running-the-model-with-sae-to-collect-activations-with-pyvene-apis"]], "Gemma-2-2B-it steering with Gemma-2-2B SAEs": [[214, "gemma-2-2b-it-steering-with-gemma-2-2b-saes"]], "Implementing SAEs as pyvene-native Interventions for model steering": [[214, "implementing-saes-as-pyvene-native-interventions-for-model-steering"]], "Interchange intervention with JumpReLU SAEs.": [[214, "interchange-intervention-with-jumprelu-saes"]], "Subspace Interventions": [[215, "subspace-interventions"]], "Subspace alignment config": [[215, "subspace-alignment-config"]], "Patch Patching on the First Subspace of Position-aligned Tokens": [[215, "patch-patching-on-the-first-subspace-of-position-aligned-tokens"]], "Introduction to pyvene": [[216, "introduction-to-pyvene"]], "Table of Contents": [[216, "table-of-contents"]], "pyvene 101": [[216, "pyvene-101"]], "Get Attention Weights": [[216, "get-attention-weights"]], "Get Attention Weights with Direct Access String": [[216, "get-attention-weights-with-direct-access-string"]], "Get Attention Weights with a Function": [[216, 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+<button onclick="toggleFullScreen()"
+  class="btn btn-sm btn-fullscreen-button"
+  title="Fullscreen mode"
+  data-bs-placement="bottom" data-bs-toggle="tooltip"
+>
+  
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+<span class="btn__icon-container">
+  <i class="fas fa-expand"></i>
+  </span>
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+</button>
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+<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode"  data-bs-placement="bottom" data-bs-toggle="tooltip">
+  <i class="theme-switch fa-solid fa-sun                fa-lg" data-mode="light" title="Light"></i>
+  <i class="theme-switch fa-solid fa-moon               fa-lg" data-mode="dark"  title="Dark"></i>
+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button>
+
+
+<button class="btn btn-sm pst-navbar-icon search-button search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass fa-lg"></i>
+</button>
+<button class="sidebar-toggle secondary-toggle btn btn-sm" title="Toggle secondary sidebar" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="fa-solid fa-list"></span>
+</button>
+</div></div>
+      
+    </div>
+  
+</div>
+</div>
+              
+              
+
+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Boundless DAS</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-performance-of-instruct-tuned-llama-7b">Factual performance of instruct-tuned LLaMA-7B</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-training-dataset-for-our-trainable-intervention-boundless-das">Create training dataset for our trainable intervention (Boundless DAS)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#boundless-das-on-position-aligned-tokens">Boundless DAS on Position-aligned Tokens</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="boundless-das">
+<h1>Boundless DAS<a class="headerlink" href="#boundless-das" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;10/05/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>This tutorial aims to reproduce one key result of <a class="reference external" href="https://arxiv.org/pdf/2305.08809">the Boundless DAS paper</a>. It uses the same pricing tag dataset as in the paper. Additionally, it focuses on finding alignment for the left boundary check only.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[2024-01-11 01:35:34,365] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span><span class="p">,</span> <span class="n">trange</span>
+<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">get_linear_schedule_with_warmup</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">CrossEntropyLoss</span>
+<span class="kn">from</span> <span class="nn">tutorial_price_tagging_utils</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">factual_sampler</span><span class="p">,</span>
+    <span class="n">bound_alignment_sampler</span><span class="p">,</span>
+    <span class="n">lower_bound_alignment_example_sampler</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_llama</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">set_seed</span><span class="p">,</span> <span class="n">count_parameters</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">llama</span> <span class="o">=</span> <span class="n">create_llama</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">llama</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>  <span class="c1"># single gpu</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">llama</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>  <span class="c1"># always no grad on the model</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "7ae4a40cfb8d44a38578fd45815ab319", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "1220a638dba243d49cc5f951cd4d9364", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "d9fd255929ed487b8440ed3dae85df58", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "37973ed21b144db99a242a249ffba7ea", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "307b905f058e48f19eab80f03dcc2496", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "53793dbe43e54aa694d2cc0aff7b9a1d", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>You are using the default legacy behaviour of the &lt;class &#39;transformers.models.llama.tokenization_llama.LlamaTokenizer&#39;&gt;. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "cbf4bf617f9a4099bf3525e0c5334ed6", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="factual-performance-of-instruct-tuned-llama-7b">
+<h2>Factual performance of instruct-tuned LLaMA-7B<a class="headerlink" href="#factual-performance-of-instruct-tuned-llama-7b" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">raw_prealign</span> <span class="o">=</span> <span class="n">factual_sampler</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="n">game</span><span class="o">=</span><span class="s2">&quot;pricing_tag&quot;</span><span class="p">)</span>
+<span class="n">prealign_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_prealign</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_prealign</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span>
+<span class="p">)</span>
+<span class="n">prealign_dataset</span><span class="o">.</span><span class="n">set_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">,</span> <span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+<span class="n">prealign_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">prealign_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">correct_count</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">prealign_dataloader</span><span class="p">)):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">llama</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="c1"># aligning forward!</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">llama</span><span class="p">(</span>
+            <span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">],</span>
+            <span class="n">labels</span><span class="o">=</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="n">actual_test_labels</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">][:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_test_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">correct_labels</span> <span class="o">=</span> <span class="n">actual_test_labels</span> <span class="o">==</span> <span class="n">pred_test_labels</span>
+
+        <span class="n">total_count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">correct_labels</span><span class="p">)</span>
+        <span class="n">correct_count</span> <span class="o">+=</span> <span class="n">correct_labels</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">current_acc</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">correct_count</span> <span class="o">/</span> <span class="n">total_count</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;[WARNING: THIS NEEDS TO BE GOOD!] prealign task accuracy: </span><span class="si">{</span><span class="n">current_acc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 625/625 [00:48&lt;00:00, 12.82it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[WARNING: THIS NEEDS TO BE GOOD!] prealign task accuracy: 0.92
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="create-training-dataset-for-our-trainable-intervention-boundless-das">
+<h2>Create training dataset for our trainable intervention (Boundless DAS)<a class="headerlink" href="#create-training-dataset-for-our-trainable-intervention-boundless-das" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">set_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="c1">###################</span>
+<span class="c1"># data loaders</span>
+<span class="c1">###################</span>
+<span class="n">raw_data</span> <span class="o">=</span> <span class="n">bound_alignment_sampler</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span> <span class="p">[</span><span class="n">lower_bound_alignment_example_sampler</span><span class="p">]</span>
+<span class="p">)</span>
+
+<span class="n">raw_train</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">raw_eval</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">raw_test</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+<span class="p">)</span>
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">train_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">train_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">eval_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">test_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="boundless-das-on-position-aligned-tokens">
+<h2>Boundless DAS on Position-aligned Tokens<a class="headerlink" href="#boundless-das-on-position-aligned-tokens" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">simple_boundless_das_position_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">intervention_type</span><span class="p">,</span> <span class="n">layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>              <span class="c1"># layer</span>
+                <span class="n">intervention_type</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">simple_boundless_das_position_config</span><span class="p">(</span>
+    <span class="nb">type</span><span class="p">(</span><span class="n">llama</span><span class="p">),</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="mi">15</span>
+<span class="p">)</span>
+<span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">llama</span><span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">t_total</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">warm_up_steps</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">t_total</span>
+<span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">()}]</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">,</span> <span class="s2">&quot;lr&quot;</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}]</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+<span class="n">scheduler</span> <span class="o">=</span> <span class="n">get_linear_schedule_with_warmup</span><span class="p">(</span>
+    <span class="n">optimizer</span><span class="p">,</span> <span class="n">num_warmup_steps</span><span class="o">=</span><span class="n">warm_up_steps</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="o">=</span><span class="n">t_total</span>
+<span class="p">)</span>
+
+
+<span class="c1"># You can define your custom compute_metrics function.</span>
+<span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+    <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">correct_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">eval_pred</span><span class="p">,</span> <span class="n">eval_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+        <span class="n">actual_test_labels</span> <span class="o">=</span> <span class="n">eval_label</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_test_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">eval_pred</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">correct_labels</span> <span class="o">=</span> <span class="n">actual_test_labels</span> <span class="o">==</span> <span class="n">pred_test_labels</span>
+        <span class="n">total_count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">correct_labels</span><span class="p">)</span>
+        <span class="n">correct_count</span> <span class="o">+=</span> <span class="n">correct_labels</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">accuracy</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">correct_count</span> <span class="o">/</span> <span class="n">total_count</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">accuracy</span><span class="p">}</span>
+
+
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">gradient_accumulation_steps</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">target_total_step</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span>
+<span class="n">temperature_start</span> <span class="o">=</span> <span class="mf">50.0</span>
+<span class="n">temperature_end</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">temperature_schedule</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">temperature_start</span><span class="p">,</span> <span class="n">temperature_end</span><span class="p">,</span> <span class="n">target_total_step</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">calculate_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="c1"># Flatten the tokens</span>
+    <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">shift_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">model_config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">)</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># Enable model parallelism</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">shift_logits</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">shift_logits</span><span class="p">,</span> <span class="n">shift_labels</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">boundary_loss</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">v</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">+=</span> <span class="n">boundary_loss</span>
+
+    <span class="k">return</span> <span class="n">loss</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>  <span class="c1"># train enables drop-off but no grads</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;llama trainable parameters: &quot;</span><span class="p">,</span> <span class="n">count_parameters</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">model</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervention trainable parameters: &quot;</span><span class="p">,</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+<span class="n">train_iterator</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">epochs</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Epoch&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">train_iterator</span><span class="p">:</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span>
+        <span class="n">train_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">80</span><span class="p">},</span>  <span class="c1"># swap 80th token</span>
+        <span class="p">)</span>
+        <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">],</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="p">)</span>
+
+        <span class="c1"># loss and backprop</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">calculate_loss</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+        <span class="n">loss_str</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="n">loss_str</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]})</span>
+
+        <span class="k">if</span> <span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">gradient_accumulation_steps</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">total_step</span> <span class="o">%</span> <span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">total_step</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                <span class="n">intervenable</span><span class="o">.</span><span class="n">set_zero_grad</span><span class="p">()</span>
+                <span class="n">intervenable</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+        <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>llama trainable parameters:  0
+intervention trainable parameters:  16777218
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 0: 100%|██████████████████████████████████████████████████████████████████████████████████████| 500/500 [07:05&lt;00:00,  1.18it/s, loss=0.5, acc=0.88]
+Epoch: 1: 100%|█████████████████████████████████████████████████████████████████████████████████████| 500/500 [07:58&lt;00:00,  1.04it/s, loss=0.39, acc=0.94]
+Epoch: 2: 100%|█████████████████████████████████████████████████████████████████████████████████████| 500/500 [08:19&lt;00:00,  1.00it/s, loss=0.35, acc=0.94]
+Epoch: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [23:23&lt;00:00, 467.83s/it]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># evaluation on the test set</span>
+<span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">test_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">80</span><span class="p">},</span>  <span class="c1"># swap 80th token</span>
+        <span class="p">)</span>
+        <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+<span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">eval_metrics</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:45&lt;00:00,  1.38it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;accuracy&#39;: 0.96}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-training-dataset-for-our-trainable-intervention-boundless-das">Create training dataset for our trainable intervention (Boundless DAS)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#boundless-das-on-position-aligned-tokens">Boundless DAS on Position-aligned Tokens</a></li>
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Causal Tracing (ROME)</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Causal Tracing (ROME)</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall">Factual recall</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#corrupted-run">Corrupted run</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#restored-run">Restored run</a></li>
+</ul>
+</li>
+</ul>
+
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="causal-tracing-rome">
+<h1>Causal Tracing (ROME)<a class="headerlink" href="#causal-tracing-rome" title="Link to this heading">#</a></h1>
+<p>Causal tracing was a methodology for locating where facts are stored in transformer LMs, introduced in the paper <a class="reference external" href="https://arxiv.org/abs/2202.05262">“Locating and Editing Factual Associations in GPT” (Meng et al., 2023)</a>. In this notebook, we will implement their method using this library and replicate the first causal tracing example in the paper (full figure 1 on page 2).</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/advance_tutorials/Causal_Tracing.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Aryaman Arora&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;11/08/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">VanillaIntervention</span><span class="p">,</span> <span class="n">Intervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+    <span class="n">ConstantSourceIntervention</span><span class="p">,</span>
+    <span class="n">LocalistRepresentationIntervention</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_gpt2</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+    <span class="n">xlab</span><span class="p">,</span> <span class="n">ylab</span><span class="p">,</span> <span class="n">ylim</span><span class="p">,</span>
+    <span class="n">scale_y_discrete</span><span class="p">,</span> <span class="n">scale_y_continuous</span><span class="p">,</span> <span class="n">ggsave</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">plotnine.scales</span> <span class="kn">import</span> <span class="n">scale_y_reverse</span><span class="p">,</span> <span class="n">scale_fill_cmap</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="n">titles</span><span class="o">=</span><span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;single restored layer in GPT2-XL&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;center of interval of 10 patched mlp layer&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;center of interval of 10 patched attn layer&quot;</span>
+<span class="p">}</span>
+
+<span class="n">colors</span><span class="o">=</span><span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Purples&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;Greens&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Reds&quot;</span>
+<span class="p">}</span> 
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="factual-recall">
+<h2>Factual recall<a class="headerlink" href="#factual-recall" title="Link to this heading">#</a></h2>
+<p>Let’s set up the model (gpt2-xl) and test it on the fact we want to causal trace: “The Space Needle is in downtown <strong>Seattle</strong>”.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cuda:0&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span> <span class="o">=</span> <span class="n">create_gpt2</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;gpt2-xl&quot;</span><span class="p">)</span>
+<span class="n">gpt</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;The Space Needle is in downtown&quot;</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">gpt</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+The Space Needle is in downtown
+_Seattle             0.9763794541358948
+_Bellev              0.0027682818472385406
+_Portland            0.0021577849984169006
+,                    0.0015149445971474051
+_Vancouver           0.0014351375866681337
+_San                 0.0013575783232226968
+_Minneapolis         0.000938268203753978
+.                    0.0007443446083925664
+_Tacoma              0.0006097281584516168
+_Washington          0.0005885555874556303
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="corrupted-run">
+<h1>Corrupted run<a class="headerlink" href="#corrupted-run" title="Link to this heading">#</a></h1>
+<p>The first step in implementing causal tracing is to corrupt the input embeddings for the subject tokens by adding Gaussian noise to them. In Meng et al., the standard deviation of the Gaussian we sample from is computed as thrice the standard deviation of embeddings over a big dataset. We encode this as a constant, <code class="docutils literal notranslate"><span class="pre">self.noise_level</span></code>.</p>
+<p>Note that the <code class="docutils literal notranslate"><span class="pre">source</span></code> argument is ignored unlike in causal interventions, since we are adding noise without reference to any other input.</p>
+<p>Our intervention config intervenes on the <code class="docutils literal notranslate"><span class="pre">block_input</span></code> of the 0th layer, i.e. the embeddings.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">NoiseIntervention</span><span class="p">(</span><span class="n">ConstantSourceIntervention</span><span class="p">,</span> <span class="n">LocalistRepresentationIntervention</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="o">=</span> <span class="n">embed_dim</span>
+        <span class="n">rs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">prng</span> <span class="o">=</span> <span class="k">lambda</span> <span class="o">*</span><span class="n">shape</span><span class="p">:</span> <span class="n">rs</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">noise</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span>
+            <span class="n">prng</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span> <span class="o">=</span> <span class="mf">0.13462981581687927</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span>
+        <span class="k">return</span> <span class="n">base</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;NoiseIntervention(embed_dim=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="si">}</span><span class="s2">)&quot;</span>
+
+
+<span class="k">def</span> <span class="nf">corrupted_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>              <span class="c1"># layer</span>
+                <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">NoiseIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let’s check that this reduced the probability of the output “_Seattle”.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The Space Needle is in downtown&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+<span class="n">config</span> <span class="o">=</span> <span class="n">corrupted_config</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">))</span>
+<span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]])}</span>
+<span class="p">)</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>_Los                 0.03294256329536438
+_San                 0.03194474056363106
+_Seattle             0.026176469400525093
+_Toronto             0.02585919387638569
+_Chicago             0.024749040603637695
+_Houston             0.024224288761615753
+_Atlanta             0.01866454817354679
+_Austin              0.017735302448272705
+_St                  0.017606761306524277
+_Denver              0.01740877516567707
+</pre></div>
+</div>
+</div>
+</div>
+<section id="restored-run">
+<h2>Restored run<a class="headerlink" href="#restored-run" title="Link to this heading">#</a></h2>
+<p>We now make a config that performs the following:</p>
+<ol class="arabic simple">
+<li><p>Corrupt input embeddings for some positions.</p></li>
+<li><p>Restore the hidden state at a particular layer for some (potentially different positions).</p></li>
+</ol>
+<p>This is how Meng et al. check where in the model the fact moves through.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">restore_corrupted_with_interval_config</span><span class="p">(</span>
+    <span class="n">layer</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">48</span><span class="p">):</span>
+    <span class="n">start</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">layer</span> <span class="o">-</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">end</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_layers</span><span class="p">,</span> <span class="n">layer</span> <span class="o">-</span> <span class="p">(</span><span class="o">-</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>       <span class="c1"># layer</span>
+                <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="p">),</span>
+        <span class="p">]</span> <span class="o">+</span> <span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>       <span class="c1"># layer</span>
+                <span class="n">stream</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+        <span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)],</span>
+        <span class="n">intervention_types</span><span class="o">=</span>\
+            <span class="p">[</span><span class="n">NoiseIntervention</span><span class="p">]</span><span class="o">+</span><span class="p">[</span><span class="n">VanillaIntervention</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">end</span><span class="o">-</span><span class="n">start</span><span class="p">),</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now let’s run this over all layers and positions! We will corrupt positions 0, 1, 2, 3 (“The Space Needle”, i.e. the subject of the fact) and restore at a single position at every layer.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># should finish within 1 min with a standard 12G GPU</span>
+<span class="n">token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; Seattle&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">token</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>7312
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">stream</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">]:</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">)):</span>
+        <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">):</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">restore_corrupted_with_interval_config</span><span class="p">(</span>
+                <span class="n">layer_i</span><span class="p">,</span> <span class="n">stream</span><span class="p">,</span> 
+                <span class="n">window</span><span class="o">=</span><span class="mi">1</span> <span class="k">if</span> <span class="n">stream</span> <span class="o">==</span> <span class="s2">&quot;block_output&quot;</span> <span class="k">else</span> <span class="mi">10</span>
+            <span class="p">)</span>
+            <span class="n">n_restores</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">representations</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+            <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="n">base</span><span class="p">,</span>
+                <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">base</span><span class="p">]</span><span class="o">*</span><span class="n">n_restores</span><span class="p">,</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="p">[[[</span><span class="n">pos_i</span><span class="p">]]]</span><span class="o">*</span><span class="n">n_restores</span><span class="p">,</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]]</span> <span class="o">+</span> <span class="p">[[[</span><span class="n">pos_i</span><span class="p">]]]</span><span class="o">*</span><span class="n">n_restores</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+            <span class="p">)</span>
+            <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+                <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+            <span class="n">prob</span> <span class="o">=</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer_i</span><span class="p">,</span> <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span> <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="n">prob</span><span class="p">})</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_rome_</span><span class="si">{</span><span class="n">stream</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The plot below should now replicate Meng et al.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">stream</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">]:</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_rome_</span><span class="si">{</span><span class="n">stream</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(Seattle)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;prob&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+    <span class="n">custom_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;The*&quot;</span><span class="p">,</span> <span class="s2">&quot;Space*&quot;</span><span class="p">,</span> <span class="s2">&quot;Need*&quot;</span><span class="p">,</span> <span class="s2">&quot;le*&quot;</span><span class="p">,</span> <span class="s2">&quot;is&quot;</span><span class="p">,</span> <span class="s2">&quot;in&quot;</span><span class="p">,</span> <span class="s2">&quot;downtown&quot;</span><span class="p">]</span>
+    <span class="n">breaks</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
+
+    <span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">))</span>    
+
+        <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;p(Seattle)&quot;</span><span class="p">))</span>
+        <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="n">colors</span><span class="p">[</span><span class="n">stream</span><span class="p">])</span> <span class="o">+</span> <span class="n">xlab</span><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">stream</span><span class="p">])</span>
+        <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">(</span>
+            <span class="n">limits</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">6.5</span><span class="p">),</span> 
+            <span class="n">breaks</span><span class="o">=</span><span class="n">breaks</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">custom_labels</span><span class="p">)</span> 
+        <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span> <span class="o">+</span> <span class="n">ylab</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span> 
+        <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_y</span>  <span class="o">=</span> <span class="n">element_text</span><span class="p">(</span><span class="n">angle</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">hjust</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="p">)</span>
+    <span class="n">ggsave</span><span class="p">(</span>
+        <span class="n">plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_rome_</span><span class="si">{</span><span class="n">stream</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.
+/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_block_output.pdf
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/522893b20be080540bd4d49f90bfe211b68fa9d7d913dd3060c49f4bc97a56ba.png"><img alt="../../_images/522893b20be080540bd4d49f90bfe211b68fa9d7d913dd3060c49f4bc97a56ba.png" src="../../_images/522893b20be080540bd4d49f90bfe211b68fa9d7d913dd3060c49f4bc97a56ba.png" style="width: 500px; height: 400px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.
+/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_mlp_activation.pdf
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/6025c3805397843f347063a6000afac59f261aa193767759b5c7eefbc5b9a5ad.png"><img alt="../../_images/6025c3805397843f347063a6000afac59f261aa193767759b5c7eefbc5b9a5ad.png" src="../../_images/6025c3805397843f347063a6000afac59f261aa193767759b5c7eefbc5b9a5ad.png" style="width: 500px; height: 400px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:587: PlotnineWarning: Saving 5 x 4 in image.
+/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-bootleg/lib/python3.8/site-packages/plotnine/ggplot.py:588: PlotnineWarning: Filename: ./tutorial_data/pyvene_rome_attention_output.pdf
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+    <h1>Intro to Distributed Alignment Search (DAS)</h1>
+    <!-- Table of contents -->
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+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
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+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Intro to Distributed Alignment Search (DAS)</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#contents">Contents</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-hierarchical-equality-task">The hierarchical equality task</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#an-algorithm-that-solves-the-equality-task">An Algorithm that Solves the Equality Task</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-algorithm-with-no-intervention">The algorithm with no intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-algorithm-with-an-intervention">The algorithm with an intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-algorithm-with-an-interchange-intervention">The algorithm with an interchange intervention</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#hand-crafting-an-mlp-to-solve-hierarchical-equality">Hand Crafting an MLP to Solve Hierarchical Equality</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#causal-abstraction">Causal abstraction</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#training-an-mlp-to-solve-hierarchical-equality">Training an MLP to Solve Hierarchical Equality</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#distributed-alignment-search">Distributed Alignment Search</a></li>
+</ul>
+
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="intro-to-distributed-alignment-search-das">
+<h1>Intro to Distributed Alignment Search (DAS)<a class="headerlink" href="#intro-to-distributed-alignment-search-das" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Atticus Geiger&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="contents">
+<h2>Contents<a class="headerlink" href="#contents" title="Link to this heading">#</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#The-hierarchical-equality-task"><span class="xref myst">The hierarchical equality task</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#An-Algorithm-that-Solves-the-Equality-Task"><span class="xref myst">An Algorithm that Solves the Equality Task</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#The-algorithm-with-no-intervention"><span class="xref myst">The algorithm with no intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#The-algorithm-with-an-intervention"><span class="xref myst">The algorithm with an intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#The-algorithm-with-an-interchange-intervention"><span class="xref myst">The algorithm with an interchange intervention</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#Hand-Crafting-an-MLP-to-Solve-Hierarchical-Equality"><span class="xref myst">Hand Crafting an MLP to Solve Hierarchical Equality</span></a></p></li>
+<li><p><a class="reference internal" href="#Training-an-MLP-to-Solve-Hierarchical-Equality"><span class="xref myst">Training an MLP to Solve Hierarchical Equality</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#Causal-abstraction"><span class="xref myst">Causal abstraction Analysis</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#Basic-intervention:-zeroing-out-part-of-a-hidden-layer"><span class="xref myst">Basic intervention: zeroing out part of a hidden layer</span></a></p></li>
+<li><p><a class="reference internal" href="#An-interchange-intervention"><span class="xref myst">An interchange intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#Alignment"><span class="xref myst">Alignment</span></a></p></li>
+<li><p><a class="reference internal" href="#Evaluation"><span class="xref myst">Evaluating an Alignment</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#Distributed-Alignment-Search"><span class="xref myst">Distributed Alignment Search (DAS)</span></a></p></li>
+</ol>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<p>This notebook is a hands-on introduction to <strong>causal abstraction analysis</strong> <a class="reference external" href="https://arxiv.org/pdf/2106.02997.pdf">Geiger*, Lu*, Icard, and Potts (2020)</a> using <strong>distributed alignment search</strong> <a class="reference external" href="https://arxiv.org/pdf/2303.02536.pdf">Geiger*, Wu*, Potts, Icard, and Goodman (2020)</a>.</p>
+<p>In causal abstraction analysis, we assess whether trained models conform to high-level causal models that we specify, not just in terms of their input–output behavior, but also in terms of their internal dynamics. The core technique is the <strong>interchange intervention</strong>, in which a causal model is provided an input and then intermediate variables are fixed to take on the values they would have for a second input.</p>
+<p>To motivate and illustrate these concepts, we’re going to focus on a hierarchical equality task, building on work by <a class="reference external" href="https://arxiv.org/abs/2006.07968">Geiger, Carstensen, Frank, and Potts (2020)</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span><span class="p">,</span> <span class="n">trange</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">get_linear_schedule_with_warmup</span>
+
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">CausalModel</span>
+<span class="kn">from</span> <span class="nn">pyvene.models.mlp.modelings_mlp</span> <span class="kn">import</span> <span class="n">MLPConfig</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_mlp_classifier</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="n">RotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">LowRankRotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;torch._C.Generator at 0x15308f04db0&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="the-hierarchical-equality-task">
+<h2>The hierarchical equality task<a class="headerlink" href="#the-hierarchical-equality-task" title="Link to this heading">#</a></h2>
+<p>This section builds on results presented in <a class="reference external" href="https://arxiv.org/abs/2006.07968">Geiger, Carstensen, Frank, and Potts (2020)</a>. We will use a hierarchical equality task (<a class="reference external" href="https://www.cambridge.org/core/services/aop-cambridge-core/content/view/7DF6F2D22838F7546AF7279679F3571D/S0140525X00015077a.pdf/div-class-title-the-codes-of-man-and-beasts-div.pdf">Premack 1983</a>) to illustrate the concepts.</p>
+<p>We define the hierarchical equality task as follows: The input is two pairs of objects and the output is <strong>True</strong> if both pairs contain the same object or if both pairs contain different objects and <strong>False</strong> otherwise.  For example, <code class="docutils literal notranslate"><span class="pre">AABB</span></code> and <code class="docutils literal notranslate"><span class="pre">ABCD</span></code> are both labeled <strong>True</strong>, while <code class="docutils literal notranslate"><span class="pre">ABCC</span></code> and <code class="docutils literal notranslate"><span class="pre">BBCD</span></code> are both labeled <strong>False</strong>.</p>
+</section>
+<section id="an-algorithm-that-solves-the-equality-task">
+<h2>An Algorithm that Solves the Equality Task<a class="headerlink" href="#an-algorithm-that-solves-the-equality-task" title="Link to this heading">#</a></h2>
+<p>Let $\mathcal{A}$ be the simple tree-structured algorithm that solves this task by applying a simple equality relation three times: Compute whether the first two inputs are equal, compute whether the second two inputs are equal, then compute whether the truth-valued outputs of these first two computations are equal.</p>
+<p>And here’s a Python implementation of $\mathcal{A}$ that supports the interventions we’ll want to do:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">randvec</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">lower</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">round</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span>
+
+
+<span class="n">embedding_dim</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">number_of_entities</span> <span class="o">=</span> <span class="mi">20</span>
+
+<span class="n">variables</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">,</span> <span class="s2">&quot;WX&quot;</span><span class="p">,</span> <span class="s2">&quot;YZ&quot;</span><span class="p">,</span> <span class="s2">&quot;O&quot;</span><span class="p">]</span>
+
+<span class="n">reps</span> <span class="o">=</span> <span class="p">[</span><span class="n">randvec</span><span class="p">(</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">lower</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_entities</span><span class="p">)]</span>
+<span class="n">values</span> <span class="o">=</span> <span class="p">{</span><span class="n">variable</span><span class="p">:</span> <span class="n">reps</span> <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">]}</span>
+<span class="n">values</span><span class="p">[</span><span class="s2">&quot;WX&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+<span class="n">values</span><span class="p">[</span><span class="s2">&quot;YZ&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+<span class="n">values</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+
+<span class="n">parents</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;YZ&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;WX&quot;</span><span class="p">,</span> <span class="s2">&quot;YZ&quot;</span><span class="p">],</span>
+<span class="p">}</span>
+
+
+<span class="k">def</span> <span class="nf">FILLER</span><span class="p">():</span>
+    <span class="k">return</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+
+<span class="n">functions</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span>
+    <span class="s2">&quot;YZ&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span>
+    <span class="s2">&quot;O&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="n">pos</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
+    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">),</span>
+    <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">2.8</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;YZ&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+<span class="p">}</span>
+
+<span class="n">equiv_classes</span> <span class="o">=</span> <span class="p">{}</span>
+
+<span class="n">equality_model</span> <span class="o">=</span> <span class="n">CausalModel</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here’s a visual depiction of the algorithm:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">equality_model</span><span class="o">.</span><span class="n">print_structure</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Timesteps:&quot;</span><span class="p">,</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">timesteps</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/51f677c367bc01dff3f9e2c315b980e86d7a34f4896c72490cb5dc9067ae3633.png" src="../../_images/51f677c367bc01dff3f9e2c315b980e86d7a34f4896c72490cb5dc9067ae3633.png" />
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Timesteps: {&#39;W&#39;: 0, &#39;X&#39;: 0, &#39;Y&#39;: 0, &#39;Z&#39;: 0, &#39;WX&#39;: 1, &#39;YZ&#39;: 1, &#39;O&#39;: 2}
+</pre></div>
+</div>
+</div>
+</div>
+<section id="the-algorithm-with-no-intervention">
+<h3>The algorithm with no intervention<a class="headerlink" href="#the-algorithm-with-no-intervention" title="Link to this heading">#</a></h3>
+<p>Let’s first observe the behavior of the algorithm when we provide an input of the form <code class="docutils literal notranslate"><span class="pre">BBCD</span></code> with no interventions. Here is a visual depiction:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">setting</span> <span class="o">=</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">3</span><span class="p">]}</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No intervention:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">setting</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">equality_model</span><span class="o">.</span><span class="n">print_setting</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>No intervention:
+ defaultdict(None, {&#39;W&#39;: array([ 0.28, -0.95]), &#39;X&#39;: array([ 0.28, -0.95]), &#39;Y&#39;: array([-0.45, -0.55]), &#39;Z&#39;: array([ 0.78, -0.83]), &#39;WX&#39;: True, &#39;YZ&#39;: False, &#39;O&#39;: False}) 
+</pre></div>
+</div>
+<img alt="../../_images/866346be324df4fb991d12d5096d03a13885f1054f4c242a9e5f14d74de36eb0.png" src="../../_images/866346be324df4fb991d12d5096d03a13885f1054f4c242a9e5f14d74de36eb0.png" />
+</div>
+</div>
+</section>
+<section id="the-algorithm-with-an-intervention">
+<h3>The algorithm with an intervention<a class="headerlink" href="#the-algorithm-with-an-intervention" title="Link to this heading">#</a></h3>
+<p>Let’s now see the behavior of the algorithm when we provide the input with an intervention setting <strong>WX</strong> to <strong>False</strong>. First, a visual depiction:</p>
+<p>And then the same computation with <code class="docutils literal notranslate"><span class="pre">compute_A</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Intervention setting WX to FALSE:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">equality_model</span><span class="o">.</span><span class="n">print_setting</span><span class="p">(</span>
+    <span class="n">equality_model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span>
+        <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Intervention setting WX to FALSE:
+</pre></div>
+</div>
+<img alt="../../_images/818676967d53f5a2f405c76ef07d201cbee212cd134853f7bd10165a28fc7379.png" src="../../_images/818676967d53f5a2f405c76ef07d201cbee212cd134853f7bd10165a28fc7379.png" />
+</div>
+</div>
+<p>Notice that, in this example, even though the left two inputs are the same, the intervention has changed the intermediate prediction for those two inputs from <strong>True</strong> to <strong>False</strong>, and thus the algorithm outputs <strong>True</strong>, since <strong>WX</strong> and <strong>YZ</strong> are both <strong>False</strong>.</p>
+</section>
+<section id="the-algorithm-with-an-interchange-intervention">
+<h3>The algorithm with an interchange intervention<a class="headerlink" href="#the-algorithm-with-an-interchange-intervention" title="Link to this heading">#</a></h3>
+<p>Finally, let’s observe the behavior of the algorithm when we provide the base input <code class="docutils literal notranslate"><span class="pre">BBCD</span></code> with an intervention setting <strong>WX</strong> to be the value it would be for the source input <code class="docutils literal notranslate"><span class="pre">ABCC</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">3</span><span class="p">]}</span>
+<span class="n">source</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">reps</span><span class="p">[</span><span class="mi">2</span><span class="p">]}</span>
+<span class="n">setting</span> <span class="o">=</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">run_interchange</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="n">source</span><span class="p">})</span>
+<span class="n">equality_model</span><span class="o">.</span><span class="n">print_setting</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/818676967d53f5a2f405c76ef07d201cbee212cd134853f7bd10165a28fc7379.png" src="../../_images/818676967d53f5a2f405c76ef07d201cbee212cd134853f7bd10165a28fc7379.png" />
+</div>
+</div>
+</section>
+</section>
+</section>
+<section id="hand-crafting-an-mlp-to-solve-hierarchical-equality">
+<h1>Hand Crafting an MLP to Solve Hierarchical Equality<a class="headerlink" href="#hand-crafting-an-mlp-to-solve-hierarchical-equality" title="Link to this heading">#</a></h1>
+<p>Before we train a network to solve the hierarchical equality task, first consider an analytical solution where we define a neural network to have weights that are handcrafted to solve the task by implementing the algorithm $\mathcal{A}$. The network is a two layer feedforward neural network that uses the ReLU function to compute the absolute difference between two vectors.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">MLPConfig</span><span class="p">(</span>
+    <span class="n">h_dim</span><span class="o">=</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span>
+    <span class="n">activation_function</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span>
+    <span class="n">n_layer</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">pdrop</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">handcrafted</span> <span class="o">=</span> <span class="n">create_mlp_classifier</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<p>The first layer of our handcrafted model computes:</p>
+<p>$ReLU(W_1[\mathbf{a}, \mathbf{b}, \mathbf{c}, \mathbf{d}]) = [max(\mathbf{a}-\mathbf{b}, 0), max(\mathbf{b}-\mathbf{a}, 0), max(\mathbf{c}-\mathbf{d}, 0), max(\mathbf{d}-\mathbf{c}, 0)]$</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">W1</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+<span class="p">]</span>
+<span class="n">handcrafted</span><span class="o">.</span><span class="n">mlp</span><span class="o">.</span><span class="n">h</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">ff1</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">W1</span><span class="p">))</span>
+<span class="n">handcrafted</span><span class="o">.</span><span class="n">mlp</span><span class="o">.</span><span class="n">h</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">ff1</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The second layer of our handcrafted model computes:</p>
+<p>$ReLU(W_2ReLU(W_1[\mathbf{a}, \mathbf{b}, \mathbf{c}, \mathbf{d}])) = [|\mathbf{a}-\mathbf{b}| - |\mathbf{c}-\mathbf{d}|, |\mathbf{c}-\mathbf{d}|-|\mathbf{a}-\mathbf{b}|, |\mathbf{a}-\mathbf{b}|, |\mathbf{c}-\mathbf{d}|,0,0,0,0]$</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">W2</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+    <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+<span class="p">]</span>
+<span class="n">handcrafted</span><span class="o">.</span><span class="n">mlp</span><span class="o">.</span><span class="n">h</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ff1</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">W2</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">handcrafted</span><span class="o">.</span><span class="n">mlp</span><span class="o">.</span><span class="n">h</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ff1</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The third layer of our handcrafted model computes the logits:</p>
+<p>$W_3 ReLU(W_2ReLU(W_1[\mathbf{a}, \mathbf{b}, \mathbf{c}, \mathbf{d}])) = [||\mathbf{a}-\mathbf{b}| - |\mathbf{c}-\mathbf{d}|| -0.999999|\mathbf{a}-\mathbf{b}|-0.999999|\mathbf{c}-\mathbf{d}|, 0]$</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">W3</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.999999</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.999999</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
+<span class="n">handcrafted</span><span class="o">.</span><span class="n">score</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">W3</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">handcrafted</span><span class="o">.</span><span class="n">score</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.00000000000001</span><span class="p">]))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can now use the causal model of $\mathcal{A}$ that we created to generate a labeled dataset for the hierarchical equality task and show that our handcrafted network solves the task with perfect accuracy.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_examples</span> <span class="o">=</span> <span class="mi">100000</span>
+
+<span class="n">examples</span> <span class="o">=</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">generate_factual_dataset</span><span class="p">(</span>
+    <span class="n">n_examples</span><span class="p">,</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span>
+<span class="p">)</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">])</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">handcrafted</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs_embeds</span><span class="o">=</span><span class="n">X</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train Results&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Train Results
+              precision    recall  f1-score   support
+
+         0.0       1.00      1.00      1.00     50050
+         1.0       1.00      1.00      1.00     49950
+
+    accuracy                           1.00    100000
+   macro avg       1.00      1.00      1.00    100000
+weighted avg       1.00      1.00      1.00    100000
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="causal-abstraction">
+<h1>Causal abstraction<a class="headerlink" href="#causal-abstraction" title="Link to this heading">#</a></h1>
+<p>The theory of <strong>causal abstraction</strong> describes the conditions that must hold for the high-level tree structured algorithm to be a <strong>simplified and faithful description</strong> of the neural network. To perform causal abstraction analysis, we need to align high-level variables in our hypothesized algorithm $\mathcal{A}$ with sets of low-level variables in the low-level neural network $\mathcal{N}$.</p>
+<p>In essence: $\mathcal{A}$ is a causal abstraction of a $\mathcal{N}$ if and only if $\mathcal{A}$ and $\mathcal{N}$ provides the same output for all interchange interventions that target aligned variables.</p>
+<p>For our handcrafted network, we align the first four neurons in the first feed-forward layer with the high-level variable ‘WX’ and align the other four neurons in that layer with ‘YZ’. Below, we create an IntervenableConfig that allows us to taget the first four and last four neurons of the first layer for an interchange intervention.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="n">model_type</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="n">handcrafted</span><span class="p">),</span>
+    <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+        <span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="mi">0</span><span class="p">,</span>  <span class="c1"># layer</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span>
+        <span class="p">),</span>
+        <span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="mi">0</span><span class="p">,</span>  <span class="c1"># layer</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span>
+        <span class="p">),</span>
+    <span class="p">],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">handcrafted</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">handcrafted</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Next we create a counterfactual equality dataset that includes interchange intervention examples. We first define a function that create an id for the three possible high-level interventions, namely targetting ‘WX’, targetting ‘YZ’, and targetting them both.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">intervention_id</span><span class="p">(</span><span class="n">intervention</span><span class="p">):</span>
+    <span class="k">if</span> <span class="s2">&quot;WX&quot;</span> <span class="ow">in</span> <span class="n">intervention</span> <span class="ow">and</span> <span class="s2">&quot;YZ&quot;</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">:</span>
+        <span class="k">return</span> <span class="mi">2</span>
+    <span class="k">if</span> <span class="s2">&quot;WX&quot;</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">:</span>
+        <span class="k">return</span> <span class="mi">0</span>
+    <span class="k">if</span> <span class="s2">&quot;YZ&quot;</span> <span class="ow">in</span> <span class="n">intervention</span><span class="p">:</span>
+        <span class="k">return</span> <span class="mi">1</span>
+
+
+<span class="n">data_size</span> <span class="o">=</span> <span class="mi">2048</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">16</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">generate_counterfactual_dataset</span><span class="p">(</span>
+    <span class="n">data_size</span><span class="p">,</span>
+    <span class="n">intervention_id</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">,</span>
+    <span class="n">sampler</span><span class="o">=</span><span class="n">equality_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This dataset has the following components:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">input_ids</span></code>: a regular set of train examples</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">base_labels</span></code>: a regular set of train labels</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">source_input_ids</span></code>: sets additional training inputs sets (here, two sets) for interchange interventions</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">labels</span></code>: a list of labels if interchange interventions are performed with ‘source_input_ids’</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">intervention_id</span></code>: a list of intervention sites (here, all <code class="docutils literal notranslate"><span class="pre">0</span></code> corresponding to our key for “V1”)</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;input_ids&quot;</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;base_labels&quot;</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([ 0.4700,  0.3500,  0.4700,  0.3500,  0.7800, -0.8300, -0.5600,  0.1800],
+       device=&#39;cuda:0&#39;)
+tensor([[-0.1600, -0.9400,  0.6600,  0.2400,  0.0700,  0.9500,  0.0700,  0.9500],
+        [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000]],
+       device=&#39;cuda:0&#39;)
+tensor([0.], device=&#39;cuda:0&#39;)
+tensor([1.], device=&#39;cuda:0&#39;)
+tensor([0], device=&#39;cuda:0&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>To evaluate the model on this dataset, we loop through batches and peform interchange interventions based on the intervention_id.</p>
+<ul class="simple">
+<li><p>When the id is 0, the first four neurons in the first layer are targetted (‘WX’ is targetted at the high-level)</p></li>
+<li><p>When the id is 1, the last four neurons in the first layer are targetted (‘YZ’ is targetted at the high-level)</p></li>
+<li><p>When the id is 2, all of the neurons in the first layer are targetted (‘WX’ and ‘YZ’ are both targetted at the high-level)</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">handcrafted</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">parameter</span> <span class="ow">in</span> <span class="n">handcrafted</span><span class="o">.</span><span class="n">get_trainable_parameters</span><span class="p">():</span>
+    <span class="n">parameter</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">)</span>
+<span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+    <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># Intervention on both high-level variables</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">handcrafted</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]},</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">1</span><span class="p">]},</span>
+            <span class="p">],</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                    <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                    <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                <span class="p">)</span>
+            <span class="p">},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="p">(</span>
+        <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span>
+    <span class="p">):</span>  <span class="c1"># Intervention on just the high-level variable &#39;WX&#39;</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">handcrafted</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]},</span> <span class="kc">None</span><span class="p">],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">])},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="p">(</span>
+        <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
+    <span class="p">):</span>  <span class="c1"># Intervention on just the high-level variable &#39;YZ&#39;</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">handcrafted</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">])},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+        <span class="p">)</span>
+    <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Below, we can see that our handcrafted neural network is a perfect implementation of the high-level algorithm.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="n">classification_report</span><span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">])</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">preds</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+         0.0       1.00      1.00      1.00       997
+         1.0       1.00      1.00      1.00      1051
+
+    accuracy                           1.00      2048
+   macro avg       1.00      1.00      1.00      2048
+weighted avg       1.00      1.00      1.00      2048
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="training-an-mlp-to-solve-hierarchical-equality">
+<h1>Training an MLP to Solve Hierarchical Equality<a class="headerlink" href="#training-an-mlp-to-solve-hierarchical-equality" title="Link to this heading">#</a></h1>
+<p>We’ve now seen how to perform causal abstraction analysis on a simple handcrafted neural networks. We turn now to training a neural network to perform the hierarchical equality task with a 4 dimensional vector embedding for each object. We define an input sampler to provide an infinite stream of new entities, rather than relying on a fixed set of vector representations.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="mi">4</span>
+
+
+<span class="k">def</span> <span class="nf">input_sampler</span><span class="p">():</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">B</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">C</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">D</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">}</span>
+    <span class="k">elif</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_examples</span> <span class="o">=</span> <span class="mi">1048576</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1024</span>
+
+<span class="n">examples</span> <span class="o">=</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">generate_factual_dataset</span><span class="p">(</span><span class="n">n_examples</span><span class="p">,</span> <span class="n">input_sampler</span><span class="p">)</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">])</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">])</span>
+
+<span class="c1"># X = X.unsqueeze(1)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The examples in this dataset are 8-dimensional vectors: the concatenation of 4 2-dimensional vectors. Here’s the first example with its label:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(tensor([-0.7200,  0.6300,  1.0000,  0.6900, -0.7200,  0.6300,  1.0000,  0.6900,
+          0.0800, -0.8800, -0.0400, -0.0400, -0.5200, -0.8500, -0.6400,  0.6400]),
+ tensor([0.]))
+</pre></div>
+</div>
+</div>
+</div>
+<p>The label for this example is determined by whether the equality value for the first two inputs matches the equality value for the second two inputs:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">left</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="n">embedding_dim</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">embedding_dim</span> <span class="p">:</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">])</span>
+
+<span class="n">left</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">right</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span> <span class="p">:</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">3</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">3</span> <span class="p">:]</span>
+<span class="p">)</span>
+
+<span class="n">right</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>False
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">int</span><span class="p">(</span><span class="n">left</span> <span class="o">==</span> <span class="n">right</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0
+</pre></div>
+</div>
+</div>
+</div>
+<p>We define a three layer neural network with a ReLU activation function this task:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">MLPConfig</span><span class="p">(</span>
+    <span class="n">h_dim</span><span class="o">=</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span>
+    <span class="n">activation_function</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span>
+    <span class="n">n_layer</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">pdrop</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">trained</span> <span class="o">=</span> <span class="n">create_mlp_classifier</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+<span class="n">trained</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>MLPForClassification(
+  (mlp): MLPModel(
+    (dropout): Dropout(p=0.0, inplace=False)
+    (h): ModuleList(
+      (0-2): 3 x MLPBlock(
+        (ff1): Linear(in_features=16, out_features=16, bias=True)
+        (act): ReLU()
+        (dropout): Dropout(p=0.0, inplace=False)
+      )
+    )
+  )
+  (score): Linear(in_features=16, out_features=2, bias=True)
+)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_ds</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="p">[</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">y</span>
+        <span class="p">],</span>
+        <span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">X</span><span class="p">,</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">TrainingArguments</span><span class="p">,</span> <span class="n">Trainer</span>
+
+<span class="n">training_args</span> <span class="o">=</span> <span class="n">TrainingArguments</span><span class="p">(</span>
+    <span class="n">output_dir</span><span class="o">=</span><span class="s2">&quot;test_trainer&quot;</span><span class="p">,</span>
+    <span class="n">evaluation_strategy</span><span class="o">=</span><span class="s2">&quot;epoch&quot;</span><span class="p">,</span>
+    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span>
+    <span class="n">num_train_epochs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">per_device_train_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">per_device_eval_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">report_to</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">trained</span><span class="p">,</span>
+    <span class="n">args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span>
+    <span class="n">train_dataset</span><span class="o">=</span><span class="n">train_ds</span><span class="p">,</span>
+    <span class="n">eval_dataset</span><span class="o">=</span><span class="n">train_ds</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">{</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">classification_report</span><span class="p">(</span>
+            <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">output_dict</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">)[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This neural network achieves perfect performance on its train set:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "267ad9a4be754be89e338aa6240214e4", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not estimate the number of tokens of the input, floating-point operations will not be computed
+Checkpoint destination directory test_trainer\checkpoint-500 already exists and is non-empty. Saving will proceed but saved results may be invalid.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;loss&#39;: 0.6401, &#39;grad_norm&#39;: 0.23116283118724823, &#39;learning_rate&#39;: 0.0008372395833333334, &#39;epoch&#39;: 0.49}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Checkpoint destination directory test_trainer\checkpoint-1000 already exists and is non-empty. Saving will proceed but saved results may be invalid.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;loss&#39;: 0.195, &#39;grad_norm&#39;: 0.18332688510417938, &#39;learning_rate&#39;: 0.0006744791666666667, &#39;epoch&#39;: 0.98}
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "6718015efcb34628b758ff359877ff36", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 0.07157168537378311, &#39;eval_accuracy&#39;: 0.9790763854980469, &#39;eval_runtime&#39;: 37.061, &#39;eval_samples_per_second&#39;: 28293.236, &#39;eval_steps_per_second&#39;: 27.63, &#39;epoch&#39;: 1.0}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Checkpoint destination directory test_trainer\checkpoint-1500 already exists and is non-empty. Saving will proceed but saved results may be invalid.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;loss&#39;: 0.0472, &#39;grad_norm&#39;: 0.15438705682754517, &#39;learning_rate&#39;: 0.00051171875, &#39;epoch&#39;: 1.46}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Checkpoint destination directory test_trainer\checkpoint-2000 already exists and is non-empty. Saving will proceed but saved results may be invalid.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;loss&#39;: 0.025, &#39;grad_norm&#39;: 0.1841142624616623, &#39;learning_rate&#39;: 0.00034895833333333334, &#39;epoch&#39;: 1.95}
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ade15e1911a840ceb47b8c372e241568", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 0.019501574337482452, &#39;eval_accuracy&#39;: 0.9946937561035156, &#39;eval_runtime&#39;: 37.7091, &#39;eval_samples_per_second&#39;: 27806.989, &#39;eval_steps_per_second&#39;: 27.155, &#39;epoch&#39;: 2.0}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Checkpoint destination directory test_trainer\checkpoint-2500 already exists and is non-empty. Saving will proceed but saved results may be invalid.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;loss&#39;: 0.0177, &#39;grad_norm&#39;: 0.18471458554267883, &#39;learning_rate&#39;: 0.00018619791666666665, &#39;epoch&#39;: 2.44}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Checkpoint destination directory test_trainer\checkpoint-3000 already exists and is non-empty. Saving will proceed but saved results may be invalid.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;loss&#39;: 0.0149, &#39;grad_norm&#39;: 0.15878142416477203, &#39;learning_rate&#39;: 2.34375e-05, &#39;epoch&#39;: 2.93}
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "6611af998ca848efbcb7ad9afb3bc344", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 0.014224954880774021, &#39;eval_accuracy&#39;: 0.9962148666381836, &#39;eval_runtime&#39;: 42.0755, &#39;eval_samples_per_second&#39;: 24921.316, &#39;eval_steps_per_second&#39;: 24.337, &#39;epoch&#39;: 3.0}
+{&#39;train_runtime&#39;: 257.663, &#39;train_samples_per_second&#39;: 12208.692, &#39;train_steps_per_second&#39;: 11.923, &#39;train_loss&#39;: 0.15329135081265122, &#39;epoch&#39;: 3.0}
+</pre></div>
+</div>
+</div>
+</div>
+<p>Next we create a separate causal model with vector representations distinct from those used in training:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">variables</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">,</span> <span class="s2">&quot;WX&quot;</span><span class="p">,</span> <span class="s2">&quot;YZ&quot;</span><span class="p">,</span> <span class="s2">&quot;O&quot;</span><span class="p">]</span>
+
+<span class="n">number_of_test_entities</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">reps</span> <span class="o">=</span> <span class="p">[</span><span class="n">randvec</span><span class="p">(</span><span class="n">embedding_dim</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_test_entities</span><span class="p">)]</span>
+<span class="n">values</span> <span class="o">=</span> <span class="p">{</span><span class="n">variable</span><span class="p">:</span> <span class="n">reps</span> <span class="k">for</span> <span class="n">variable</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">]}</span>
+<span class="n">values</span><span class="p">[</span><span class="s2">&quot;WX&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+<span class="n">values</span><span class="p">[</span><span class="s2">&quot;YZ&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+<span class="n">values</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+
+<span class="n">parents</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;YZ&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;WX&quot;</span><span class="p">,</span> <span class="s2">&quot;YZ&quot;</span><span class="p">],</span>
+<span class="p">}</span>
+
+
+<span class="k">def</span> <span class="nf">FILLER</span><span class="p">():</span>
+    <span class="k">return</span> <span class="n">reps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+
+<span class="n">functions</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">FILLER</span><span class="p">,</span>
+    <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span>
+    <span class="s2">&quot;YZ&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span>
+    <span class="s2">&quot;O&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="n">pos</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
+    <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">),</span>
+    <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="s2">&quot;WX&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;YZ&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+<span class="p">}</span>
+
+
+<span class="n">test_equality_model</span> <span class="o">=</span> <span class="n">CausalModel</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>hi!
+</pre></div>
+</div>
+</div>
+</div>
+<p>Our trained model generalizes perfectly this test set consisting of distinct vectors:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">examples</span> <span class="o">=</span> <span class="n">test_equality_model</span><span class="o">.</span><span class="n">generate_factual_dataset</span><span class="p">(</span><span class="mi">10000</span><span class="p">,</span> <span class="n">input_sampler</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test Results&quot;</span><span class="p">)</span>
+
+<span class="n">test_ds</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="p">[</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+            <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span>
+        <span class="p">],</span>
+        <span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">]),</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+
+<span class="n">test_preds</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_ds</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">]</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">test_preds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test Results
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "171e7809005a4020ab22e408905323ff", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+         0.0       0.99      1.00      1.00      5026
+         1.0       1.00      0.99      1.00      4974
+
+    accuracy                           1.00     10000
+   macro avg       1.00      1.00      1.00     10000
+weighted avg       1.00      1.00      1.00     10000
+</pre></div>
+</div>
+</div>
+</div>
+<p>Does it implement our high-level model of the problem, though?</p>
+</section>
+<section id="distributed-alignment-search">
+<h1>Distributed Alignment Search<a class="headerlink" href="#distributed-alignment-search" title="Link to this heading">#</a></h1>
+<p>We previously handcrafted the weights of a network so the two high-level variables are perfectly stored in two non-overlapping sets of neurons in the first layer of the network. However, the trained network won’t have axis aligned representations of high-level concepts. Rather, the two high-level variables will be encoded in multidimensional linear subspaces of the first layer in the network.</p>
+<p>To learn these subspaces, we define an IntervenableConfig that allows us to target the first layer of in the network after it has been rotated by an orthogonal matrix:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="n">model_type</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="n">trained</span><span class="p">),</span>
+    <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+        <span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="mi">0</span><span class="p">,</span>  <span class="c1"># layer</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit is now aligne with tokens</span>
+            <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># binary partition with equal sizes</span>
+            <span class="n">intervention_link_key</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="mi">0</span><span class="p">,</span>  <span class="c1"># layer</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit is now aligne with tokens</span>
+            <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># binary partition with equal sizes,</span>
+            <span class="n">intervention_link_key</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">RotatedSpaceIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">trained</span><span class="p">,</span> <span class="n">use_fast</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.
+
+In case multiple location tags are passed only the first one will be considered
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">gradient_accumulation_steps</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">target_total_step</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span>
+
+<span class="n">t_total</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span><span class="p">)</span>
+<span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">()}]</span>
+    <span class="k">break</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+    <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">correct_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">eval_pred</span><span class="p">,</span> <span class="n">eval_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+        <span class="n">total_count</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">correct_count</span> <span class="o">+=</span> <span class="n">eval_pred</span> <span class="o">==</span> <span class="n">eval_label</span>
+    <span class="n">accuracy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">correct_count</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">total_count</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">accuracy</span><span class="p">}</span>
+
+
+<span class="k">def</span> <span class="nf">compute_loss</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+    <span class="n">CE</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">CE</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">batched_random_sampler</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="n">batch_indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))]</span>
+    <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">batch_indices</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">b_i</span> <span class="ow">in</span> <span class="n">batch_indices</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b_i</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">(</span><span class="n">b_i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">):</span>
+            <span class="k">yield</span> <span class="n">i</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">input_sampler</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">B</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">C</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">D</span> <span class="o">=</span> <span class="n">randvec</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;output_var&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">}</span>
+        <span class="p">])</span>
+    <span class="k">elif</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;output_var&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;WX&#39;</span> <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;output_var_value&#39;</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">}</span>
+        <span class="p">])</span>
+    <span class="k">elif</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;output_var&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;WX&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;output_var_value&#39;</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">}</span>
+        <span class="p">])</span>
+    <span class="k">elif</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;output_var&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;YZ&#39;</span> <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;output_var_value&#39;</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">}</span>
+        <span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">B</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;W&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">:</span> <span class="n">A</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">:</span> <span class="n">C</span><span class="p">,</span> <span class="s2">&quot;Z&quot;</span><span class="p">:</span> <span class="n">D</span><span class="p">}</span>
+        <span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We again generate a counterfactual dataset using our high-level causal model:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_examples</span> <span class="o">=</span> <span class="mi">1280000</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">6400</span>
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">equality_model</span><span class="o">.</span><span class="n">generate_counterfactual_dataset</span><span class="p">(</span>
+    <span class="n">n_examples</span><span class="p">,</span> <span class="n">intervention_id</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">input_sampler</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Then we train the orthgonal matrix to be such that the first four dimensions in the rotated space encode the high-level variable ‘WX’ and the second four dimensions encode the high-level variable ‘YZ’.</p>
+<p>Again, we check the intervention_id for each batch of training data in order to determine whether to intervene of the first four rotated dimensions (‘WX’ is targetted at the high-level), the last four rotated dimensions (‘YZ’ is targetted at the high-level), or all of the dimensions (‘WX’ and ‘YZ’ are both targetted at the high-level).</p>
+<p>We can train the rotation matrix such that we get perfect interchange intervention accuracy, meaning the trained network perfectly implements the high-level algorithm on the training data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>  <span class="c1"># train enables drop-off but no grads</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervention trainable parameters: &quot;</span><span class="p">,</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+<span class="n">train_iterator</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">epochs</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Epoch&quot;</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">train_iterator</span><span class="p">:</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span>
+        <span class="n">DataLoader</span><span class="p">(</span>
+            <span class="n">train_dataset</span><span class="p">,</span>
+            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+            <span class="n">sampler</span><span class="o">=</span><span class="n">batched_random_sampler</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">),</span>
+        <span class="p">),</span>
+        <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="n">leave</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">epoch_iterator</span><span class="p">:</span>
+        <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">batch</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">batch</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">[</span>
+                    <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]},</span>
+                    <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">1</span><span class="p">]},</span>
+                <span class="p">],</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+                <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)]]</span>
+                    <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">[{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]},</span> <span class="kc">None</span><span class="p">],</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+                <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                    <span class="kc">None</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]}],</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                        <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+                <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span>
+                    <span class="kc">None</span><span class="p">,</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)]]</span>
+                    <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+            <span class="n">counterfactual_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+        <span class="p">)</span>
+
+        <span class="c1"># loss and backprop</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">compute_loss</span><span class="p">(</span>
+            <span class="n">counterfactual_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="n">loss</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]})</span>
+
+        <span class="k">if</span> <span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">gradient_accumulation_steps</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">total_step</span> <span class="o">%</span> <span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+            <span class="n">intervenable</span><span class="o">.</span><span class="n">set_zero_grad</span><span class="p">()</span>
+        <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>intervention trainable parameters:  256
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 0: 0it [00:00, ?it/s] [00:00&lt;?, ?it/s]
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 0: 200it [01:17,  2.59it/s, loss=tensor(0.4580, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.872]
+Epoch: 1: 200it [00:54,  3.69it/s, loss=tensor(0.4646, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.89] 
+Epoch: 2: 200it [00:53,  3.71it/s, loss=tensor(0.1925, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.962]
+Epoch: 3: 200it [00:53,  3.71it/s, loss=tensor(0.5047, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.837]
+Epoch: 4: 200it [00:54,  3.70it/s, loss=tensor(0.1448, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.969]
+Epoch: 5: 200it [00:52,  3.81it/s, loss=tensor(0.1444, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.967]
+Epoch: 6: 200it [00:58,  3.40it/s, loss=tensor(0.1562, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.97] 
+Epoch: 7: 200it [01:04,  3.09it/s, loss=tensor(0.1703, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.958]
+Epoch: 8: 200it [01:05,  3.07it/s, loss=tensor(0.1553, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.959]
+Epoch: 9: 200it [01:04,  3.10it/s, loss=tensor(0.1505, device=&#39;cuda:0&#39;, grad_fn=&lt;NllLossBackward0&gt;), acc=0.967]
+Epoch: 100%|██████████| 10/10 [09:59&lt;00:00, 59.91s/it]
+</pre></div>
+</div>
+</div>
+</div>
+<p>What’s more, is it generalizes unseen test data:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">test_dataset</span> <span class="o">=</span> <span class="n">test_equality_model</span><span class="o">.</span><span class="n">generate_counterfactual_dataset</span><span class="p">(</span>
+    <span class="mi">10000</span><span class="p">,</span> <span class="n">intervention_id</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">input_sampler</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">batch</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">batch</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">[</span>
+                    <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]},</span>
+                    <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">1</span><span class="p">]},</span>
+                <span class="p">],</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+                <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)]]</span>
+                    <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">[{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]},</span> <span class="kc">None</span><span class="p">],</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+                        <span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+                <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                    <span class="kc">None</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;intervention_id&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]}],</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                        <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+                    <span class="p">)</span>
+                <span class="p">},</span>
+                <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span>
+                    <span class="kc">None</span><span class="p">,</span>
+                    <span class="p">[[</span><span class="n">_</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">embedding_dim</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)]]</span>
+                    <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span>
+                <span class="p">],</span>
+            <span class="p">)</span>
+        <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">eval_labels</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">()))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test:   0%|          | 0/2 [00:00&lt;?, ?it/s]
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████| 2/2 [00:00&lt;00:00, 14.01it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+         0.0       0.98      0.96      0.97      6407
+         1.0       0.96      0.98      0.97      6393
+
+    accuracy                           0.97     12800
+   macro avg       0.97      0.97      0.97     12800
+weighted avg       0.97      0.97      0.97     12800
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
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+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Intro to Distributed Alignment Search (DAS)</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#contents">Contents</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-hierarchical-equality-task">The hierarchical equality task</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#an-algorithm-that-solves-the-equality-task">An Algorithm that Solves the Equality Task</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-algorithm-with-no-intervention">The algorithm with no intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-algorithm-with-an-intervention">The algorithm with an intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-algorithm-with-an-interchange-intervention">The algorithm with an interchange intervention</a></li>
+</ul>
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+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#hand-crafting-an-mlp-to-solve-hierarchical-equality">Hand Crafting an MLP to Solve Hierarchical Equality</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#causal-abstraction">Causal abstraction</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#training-an-mlp-to-solve-hierarchical-equality">Training an MLP to Solve Hierarchical Equality</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#distributed-alignment-search">Distributed Alignment Search</a></li>
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new file mode 100644
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+  <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"  title="System Settings"></i>
+</button>
+
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+<button class="btn btn-sm pst-navbar-icon search-button search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+</button>
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+      
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+</div>
+              
+              
+
+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Replicating the IOI paper</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#setup">Setup</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#path-patching-config">Path patching config</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-utils">Dataset + Utils</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#name-mover-heads">Name mover heads</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="replicating-the-ioi-paper">
+<h1>Replicating the IOI paper<a class="headerlink" href="#replicating-the-ioi-paper" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Aryaman Arora&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;1/24/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>We’re going to try to replicate some results of the original IOI paper (<a class="reference external" href="https://arxiv.org/abs/2211.00593">Wang et al., 2022</a>) using <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>, as a demonstration of path patching and verification of their results.</p>
+</section>
+<section id="setup">
+<h2>Setup<a class="headerlink" href="#setup" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/frankaging/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tutorial_ioi_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">scale_y_reverse</span><span class="p">,</span>
+    <span class="n">scale_fill_cmap</span><span class="p">,</span>
+    <span class="n">geom_text</span><span class="p">,</span>
+    <span class="n">theme_bw</span><span class="p">,</span>
+    <span class="n">xlim</span><span class="p">,</span>
+    <span class="n">ylim</span><span class="p">,</span>
+    <span class="n">scale_x_continuous</span>
+<span class="p">)</span>
+
+<span class="c1"># please try not to do this, the plot somehow throw warnings though :(</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2_lm</span><span class="p">(</span><span class="n">cache_dir</span><span class="o">=</span><span class="s2">&quot;/Users/aryamanarora/.cache/huggingface/hub/&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+
+<span class="n">titles</span><span class="o">=</span><span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;single restored layer in GPT2-XL&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;center of interval of 10 patched mlp layer&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;center of interval of 10 patched attn layer&quot;</span>
+<span class="p">}</span>
+
+<span class="n">colors</span><span class="o">=</span><span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Purples&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;Greens&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Reds&quot;</span>
+<span class="p">}</span> 
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace">                         </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span> <span class="p">[</span><span class="mi">58</span><span class="p">],</span> <span class="n">line</span> <span class="mi">27</span>
+<span class="g g-Whitespace">     </span><span class="mi">23</span> <span class="kn">import</span> <span class="nn">warnings</span>
+<span class="g g-Whitespace">     </span><span class="mi">25</span> <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">27</span> <span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2_lm</span><span class="p">(</span><span class="n">cache_dir</span><span class="o">=</span><span class="s2">&quot;/Users/aryamanarora/.cache/huggingface/hub/&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">28</span> <span class="n">_</span> <span class="o">=</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">30</span> <span class="n">titles</span><span class="o">=</span><span class="p">{</span>
+<span class="g g-Whitespace">     </span><span class="mi">31</span>     <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;single restored layer in GPT2-XL&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">32</span>     <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;center of interval of 10 patched mlp layer&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">33</span>     <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;center of interval of 10 patched attn layer&quot;</span>
+<span class="g g-Whitespace">     </span><span class="mi">34</span> <span class="p">}</span>
+
+<span class="nn">File ~/Documents/Code/pyvene/pyvene/models/gpt2/modelings_intervenable_gpt2.py:84,</span> in <span class="ni">create_gpt2_lm</span><span class="nt">(name, config, cache_dir)</span>
+<span class="g g-Whitespace">     </span><span class="mi">81</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;Creates a GPT2 LM, config, and tokenizer from the given name and revision&quot;&quot;&quot;</span>
+<span class="g g-Whitespace">     </span><span class="mi">82</span> <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">GPT2LMHeadModel</span><span class="p">,</span> <span class="n">GPT2Tokenizer</span><span class="p">,</span> <span class="n">GPT2Config</span>
+<span class="ne">---&gt; </span><span class="mi">84</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">GPT2Tokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">85</span> <span class="k">if</span> <span class="n">config</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+<span class="g g-Whitespace">     </span><span class="mi">86</span>     <span class="n">config</span> <span class="o">=</span> <span class="n">GPT2Config</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1968,</span> in <span class="ni">PreTrainedTokenizerBase.from_pretrained</span><span class="nt">(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, *init_inputs, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1965</span> <span class="k">if</span> <span class="s2">&quot;tokenizer_file&quot;</span> <span class="ow">in</span> <span class="n">vocab_files</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1966</span>     <span class="c1"># Try to get the tokenizer config to see if there are versioned tokenizer files.</span>
+<span class="g g-Whitespace">   </span><span class="mi">1967</span>     <span class="n">fast_tokenizer_file</span> <span class="o">=</span> <span class="n">FULL_TOKENIZER_FILE</span>
+<span class="ne">-&gt; </span><span class="mi">1968</span>     <span class="n">resolved_config_file</span> <span class="o">=</span> <span class="n">cached_file</span><span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">1969</span>         <span class="n">pretrained_model_name_or_path</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1970</span>         <span class="n">TOKENIZER_CONFIG_FILE</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1971</span>         <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1972</span>         <span class="n">force_download</span><span class="o">=</span><span class="n">force_download</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1973</span>         <span class="n">resume_download</span><span class="o">=</span><span class="n">resume_download</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1974</span>         <span class="n">proxies</span><span class="o">=</span><span class="n">proxies</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1975</span>         <span class="n">token</span><span class="o">=</span><span class="n">token</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1976</span>         <span class="n">revision</span><span class="o">=</span><span class="n">revision</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1977</span>         <span class="n">local_files_only</span><span class="o">=</span><span class="n">local_files_only</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1978</span>         <span class="n">subfolder</span><span class="o">=</span><span class="n">subfolder</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1979</span>         <span class="n">user_agent</span><span class="o">=</span><span class="n">user_agent</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1980</span>         <span class="n">_raise_exceptions_for_missing_entries</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1981</span>         <span class="n">_raise_exceptions_for_connection_errors</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1982</span>         <span class="n">_commit_hash</span><span class="o">=</span><span class="n">commit_hash</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1983</span>     <span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1984</span>     <span class="n">commit_hash</span> <span class="o">=</span> <span class="n">extract_commit_hash</span><span class="p">(</span><span class="n">resolved_config_file</span><span class="p">,</span> <span class="n">commit_hash</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1985</span>     <span class="k">if</span> <span class="n">resolved_config_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/transformers/utils/hub.py:429,</span> in <span class="ni">cached_file</span><span class="nt">(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">426</span> <span class="n">user_agent</span> <span class="o">=</span> <span class="n">http_user_agent</span><span class="p">(</span><span class="n">user_agent</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">427</span> <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">428</span>     <span class="c1"># Load from URL or cache if already cached</span>
+<span class="ne">--&gt; </span><span class="mi">429</span>     <span class="n">resolved_file</span> <span class="o">=</span> <span class="n">hf_hub_download</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">430</span>         <span class="n">path_or_repo_id</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">431</span>         <span class="n">filename</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">432</span>         <span class="n">subfolder</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">subfolder</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">subfolder</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">433</span>         <span class="n">repo_type</span><span class="o">=</span><span class="n">repo_type</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">434</span>         <span class="n">revision</span><span class="o">=</span><span class="n">revision</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">435</span>         <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">436</span>         <span class="n">user_agent</span><span class="o">=</span><span class="n">user_agent</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">437</span>         <span class="n">force_download</span><span class="o">=</span><span class="n">force_download</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">438</span>         <span class="n">proxies</span><span class="o">=</span><span class="n">proxies</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">439</span>         <span class="n">resume_download</span><span class="o">=</span><span class="n">resume_download</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">440</span>         <span class="n">token</span><span class="o">=</span><span class="n">token</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">441</span>         <span class="n">local_files_only</span><span class="o">=</span><span class="n">local_files_only</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">442</span>     <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">443</span> <span class="k">except</span> <span class="n">GatedRepoError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">444</span>     <span class="k">raise</span> <span class="ne">EnvironmentError</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">445</span>         <span class="s2">&quot;You are trying to access a gated repo.</span><span class="se">\n</span><span class="s2">Make sure to request access at &quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">446</span>         <span class="sa">f</span><span class="s2">&quot;https://huggingface.co/</span><span class="si">{</span><span class="n">path_or_repo_id</span><span class="si">}</span><span class="s2"> and pass a token having permission to this repo either &quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">447</span>         <span class="s2">&quot;by logging in with `huggingface-cli login` or by passing `token=&lt;your_token&gt;`.&quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">448</span>     <span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py:118,</span> in <span class="ni">validate_hf_hub_args.&lt;locals&gt;._inner_fn</span><span class="nt">(*args, **kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">115</span> <span class="k">if</span> <span class="n">check_use_auth_token</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">116</span>     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">smoothly_deprecate_use_auth_token</span><span class="p">(</span><span class="n">fn_name</span><span class="o">=</span><span class="n">fn</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">has_token</span><span class="o">=</span><span class="n">has_token</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">118</span> <span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:1232,</span> in <span class="ni">hf_hub_download</span><span class="nt">(repo_id, filename, subfolder, repo_type, revision, endpoint, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1230</span> <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1231</span>     <span class="k">try</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1232</span>         <span class="n">metadata</span> <span class="o">=</span> <span class="n">get_hf_file_metadata</span><span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">1233</span>             <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1234</span>             <span class="n">token</span><span class="o">=</span><span class="n">token</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1235</span>             <span class="n">proxies</span><span class="o">=</span><span class="n">proxies</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1236</span>             <span class="n">timeout</span><span class="o">=</span><span class="n">etag_timeout</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1237</span>         <span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1238</span>     <span class="k">except</span> <span class="n">EntryNotFoundError</span> <span class="k">as</span> <span class="n">http_error</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1239</span>         <span class="c1"># Cache the non-existence of the file and raise</span>
+<span class="g g-Whitespace">   </span><span class="mi">1240</span>         <span class="n">commit_hash</span> <span class="o">=</span> <span class="n">http_error</span><span class="o">.</span><span class="n">response</span><span class="o">.</span><span class="n">headers</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">HUGGINGFACE_HEADER_X_REPO_COMMIT</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py:118,</span> in <span class="ni">validate_hf_hub_args.&lt;locals&gt;._inner_fn</span><span class="nt">(*args, **kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">115</span> <span class="k">if</span> <span class="n">check_use_auth_token</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">116</span>     <span class="n">kwargs</span> <span class="o">=</span> <span class="n">smoothly_deprecate_use_auth_token</span><span class="p">(</span><span class="n">fn_name</span><span class="o">=</span><span class="n">fn</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">has_token</span><span class="o">=</span><span class="n">has_token</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">118</span> <span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:1599,</span> in <span class="ni">get_hf_file_metadata</span><span class="nt">(url, token, proxies, timeout)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1596</span> <span class="n">headers</span><span class="p">[</span><span class="s2">&quot;Accept-Encoding&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;identity&quot;</span>  <span class="c1"># prevent any compression =&gt; we want to know the real size of the file</span>
+<span class="g g-Whitespace">   </span><span class="mi">1598</span> <span class="c1"># Retrieve metadata</span>
+<span class="ne">-&gt; </span><span class="mi">1599</span> <span class="n">r</span> <span class="o">=</span> <span class="n">_request_wrapper</span><span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">1600</span>     <span class="n">method</span><span class="o">=</span><span class="s2">&quot;HEAD&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1601</span>     <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1602</span>     <span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1603</span>     <span class="n">allow_redirects</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1604</span>     <span class="n">follow_relative_redirects</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1605</span>     <span class="n">proxies</span><span class="o">=</span><span class="n">proxies</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1606</span>     <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1607</span> <span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1608</span> <span class="n">hf_raise_for_status</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1610</span> <span class="c1"># Return</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:417,</span> in <span class="ni">_request_wrapper</span><span class="nt">(method, url, max_retries, base_wait_time, max_wait_time, timeout, follow_relative_redirects, **params)</span>
+<span class="g g-Whitespace">    </span><span class="mi">415</span> <span class="c1"># 2. Force relative redirection</span>
+<span class="g g-Whitespace">    </span><span class="mi">416</span> <span class="k">if</span> <span class="n">follow_relative_redirects</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">417</span>     <span class="n">response</span> <span class="o">=</span> <span class="n">_request_wrapper</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">418</span>         <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">419</span>         <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">420</span>         <span class="n">max_retries</span><span class="o">=</span><span class="n">max_retries</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">421</span>         <span class="n">base_wait_time</span><span class="o">=</span><span class="n">base_wait_time</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">422</span>         <span class="n">max_wait_time</span><span class="o">=</span><span class="n">max_wait_time</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">423</span>         <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">424</span>         <span class="n">follow_relative_redirects</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">425</span>         <span class="o">**</span><span class="n">params</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">426</span>     <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">428</span>     <span class="c1"># If redirection, we redirect only relative paths.</span>
+<span class="g g-Whitespace">    </span><span class="mi">429</span>     <span class="c1"># This is useful in case of a renamed repository.</span>
+<span class="g g-Whitespace">    </span><span class="mi">430</span>     <span class="k">if</span> <span class="mi">300</span> <span class="o">&lt;=</span> <span class="n">response</span><span class="o">.</span><span class="n">status_code</span> <span class="o">&lt;=</span> <span class="mi">399</span><span class="p">:</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/file_download.py:452,</span> in <span class="ni">_request_wrapper</span><span class="nt">(method, url, max_retries, base_wait_time, max_wait_time, timeout, follow_relative_redirects, **params)</span>
+<span class="g g-Whitespace">    </span><span class="mi">449</span>     <span class="k">return</span> <span class="n">response</span>
+<span class="g g-Whitespace">    </span><span class="mi">451</span> <span class="c1"># 3. Exponential backoff</span>
+<span class="ne">--&gt; </span><span class="mi">452</span> <span class="k">return</span> <span class="n">http_backoff</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">453</span>     <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">454</span>     <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">455</span>     <span class="n">max_retries</span><span class="o">=</span><span class="n">max_retries</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">456</span>     <span class="n">base_wait_time</span><span class="o">=</span><span class="n">base_wait_time</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">457</span>     <span class="n">max_wait_time</span><span class="o">=</span><span class="n">max_wait_time</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">458</span>     <span class="n">retry_on_exceptions</span><span class="o">=</span><span class="p">(</span><span class="n">Timeout</span><span class="p">,</span> <span class="n">ProxyError</span><span class="p">),</span>
+<span class="g g-Whitespace">    </span><span class="mi">459</span>     <span class="n">retry_on_status_codes</span><span class="o">=</span><span class="p">(),</span>
+<span class="g g-Whitespace">    </span><span class="mi">460</span>     <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">461</span>     <span class="o">**</span><span class="n">params</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">462</span> <span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_http.py:258,</span> in <span class="ni">http_backoff</span><span class="nt">(method, url, max_retries, base_wait_time, max_wait_time, retry_on_exceptions, retry_on_status_codes, **kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">255</span>     <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;data&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="n">io_obj_initial_pos</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">257</span> <span class="c1"># Perform request and return if status_code is not in the retry list.</span>
+<span class="ne">--&gt; </span><span class="mi">258</span> <span class="n">response</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">request</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">259</span> <span class="k">if</span> <span class="n">response</span><span class="o">.</span><span class="n">status_code</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">retry_on_status_codes</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">260</span>     <span class="k">return</span> <span class="n">response</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/requests/sessions.py:589,</span> in <span class="ni">Session.request</span><span class="nt">(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)</span>
+<span class="g g-Whitespace">    </span><span class="mi">584</span> <span class="n">send_kwargs</span> <span class="o">=</span> <span class="p">{</span>
+<span class="g g-Whitespace">    </span><span class="mi">585</span>     <span class="s2">&quot;timeout&quot;</span><span class="p">:</span> <span class="n">timeout</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">586</span>     <span class="s2">&quot;allow_redirects&quot;</span><span class="p">:</span> <span class="n">allow_redirects</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">587</span> <span class="p">}</span>
+<span class="g g-Whitespace">    </span><span class="mi">588</span> <span class="n">send_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">settings</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">589</span> <span class="n">resp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">send</span><span class="p">(</span><span class="n">prep</span><span class="p">,</span> <span class="o">**</span><span class="n">send_kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">591</span> <span class="k">return</span> <span class="n">resp</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/requests/sessions.py:703,</span> in <span class="ni">Session.send</span><span class="nt">(self, request, **kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">700</span> <span class="n">start</span> <span class="o">=</span> <span class="n">preferred_clock</span><span class="p">()</span>
+<span class="g g-Whitespace">    </span><span class="mi">702</span> <span class="c1"># Send the request</span>
+<span class="ne">--&gt; </span><span class="mi">703</span> <span class="n">r</span> <span class="o">=</span> <span class="n">adapter</span><span class="o">.</span><span class="n">send</span><span class="p">(</span><span class="n">request</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">705</span> <span class="c1"># Total elapsed time of the request (approximately)</span>
+<span class="g g-Whitespace">    </span><span class="mi">706</span> <span class="n">elapsed</span> <span class="o">=</span> <span class="n">preferred_clock</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/huggingface_hub/utils/_http.py:63,</span> in <span class="ni">UniqueRequestIdAdapter.send</span><span class="nt">(self, request, *args, **kwargs)</span>
+<span class="g g-Whitespace">     </span><span class="mi">61</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;Catch any RequestException to append request id to the error message for debugging.&quot;&quot;&quot;</span>
+<span class="g g-Whitespace">     </span><span class="mi">62</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">---&gt; </span><span class="mi">63</span>     <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">send</span><span class="p">(</span><span class="n">request</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">64</span> <span class="k">except</span> <span class="n">requests</span><span class="o">.</span><span class="n">RequestException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+<span class="g g-Whitespace">     </span><span class="mi">65</span>     <span class="n">request_id</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">headers</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">X_AMZN_TRACE_ID</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/requests/adapters.py:486,</span> in <span class="ni">HTTPAdapter.send</span><span class="nt">(self, request, stream, timeout, verify, cert, proxies)</span>
+<span class="g g-Whitespace">    </span><span class="mi">483</span>     <span class="n">timeout</span> <span class="o">=</span> <span class="n">TimeoutSauce</span><span class="p">(</span><span class="n">connect</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span> <span class="n">read</span><span class="o">=</span><span class="n">timeout</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">485</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">486</span>     <span class="n">resp</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">487</span>         <span class="n">method</span><span class="o">=</span><span class="n">request</span><span class="o">.</span><span class="n">method</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">488</span>         <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">489</span>         <span class="n">body</span><span class="o">=</span><span class="n">request</span><span class="o">.</span><span class="n">body</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">490</span>         <span class="n">headers</span><span class="o">=</span><span class="n">request</span><span class="o">.</span><span class="n">headers</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">491</span>         <span class="n">redirect</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">492</span>         <span class="n">assert_same_host</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">493</span>         <span class="n">preload_content</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">494</span>         <span class="n">decode_content</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">495</span>         <span class="n">retries</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_retries</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">496</span>         <span class="n">timeout</span><span class="o">=</span><span class="n">timeout</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">497</span>         <span class="n">chunked</span><span class="o">=</span><span class="n">chunked</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">498</span>     <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">500</span> <span class="k">except</span> <span class="p">(</span><span class="n">ProtocolError</span><span class="p">,</span> <span class="ne">OSError</span><span class="p">)</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">501</span>     <span class="k">raise</span> <span class="ne">ConnectionError</span><span class="p">(</span><span class="n">err</span><span class="p">,</span> <span class="n">request</span><span class="o">=</span><span class="n">request</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connectionpool.py:703,</span> in <span class="ni">HTTPConnectionPool.urlopen</span><span class="nt">(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)</span>
+<span class="g g-Whitespace">    </span><span class="mi">700</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_proxy</span><span class="p">(</span><span class="n">conn</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">702</span> <span class="c1"># Make the request on the httplib connection object.</span>
+<span class="ne">--&gt; </span><span class="mi">703</span> <span class="n">httplib_response</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_request</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">704</span>     <span class="n">conn</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">705</span>     <span class="n">method</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">706</span>     <span class="n">url</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">707</span>     <span class="n">timeout</span><span class="o">=</span><span class="n">timeout_obj</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">708</span>     <span class="n">body</span><span class="o">=</span><span class="n">body</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">709</span>     <span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">710</span>     <span class="n">chunked</span><span class="o">=</span><span class="n">chunked</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">711</span> <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">713</span> <span class="c1"># If we&#39;re going to release the connection in ``finally:``, then</span>
+<span class="g g-Whitespace">    </span><span class="mi">714</span> <span class="c1"># the response doesn&#39;t need to know about the connection. Otherwise</span>
+<span class="g g-Whitespace">    </span><span class="mi">715</span> <span class="c1"># it will also try to release it and we&#39;ll have a double-release</span>
+<span class="g g-Whitespace">    </span><span class="mi">716</span> <span class="c1"># mess.</span>
+<span class="g g-Whitespace">    </span><span class="mi">717</span> <span class="n">response_conn</span> <span class="o">=</span> <span class="n">conn</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">release_conn</span> <span class="k">else</span> <span class="kc">None</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connectionpool.py:386,</span> in <span class="ni">HTTPConnectionPool._make_request</span><span class="nt">(self, conn, method, url, timeout, chunked, **httplib_request_kw)</span>
+<span class="g g-Whitespace">    </span><span class="mi">384</span> <span class="c1"># Trigger any extra validation we need to do.</span>
+<span class="g g-Whitespace">    </span><span class="mi">385</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">386</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_validate_conn</span><span class="p">(</span><span class="n">conn</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">387</span> <span class="k">except</span> <span class="p">(</span><span class="n">SocketTimeout</span><span class="p">,</span> <span class="n">BaseSSLError</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">388</span>     <span class="c1"># Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.</span>
+<span class="g g-Whitespace">    </span><span class="mi">389</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_raise_timeout</span><span class="p">(</span><span class="n">err</span><span class="o">=</span><span class="n">e</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="n">timeout_value</span><span class="o">=</span><span class="n">conn</span><span class="o">.</span><span class="n">timeout</span><span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connectionpool.py:1042,</span> in <span class="ni">HTTPSConnectionPool._validate_conn</span><span class="nt">(self, conn)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1040</span> <span class="c1"># Force connect early to allow us to validate the connection.</span>
+<span class="g g-Whitespace">   </span><span class="mi">1041</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="s2">&quot;sock&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>  <span class="c1"># AppEngine might not have  `.sock`</span>
+<span class="ne">-&gt; </span><span class="mi">1042</span>     <span class="n">conn</span><span class="o">.</span><span class="n">connect</span><span class="p">()</span>
+<span class="g g-Whitespace">   </span><span class="mi">1044</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">conn</span><span class="o">.</span><span class="n">is_verified</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1045</span>     <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">1046</span>         <span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">1047</span>             <span class="s2">&quot;Unverified HTTPS request is being made to host &#39;</span><span class="si">%s</span><span class="s2">&#39;. &quot;</span>
+   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1052</span>         <span class="n">InsecureRequestWarning</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1053</span>     <span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connection.py:358,</span> in <span class="ni">HTTPSConnection.connect</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">    </span><span class="mi">356</span> <span class="k">def</span> <span class="nf">connect</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">357</span>     <span class="c1"># Add certificate verification</span>
+<span class="ne">--&gt; </span><span class="mi">358</span>     <span class="bp">self</span><span class="o">.</span><span class="n">sock</span> <span class="o">=</span> <span class="n">conn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_conn</span><span class="p">()</span>
+<span class="g g-Whitespace">    </span><span class="mi">359</span>     <span class="n">hostname</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">host</span>
+<span class="g g-Whitespace">    </span><span class="mi">360</span>     <span class="n">tls_in_tls</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/urllib3/connection.py:174,</span> in <span class="ni">HTTPConnection._new_conn</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">    </span><span class="mi">171</span>     <span class="n">extra_kw</span><span class="p">[</span><span class="s2">&quot;socket_options&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">socket_options</span>
+<span class="g g-Whitespace">    </span><span class="mi">173</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">174</span>     <span class="n">conn</span> <span class="o">=</span> <span class="n">connection</span><span class="o">.</span><span class="n">create_connection</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">175</span>         <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dns_host</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">port</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">,</span> <span class="o">**</span><span class="n">extra_kw</span>
+<span class="g g-Whitespace">    </span><span class="mi">176</span>     <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">178</span> <span class="k">except</span> <span class="n">SocketTimeout</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">179</span>     <span class="k">raise</span> <span class="n">ConnectTimeoutError</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">180</span>         <span class="bp">self</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">181</span>         <span class="s2">&quot;Connection to </span><span class="si">%s</span><span class="s2"> timed out. (connect timeout=</span><span class="si">%s</span><span class="s2">)&quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">182</span>         <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">host</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">),</span>
+<span class="g g-Whitespace">    </span><span class="mi">183</span>     <span class="p">)</span>
+
+<span class="nn">File ~/opt/miniconda3/lib/python3.9/site-packages/urllib3/util/connection.py:85,</span> in <span class="ni">create_connection</span><span class="nt">(address, timeout, source_address, socket_options)</span>
+<span class="g g-Whitespace">     </span><span class="mi">83</span>     <span class="k">if</span> <span class="n">source_address</span><span class="p">:</span>
+<span class="g g-Whitespace">     </span><span class="mi">84</span>         <span class="n">sock</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">source_address</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">85</span>     <span class="n">sock</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">sa</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">86</span>     <span class="k">return</span> <span class="n">sock</span>
+<span class="g g-Whitespace">     </span><span class="mi">88</span> <span class="k">except</span> <span class="n">socket</span><span class="o">.</span><span class="n">error</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+
+<span class="ne">KeyboardInterrupt</span>: 
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="path-patching-config">
+<h2>Path patching config<a class="headerlink" href="#path-patching-config" title="Link to this heading">#</a></h2>
+<p>This is taken from the <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> 101 tutorial. Basically, we’ll intervene at all positions for a single attention head, and restore the base input for all upstream model components. This will get the direct effect of the intervention on the logits.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">path_patching_config</span><span class="p">(</span>
+    <span class="n">layer</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">,</span> 
+    <span class="n">component</span><span class="o">=</span><span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s2">&quot;h.pos&quot;</span>
+<span class="p">):</span>
+    <span class="n">intervening_component</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">component</span><span class="p">,</span> <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="n">unit</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}]</span>
+    <span class="n">restoring_components</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">component</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;mlp_&quot;</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layer</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
+        <span class="p">]</span>
+    <span class="n">intervenable_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">intervening_component</span> <span class="o">+</span> <span class="n">restoring_components</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">intervenable_config</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">restoring_components</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="dataset-utils">
+<h2>Dataset + Utils<a class="headerlink" href="#dataset-utils" title="Link to this heading">#</a></h2>
+<p>Just sampling prompts for the IOI task.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">test_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+    <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">,</span>
+    <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">,</span>
+    <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">,</span>
+    <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">D_test</span> <span class="o">=</span> <span class="n">test_distribution</span><span class="o">.</span><span class="n">sample_das</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">base_patterns</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;ABB&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">source_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;DCE&quot;</span><span class="p">],</span>
+    <span class="n">labels</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">samples_per_combination</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">+</span> <span class="n">test_distribution</span><span class="o">.</span><span class="n">sample_das</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">base_patterns</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;BAB&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">source_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;DCE&quot;</span><span class="p">],</span>
+    <span class="n">labels</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">samples_per_combination</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+
+<span class="k">def</span> <span class="nf">get_last_token</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">):</span>
+    <span class="n">last_token_indices</span> <span class="o">=</span> <span class="n">attention_mask</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">batch_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">logits</span><span class="p">[</span><span class="n">batch_indices</span><span class="p">,</span> <span class="n">last_token_indices</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">D_test</span><span class="o">.</span><span class="n">batches</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">source</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">tokens</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+    <span class="k">break</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;===PROMPT=== Then, Justin and Bryan went to the hospital. Justin gave a necklace to&gt;] torch.Size([1, 15])  Bryan
+[&lt;===PROMPT=== Then, Courtney and Thomas went to the hospital. Ashley gave a necklace to&gt;] torch.Size([1, 15])  None
+</pre></div>
+</div>
+</div>
+</div>
+<p>We’re also implementing the logit diff metric, which checks the difference in logits between the two names in the sentence. Positive logit diff means a correct prediction is more likely (the IO, i.e. non-subject name).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compute_logit_diff</span><span class="p">(</span><span class="n">logits</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
+    <span class="n">base_logit</span> <span class="o">=</span> <span class="n">get_last_token</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">])</span>
+    <span class="n">base_label</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">logit_diffs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">base_logit</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
+        <span class="n">correct_name</span> <span class="o">=</span> <span class="n">base_label</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]</span>
+        <span class="n">other_name</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">prompts</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]</span><span class="o">.</span><span class="n">s_name</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">logit_diffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">base_logit</span><span class="p">[</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">correct_name</span><span class="p">]</span> <span class="o">-</span> <span class="n">base_logit</span><span class="p">[</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">other_name</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">logit_diffs</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>They reported a baseline logit difference of 3.56 and a task accuracy of 99.3% for GPT-2 in the paper. Let’s check if this holds on our dataset (it pretty much does):</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="n">logit_diffs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">argmax_acc</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">D_test</span><span class="o">.</span><span class="n">batches</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+        <span class="n">base_label</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">base_logit</span> <span class="o">=</span> <span class="n">get_last_token</span><span class="p">(</span><span class="n">gpt2</span><span class="p">(</span><span class="o">**</span><span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span><span class="p">)</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">])</span>
+        <span class="n">src_logit</span> <span class="o">=</span> <span class="n">get_last_token</span><span class="p">(</span><span class="n">gpt2</span><span class="p">(</span><span class="o">**</span><span class="n">batch</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">tokens</span><span class="p">)</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">tokens</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
+            <span class="n">other_name</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">batch</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">prompts</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]</span><span class="o">.</span><span class="n">s_name</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">correct_name</span> <span class="o">=</span> <span class="n">base_label</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]</span>
+
+            <span class="c1"># logit diff</span>
+            <span class="n">logit_diffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">base_logit</span><span class="p">[</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">base_label</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]]</span> <span class="o">-</span> <span class="n">base_logit</span><span class="p">[</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">other_name</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+
+            <span class="c1"># baseline accuracy</span>
+            <span class="n">argmax</span> <span class="o">=</span> <span class="n">base_logit</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">argmax</span> <span class="o">==</span> <span class="n">base_label</span><span class="p">[</span><span class="n">batch_i</span><span class="p">]:</span>
+                <span class="n">argmax_acc</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="n">logit_diff</span> <span class="o">=</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">logit_diffs</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">logit_diffs</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;avg logit diff:&quot;</span><span class="p">,</span> <span class="n">logit_diff</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;argmax acc:&quot;</span><span class="p">,</span> <span class="n">argmax_acc</span> <span class="o">/</span> <span class="mi">50</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 10/10 [00:02&lt;00:00,  4.54it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>avg logit diff: 3.7562549114227295
+argmax acc: 0.94
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="name-mover-heads">
+<h2>Name mover heads<a class="headerlink" href="#name-mover-heads" title="Link to this heading">#</a></h2>
+<p>We will replicate figure 3, which identifies heads which directly affect the logits.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">12</span><span class="p">):</span>
+        <span class="n">intervenable_config</span><span class="p">,</span> <span class="n">num_restores</span> <span class="o">=</span> <span class="n">path_patching_config</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
+        <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">intervenable_config</span><span class="p">,</span> <span class="n">gpt2</span><span class="p">,</span> <span class="n">use_fast</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">head</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_head</span><span class="p">):</span>
+            <span class="n">eval_labels</span><span class="p">,</span> <span class="n">eval_preds</span><span class="p">,</span> <span class="n">logit_diffs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">batch_dataset</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">D_test</span><span class="o">.</span><span class="n">batches</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="mi">50</span><span class="p">):</span>
+                <span class="c1"># prepare</span>
+                <span class="n">base_inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span>
+                <span class="n">source_inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">tokens</span>
+                <span class="n">labels</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+                <span class="n">pos</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+                <span class="c1"># inference</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span> 
+                    <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">source_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span> <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]}],</span>
+                    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">((</span>
+                        <span class="p">[[[[</span><span class="n">head</span><span class="p">]],</span> <span class="p">[</span><span class="n">pos</span><span class="p">]]]</span><span class="o">+</span><span class="p">[[</span><span class="n">pos</span><span class="p">]]</span><span class="o">*</span><span class="n">num_restores</span><span class="p">,</span> 
+                        <span class="p">[[[[</span><span class="n">head</span><span class="p">]],</span> <span class="p">[</span><span class="n">pos</span><span class="p">]]]</span><span class="o">+</span><span class="p">[[</span><span class="n">pos</span><span class="p">]]</span><span class="o">*</span><span class="n">num_restores</span>
+                    <span class="p">))}</span>
+                <span class="p">)</span>
+                <span class="n">logit_diffs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">compute_logit_diff</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">batch_dataset</span><span class="p">))</span>
+                <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">labels</span><span class="p">]</span>
+                <span class="n">last_token_logits</span> <span class="o">=</span> <span class="n">get_last_token</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+                <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">last_token_logits</span><span class="p">]</span>
+            
+            <span class="c1"># metrics</span>
+            <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+                <span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">mean_logit_diff</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">logit_diffs</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">logit_diffs</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;head&quot;</span><span class="p">:</span> <span class="n">head</span><span class="p">,</span> <span class="s2">&quot;logit_diff&quot;</span><span class="p">:</span> <span class="n">mean_logit_diff</span><span class="p">,</span> <span class="o">**</span><span class="n">eval_metrics</span><span class="p">})</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.
+
+In case multiple location tags are passed only the first one will be considered
+100%|██████████| 50/50 [00:07&lt;00:00,  6.68it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 0, &#39;logit_diff&#39;: tensor(3.7504), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3469), &#39;label_logit&#39;: -74.34694038391113, &#39;label_prob&#39;: 0.5380359962582588}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.44it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 1, &#39;logit_diff&#39;: tensor(3.7568), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4057), &#39;label_logit&#39;: -74.40572700500488, &#39;label_prob&#39;: 0.5395114549994469}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.22it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 2, &#39;logit_diff&#39;: tensor(3.8010), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4281), &#39;label_logit&#39;: -74.42814888000488, &#39;label_prob&#39;: 0.5384641465544701}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.38it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 3, &#39;logit_diff&#39;: tensor(3.7179), &#39;accuracy&#39;: 0.92, &#39;kl_div&#39;: tensor(-74.5966), &#39;label_logit&#39;: -74.59663963317871, &#39;label_prob&#39;: 0.5215314196050167}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:08&lt;00:00,  5.84it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 4, &#39;logit_diff&#39;: tensor(3.7548), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4089), &#39;label_logit&#39;: -74.40891792297363, &#39;label_prob&#39;: 0.5398365586996079}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.36it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 5, &#39;logit_diff&#39;: tensor(3.7737), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4948), &#39;label_logit&#39;: -74.49480590820312, &#39;label_prob&#39;: 0.5443936404585838}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:08&lt;00:00,  6.24it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 6, &#39;logit_diff&#39;: tensor(3.8182), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4737), &#39;label_logit&#39;: -74.47373725891113, &#39;label_prob&#39;: 0.5551303905248642}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.79it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 7, &#39;logit_diff&#39;: tensor(3.7553), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4145), &#39;label_logit&#39;: -74.41451263427734, &#39;label_prob&#39;: 0.5396078166365623}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  7.13it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 8, &#39;logit_diff&#39;: tensor(3.7844), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4013), &#39;label_logit&#39;: -74.40127014160156, &#39;label_prob&#39;: 0.536662351489067}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  7.08it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 9, &#39;logit_diff&#39;: tensor(3.7546), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4106), &#39;label_logit&#39;: -74.41055702209472, &#39;label_prob&#39;: 0.5401303231716156}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.89it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 10, &#39;logit_diff&#39;: tensor(3.5075), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.2388), &#39;label_logit&#39;: -74.23884353637695, &#39;label_prob&#39;: 0.5428883665800095}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.37it/s]
+WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.
+
+In case multiple location tags are passed only the first one will be considered
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 8, &#39;head&#39;: 11, &#39;logit_diff&#39;: tensor(3.7405), &#39;accuracy&#39;: 0.92, &#39;kl_div&#39;: tensor(-74.6022), &#39;label_logit&#39;: -74.60216705322266, &#39;label_prob&#39;: 0.5177586142718792}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.30it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 0, &#39;logit_diff&#39;: tensor(3.7050), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.6208), &#39;label_logit&#39;: -74.620824508667, &#39;label_prob&#39;: 0.5102730639278888}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.15it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 1, &#39;logit_diff&#39;: tensor(3.7559), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4151), &#39;label_logit&#39;: -74.41508010864258, &#39;label_prob&#39;: 0.5392078700661659}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.28it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 2, &#39;logit_diff&#39;: tensor(3.7019), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.6157), &#39;label_logit&#39;: -74.61571449279785, &#39;label_prob&#39;: 0.5136157578229904}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:08&lt;00:00,  6.16it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 3, &#39;logit_diff&#39;: tensor(3.7431), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3672), &#39;label_logit&#39;: -74.3671482849121, &#39;label_prob&#39;: 0.5368358224630356}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.46it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 4, &#39;logit_diff&#39;: tensor(3.7632), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4501), &#39;label_logit&#39;: -74.45014938354493, &#39;label_prob&#39;: 0.5293045191466809}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.86it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 5, &#39;logit_diff&#39;: tensor(3.7335), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4766), &#39;label_logit&#39;: -74.47663719177245, &#39;label_prob&#39;: 0.5465028408169746}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:08&lt;00:00,  5.85it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 6, &#39;logit_diff&#39;: tensor(2.7914), &#39;accuracy&#39;: 0.7, &#39;kl_div&#39;: tensor(-76.0374), &#39;label_logit&#39;: -76.03742889404298, &#39;label_prob&#39;: 0.29187622375786304}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  7.05it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 7, &#39;logit_diff&#39;: tensor(3.7026), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3311), &#39;label_logit&#39;: -74.33110733032227, &#39;label_prob&#39;: 0.5488018499314785}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  7.05it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 8, &#39;logit_diff&#39;: tensor(3.7859), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.8128), &#39;label_logit&#39;: -74.81278228759766, &#39;label_prob&#39;: 0.4744531024992466}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.61it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 9, &#39;logit_diff&#39;: tensor(1.4911), &#39;accuracy&#39;: 0.22, &#39;kl_div&#39;: tensor(-77.3908), &#39;label_logit&#39;: -77.39078643798828, &#39;label_prob&#39;: 0.12255394758656621}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.70it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 10, &#39;logit_diff&#39;: tensor(3.7551), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3962), &#39;label_logit&#39;: -74.39617370605468, &#39;label_prob&#39;: 0.5393955698609352}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.31it/s]
+WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.
+
+In case multiple location tags are passed only the first one will be considered
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 9, &#39;head&#39;: 11, &#39;logit_diff&#39;: tensor(3.7567), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4002), &#39;label_logit&#39;: -74.40017051696778, &#39;label_prob&#39;: 0.5373660787940026}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.85it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 0, &#39;logit_diff&#39;: tensor(3.1255), &#39;accuracy&#39;: 0.66, &#39;kl_div&#39;: tensor(-75.9131), &#39;label_logit&#39;: -75.91307647705078, &#39;label_prob&#39;: 0.3100175105780363}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.76it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 1, &#39;logit_diff&#39;: tensor(3.6291), &#39;accuracy&#39;: 0.92, &#39;kl_div&#39;: tensor(-74.7467), &#39;label_logit&#39;: -74.74669570922852, &#39;label_prob&#39;: 0.4808885481953621}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.71it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 2, &#39;logit_diff&#39;: tensor(3.6425), &#39;accuracy&#39;: 0.88, &#39;kl_div&#39;: tensor(-74.8445), &#39;label_logit&#39;: -74.84453872680665, &#39;label_prob&#39;: 0.4534783412516117}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.57it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 3, &#39;logit_diff&#39;: tensor(3.7048), &#39;accuracy&#39;: 0.92, &#39;kl_div&#39;: tensor(-74.5217), &#39;label_logit&#39;: -74.52171600341796, &#39;label_prob&#39;: 0.5153644406795501}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.59it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 4, &#39;logit_diff&#39;: tensor(3.7698), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.6806), &#39;label_logit&#39;: -74.68061622619629, &#39;label_prob&#39;: 0.5424765661358834}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.19it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 5, &#39;logit_diff&#39;: tensor(3.7580), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3888), &#39;label_logit&#39;: -74.3887621307373, &#39;label_prob&#39;: 0.5407887950539589}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  7.00it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 6, &#39;logit_diff&#39;: tensor(3.4450), &#39;accuracy&#39;: 0.9, &#39;kl_div&#39;: tensor(-75.0385), &#39;label_logit&#39;: -75.0384928894043, &#39;label_prob&#39;: 0.4376738278567791}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.58it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 7, &#39;logit_diff&#39;: tensor(5.1090), &#39;accuracy&#39;: 0.98, &#39;kl_div&#39;: tensor(-72.2116), &#39;label_logit&#39;: -72.21165046691894, &#39;label_prob&#39;: 0.8099988362193108}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.89it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 8, &#39;logit_diff&#39;: tensor(3.7607), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4309), &#39;label_logit&#39;: -74.43088127136231, &#39;label_prob&#39;: 0.5412813138961792}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.34it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 9, &#39;logit_diff&#39;: tensor(3.7759), &#39;accuracy&#39;: 0.92, &#39;kl_div&#39;: tensor(-74.4457), &#39;label_logit&#39;: -74.44571556091309, &#39;label_prob&#39;: 0.5376637886464596}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.46it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 10, &#39;logit_diff&#39;: tensor(3.1971), &#39;accuracy&#39;: 0.8, &#39;kl_div&#39;: tensor(-75.1706), &#39;label_logit&#39;: -75.17062294006348, &#39;label_prob&#39;: 0.38778901934623716}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.67it/s]
+WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.
+
+In case multiple location tags are passed only the first one will be considered
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 10, &#39;head&#39;: 11, &#39;logit_diff&#39;: tensor(3.7515), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4513), &#39;label_logit&#39;: -74.4512720489502, &#39;label_prob&#39;: 0.5345661920309067}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.49it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 0, &#39;logit_diff&#39;: tensor(3.7527), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4928), &#39;label_logit&#39;: -74.49275856018066, &#39;label_prob&#39;: 0.5453835587203503}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.53it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 1, &#39;logit_diff&#39;: tensor(3.6884), &#39;accuracy&#39;: 0.9, &#39;kl_div&#39;: tensor(-74.3526), &#39;label_logit&#39;: -74.35264060974121, &#39;label_prob&#39;: 0.5213113659620285}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.46it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 2, &#39;logit_diff&#39;: tensor(4.2981), &#39;accuracy&#39;: 0.88, &#39;kl_div&#39;: tensor(-74.8699), &#39;label_logit&#39;: -74.86986404418946, &#39;label_prob&#39;: 0.4666699156165123}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.71it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 3, &#39;logit_diff&#39;: tensor(3.7677), &#39;accuracy&#39;: 0.9, &#39;kl_div&#39;: tensor(-74.9174), &#39;label_logit&#39;: -74.91741744995117, &#39;label_prob&#39;: 0.47216988608241084}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.60it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 4, &#39;logit_diff&#39;: tensor(3.7532), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3741), &#39;label_logit&#39;: -74.37413780212403, &#39;label_prob&#39;: 0.5394928365945816}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:06&lt;00:00,  7.54it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 5, &#39;logit_diff&#39;: tensor(3.7504), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.3879), &#39;label_logit&#39;: -74.38786392211914, &#39;label_prob&#39;: 0.5406103874742985}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.85it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 6, &#39;logit_diff&#39;: tensor(3.7768), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.6573), &#39;label_logit&#39;: -74.65733688354493, &#39;label_prob&#39;: 0.4942662340402603}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.83it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 7, &#39;logit_diff&#39;: tensor(3.7518), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.4400), &#39;label_logit&#39;: -74.44003486633301, &#39;label_prob&#39;: 0.5410507157444954}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.28it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 8, &#39;logit_diff&#39;: tensor(3.7459), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.9492), &#39;label_logit&#39;: -74.94922843933105, &#39;label_prob&#39;: 0.535863026380539}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:08&lt;00:00,  6.13it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 9, &#39;logit_diff&#39;: tensor(3.6901), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-74.6614), &#39;label_logit&#39;: -74.66142028808594, &#39;label_prob&#39;: 0.4914996309578419}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.31it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 10, &#39;logit_diff&#39;: tensor(4.5615), &#39;accuracy&#39;: 0.96, &#39;kl_div&#39;: tensor(-72.9968), &#39;label_logit&#39;: -72.99680526733398, &#39;label_prob&#39;: 0.7122889611124993}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 50/50 [00:07&lt;00:00,  6.59it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;layer&#39;: 11, &#39;head&#39;: 11, &#39;logit_diff&#39;: tensor(3.7668), &#39;accuracy&#39;: 0.94, &#39;kl_div&#39;: tensor(-76.5733), &#39;label_logit&#39;: -76.57328651428223, &#39;label_prob&#39;: 0.507421883046627}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;logit_diff&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;logit_diff&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;logit_diff_relative&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;logit_diff&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">logit_diff</span><span class="p">)</span> <span class="o">/</span> <span class="n">logit_diff</span>
+<span class="n">lim</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;logit_diff_relative&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;logit_diff_relative&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;head&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;logit_diff_relative&quot;</span><span class="p">))</span> <span class="o">+</span> <span class="n">geom_tile</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;RdBu&quot;</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="n">lim</span><span class="p">,</span> <span class="n">lim</span><span class="p">))</span> <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">(</span><span class="n">expand</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;formatted&quot;</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_bw</span><span class="p">()</span> <span class="o">+</span> <span class="n">scale_x_continuous</span><span class="p">(</span><span class="n">expand</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/dfc3bb8512eb7e3c6be222658aee1fe5a23474554a0ab68a0beb92752d1b4669.svg" src="../../_images/dfc3bb8512eb7e3c6be222658aee1fe5a23474554a0ab68a0beb92752d1b4669.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#setup">Setup</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#path-patching-config">Path patching config</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-utils">Dataset + Utils</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#name-mover-heads">Name mover heads</a></li>
+</ul>
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diff --git a/tutorials/advanced_tutorials/IOI_with_DAS.html b/tutorials/advanced_tutorials/IOI_with_DAS.html
new file mode 100644
index 00000000..cd569f20
--- /dev/null
+++ b/tutorials/advanced_tutorials/IOI_with_DAS.html
@@ -0,0 +1,2688 @@
+
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>IOI with DAS</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall">Factual recall</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-the-name-position-information-in-the-main-residual-stream-and-mlp-activations">Localizing the name position information in the main residual stream and MLP activations</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-in-across-multiple-tokens-at-layer-7-one-layer-earlier-than-layer-8">Localizing in across multiple tokens at layer 7, one layer earlier than layer 8</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-in-different-streams">Localizing in different streams</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-in-each-individual-head-at-layer-7-and-8">Localizing in each individual head at layer 7 and 8</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-with-group-of-heads-using-rankings-of-missing-out-head-iia">Localizing with group of heads using rankings of missing out head IIA</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-the-correct-io-name-with-vanilla-causal-abstraction-no-training">Localizing the correct IO name with vanilla causal abstraction (no training)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-the-correct-io-name-with-das-20-names-only-with-20-dimension-das">Localizing the correct IO name with DAS (20 names only with 20 dimension DAS)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#head-s-query-representations-to-trace-name-mover-head">Head’s Query Representations to Trace Name Mover Head</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#use-boundless-das-to-find-information">Use Boundless DAS to find information</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#das-and-boundless-das-weight-matrix-and-head-importance">DAS and Boundless DAS weight matrix and head importance</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="ioi-with-das">
+<h1>IOI with DAS<a class="headerlink" href="#ioi-with-das" title="Link to this heading">#</a></h1>
+<p><a class="reference external" href="https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/Indirect%20object%20identification%20(IOI)%20circuit%20with%20DAS.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;12/31/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>This tutorial aims to replicate key findings from previous mechanistic interpretability papers that examine the IOI task using this library. It focuses on identifying alignments for the name position information and the output IO name at various locations within the neural networks.</p>
+<p>Additionally, the tutorial seeks to provide new insights into GPT-2’s causal mechanisms in solving this task by analyzing how information is stored and processed across different attention heads.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tutorial_ioi_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span><span class="p">,</span> <span class="n">sigmoid_boundary</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+    <span class="n">LowRankRotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">SkipIntervention</span><span class="p">,</span>
+    <span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_gpt2_lm</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+    <span class="n">scale_y_reverse</span><span class="p">,</span>
+    <span class="n">scale_fill_cmap</span><span class="p">,</span>
+    <span class="n">geom_text</span><span class="p">,</span>
+    <span class="n">scale_fill_gradient</span><span class="p">,</span>
+    <span class="n">geom_point</span><span class="p">,</span>
+    <span class="n">geom_line</span><span class="p">,</span>
+    <span class="n">theme_minimal</span><span class="p">,</span>
+    <span class="n">ylim</span><span class="p">,</span>
+    <span class="n">ggtitle</span><span class="p">,</span>
+    <span class="n">ggsave</span><span class="p">,</span>
+    <span class="n">labs</span><span class="p">,</span>
+    <span class="n">scale_x_discrete</span><span class="p">,</span>
+    <span class="n">geom_histogram</span><span class="p">,</span>
+    <span class="n">scale_fill_manual</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># please try not to do this, the plot somehow throw warnings though :(</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">create_gpt2_lm</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="factual-recall">
+<h2>Factual recall<a class="headerlink" href="#factual-recall" title="Link to this heading">#</a></h2>
+<p>We first check IOI task performance of GPT-2.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">transformers</span><span class="o">.</span><span class="n">set_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+
+<span class="k">def</span> <span class="nf">get_last_token</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">):</span>
+    <span class="n">last_token_indices</span> <span class="o">=</span> <span class="n">attention_mask</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">batch_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">logits</span><span class="p">[</span><span class="n">batch_indices</span><span class="p">,</span> <span class="n">last_token_indices</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">train_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+    <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">NAMES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES_PATH</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">D_train</span> <span class="o">=</span> <span class="n">train_distribution</span><span class="o">.</span><span class="n">sample_das</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">base_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ABB&quot;</span><span class="p">,</span> <span class="s2">&quot;BAB&quot;</span><span class="p">],</span>
+    <span class="n">source_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ABB&quot;</span><span class="p">,</span> <span class="s2">&quot;BAB&quot;</span><span class="p">],</span>
+    <span class="n">labels</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">samples_per_combination</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">correct_count</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">batch_dataset</span> <span class="ow">in</span> <span class="n">D_train</span><span class="o">.</span><span class="n">batches</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">30</span><span class="p">):</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span>
+
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">labels</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">answer_tokens</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">gpt2</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span>
+        <span class="n">logits</span> <span class="o">=</span> <span class="n">get_last_token</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;attention_mask&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">pred_labels</span> <span class="o">=</span> <span class="n">logits</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">correct_labels</span> <span class="o">=</span> <span class="n">labels</span> <span class="o">==</span> <span class="n">pred_labels</span>
+
+        <span class="n">total_count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">correct_labels</span><span class="p">)</span>
+        <span class="n">correct_count</span> <span class="o">+=</span> <span class="n">correct_labels</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">current_acc</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">correct_count</span> <span class="o">/</span> <span class="n">total_count</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;gpt-2 IOI task accuracy: </span><span class="si">{</span><span class="n">current_acc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>gpt-2 IOI task accuracy: 0.91
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-the-name-position-information-in-the-main-residual-stream-and-mlp-activations">
+<h2>Localizing the name position information in the main residual stream and MLP activations<a class="headerlink" href="#localizing-the-name-position-information-in-the-main-residual-stream-and-mlp-activations" title="Link to this heading">#</a></h2>
+<p>This section reproduces main results in <a class="reference external" href="https://arxiv.org/pdf/2311.17030.pdf">this paper</a>. To address the IOI task, previous studies have shown that GPT-2 creates representations for a causal variable representing the correct name position (either the first or second name mentioned in a sentence). Our objective is to utilize our library to pinpoint the location of name position information at each layer and across several positions, not just at the last token position.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">block_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">positions</span><span class="o">=</span><span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">17</span><span class="p">],</span>
+    <span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="n">stream</span><span class="o">=</span><span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>finding name position at: pos-&gt;13, layers-&gt;0, stream-&gt;block_output
+</pre></div>
+</div>
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">AssertionError</span><span class="g g-Whitespace">                            </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span> <span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">block_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+<span class="g g-Whitespace">      </span><span class="mi">2</span>     <span class="n">gpt2</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">3</span>     <span class="n">tokenizer</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span>     <span class="n">positions</span><span class="o">=</span><span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">17</span><span class="p">],</span>
+<span class="g g-Whitespace">      </span><span class="mi">5</span>     <span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+<span class="g g-Whitespace">      </span><span class="mi">6</span>     <span class="n">stream</span><span class="o">=</span><span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">7</span>     <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">8</span>     <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">9</span> <span class="p">)</span>
+
+<span class="nn">File ~/Documents/Code/pyvene/tutorials/advanced_tutorials/tutorial_ioi_utils.py:704,</span> in <span class="ni">find_variable_at</span><span class="nt">(gpt2, tokenizer, positions, layers, stream, heads, low_rank_dimension, aligning_variable, do_vanilla_intervention, do_boundless_das, seed, return_intervenable, debug)</span>
+<span class="g g-Whitespace">    </span><span class="mi">699</span> <span class="c1"># prepare label</span>
+<span class="g g-Whitespace">    </span><span class="mi">700</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">701</span>     <span class="n">gpt2</span><span class="o">.</span><span class="n">device</span>
+<span class="g g-Whitespace">    </span><span class="mi">702</span> <span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">704</span> <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="mi">18</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">705</span> <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="mi">18</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">707</span> <span class="k">if</span> <span class="n">heads</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+<span class="ne">AssertionError</span>: 
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># these lines are comment out since we have run the experiments.</span>
+<span class="c1"># df = pd.DataFrame(block_out_data)</span>
+<span class="c1"># df.to_csv(&quot;./tutorial_data/block_out_df.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/block_out_df.csv&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token position&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token position&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;token position&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+
+<span class="c1"># Use format string to keep two decimal places</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="n">block_out_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;token position&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Purples&quot;</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_label&quot;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Main Residual Streams&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span><span class="n">block_out_plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/block_out_plot.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="n">block_out_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/e9987d909d2d1b0c83b17b98d174bc4b7b24147e1ba714fd1e9b5b3711262820.png"><img alt="../../_images/e9987d909d2d1b0c83b17b98d174bc4b7b24147e1ba714fd1e9b5b3711262820.png" src="../../_images/e9987d909d2d1b0c83b17b98d174bc4b7b24147e1ba714fd1e9b5b3711262820.png" style="width: 640px; height: 480px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (640 x 480)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mlp_activation_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">positions</span><span class="o">=</span><span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">17</span><span class="p">],</span>
+    <span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="n">stream</span><span class="o">=</span><span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.DataFrame(mlp_activation_data)</span>
+<span class="c1"># df.to_csv(&quot;./tutorial_data/mlp_activation_df.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/mlp_activation_df.csv&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token position&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token position&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;token position&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+
+<span class="c1"># Use format string to keep two decimal places</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="n">mlp_activaiton_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;token position&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Greens&quot;</span><span class="p">,</span> <span class="n">limits</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_label&quot;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;MLP Activations&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span><span class="n">mlp_activaiton_plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/mlp_activaiton_plot.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="n">mlp_activaiton_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/c925fb81fb3b668bf0bc074d8fb475325f8b4ef7c8b86402bacd25af5d35e39f.png"><img alt="../../_images/c925fb81fb3b668bf0bc074d8fb475325f8b4ef7c8b86402bacd25af5d35e39f.png" src="../../_images/c925fb81fb3b668bf0bc074d8fb475325f8b4ef7c8b86402bacd25af5d35e39f.png" style="width: 640px; height: 480px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (640 x 480)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-in-across-multiple-tokens-at-layer-7-one-layer-earlier-than-layer-8">
+<h2>Localizing in across multiple tokens at layer 7, one layer earlier than layer 8<a class="headerlink" href="#localizing-in-across-multiple-tokens-at-layer-7-one-layer-earlier-than-layer-8" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">block_out_across_tokens_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">]],</span>
+    <span class="p">[</span><span class="mi">7</span><span class="p">],</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-in-different-streams">
+<h2>Localizing in different streams<a class="headerlink" href="#localizing-in-different-streams" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">attn_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">block_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;block_input&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">mlp_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">mlp_act_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">attn_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">mlp_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">attn_value_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">block_output_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Block Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Output</span><span class="se">\n</span><span class="s2">(After Head Mixing)&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Value Output</span><span class="se">\n</span><span class="s2">(Before Head Mixing)&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">other_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position w/ A Single DAS Direction&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">other_locations_plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/other_locations_plot.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span>
+<span class="p">)</span>
+<span class="n">other_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/4eb1fdd576890c32323ce9c486fb0d5c982145e923a9902ad268114f8761f307.png"><img alt="../../_images/4eb1fdd576890c32323ce9c486fb0d5c982145e923a9902ad268114f8761f307.png" src="../../_images/4eb1fdd576890c32323ce9c486fb0d5c982145e923a9902ad268114f8761f307.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># all_streams_df = pd.DataFrame(</span>
+<span class="c1">#     block_output_data+attn_out_data+</span>
+<span class="c1">#     mlp_out_data+attn_value_out_data+</span>
+<span class="c1">#     mlp_act_data+mlp_input_data+</span>
+<span class="c1">#     block_input_data+attn_input_data</span>
+<span class="c1"># )</span>
+<span class="c1"># all_streams_df.to_csv(&quot;./tutorial_data/all_streams_df.csv&quot;)</span>
+<span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">})</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Activations&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position w/ A Single DAS Direction&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">all_mlp_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/all_mlp_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/a9d681777ff4189db56e2333de5b721197ed39bd911b039af373c2084f2b9444.png"><img alt="../../_images/a9d681777ff4189db56e2333de5b721197ed39bd911b039af373c2084f2b9444.png" src="../../_images/a9d681777ff4189db56e2333de5b721197ed39bd911b039af373c2084f2b9444.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-in-each-individual-head-at-layer-7-and-8">
+<h2>Localizing in each individual head at layer 7 and 8<a class="headerlink" href="#localizing-in-each-individual-head-at-layer-7-and-8" title="Link to this heading">#</a></h2>
+<p>We further investigate whether any heads in the 7th and 8th layers specialize in name position information. Note that we consider each head individually in this analysis. Therefore, if the name position information is distributed across tokens, it would not be detectable in this context.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_attn_value_out_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
+    <span class="n">_head_attn_value_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="p">[</span><span class="mi">17</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="p">]</span>
+    <span class="p">)</span>
+    <span class="n">head_attn_value_out_data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">_head_attn_value_out_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">head_attn_value_out_data</span><span class="p">)</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-with-group-of-heads-using-rankings-of-missing-out-head-iia">
+<h2>Localizing with group of heads using rankings of missing out head IIA<a class="headerlink" href="#localizing-with-group-of-heads-using-rankings-of-missing-out-head-iia" title="Link to this heading">#</a></h2>
+<p>When examining each individual head, localizing the name position information was not possible. Why is this? The information is likely distributed across multiple heads. To further investigate, let’s perform an additional check by localizing in the concatenated head representations, leaving only one head out at a time. We leave one head out to determine if omitting specific heads results in significant drops in IIA.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">7</span>
+
+<span class="n">head_attn_value_out_mo_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;evaluating grouped IIA without head&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="n">_head_attn_value_out_mo_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)])</span> <span class="o">-</span> <span class="p">{</span><span class="n">i</span><span class="p">}),</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_attn_value_out_mo_data</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_attn_value_out_mo_data</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_head_attn_value_out_mo_data</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_rank_list</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">anchor_acc</span> <span class="o">=</span> <span class="mf">0.48</span>
+<span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">head_attn_value_out_mo_data</span><span class="p">:</span>
+    <span class="n">head_rank_list</span> <span class="o">+=</span> <span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">],</span> <span class="n">anchor_acc</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">])]</span>
+<span class="n">head_rank_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">head_rank_list</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">head_attn_value_out_mo_data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">head_drop_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;mo_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Leave-One-Out (LOO) Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]])</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_drop_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_drop_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_drop_plot</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_attn_value_out_cumulative_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">current_heads</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">head_rank_list</span><span class="p">:</span>
+    <span class="n">current_heads</span> <span class="o">+=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;evaluating grouped IIA adding head&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">_head_attn_value_out_cumulative_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="n">current_heads</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_attn_value_out_cumulative_data</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">head_attn_value_out_cumulative_data</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_head_attn_value_out_cumulative_data</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">head_attn_value_out_cumulative_data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="n">head_acc_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;adding_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;adding_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Cumulative Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;+ head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]])</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_acc_plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_acc_plot.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span>
+<span class="p">)</span>
+<span class="n">head_acc_plot</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-the-correct-io-name-with-vanilla-causal-abstraction-no-training">
+<h2>Localizing the correct IO name with vanilla causal abstraction (no training)<a class="headerlink" href="#localizing-the-correct-io-name-with-vanilla-causal-abstraction-no-training" title="Link to this heading">#</a></h2>
+<p>We can further localize the correct IO name in the output streams. We use vanilla interchange intervention, so there is no training just activation swap.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">attn_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>  <span class="c1"># now we are localizing the IO name</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  <span class="c1"># we avoid learning DAS for this since outspace is large here.</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_act_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_value_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_output_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_vanilla_intervention</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="n">block_output_data</span>
+    <span class="o">+</span> <span class="n">attn_out_data</span>
+    <span class="o">+</span> <span class="n">mlp_out_data</span>
+    <span class="o">+</span> <span class="n">attn_value_out_data</span>
+    <span class="o">+</span> <span class="n">mlp_act_data</span>
+    <span class="o">+</span> <span class="n">mlp_input_data</span>
+    <span class="o">+</span> <span class="n">block_input_data</span>
+    <span class="o">+</span> <span class="n">attn_input_data</span>
+<span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Block Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Output</span><span class="se">\n</span><span class="s2">(After Head Mixing)&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Value Output</span><span class="se">\n</span><span class="s2">(Before Head Mixing)&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">other_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Correct IO Name (20) w/ Interchange Intervention (Original Basis)&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">other_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/IO_name_other_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">other_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/495a39be446770714619153e861d9e48c1e50e473bbb070647af3440a710b058.png"><img alt="../../_images/495a39be446770714619153e861d9e48c1e50e473bbb070647af3440a710b058.png" src="../../_images/495a39be446770714619153e861d9e48c1e50e473bbb070647af3440a710b058.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">})</span>
+<span class="p">]</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Activations&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Correct IO Name (20) w/ Interchange Intervention (Original Basis)&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">all_mlp_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/IO_name_all_mlp_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/3cb60b7e6e568804c2e8855e0d2c1bba450a304fe8221e2c5cc2d6fbb175439e.png"><img alt="../../_images/3cb60b7e6e568804c2e8855e0d2c1bba450a304fe8221e2c5cc2d6fbb175439e.png" src="../../_images/3cb60b7e6e568804c2e8855e0d2c1bba450a304fe8221e2c5cc2d6fbb175439e.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="localizing-the-correct-io-name-with-das-20-names-only-with-20-dimension-das">
+<h2>Localizing the correct IO name with DAS (20 names only with 20 dimension DAS)<a class="headerlink" href="#localizing-the-correct-io-name-with-das-20-names-only-with-20-dimension-das" title="Link to this heading">#</a></h2>
+<p>We now localize the IO name with DAS. To make things easier, we restrict the dataset to contain 20 distinct names. Training and evaluation share the same set of names. We use a fixed dimension size of 20 for DAS training. Everything else is kept the same as previous experiments.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">attn_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>  <span class="c1"># now we are localizing the IO name</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_act_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_value_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_output_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># all_streams_io_name_df = pd.DataFrame(</span>
+<span class="c1">#     block_output_data+</span>
+<span class="c1">#     attn_out_data+mlp_out_data+</span>
+<span class="c1">#     attn_value_out_data+mlp_act_data+</span>
+<span class="c1">#     mlp_input_data+block_input_data+attn_input_data</span>
+<span class="c1"># )</span>
+<span class="c1"># all_streams_io_name_df.to_csv(&quot;./tutorial_data/all_streams_io_name_df.csv&quot;)</span>
+<span class="n">all_streams_io_name_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_io_name_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_io_name_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_io_name_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_io_name_df</span><span class="p">[</span>
+    <span class="n">all_streams_io_name_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Block Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Output</span><span class="se">\n</span><span class="s2">(After Head Mixing)&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Value Output</span><span class="se">\n</span><span class="s2">(Before Head Mixing)&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">other_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Correct IO Name (20) w/ DAS (Learned Basis)&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">other_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/DAS_IO_name_other_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">other_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/740112e374c50c87fcb4b353651e3ac260de0b8a13531f8bd108f57633bd1a70.png"><img alt="../../_images/740112e374c50c87fcb4b353651e3ac260de0b8a13531f8bd108f57633bd1a70.png" src="../../_images/740112e374c50c87fcb4b353651e3ac260de0b8a13531f8bd108f57633bd1a70.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># all_streams_df = pd.DataFrame(</span>
+<span class="c1">#     block_output_data+attn_out_data+</span>
+<span class="c1">#     mlp_out_data+attn_value_out_data+</span>
+<span class="c1">#     mlp_act_data+mlp_input_data+</span>
+<span class="c1">#     block_input_data+attn_input_data</span>
+<span class="c1"># )</span>
+<span class="c1"># all_streams_df.to_csv(&quot;./tutorial_data/all_streams_df.csv&quot;)</span>
+<span class="n">all_streams_io_name_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_io_name_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_io_name_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_io_name_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_io_name_df</span><span class="p">[</span>
+    <span class="n">all_streams_io_name_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">})</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Activations&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position w/ DAS (Learned Basis)&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">all_mlp_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/DAS_IO_name_all_mlp_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/ce52c762453051be57d204f305737df463b4a354458359246906815a0b49aed2.png"><img alt="../../_images/ce52c762453051be57d204f305737df463b4a354458359246906815a0b49aed2.png" src="../../_images/ce52c762453051be57d204f305737df463b4a354458359246906815a0b49aed2.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="head-s-query-representations-to-trace-name-mover-head">
+<h2>Head’s Query Representations to Trace Name Mover Head<a class="headerlink" href="#head-s-query-representations-to-trace-name-mover-head" title="Link to this heading">#</a></h2>
+<p>To use the name position information to copy the IO name, responsible heads need to take in name position information, and process it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">9</span>
+<span class="n">head_query_output_mo_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
+    <span class="n">_head_query_output_mo_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_query_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)])</span> <span class="o">-</span> <span class="p">{</span><span class="n">i</span><span class="p">})),</span>
+        <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_query_output_mo_data</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_query_output_mo_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_head_query_output_mo_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">9</span>
+<span class="c1"># df = pd.DataFrame(head_query_output_mo_data)</span>
+<span class="c1"># df.to_csv(f&quot;./tutorial_data/layer_{layer}_head_query_output_mo_data.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_query_output_mo_data.csv&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">head_query_output_mo_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;mo_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Leave-One-Out (LOO) Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]])</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position for Each Head (Query) Before Self-Attention&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_query_output_mo_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_query_output_mo_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_query_output_mo_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/ae5f93b8f042d6e571475d7de414286eaf6214da23944a580baae06ce44a2b52.png"><img alt="../../_images/ae5f93b8f042d6e571475d7de414286eaf6214da23944a580baae06ce44a2b52.png" src="../../_images/ae5f93b8f042d6e571475d7de414286eaf6214da23944a580baae06ce44a2b52.png" style="width: 1000px; height: 200px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1000 x 200)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_attention_value_output_mo_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
+    <span class="n">_head_attention_value_output_mo_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)])</span> <span class="o">-</span> <span class="p">{</span><span class="n">i</span><span class="p">})),</span>
+        <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+        <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+        <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_attention_value_output_mo_data</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_attention_value_output_mo_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_head_attention_value_output_mo_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.DataFrame(head_attention_value_output_mo_data)</span>
+<span class="c1"># df.to_csv(f&quot;./tutorial_data/layer_{layer}_head_attention_value_output_mo_data.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attention_value_output_mo_data.csv&quot;</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">head_attention_value_output_mo_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;mo_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Leave-One-Out (LOO) Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]])</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;IO Name for Each Head (Value) after Self-Attention&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_attention_value_output_mo_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attention_value_output_mo_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_attention_value_output_mo_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/86c810bf678fbd3cbf2ecdc10ed0ce804528334e27db0ca51a198a7bf625e417.png"><img alt="../../_images/86c810bf678fbd3cbf2ecdc10ed0ce804528334e27db0ca51a198a7bf625e417.png" src="../../_images/86c810bf678fbd3cbf2ecdc10ed0ce804528334e27db0ca51a198a7bf625e417.png" style="width: 1000px; height: 200px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1000 x 200)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_attn_value_out_cumulative_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">current_heads</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">]:</span>
+    <span class="n">current_heads</span> <span class="o">+=</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;evaluating grouped IIA adding head&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="n">_head_attn_value_out_cumulative_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="n">current_heads</span><span class="p">),</span>
+        <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+        <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+        <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_attn_value_out_cumulative_data</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_attn_value_out_cumulative_data</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_head_attn_value_out_cumulative_data</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.DataFrame(head_attn_value_out_cumulative_data)</span>
+<span class="c1"># df.to_csv(f&quot;./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_data.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;./tutorial_data/DAS_IO_layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attn_value_out_cumulative_data.csv&quot;</span>
+<span class="p">)</span>
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="n">head_acc_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;adding_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;adding_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Cumulative Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;+ head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]])</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;IO Name for Cumulative Head (Value) after Self-Attention&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_acc_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/DAS_IO_layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attn_value_out_cumulative_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_acc_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/c0f343f8181fac70426959817b6d7bed20fbbf9e2ced923bd60af5ae3dd76796.png"><img alt="../../_images/c0f343f8181fac70426959817b6d7bed20fbbf9e2ced923bd60af5ae3dd76796.png" src="../../_images/c0f343f8181fac70426959817b6d7bed20fbbf9e2ced923bd60af5ae3dd76796.png" style="width: 1000px; height: 200px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1000 x 200)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">head_query_output_mo_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
+    <span class="n">_head_query_output_mo_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_query_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)])</span> <span class="o">-</span> <span class="p">{</span><span class="n">i</span><span class="p">})),</span>
+        <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_query_output_mo_data</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_query_output_mo_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_head_query_output_mo_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="c1"># df = pd.DataFrame(head_query_output_mo_data)</span>
+<span class="c1"># df.to_csv(f&quot;./tutorial_data/layer_{layer}_head_query_output_mo_data.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_query_output_mo_data.csv&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">head_query_output_mo_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;mo_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Leave-One-Out (LOO) Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]])</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position for Each Head (Query) Before Self-Attention&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_query_output_mo_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_query_output_mo_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_query_output_mo_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/b43cebf3af23ff11bba516655e0bdd08a79ea3da27bb9f48ced1527e36bf87e8.png"><img alt="../../_images/b43cebf3af23ff11bba516655e0bdd08a79ea3da27bb9f48ced1527e36bf87e8.png" src="../../_images/b43cebf3af23ff11bba516655e0bdd08a79ea3da27bb9f48ced1527e36bf87e8.png" style="width: 1000px; height: 200px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1000 x 200)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_attention_value_output_mo_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
+    <span class="n">_head_attention_value_output_mo_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)])</span> <span class="o">-</span> <span class="p">{</span><span class="n">i</span><span class="p">})),</span>
+        <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+        <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+        <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_attention_value_output_mo_data</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_attention_value_output_mo_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_head_attention_value_output_mo_data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.DataFrame(head_attention_value_output_mo_data)</span>
+<span class="c1"># df.to_csv(f&quot;./tutorial_data/layer_{layer}_head_attention_value_output_mo_data.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attention_value_output_mo_data.csv&quot;</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">head_attention_value_output_mo_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;mo_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;mo_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Leave-One-Out (LOO) Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;mo_head&quot;</span><span class="p">]])</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;IO Name for Each Head (Value) after Self-Attention&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_attention_value_output_mo_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attention_value_output_mo_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_attention_value_output_mo_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/92151326e27ed27ba1ef81367138fc690c27f638f7366c58e68dea69e26ec6d4.png"><img alt="../../_images/92151326e27ed27ba1ef81367138fc690c27f638f7366c58e68dea69e26ec6d4.png" src="../../_images/92151326e27ed27ba1ef81367138fc690c27f638f7366c58e68dea69e26ec6d4.png" style="width: 1000px; height: 200px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1000 x 200)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">head_attn_value_out_cumulative_data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">current_heads</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]:</span>
+    <span class="n">current_heads</span> <span class="o">+=</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;evaluating grouped IIA adding head&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="n">_head_attn_value_out_cumulative_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+        <span class="n">gpt2</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+        <span class="p">[</span><span class="n">layer</span><span class="p">],</span>
+        <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+        <span class="n">heads</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="n">current_heads</span><span class="p">),</span>
+        <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+        <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+        <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">_head_attn_value_out_cumulative_data</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="n">head_attn_value_out_cumulative_data</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_head_attn_value_out_cumulative_data</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.DataFrame(head_attn_value_out_cumulative_data)</span>
+<span class="c1"># df.to_csv(f&quot;./tutorial_data/DAS_IO_layer_{layer}_head_attn_value_out_cumulative_data.csv&quot;)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;./tutorial_data/DAS_IO_layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attn_value_out_cumulative_data.csv&quot;</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head_cat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(),</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">head_acc_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;adding_head_cat&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;adding_head&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="s2">&quot;dodge&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Cumulative Head Index (</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">th Layer)&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>  <span class="c1"># Add axis labels</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>  <span class="c1"># Use a minimal theme</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_x_discrete</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;+ head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;adding_head&quot;</span><span class="p">]])</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;IO Name for Cumulative Head (Value) after Self-Attention&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">head_acc_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/DAS_IO_layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_head_attn_value_out_cumulative_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">head_acc_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/8f403b7248becfbb27ca726da099de64aab46a69a5d1c210a44c9f1c9afcc058.png"><img alt="../../_images/8f403b7248becfbb27ca726da099de64aab46a69a5d1c210a44c9f1c9afcc058.png" src="../../_images/8f403b7248becfbb27ca726da099de64aab46a69a5d1c210a44c9f1c9afcc058.png" style="width: 1000px; height: 200px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1000 x 200)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="use-boundless-das-to-find-information">
+<h2>Use Boundless DAS to find information<a class="headerlink" href="#use-boundless-das-to-find-information" title="Link to this heading">#</a></h2>
+<p>Instead of starting with a fixed number of DAS dimension, we can also use Boundless DAS (Wu et. al., 2023) to dynamically find a good dimension by learning the boundary. Here, we use Boundless DAS to find alignments for the name position information variable as well as the correct IO name variable. Very similar results, yet alignments with the IO name seems to be consistently better.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">attn_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>  <span class="c1"># now we are localizing the IO name</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_act_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_value_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_output_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># all_streams_io_name_df = pd.DataFrame(</span>
+<span class="c1">#     block_output_data+</span>
+<span class="c1">#     attn_out_data+mlp_out_data+</span>
+<span class="c1">#     attn_value_out_data+mlp_act_data+</span>
+<span class="c1">#     mlp_input_data+block_input_data+attn_input_data</span>
+<span class="c1"># )</span>
+<span class="c1"># all_streams_io_name_df.to_csv(&quot;./tutorial_data/all_streams_boundless_das_df.csv&quot;)</span>
+<span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_boundless_das_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Block Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Output</span><span class="se">\n</span><span class="s2">(After Head Mixing)&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Value Output</span><span class="se">\n</span><span class="s2">(Before Head Mixing)&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">other_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position w/ Boundless DAS&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">other_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/Boundless_DAS_other_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">other_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/7725b3c3b31b963a269bc9cb673c3fd0795f5cb25290fd37d3c8c483e24ba67d.png"><img alt="../../_images/7725b3c3b31b963a269bc9cb673c3fd0795f5cb25290fd37d3c8c483e24ba67d.png" src="../../_images/7725b3c3b31b963a269bc9cb673c3fd0795f5cb25290fd37d3c8c483e24ba67d.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_boundless_das_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">})</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Activations&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Name Position w/ Boundless DAS&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">all_mlp_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/Boundless_DAS_all_mlp_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/bb77bdccb046f5e77b9ea0a5312189dfe2984f404288060a908abd8077786292.png"><img alt="../../_images/bb77bdccb046f5e77b9ea0a5312189dfe2984f404288060a908abd8077786292.png" src="../../_images/bb77bdccb046f5e77b9ea0a5312189dfe2984f404288060a908abd8077786292.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">attn_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>  <span class="c1"># now we are localizing the IO name</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_input_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_act_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">mlp_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">attn_value_out_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">block_output_data</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># all_streams_io_name_df = pd.DataFrame(</span>
+<span class="c1">#     block_output_data+</span>
+<span class="c1">#     attn_out_data+mlp_out_data+</span>
+<span class="c1">#     attn_value_out_data+mlp_act_data+</span>
+<span class="c1">#     mlp_input_data+block_input_data+attn_input_data</span>
+<span class="c1"># )</span>
+<span class="c1"># all_streams_io_name_df.to_csv(&quot;./tutorial_data/all_streams_io_name_boundless_das_df.csv&quot;)</span>
+<span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_io_name_boundless_das_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Block Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Output</span><span class="se">\n</span><span class="s2">(After Head Mixing)&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">:</span> <span class="s2">&quot;Attention Value Output</span><span class="se">\n</span><span class="s2">(Before Head Mixing)&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">other_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Correct IO Name (20) w/ Boundless DAS&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">other_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/Boundless_DAS_IO_name_other_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">other_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/fc9a15b5e5ca3ec9f1fd7e08bfd643759f324f90fdc940dfa5a9304a600227da.png"><img alt="../../_images/fc9a15b5e5ca3ec9f1fd7e08bfd643759f324f90fdc940dfa5a9304a600227da.png" src="../../_images/fc9a15b5e5ca3ec9f1fd7e08bfd643759f324f90fdc940dfa5a9304a600227da.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_streams_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/all_streams_io_name_boundless_das_df.csv&quot;</span><span class="p">)</span>
+<span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_streams_df</span><span class="p">[</span>
+    <span class="n">all_streams_df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">({</span><span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">,</span> <span class="s2">&quot;mlp_activation&quot;</span><span class="p">})</span>
+<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">stream_labels</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_input&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Input&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mlp_activation&quot;</span><span class="p">:</span> <span class="s2">&quot;MLP Activations&quot;</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;stream&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">stream_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">custom_format</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">custom_format</span><span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;stream&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_line</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_point</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_text</span><span class="p">(</span>
+        <span class="n">aes</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;IIA_formatted&quot;</span><span class="p">),</span> <span class="n">nudge_y</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">False</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme_minimal</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Correct IO Name (20) w/ Boundless DAS&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">all_mlp_locations_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/Boundless_DAS_IO_name_all_mlp_locations_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">all_mlp_locations_plot</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/53f9d0302373b51b72a09eb116ccd675a00d8316d611a91cb91f0912c3cb9343.png"><img alt="../../_images/53f9d0302373b51b72a09eb116ccd675a00d8316d611a91cb91f0912c3cb9343.png" src="../../_images/53f9d0302373b51b72a09eb116ccd675a00d8316d611a91cb91f0912c3cb9343.png" style="width: 1200px; height: 300px;" /></a>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Figure Size: (1200 x 300)&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="das-and-boundless-das-weight-matrix-and-head-importance">
+<h2>DAS and Boundless DAS weight matrix and head importance<a class="headerlink" href="#das-and-boundless-das-weight-matrix-and-head-importance" title="Link to this heading">#</a></h2>
+<p>DAS-based methods learn a subspace that is a linear combination of axes in the original basis. Will the learned linear combination (i.e., the learned weights) interpretable by showing head’s importance when applied on top of the attention value output stream before the head mixing? In this section, we investigate this question for both DAS as well as Boundless DAS when aligning with the name position variable as well as the IO name variable.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">boundless_das_intervenable</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">8</span><span class="p">],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">return_intervenable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>finding name position at: pos-&gt;17, layers-&gt;8, stream-&gt;attention_value_output
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervention</span> <span class="o">=</span> <span class="n">boundless_das_intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span>
+    <span class="s2">&quot;layer_8_repr_attention_value_output_unit_pos_nunit_1#0&quot;</span>
+<span class="p">]</span>
+<span class="n">boundary_mask</span> <span class="o">=</span> <span class="n">sigmoid_boundary</span><span class="p">(</span>
+    <span class="n">intervention</span><span class="o">.</span><span class="n">intervention_population</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="mf">0.0</span><span class="p">,</span>
+    <span class="n">intervention</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">),</span>
+    <span class="n">intervention</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">learned_weights</span> <span class="o">=</span> <span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span> <span class="o">*</span> <span class="n">boundary_mask</span><span class="p">)[</span>
+    <span class="p">:,</span> <span class="p">:</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">768</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="n">headwise_learned_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">learned_weights</span><span class="p">,</span> <span class="n">chunks</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">sampled_weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">headwise_learned_weights</span><span class="p">]</span>
+
+<span class="c1"># Assuming your_list is your 2D list where each sublist is a NumPy array</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sampled_weights</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">sublist</span>
+    <span class="p">],</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="c1"># Plotting</span>
+<span class="n">boundless_das_weight_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;Group&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_histogram</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~Group&quot;</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span>  <span class="c1"># Arrange in a grid with 3 columns</span>
+        <span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span> <span class="n">subplots_adjust</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">,</span> <span class="s2">&quot;hspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">}</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">boundless_das_weight_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/Boundless_DAS_weight_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">boundless_das_weight_plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/7cf499431f3c7362117ca1ffa99f0fa159dc9c59b16d5108f71d62f8108156c6.svg" src="../../_images/7cf499431f3c7362117ca1ffa99f0fa159dc9c59b16d5108f71d62f8108156c6.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">das_intervenable</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">8</span><span class="p">],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;position&quot;</span><span class="p">,</span>
+    <span class="n">return_intervenable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>finding name position at: pos-&gt;17, layers-&gt;8, stream-&gt;attention_value_output
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervention</span> <span class="o">=</span> <span class="n">das_intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span>
+    <span class="s2">&quot;layer_8_repr_attention_value_output_unit_pos_nunit_1#0&quot;</span>
+<span class="p">]</span>
+<span class="n">learned_weights</span> <span class="o">=</span> <span class="n">intervention</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span>
+<span class="n">headwise_learned_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">learned_weights</span><span class="p">,</span> <span class="n">chunks</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">sampled_weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">headwise_learned_weights</span><span class="p">]</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sampled_weights</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">sublist</span>
+    <span class="p">],</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="c1"># Plotting</span>
+<span class="n">das_weight_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;Group&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_histogram</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~Group&quot;</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span>  <span class="c1"># Arrange in a grid with 3 columns</span>
+        <span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span> <span class="n">subplots_adjust</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">,</span> <span class="s2">&quot;hspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">}</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span><span class="n">das_weight_plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/DAS_weight_plot.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">das_weight_plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/aebce4a12767db3fd69964bc17f4268415d639eda8d92d842d7ded3ba1828056.svg" src="../../_images/aebce4a12767db3fd69964bc17f4268415d639eda8d92d842d7ded3ba1828056.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">boundless_das_intervenable</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">9</span><span class="p">],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">do_boundless_das</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">return_intervenable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>finding name position at: pos-&gt;17, layers-&gt;9, stream-&gt;attention_value_output
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervention</span> <span class="o">=</span> <span class="n">boundless_das_intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span>
+    <span class="s2">&quot;layer_9_repr_attention_value_output_unit_pos_nunit_1#0&quot;</span>
+<span class="p">]</span>
+<span class="n">boundary_mask</span> <span class="o">=</span> <span class="n">sigmoid_boundary</span><span class="p">(</span>
+    <span class="n">intervention</span><span class="o">.</span><span class="n">intervention_population</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="mf">0.0</span><span class="p">,</span>
+    <span class="n">intervention</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">),</span>
+    <span class="n">intervention</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">learned_weights</span> <span class="o">=</span> <span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span> <span class="o">*</span> <span class="n">boundary_mask</span><span class="p">)[</span>
+    <span class="p">:,</span> <span class="p">:</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">intervention</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">768</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="n">headwise_learned_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">learned_weights</span><span class="p">,</span> <span class="n">chunks</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">sampled_weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">headwise_learned_weights</span><span class="p">]</span>
+
+<span class="c1"># Assuming your_list is your 2D list where each sublist is a NumPy array</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sampled_weights</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">sublist</span>
+    <span class="p">],</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="c1"># Plotting</span>
+<span class="n">boundless_das_weight_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;Group&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_histogram</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~Group&quot;</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span>  <span class="c1"># Arrange in a grid with 3 columns</span>
+        <span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span> <span class="n">subplots_adjust</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">,</span> <span class="s2">&quot;hspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">}</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">boundless_das_weight_plot</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/Boundless_DAS_IO_name_weight_plot.pdf&quot;</span><span class="p">,</span>
+    <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">boundless_das_weight_plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/6ac0fedb49fc4a4d44a6941c256db0b3a7d64421a4712f3137c5991b235b3895.svg" src="../../_images/6ac0fedb49fc4a4d44a6941c256db0b3a7d64421a4712f3137c5991b235b3895.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">das_intervenable</span> <span class="o">=</span> <span class="n">find_variable_at</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="p">[</span><span class="mi">17</span><span class="p">],</span>
+    <span class="p">[</span><span class="mi">9</span><span class="p">],</span>
+    <span class="s2">&quot;attention_value_output&quot;</span><span class="p">,</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">aligning_variable</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">return_intervenable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>finding name position at: pos-&gt;17, layers-&gt;9, stream-&gt;attention_value_output
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervention</span> <span class="o">=</span> <span class="n">das_intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span>
+    <span class="s2">&quot;layer_9_repr_attention_value_output_unit_pos_nunit_1#0&quot;</span>
+<span class="p">]</span>
+<span class="n">learned_weights</span> <span class="o">=</span> <span class="n">intervention</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">weight</span>
+<span class="n">headwise_learned_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="n">learned_weights</span><span class="p">,</span> <span class="n">chunks</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">sampled_weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">headwise_learned_weights</span><span class="p">]</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sampled_weights</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">sublist</span>
+    <span class="p">],</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Group&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;Head </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="c1"># Plotting</span>
+<span class="n">das_weight_plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;Group&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">geom_histogram</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~Group&quot;</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span>  <span class="c1"># Arrange in a grid with 3 columns</span>
+        <span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span> <span class="n">subplots_adjust</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">,</span> <span class="s2">&quot;hspace&quot;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">}</span>
+    <span class="p">)</span>
+    <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;Count&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span><span class="n">das_weight_plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/DAS_IO_name_weight_plot.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">das_weight_plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/3a9c1a0fea1e610ed044635a984b68db3aff6f1f3df1a6771625efc2cc01d108.svg" src="../../_images/3a9c1a0fea1e610ed044635a984b68db3aff6f1f3df1a6771625efc2cc01d108.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Findings:</strong> DAS learned weights map very well to head importance! In the plot above, we can see that Heads 6 and 10 have many non-zero entries, indicating that these neurons contribute significantly to aligning with the name position information. Boundless DAS shows a less salient result, suggesting that it learns a more distributed representation.</p>
+</section>
+</section>
+
+
+                </article>
+              
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+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Replicating the IOI paper</p>
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+    <i class="fa-solid fa-list"></i> Contents
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall">Factual recall</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-the-name-position-information-in-the-main-residual-stream-and-mlp-activations">Localizing the name position information in the main residual stream and MLP activations</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-in-across-multiple-tokens-at-layer-7-one-layer-earlier-than-layer-8">Localizing in across multiple tokens at layer 7, one layer earlier than layer 8</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-in-different-streams">Localizing in different streams</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-in-each-individual-head-at-layer-7-and-8">Localizing in each individual head at layer 7 and 8</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-with-group-of-heads-using-rankings-of-missing-out-head-iia">Localizing with group of heads using rankings of missing out head IIA</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-the-correct-io-name-with-vanilla-causal-abstraction-no-training">Localizing the correct IO name with vanilla causal abstraction (no training)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#localizing-the-correct-io-name-with-das-20-names-only-with-20-dimension-das">Localizing the correct IO name with DAS (20 names only with 20 dimension DAS)</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#head-s-query-representations-to-trace-name-mover-head">Head’s Query Representations to Trace Name Mover Head</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#use-boundless-das-to-find-information">Use Boundless DAS to find information</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#das-and-boundless-das-weight-matrix-and-head-importance">DAS and Boundless DAS weight matrix and head importance</a></li>
+</ul>
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diff --git a/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html b/tutorials/advanced_tutorials/IOI_with_Mask_Intervention.html
new file mode 100644
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>IOI with Masked Interventions</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="ioi-with-masked-interventions">
+<h1>IOI with Masked Interventions<a class="headerlink" href="#ioi-with-masked-interventions" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;01/31/2024&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>This tutorial analyzes the IOI task with a new type of trainable intervention, masked interchange intervention</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;../..&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tutorial_ioi_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+    <span class="n">scale_y_reverse</span><span class="p">,</span>
+    <span class="n">scale_fill_cmap</span><span class="p">,</span>
+    <span class="n">geom_text</span><span class="p">,</span>
+    <span class="n">scale_fill_gradient</span><span class="p">,</span>
+    <span class="n">geom_point</span><span class="p">,</span>
+    <span class="n">geom_line</span><span class="p">,</span>
+    <span class="n">theme_minimal</span><span class="p">,</span>
+    <span class="n">ylim</span><span class="p">,</span>
+    <span class="n">ggtitle</span><span class="p">,</span>
+    <span class="n">ggsave</span><span class="p">,</span>
+    <span class="n">labs</span><span class="p">,</span>
+    <span class="n">scale_x_discrete</span><span class="p">,</span>
+    <span class="n">geom_histogram</span><span class="p">,</span>
+    <span class="n">scale_fill_manual</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># please try not to do this, the plot somehow throw warnings though :(</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2_lm</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+    <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">NAMES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+    <span class="n">prefix_len</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">prefixes</span><span class="o">=</span><span class="n">PREFIXES</span><span class="p">,</span>
+    <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span>
+<span class="p">)</span>
+
+<span class="n">test_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+    <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">NAMES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+    <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+    <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+    <span class="n">prefix_len</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">prefixes</span><span class="o">=</span><span class="n">PREFIXES</span><span class="p">,</span>
+    <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span>
+<span class="p">)</span>
+
+<span class="n">aligning_variable</span> <span class="o">=</span> <span class="s2">&quot;position&quot;</span>
+<span class="n">aligning_stream</span> <span class="o">=</span> <span class="s2">&quot;block_output&quot;</span>
+<span class="n">aligning_pos</span> <span class="o">=</span> <span class="mi">17</span>
+<span class="n">aligning_layer</span> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">20</span>
+<span class="n">eval_every</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">initial_lr</span> <span class="o">=</span> <span class="mf">1e-4</span>
+<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">50</span>
+
+<span class="k">if</span> <span class="n">aligning_variable</span> <span class="o">==</span> <span class="s2">&quot;name&quot;</span><span class="p">:</span>
+    <span class="c1"># we hacky the distribution a little</span>
+    <span class="n">train_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+        <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[:</span><span class="mi">20</span><span class="p">],</span>
+        <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+        <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+        <span class="n">prefix_len</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">prefixes</span><span class="o">=</span><span class="n">PREFIXES</span><span class="p">,</span>
+        <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="n">test_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+        <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[:</span><span class="mi">20</span><span class="p">],</span>
+        <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+        <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+        <span class="n">prefix_len</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">prefixes</span><span class="o">=</span><span class="n">PREFIXES</span><span class="p">,</span>
+        <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span>
+    <span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">train_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+        <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">NAMES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+        <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+        <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
+        <span class="n">prefix_len</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">prefixes</span><span class="o">=</span><span class="n">PREFIXES</span><span class="p">,</span>
+        <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="n">test_distribution</span> <span class="o">=</span> <span class="n">PromptDistribution</span><span class="p">(</span>
+        <span class="n">names</span><span class="o">=</span><span class="n">NAMES</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">NAMES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+        <span class="n">objects</span><span class="o">=</span><span class="n">OBJECTS</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">OBJECTS</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+        <span class="n">places</span><span class="o">=</span><span class="n">PLACES</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">PLACES</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span> <span class="p">:],</span>
+        <span class="n">prefix_len</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">prefixes</span><span class="o">=</span><span class="n">PREFIXES</span><span class="p">,</span>
+        <span class="n">templates</span><span class="o">=</span><span class="n">TEMPLATES</span><span class="p">[</span><span class="mi">2</span><span class="p">:],</span>
+    <span class="p">)</span>
+
+<span class="n">D_train</span> <span class="o">=</span> <span class="n">train_distribution</span><span class="o">.</span><span class="n">sample_das</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">base_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ABB&quot;</span><span class="p">,</span> <span class="s2">&quot;BAB&quot;</span><span class="p">],</span>
+    <span class="n">source_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ABB&quot;</span><span class="p">,</span> <span class="s2">&quot;BAB&quot;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">aligning_variable</span> <span class="o">==</span> <span class="s2">&quot;position&quot;</span>
+    <span class="k">else</span> <span class="p">[</span><span class="s2">&quot;CDD&quot;</span><span class="p">,</span> <span class="s2">&quot;DCD&quot;</span><span class="p">],</span>
+    <span class="n">labels</span><span class="o">=</span><span class="n">aligning_variable</span><span class="p">,</span>
+    <span class="n">samples_per_combination</span><span class="o">=</span><span class="mi">50</span> <span class="k">if</span> <span class="n">aligning_variable</span> <span class="o">==</span> <span class="s2">&quot;position&quot;</span> <span class="k">else</span> <span class="mi">50</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">D_test</span> <span class="o">=</span> <span class="n">test_distribution</span><span class="o">.</span><span class="n">sample_das</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">base_patterns</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;ABB&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">source_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;BAB&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="n">aligning_variable</span> <span class="o">==</span> <span class="s2">&quot;position&quot;</span> <span class="k">else</span> <span class="p">[</span><span class="s2">&quot;DCD&quot;</span><span class="p">],</span>
+    <span class="n">labels</span><span class="o">=</span><span class="n">aligning_variable</span><span class="p">,</span>
+    <span class="n">samples_per_combination</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">+</span> <span class="n">test_distribution</span><span class="o">.</span><span class="n">sample_das</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">base_patterns</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;BAB&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">source_patterns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ABB&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="n">aligning_variable</span> <span class="o">==</span> <span class="s2">&quot;position&quot;</span> <span class="k">else</span> <span class="p">[</span><span class="s2">&quot;CDD&quot;</span><span class="p">],</span>
+    <span class="n">labels</span><span class="o">=</span><span class="n">aligning_variable</span><span class="p">,</span>
+    <span class="n">samples_per_combination</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">calculate_loss_with_mask</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">intervenable</span><span class="p">,</span> <span class="n">coeff</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">calculate_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">mask_loss</span> <span class="o">=</span> <span class="n">coeff</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">mask</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">loss</span> <span class="o">+=</span> <span class="n">mask_loss</span>
+    <span class="k">return</span> <span class="n">loss</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">aligning_stream</span><span class="p">,</span> <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">aligning_layer</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">SigmoidMaskIntervention</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt2</span><span class="p">)</span>
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+
+<span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span>
+    <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">get_trainable_parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">initial_lr</span>
+<span class="p">)</span>
+<span class="n">target_total_step</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">D_train</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">n_epochs</span>
+<span class="n">temperature_start</span> <span class="o">=</span> <span class="mf">1e-2</span>
+<span class="n">temperature_end</span> <span class="o">=</span> <span class="mf">1e-7</span>
+<span class="n">temperature_schedule</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span>
+        <span class="n">temperature_start</span><span class="p">,</span> <span class="n">temperature_end</span><span class="p">,</span> <span class="n">target_total_step</span>
+    <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">LinearLR</span><span class="p">(</span>
+    <span class="n">optimizer</span><span class="p">,</span> <span class="n">end_factor</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">total_iters</span><span class="o">=</span><span class="n">n_epochs</span>
+<span class="p">)</span>
+
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">)):</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">batch_dataset</span> <span class="ow">in</span> <span class="n">D_train</span><span class="o">.</span><span class="n">batches</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">):</span>
+        <span class="c1"># prepare base</span>
+        <span class="n">base_inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">base_inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">base_inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="c1"># prepare source</span>
+        <span class="n">source_inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">tokens</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">source_inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">source_inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="c1"># prepare label</span>
+        <span class="n">labels</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
+            <span class="n">gpt2</span><span class="o">.</span><span class="n">device</span>
+        <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="mi">18</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="mi">18</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">source_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">aligning_pos</span>
+            <span class="p">},</span>
+        <span class="p">)</span>
+        <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">],</span> <span class="p">[</span><span class="n">labels</span><span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">calculate_loss_with_mask</span><span class="p">(</span>
+            <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">pv_gpt2</span>
+        <span class="p">)</span>
+        <span class="n">loss_str</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+        <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+        <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">set_zero_grad</span><span class="p">()</span>
+        <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span>
+            <span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span>
+        
+<span class="c1"># eval</span>
+<span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">batch_dataset</span> <span class="ow">in</span> <span class="n">D_test</span><span class="o">.</span><span class="n">batches</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">):</span>
+        <span class="c1"># prepare base</span>
+        <span class="n">base_inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">tokens</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">base_inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">base_inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="c1"># prepare source</span>
+        <span class="n">source_inputs</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">tokens</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">source_inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">source_inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="c1"># prepare label</span>
+        <span class="n">labels</span> <span class="o">=</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">patched_answer_tokens</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="mi">18</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="o">==</span> <span class="mi">18</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">batch_dataset</span><span class="o">.</span><span class="n">source</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">base_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">source_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">aligning_pos</span>
+            <span class="p">},</span>
+        <span class="p">)</span>
+        <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">labels</span><span class="p">]</span>
+        <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+<span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">mask</span>
+    <span class="n">temperature</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">temperature</span>
+    <span class="k">break</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">eval_metrics</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;bits turned on: &quot;</span><span class="p">,</span> 
+    <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">mask</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">temperature</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:17&lt;00:00,  2.78it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;accuracy&#39;: 0.58, &#39;kl_div&#39;: tensor(-77.9607, device=&#39;cuda:0&#39;)}
+bits turned on:  tensor(534.7392, device=&#39;cuda:0&#39;, grad_fn=&lt;SumBackward0&gt;)
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bit_map</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">mask</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">temperature</span><span class="p">))</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert the list to a DataFrame</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Value&#39;</span><span class="p">:</span> <span class="n">bit_map</span><span class="p">,</span> <span class="s1">&#39;Index&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bit_map</span><span class="p">))})</span>
+
+<span class="c1"># Create a barplot</span>
+<span class="n">plot</span> <span class="o">=</span> <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;Index&#39;</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="s1">&#39;Value&#39;</span><span class="p">))</span> <span class="o">+</span> <span class="n">geom_bar</span><span class="p">()</span> <span class="o">+</span> \
+       <span class="n">ggtitle</span><span class="p">(</span><span class="s1">&#39;Bitmap Visualization of Intervening Dimensions&#39;</span><span class="p">)</span> <span class="o">+</span> \
+       <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/da21608480b9859d2fe2f3e836f1b77b148b9773f073caff4fcc4570379731f6.png"><img alt="../../_images/da21608480b9859d2fe2f3e836f1b77b148b9773f073caff4fcc4570379731f6.png" src="../../_images/da21608480b9859d2fe2f3e836f1b77b148b9773f073caff4fcc4570379731f6.png" style="width: 800px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
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diff --git a/tutorials/advanced_tutorials/Interventions_with_BLIP.html b/tutorials/advanced_tutorials/Interventions_with_BLIP.html
new file mode 100644
index 00000000..c3bf3221
--- /dev/null
+++ b/tutorials/advanced_tutorials/Interventions_with_BLIP.html
@@ -0,0 +1,1464 @@
+
+<!DOCTYPE html>
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Intervening on Vision-Language Models</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#setup">Setup</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-model-and-test-inference">Load model and test inference</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#vanilla-intervention">Vanilla intervention</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#causal-tracing">Causal tracing</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#varying-levels-of-noise">Varying levels of noise</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sampling-noise-and-running-repeatedly">Sampling noise and running repeatedly</a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="intervening-on-vision-language-models">
+<h1>Intervening on Vision-Language Models<a class="headerlink" href="#intervening-on-vision-language-models" title="Link to this heading">#</a></h1>
+<p>This is a quick tutorial for running interventions on the decoder component of BLIP for visual question answering (support for the text/image encoders TBD). This is partially based on an earlier paper I was on, <a class="reference external" href="https://arxiv.org/abs/2308.14179">“Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP” (Palit et al., 2023)</a>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Aryaman Arora&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;12/27/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="setup">
+<h2>Setup<a class="headerlink" href="#setup" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">VanillaIntervention</span><span class="p">,</span> <span class="n">Intervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_blip</span>
+<span class="kn">from</span> <span class="nn">pyvene.models.blip.modelings_blip</span> <span class="kn">import</span> <span class="n">BlipWrapper</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">plotnine.scales</span> <span class="kn">import</span> <span class="n">scale_y_reverse</span><span class="p">,</span> <span class="n">scale_fill_cmap</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="load-model-and-test-inference">
+<h2>Load model and test inference<a class="headerlink" href="#load-model-and-test-inference" title="Link to this heading">#</a></h2>
+<p>We’ll load BLIPForQuestionAnswering and use a special <code class="docutils literal notranslate"><span class="pre">BlipWrapper</span></code> to enable easy access of decoder logits; this doesn’t modify the model’s computations in any way.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cuda:0&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="n">config</span><span class="p">,</span> <span class="n">processor</span><span class="p">,</span> <span class="n">blip</span> <span class="o">=</span> <span class="n">create_blip</span><span class="p">(</span>
+    <span class="n">cache_dir</span><span class="o">=</span><span class="s2">&quot;/nlp/scr/aryaman/.cache/huggingface/hub&quot;</span>  <span class="c1"># change to your local dir</span>
+<span class="p">)</span>
+<span class="n">blip</span> <span class="o">=</span> <span class="n">BlipWrapper</span><span class="p">(</span><span class="n">blip</span><span class="p">)</span>
+<span class="n">blip</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BlipWrapper(
+  (model_vis): BlipVisionModel(
+    (embeddings): BlipVisionEmbeddings(
+      (patch_embedding): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
+    )
+    (encoder): BlipEncoder(
+      (layers): ModuleList(
+        (0-11): 12 x BlipEncoderLayer(
+          (self_attn): BlipAttention(
+            (dropout): Dropout(p=0.0, inplace=False)
+            (qkv): Linear(in_features=768, out_features=2304, bias=True)
+            (projection): Linear(in_features=768, out_features=768, bias=True)
+          )
+          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          (mlp): BlipMLP(
+            (activation_fn): GELUActivation()
+            (fc1): Linear(in_features=768, out_features=3072, bias=True)
+            (fc2): Linear(in_features=3072, out_features=768, bias=True)
+          )
+          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+        )
+      )
+    )
+    (post_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+  )
+  (model_text_enc): BlipTextModel(
+    (embeddings): BlipTextEmbeddings(
+      (word_embeddings): Embedding(30524, 768, padding_idx=0)
+      (position_embeddings): Embedding(512, 768)
+      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+      (dropout): Dropout(p=0.0, inplace=False)
+    )
+    (encoder): BlipTextEncoder(
+      (layer): ModuleList(
+        (0-11): 12 x BlipTextLayer(
+          (attention): BlipTextAttention(
+            (self): BlipTextSelfAttention(
+              (query): Linear(in_features=768, out_features=768, bias=True)
+              (key): Linear(in_features=768, out_features=768, bias=True)
+              (value): Linear(in_features=768, out_features=768, bias=True)
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+            (output): BlipTextSelfOutput(
+              (dense): Linear(in_features=768, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+          (crossattention): BlipTextAttention(
+            (self): BlipTextSelfAttention(
+              (query): Linear(in_features=768, out_features=768, bias=True)
+              (key): Linear(in_features=768, out_features=768, bias=True)
+              (value): Linear(in_features=768, out_features=768, bias=True)
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+            (output): BlipTextSelfOutput(
+              (dense): Linear(in_features=768, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+          (intermediate): BlipTextIntermediate(
+            (dense): Linear(in_features=768, out_features=3072, bias=True)
+            (intermediate_act_fn): GELUActivation()
+          )
+          (output): BlipTextOutput(
+            (dense): Linear(in_features=3072, out_features=768, bias=True)
+            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+            (dropout): Dropout(p=0.0, inplace=False)
+          )
+        )
+      )
+    )
+  )
+  (model_text_dec): BlipTextLMHeadModel(
+    (bert): BlipTextModel(
+      (embeddings): BlipTextEmbeddings(
+        (word_embeddings): Embedding(30524, 768, padding_idx=0)
+        (position_embeddings): Embedding(512, 768)
+        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.0, inplace=False)
+      )
+      (encoder): BlipTextEncoder(
+        (layer): ModuleList(
+          (0-11): 12 x BlipTextLayer(
+            (attention): BlipTextAttention(
+              (self): BlipTextSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+              )
+              (output): BlipTextSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (crossattention): BlipTextAttention(
+              (self): BlipTextSelfAttention(
+                (query): Linear(in_features=768, out_features=768, bias=True)
+                (key): Linear(in_features=768, out_features=768, bias=True)
+                (value): Linear(in_features=768, out_features=768, bias=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+              )
+              (output): BlipTextSelfOutput(
+                (dense): Linear(in_features=768, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (intermediate): BlipTextIntermediate(
+              (dense): Linear(in_features=768, out_features=3072, bias=True)
+              (intermediate_act_fn): GELUActivation()
+            )
+            (output): BlipTextOutput(
+              (dense): Linear(in_features=3072, out_features=768, bias=True)
+              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+        )
+      )
+    )
+    (cls): BlipTextOnlyMLMHead(
+      (predictions): BlipTextLMPredictionHead(
+        (transform): BlipTextPredictionHeadTransform(
+          (dense): Linear(in_features=768, out_features=768, bias=True)
+          (transform_act_fn): GELUActivation()
+          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
+        )
+        (decoder): Linear(in_features=768, out_features=30524, bias=True)
+      )
+    )
+  )
+)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now testing some QA on a simple image.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">img_url</span> <span class="o">=</span> <span class="s2">&quot;http://images.cocodataset.org/train2017/000000458864.jpg&quot;</span>
+<span class="n">raw_image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">img_url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">raw</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;RGB&quot;</span><span class="p">)</span>
+<span class="n">raw_image</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/8f16a324ed49faef17dc8b20bffbfc319332532c3e538ce72652eff07848983d.png" src="../../_images/8f16a324ed49faef17dc8b20bffbfc319332532c3e538ce72652eff07848983d.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Pretty print the top n values of a distribution over the vocabulary&quot;&quot;&quot;</span>
+    <span class="n">probs</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">top_values</span><span class="p">,</span> <span class="n">top_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_values</span><span class="p">)):</span>
+        <span class="n">tok</span> <span class="o">=</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">top_indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">tok</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">top_indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s2">&gt;10</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">top_values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s2">&gt;10.5f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">probs</span><span class="p">[</span><span class="n">top_indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="si">:</span><span class="s2">&gt;10.2%</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+
+<span class="n">question</span> <span class="o">=</span> <span class="s2">&quot;what is the color of the shirt&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">----&quot;</span><span class="p">)</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">processor</span><span class="p">(</span><span class="n">raw_image</span><span class="p">,</span> <span class="n">question</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+<span class="n">out</span> <span class="o">=</span> <span class="n">blip</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">processor</span><span class="p">,</span> <span class="n">out</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+
+<span class="n">question</span> <span class="o">=</span> <span class="s2">&quot;what is the color of the animal&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">----&quot;</span><span class="p">)</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">processor</span><span class="p">(</span><span class="n">raw_image</span><span class="p">,</span> <span class="n">question</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+<span class="n">out</span> <span class="o">=</span> <span class="n">blip</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">processor</span><span class="p">,</span> <span class="n">out</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>what is the color of the shirt 
+----
+torch.Size([1, 9])
+torch.Size([1, 1, 30524])
+red                        2417   10.64493     61.74%
+white                      2317    9.11390     13.35%
+pink                       5061    8.10488      4.87%
+green                      2665    7.13710      1.85%
+blue                       2630    6.71536      1.21%
+gray                       3897    6.63115      1.12%
+black                      2304    6.52738      1.01%
+brown                      2829    6.42861      0.91%
+maroon                    22222    6.03666      0.62%
+multi                      4800    5.35350      0.31%
+what is the color of the animal 
+----
+torch.Size([1, 9])
+torch.Size([1, 1, 30524])
+brown                      2829   11.42929     88.94%
+red                        2417    6.36936      0.56%
+reddish                   14182    5.54194      0.25%
+dark                       2601    5.49398      0.24%
+tan                        9092    5.24055      0.18%
+light                      2422    5.15968      0.17%
+brownish                  19437    4.64057      0.10%
+orange                     4589    4.52101      0.09%
+rust                      18399    4.26794      0.07%
+it                         2009    4.12371      0.06%
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="vanilla-intervention">
+<h2>Vanilla intervention<a class="headerlink" href="#vanilla-intervention" title="Link to this heading">#</a></h2>
+<p>Let’s see at what transformer blocks and position we can swap activations in order to make the model tell the colour of the shirt instead of the animal.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">simple_position_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">intervention_type</span><span class="p">,</span> <span class="n">layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>  <span class="c1"># layer</span>
+                <span class="n">intervention_type</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">processor</span><span class="p">(</span><span class="n">raw_image</span><span class="p">,</span> <span class="s2">&quot;what is the color of the animal&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
+    <span class="n">device</span>
+<span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">processor</span><span class="p">(</span><span class="n">raw_image</span><span class="p">,</span> <span class="s2">&quot;what is the color of the shirt&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
+        <span class="n">device</span>
+    <span class="p">)</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)):</span>
+        <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">9</span><span class="p">):</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">simple_position_config</span><span class="p">(</span>
+                <span class="nb">type</span><span class="p">(</span><span class="n">blip</span><span class="p">),</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="n">layer_i</span>
+            <span class="p">)</span>
+            <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">blip</span><span class="p">)</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="n">pos_i</span><span class="p">]]],</span> <span class="p">[[[</span><span class="n">pos_i</span><span class="p">]]])}</span>
+            <span class="p">)</span>
+            <span class="n">logits</span> <span class="o">=</span> <span class="n">counterfactual_outputs</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="c1"># top_vals(processor, logits, n=1)</span>
+            <span class="n">logits</span> <span class="o">=</span> <span class="n">logits</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">p_brown</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="mi">2829</span><span class="p">]</span>
+            <span class="n">p_red</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="mi">2417</span><span class="p">]</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer_i</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;p(brown)&quot;</span><span class="p">:</span> <span class="n">p_brown</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+                    <span class="s2">&quot;p(red)&quot;</span><span class="p">:</span> <span class="n">p_red</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:15&lt;00:00,  1.32s/it]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(red)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(red)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;p(red)&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Purples&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/abd44e33b4e85f93355b7463f8587ee66194280e12ad88b4c3f9959df0c8b472.png"><img alt="../../_images/abd44e33b4e85f93355b7463f8587ee66194280e12ad88b4c3f9959df0c8b472.png" src="../../_images/abd44e33b4e85f93355b7463f8587ee66194280e12ad88b4c3f9959df0c8b472.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="causal-tracing">
+<h2>Causal tracing<a class="headerlink" href="#causal-tracing" title="Link to this heading">#</a></h2>
+<p>This replicates the Gaussian interventions from <a class="reference external" href="https://arxiv.org/abs/2202.05262">“Locating and Editing Factual Associations in GPT” (Meng et al., 2023)</a> that were also used on BLIP in the paper I linked at the start, i.e. this is a full port into pyvene of the original code for causal tracing on BLIP.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">NoiseIntervention</span><span class="p">(</span><span class="n">Intervention</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span> <span class="o">=</span> <span class="n">embed_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span> <span class="o">=</span> <span class="mf">1.0</span>
+
+    <span class="k">def</span> <span class="nf">set_interchange_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interchange_dim</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span> <span class="o">=</span> <span class="n">interchange_dim</span>
+
+    <span class="k">def</span> <span class="nf">set_noise_level</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">noise_level</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span> <span class="o">=</span> <span class="n">noise_level</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="p">):</span>
+        <span class="c1"># sample gaussian noise</span>
+        <span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">])</span>
+        <span class="n">stdev</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">])</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">noise_level</span>
+        <span class="n">noise</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">stdev</span><span class="p">)</span>
+
+        <span class="c1"># interchange</span>
+        <span class="n">base</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">interchange_dim</span><span class="p">]</span> <span class="o">+=</span> <span class="n">noise</span>
+
+        <span class="k">return</span> <span class="n">base</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;NoiseIntervention(embed_dim=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="si">}</span><span class="s2">)&quot;</span>
+
+
+<span class="k">def</span> <span class="nf">make_noise_intervention</span><span class="p">(</span><span class="n">noise_level</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">proj_dim</span><span class="p">,</span> <span class="n">subspace_partition</span><span class="p">):</span>
+        <span class="n">intervention</span> <span class="o">=</span> <span class="n">NoiseIntervention</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+        <span class="n">intervention</span><span class="o">.</span><span class="n">set_noise_level</span><span class="p">(</span><span class="n">noise_level</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">intervention</span>
+
+    <span class="k">return</span> <span class="n">func</span>
+
+
+<span class="k">def</span> <span class="nf">corrupted_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">noise_level</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>  <span class="c1"># layer</span>
+                <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">make_noise_intervention</span><span class="p">(</span><span class="n">noise_level</span><span class="p">),</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="k">def</span> <span class="nf">restore_corrupted_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">noise_level</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>  <span class="c1"># layer</span>
+                <span class="s2">&quot;block_input&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="p">),</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>  <span class="c1"># layer</span>
+                <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">make_noise_intervention</span><span class="p">(</span><span class="n">noise_level</span><span class="p">),</span>
+            <span class="n">VanillaIntervention</span><span class="p">,</span>
+        <span class="p">],</span>
+        <span class="c1"># mode=&#39;serial&#39;</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="varying-levels-of-noise">
+<h3>Varying levels of noise<a class="headerlink" href="#varying-levels-of-noise" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="n">inputs</span>
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">noise_level</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">]:</span>
+        <span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">9</span><span class="p">):</span>
+                <span class="n">config</span> <span class="o">=</span> <span class="n">restore_corrupted_config</span><span class="p">(</span>
+                    <span class="nb">type</span><span class="p">(</span><span class="n">blip</span><span class="p">),</span> <span class="n">layer_i</span><span class="p">,</span> <span class="n">noise_level</span><span class="o">=</span><span class="n">noise_level</span>
+                <span class="p">)</span>
+                <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">blip</span><span class="p">)</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">base</span><span class="p">,</span>
+                    <span class="p">[</span><span class="n">base</span><span class="p">,</span> <span class="n">base</span><span class="p">],</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                            <span class="p">[</span>
+                                <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span>
+                                <span class="p">[</span>
+                                    <span class="p">[</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                    <span class="p">]</span>
+                                <span class="p">],</span>
+                            <span class="p">],</span>
+                            <span class="p">[</span>
+                                <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span>
+                                <span class="p">[</span>
+                                    <span class="p">[</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                    <span class="p">]</span>
+                                <span class="p">],</span>
+                            <span class="p">],</span>
+                        <span class="p">)</span>
+                    <span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">logits</span> <span class="o">=</span> <span class="n">counterfactual_outputs</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="n">p_brown</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="mi">2829</span><span class="p">]</span>
+                <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer_i</span><span class="p">,</span>
+                        <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                        <span class="s2">&quot;p(brown)&quot;</span><span class="p">:</span> <span class="n">p_brown</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+                        <span class="s2">&quot;noise_level&quot;</span><span class="p">:</span> <span class="n">noise_level</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">)</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">avg_p</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;noise_level&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">noise_level</span><span class="p">][</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;average prob: </span><span class="si">{</span><span class="n">avg_p</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 88.95%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 59.44%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.79s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 11.18%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.79s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 5.78%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 4.00%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.79s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 5.99%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob: 4.40%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Purples&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;noise_level&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/779c28ba365c07a9508d027b0ac409537ec545e329e75f73fd320ecc20447b72.png"><img alt="../../_images/779c28ba365c07a9508d027b0ac409537ec545e329e75f73fd320ecc20447b72.png" src="../../_images/779c28ba365c07a9508d027b0ac409537ec545e329e75f73fd320ecc20447b72.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="sampling-noise-and-running-repeatedly">
+<h3>Sampling noise and running repeatedly<a class="headerlink" href="#sampling-noise-and-running-repeatedly" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="n">inputs</span>
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">9</span><span class="p">):</span>
+                <span class="n">config</span> <span class="o">=</span> <span class="n">restore_corrupted_config</span><span class="p">(</span>
+                    <span class="nb">type</span><span class="p">(</span><span class="n">blip</span><span class="p">),</span> <span class="n">layer_i</span><span class="p">,</span> <span class="n">noise_level</span><span class="o">=</span><span class="mf">1.0</span>
+                <span class="p">)</span>
+                <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">blip</span><span class="p">)</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">base</span><span class="p">,</span>
+                    <span class="p">[</span><span class="n">base</span><span class="p">,</span> <span class="n">base</span><span class="p">],</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                            <span class="p">[</span>
+                                <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span>
+                                <span class="p">[</span>
+                                    <span class="p">[</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                    <span class="p">]</span>
+                                <span class="p">],</span>
+                            <span class="p">],</span>
+                            <span class="p">[</span>
+                                <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span>
+                                <span class="p">[</span>
+                                    <span class="p">[</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                        <span class="n">pos_i</span><span class="p">,</span>
+                                    <span class="p">]</span>
+                                <span class="p">],</span>
+                            <span class="p">],</span>
+                        <span class="p">)</span>
+                    <span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">logits</span> <span class="o">=</span> <span class="n">counterfactual_outputs</span><span class="p">[</span><span class="s2">&quot;decoder_logits&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="n">p_brown</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="mi">2829</span><span class="p">]</span>
+                <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="p">{</span>
+                        <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer_i</span><span class="p">,</span>
+                        <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                        <span class="s2">&quot;p(brown)&quot;</span><span class="p">:</span> <span class="n">p_brown</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+                        <span class="s2">&quot;iteration&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span>
+                    <span class="p">}</span>
+                <span class="p">)</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">avg_p</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;iteration&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">][</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;average prob so far: </span><span class="si">{</span><span class="n">avg_p</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 9.13%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 7.91%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 7.51%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 8.36%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.79s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 9.84%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 8.33%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 8.20%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 7.92%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 8.24%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:21&lt;00:00,  1.78s/it]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>average prob so far: 8.59%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Purples&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;iteration&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/d600e321372b924d5012fb12011690c76589d8f5fd115ce7801e611c63682f0b.png"><img alt="../../_images/d600e321372b924d5012fb12011690c76589d8f5fd115ce7801e611c63682f0b.png" src="../../_images/d600e321372b924d5012fb12011690c76589d8f5fd115ce7801e611c63682f0b.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="s2">&quot;pos&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;p(brown)&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Purples&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>     layer  pos  p(brown)  iteration
+0        0    0  0.008152        4.5
+1        0    1  0.015465        4.5
+2        0    2  0.022314        4.5
+3        0    3  0.003820        4.5
+4        0    4  0.025465        4.5
+..     ...  ...       ...        ...
+103     11    4  0.193646        4.5
+104     11    5  0.137404        4.5
+105     11    6  0.209949        4.5
+106     11    7  0.234223        4.5
+107     11    8  0.048529        4.5
+
+[108 rows x 4 columns]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/cca78cf50fa435c957f29630bdbde741defbaf5a9a04e934e6a9e067cbe4ef88.png"><img alt="../../_images/cca78cf50fa435c957f29630bdbde741defbaf5a9a04e934e6a9e067cbe4ef88.png" src="../../_images/cca78cf50fa435c957f29630bdbde741defbaf5a9a04e934e6a9e067cbe4ef88.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Nested Hierarchical Structure with MQNLI</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-Up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-mqnli-task">The MQNLI Task</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#example-mqnli-input">Example MQNLI Input</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mqnli-with-an-intervention">MQNLI with an Intervention</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training-a-model-on-mqnli">Training a Model on MQNLI</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#interpreting-our-model">Interpreting Our Model</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluate-das-alignment">Evaluate DAS Alignment</a></li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="nested-hierarchical-structure-with-mqnli">
+<h1>Nested Hierarchical Structure with MQNLI<a class="headerlink" href="#nested-hierarchical-structure-with-mqnli" title="Link to this heading">#</a></h1>
+<p>In this notebook, we use <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> to analyze language models on a complex heirarchical task: multiply-quantified natural language inference (MQNLI).
+We begin by describing the causal structure that generates our data through semantic composition. Then, we analyze whether models that learn this task employ the same compositional structure to solve it.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Amir Zur&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="set-up">
+<h2>Set-Up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">load_ext</span> autoreload
+<span class="o">%</span><span class="k">autoreload</span> 2
+<span class="kn">import</span> <span class="nn">pyvene</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">json</span> 
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span><span class="p">,</span> <span class="n">trange</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">CrossEntropyLoss</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">TrainingArguments</span><span class="p">,</span> <span class="n">Trainer</span>
+<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
+
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">CausalModel</span>
+<span class="c1"># from pyvene.models.configuration_intervenable_model import RepresentationConfig</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">RotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">pyvene.models.gpt2.modelings_intervenable_gpt2</span> <span class="kn">import</span> <span class="n">create_gpt2_lm</span>
+<span class="c1"># from pyvene.models.gpt_neox.modelings_intervenable_gpt_neox import create_gpt_neox</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TRAIN_DIR</span> <span class="o">=</span> <span class="s1">&#39;mqnli_factual&#39;</span>
+<span class="n">DAS_DIR</span> <span class="o">=</span> <span class="s1">&#39;mqnli_das&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;torch._C.Generator at 0x169a1e510b0&gt;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="the-mqnli-task">
+<h2>The MQNLI Task<a class="headerlink" href="#the-mqnli-task" title="Link to this heading">#</a></h2>
+<p>Multiply-quantified natural language inference (MQNLI) is a variant of natural language inference (NLI) with a highly structured composition. Each datapoint is a pair of sentences, and each label is the logical relation between them (e.g., entailment, reverse entailment, no relation). Crucially, the logical relation can be computed compositionally: first compute the relation between the two sentences’ nouns, then their determiners, and then combine these intermediate computations to find the relation between the sentences’ noun phrases (NPs).</p>
+<p>In this section, we walk through the high-level causal structure of MQNLI with examples from the MQNLI dataset. We encourage first-time readers to read the original paper, <a class="reference external" href="https://arxiv.org/pdf/1810.13033.pdf">Geiger, Cases, Karttunen, and Potts (2018)</a>, before getting started.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># JSON files generated from adapting MQNLI codebase</span>
+<span class="c1"># https://github.com/atticusg/MultiplyQuantifiedData</span>
+
+<span class="k">class</span> <span class="nc">Hashabledict</span><span class="p">(</span><span class="nb">dict</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;tutorial_data/mqnli_q_projectivity.json&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">determiner_signatures</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+    <span class="n">determiner_signatures</span> <span class="o">=</span> <span class="n">Hashabledict</span><span class="p">({</span>
+        <span class="n">q1</span><span class="p">:</span> <span class="n">Hashabledict</span><span class="p">({</span>
+            <span class="n">q2</span><span class="p">:</span> <span class="n">Hashabledict</span><span class="p">({</span>
+                <span class="n">r1</span><span class="p">:</span> <span class="n">Hashabledict</span><span class="p">({</span>
+                    <span class="n">r2</span><span class="p">:</span> <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q1</span><span class="p">][</span><span class="n">q2</span><span class="p">][</span><span class="n">r1</span><span class="p">][</span><span class="n">r2</span><span class="p">]</span>  
+                    <span class="k">for</span> <span class="n">r2</span> <span class="ow">in</span> <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q1</span><span class="p">][</span><span class="n">q2</span><span class="p">][</span><span class="n">r1</span><span class="p">]</span>
+                <span class="p">})</span>  
+                <span class="k">for</span> <span class="n">r1</span> <span class="ow">in</span> <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q1</span><span class="p">][</span><span class="n">q2</span><span class="p">]</span>
+            <span class="p">})</span>
+            <span class="k">for</span> <span class="n">q2</span> <span class="ow">in</span> <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q1</span><span class="p">]</span>
+        <span class="p">})</span>
+        <span class="k">for</span> <span class="n">q1</span> <span class="ow">in</span> <span class="n">determiner_signatures</span>
+    <span class="p">})</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;tutorial_data/mqnli_neg_signature.json&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">negation_signature</span> <span class="o">=</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;tutorial_data/mqnli_empty_signature.json&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">emptystring_signature</span> <span class="o">=</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;tutorial_data/mqnli_cont_signature.json&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">compose_contradiction_signature</span> <span class="o">=</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;tutorial_data/mqnli_neg_cont_signature.json&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+    <span class="n">compose_neg_contradiction_signature</span> <span class="o">=</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parents</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;N_P_O&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;N_H_O&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;N_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;N_P_O&quot;</span><span class="p">,</span> <span class="s2">&quot;N_H_O&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_P_O&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Adj_H_O&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Adj_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Adj_P_O&quot;</span><span class="p">,</span> <span class="s2">&quot;Adj_H_O&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;NP_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Adj_O&quot;</span><span class="p">,</span> <span class="s2">&quot;N_O&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Q_P_O&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Q_H_O&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Q_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Q_P_O&quot;</span><span class="p">,</span> <span class="s2">&quot;Q_H_O&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;V_P&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;V_H&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;V_P&quot;</span><span class="p">,</span> <span class="s2">&quot;V_H&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Adv_P&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Adv_H&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Adv&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Adv_P&quot;</span><span class="p">,</span> <span class="s2">&quot;Adv_H&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;VP&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Adv&quot;</span><span class="p">,</span> <span class="s2">&quot;V&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;QP_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Q_O&quot;</span><span class="p">,</span> <span class="s2">&quot;NP_O&quot;</span><span class="p">,</span> <span class="s2">&quot;VP&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Neg_P&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Neg_H&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Neg&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Neg_P&quot;</span><span class="p">,</span> <span class="s2">&quot;Neg_H&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;NegP&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Neg&quot;</span><span class="p">,</span> <span class="s2">&quot;QP_O&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;N_P_S&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;N_H_S&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;N_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;N_P_S&quot;</span><span class="p">,</span> <span class="s2">&quot;N_H_S&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_P_S&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Adj_H_S&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Adj_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Adj_P_S&quot;</span><span class="p">,</span> <span class="s2">&quot;Adj_H_S&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;NP_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Adj_S&quot;</span><span class="p">,</span> <span class="s2">&quot;N_S&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Q_P_S&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Q_H_S&quot;</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">&quot;Q_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Q_P_S&quot;</span><span class="p">,</span> <span class="s2">&quot;Q_H_S&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;QP_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Q_S&quot;</span><span class="p">,</span> <span class="s2">&quot;NP_S&quot;</span><span class="p">,</span> <span class="s2">&quot;NegP&quot;</span><span class="p">]</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">EMPTY</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+<span class="n">IND</span> <span class="o">=</span> <span class="s2">&quot;independence&quot;</span>
+<span class="n">EQV</span> <span class="o">=</span> <span class="s2">&quot;equivalence&quot;</span>
+<span class="n">ENT</span> <span class="o">=</span> <span class="s2">&quot;entails&quot;</span>
+<span class="n">REV</span> <span class="o">=</span> <span class="s2">&quot;reverse entails&quot;</span>
+<span class="n">CON</span> <span class="o">=</span> <span class="s2">&quot;contradiction&quot;</span>
+<span class="n">ALT</span> <span class="o">=</span> <span class="s2">&quot;alternation&quot;</span>
+<span class="n">COV</span> <span class="o">=</span> <span class="s2">&quot;cover&quot;</span>
+<span class="c1"># all possible relation values from the original paper </span>
+<span class="c1"># (https://arxiv.org/pdf/1810.13033.pdf)</span>
+<span class="n">RELATIONS</span> <span class="o">=</span> <span class="p">[</span><span class="n">IND</span><span class="p">,</span> <span class="n">EQV</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">,</span> <span class="n">CON</span><span class="p">,</span> <span class="n">ALT</span><span class="p">,</span> <span class="n">COV</span><span class="p">]</span>
+
+<span class="n">Q_VALUES</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q1</span><span class="p">][</span><span class="n">q2</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">q1</span> <span class="ow">in</span> <span class="n">determiner_signatures</span> <span class="k">for</span> <span class="n">q2</span> <span class="ow">in</span> <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q1</span><span class="p">]</span>
+<span class="p">]</span>
+
+<span class="n">values</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;N_P_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;tree&quot;</span><span class="p">,</span> <span class="s2">&quot;rock&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;N_H_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;tree&quot;</span><span class="p">,</span> <span class="s2">&quot;rock&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;N_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_P_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;happy&quot;</span><span class="p">,</span> <span class="s2">&quot;sad&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_H_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;happy&quot;</span><span class="p">,</span> <span class="s2">&quot;sad&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;NP_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;Q_P_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;some&quot;</span><span class="p">,</span> <span class="s2">&quot;every&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Q_H_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;some&quot;</span><span class="p">,</span> <span class="s2">&quot;every&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Q_O&quot;</span><span class="p">:</span> <span class="n">Q_VALUES</span><span class="p">,</span>
+    <span class="s2">&quot;V_P&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;climbed&quot;</span><span class="p">,</span> <span class="s2">&quot;threw&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;V_H&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;climbed&quot;</span><span class="p">,</span> <span class="s2">&quot;threw&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">],</span>
+    <span class="s2">&quot;Adv_P&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;energetically&quot;</span><span class="p">,</span> <span class="s2">&quot;joyfully&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Adv_H&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;energetically&quot;</span><span class="p">,</span> <span class="s2">&quot;joyfully&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Adv&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;VP&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;QP_O&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;Neg_P&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;not&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Neg_H&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;not&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Neg&quot;</span><span class="p">:</span> <span class="p">[</span>
+        <span class="n">negation_signature</span><span class="p">,</span> 
+        <span class="n">emptystring_signature</span><span class="p">,</span> 
+        <span class="n">compose_contradiction_signature</span><span class="p">,</span> 
+        <span class="n">compose_neg_contradiction_signature</span>
+    <span class="p">],</span>
+    <span class="s2">&quot;NegP&quot;</span><span class="p">:</span> <span class="n">RELATIONS</span><span class="p">,</span>
+    <span class="s2">&quot;N_P_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;child&quot;</span><span class="p">,</span> <span class="s2">&quot;dog&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;N_H_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;child&quot;</span><span class="p">,</span> <span class="s2">&quot;dog&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;N_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_P_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;little&quot;</span><span class="p">,</span> <span class="s2">&quot;cute&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_H_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;little&quot;</span><span class="p">,</span> <span class="s2">&quot;cute&quot;</span><span class="p">,</span> <span class="n">EMPTY</span><span class="p">],</span>
+    <span class="s2">&quot;Adj_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;NP_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">EQV</span><span class="p">,</span> <span class="n">IND</span><span class="p">,</span> <span class="n">ENT</span><span class="p">,</span> <span class="n">REV</span><span class="p">],</span>
+    <span class="s2">&quot;Q_P_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;some&quot;</span><span class="p">,</span> <span class="s2">&quot;every&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Q_H_S&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;some&quot;</span><span class="p">,</span> <span class="s2">&quot;every&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Q_S&quot;</span><span class="p">:</span> <span class="n">Q_VALUES</span><span class="p">,</span>
+    <span class="s2">&quot;QP_S&quot;</span><span class="p">:</span> <span class="n">RELATIONS</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># adapted from original code for MQNLI:</span>
+<span class="c1"># https://github.com/atticusg/MultiplyQuantifiedData/blob/master/natural_logic_model.py</span>
+
+<span class="k">def</span> <span class="nf">adj_merge</span><span class="p">(</span><span class="n">adj_p</span><span class="p">,</span> <span class="n">adj_h</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">adj_p</span> <span class="o">==</span> <span class="n">adj_h</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">EQV</span>
+    <span class="k">if</span> <span class="n">adj_p</span> <span class="o">==</span> <span class="n">EMPTY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">REV</span>
+    <span class="k">if</span> <span class="n">adj_h</span> <span class="o">==</span> <span class="n">EMPTY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">ENT</span>
+    <span class="k">return</span> <span class="n">IND</span>
+
+<span class="n">adv_merge</span> <span class="o">=</span> <span class="n">adj_merge</span>
+
+<span class="k">def</span> <span class="nf">noun_phrase</span><span class="p">(</span><span class="n">adj</span><span class="p">,</span> <span class="n">noun</span><span class="p">):</span>
+    <span class="c1">#merges a noun relation with an adjective relation</span>
+    <span class="c1">#or a verb relation with an adverb relation</span>
+    <span class="c1"># makes sense: if the objects are the same, then adjective&#39;s relation holds</span>
+    <span class="c1"># otherwise, they&#39;re independent</span>
+    <span class="k">if</span> <span class="n">noun</span> <span class="o">==</span> <span class="n">EQV</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">adj</span>
+    <span class="k">return</span> <span class="n">IND</span>
+
+<span class="n">verb_phrase</span> <span class="o">=</span> <span class="n">noun_phrase</span>
+
+<span class="k">def</span> <span class="nf">negation_merge</span><span class="p">(</span><span class="n">neg_p</span><span class="p">,</span> <span class="n">neg_h</span><span class="p">):</span>
+    <span class="c1">#merges negation</span>
+    <span class="k">if</span> <span class="n">neg_p</span> <span class="o">==</span> <span class="n">neg_h</span> <span class="ow">and</span> <span class="n">neg_p</span> <span class="o">==</span> <span class="n">EMPTY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">emptystring_signature</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">neg_p</span> <span class="o">==</span> <span class="n">neg_h</span> <span class="ow">and</span> <span class="n">neg_p</span> <span class="o">!=</span> <span class="n">EMPTY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">negation_signature</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">neg_p</span> <span class="o">==</span> <span class="n">EMPTY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">compose_contradiction_signature</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">neg_p</span> <span class="o">!=</span> <span class="n">EMPTY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Hashabledict</span><span class="p">(</span><span class="n">compose_neg_contradiction_signature</span><span class="p">)</span>
+
+<span class="n">negation_phrase</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">neg</span><span class="p">,</span> <span class="n">qp</span><span class="p">:</span> <span class="n">neg</span><span class="p">[</span><span class="n">qp</span><span class="p">]</span>
+
+<span class="n">noun_merge</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">n_p</span><span class="p">,</span> <span class="n">n_h</span><span class="p">:</span> <span class="n">EQV</span> <span class="k">if</span> <span class="n">n_p</span> <span class="o">==</span> <span class="n">n_h</span> <span class="k">else</span> <span class="n">IND</span>
+<span class="n">verb_merge</span> <span class="o">=</span> <span class="n">noun_merge</span>
+
+<span class="n">quantifier_merge</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">q_p</span><span class="p">,</span> <span class="n">q_h</span><span class="p">:</span> <span class="n">determiner_signatures</span><span class="p">[</span><span class="n">q_p</span><span class="p">][</span><span class="n">q_h</span><span class="p">]</span>
+
+<span class="n">quantifier_phrase</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">q</span><span class="p">,</span> <span class="n">np</span><span class="p">,</span> <span class="n">vp</span><span class="p">:</span> <span class="n">q</span><span class="p">[</span><span class="n">np</span><span class="p">][</span><span class="n">vp</span><span class="p">]</span>
+
+<span class="n">functions</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;N_P_O&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;tree&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;N_H_O&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;tree&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;N_O&quot;</span><span class="p">:</span> <span class="n">noun_merge</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_P_O&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;happy&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_H_O&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;happy&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_O&quot;</span><span class="p">:</span> <span class="n">adj_merge</span><span class="p">,</span>
+    <span class="s2">&quot;NP_O&quot;</span><span class="p">:</span> <span class="n">noun_phrase</span><span class="p">,</span>
+    <span class="s2">&quot;Q_P_O&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Q_H_O&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Q_O&quot;</span><span class="p">:</span> <span class="n">quantifier_merge</span><span class="p">,</span>
+    <span class="s2">&quot;V_P&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;climbed&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;V_H&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;climbed&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">verb_merge</span><span class="p">,</span>
+    <span class="s2">&quot;Adv_P&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;energetically&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adv_H&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;energetically&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adv&quot;</span><span class="p">:</span> <span class="n">adv_merge</span><span class="p">,</span>
+    <span class="s2">&quot;VP&quot;</span><span class="p">:</span> <span class="n">verb_phrase</span><span class="p">,</span>
+    <span class="s2">&quot;QP_O&quot;</span><span class="p">:</span> <span class="n">quantifier_phrase</span><span class="p">,</span>
+    <span class="s2">&quot;Neg_P&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;not&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Neg_H&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;not&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Neg&quot;</span><span class="p">:</span> <span class="n">negation_merge</span><span class="p">,</span>
+    <span class="s2">&quot;NegP&quot;</span><span class="p">:</span> <span class="n">negation_phrase</span><span class="p">,</span>
+    <span class="s2">&quot;N_P_S&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;dog&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;N_H_S&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;dog&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;N_S&quot;</span><span class="p">:</span> <span class="n">noun_merge</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_P_S&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;cute&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_H_S&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;cute&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_S&quot;</span><span class="p">:</span> <span class="n">adj_merge</span><span class="p">,</span>
+    <span class="s2">&quot;NP_S&quot;</span><span class="p">:</span> <span class="n">noun_phrase</span><span class="p">,</span>
+    <span class="s2">&quot;Q_P_S&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Q_H_S&quot;</span><span class="p">:</span> <span class="k">lambda</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Q_S&quot;</span><span class="p">:</span> <span class="n">quantifier_merge</span><span class="p">,</span>
+    <span class="s2">&quot;QP_S&quot;</span><span class="p">:</span> <span class="n">quantifier_phrase</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># coordinates to display the MQNLI tree</span>
+<span class="n">pos</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;N_P_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">),</span>
+    <span class="s2">&quot;N_H_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">),</span>
+    <span class="s2">&quot;N_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">31</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">),</span>
+    <span class="s2">&quot;Adj_P_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="s2">&quot;Adj_H_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">26</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
+    <span class="s2">&quot;Adj_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">27</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="s2">&quot;NP_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">29</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;Q_P_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">24</span><span class="p">,</span> <span class="mf">1.3</span><span class="p">),</span>
+    <span class="s2">&quot;Q_H_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">22</span><span class="p">,</span> <span class="mf">1.7</span><span class="p">),</span>
+    <span class="s2">&quot;Q_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">23</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">),</span>
+    <span class="s2">&quot;V_P&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">21</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="s2">&quot;V_H&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">19</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
+    <span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="s2">&quot;Adv_P&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.3</span><span class="p">),</span>
+    <span class="s2">&quot;Adv_H&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">),</span>
+    <span class="s2">&quot;Adv&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">),</span>
+    <span class="s2">&quot;VP&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;QP_O&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+    <span class="s2">&quot;Neg_P&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">),</span>
+    <span class="s2">&quot;Neg_H&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+    <span class="s2">&quot;Neg&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span>
+    <span class="s2">&quot;NegP&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+    <span class="s2">&quot;N_P_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span>
+    <span class="s2">&quot;N_H_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mf">2.8</span><span class="p">),</span>
+    <span class="s2">&quot;N_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mf">3.3</span><span class="p">),</span>
+    <span class="s2">&quot;Adj_P_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">),</span>
+    <span class="s2">&quot;Adj_H_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="s2">&quot;Adj_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">),</span>
+    <span class="s2">&quot;NP_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mf">4.3</span><span class="p">),</span>
+    <span class="s2">&quot;Q_P_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.2</span><span class="p">),</span>
+    <span class="s2">&quot;Q_H_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span>
+    <span class="s2">&quot;Q_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+    <span class="s2">&quot;QP_S&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">variables</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">parents</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>  <span class="c1"># pretty sure this preserves order?</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mqlni_model</span> <span class="o">=</span> <span class="n">CausalModel</span><span class="p">(</span><span class="n">variables</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">parents</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We begin by visualizing the high-level structure of the MQNLI task. All leaf nodes consist of text entries (e.g., Adj_P_O might be “fun”, N_H_O might be “child”), with one copy corresponding to the premise sentence (P) and the other corresponding to the hypothesis (H). All other nodes take on relation values (e.g., entailment, reverse entailment, no relation), or mappings between relations (e.g., (entailment, entailment) –&gt; no entailment).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">print_structure</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/d1c6b90794f1c5c0fcfa0931492742db78295ea701f49c67bfe4f5609cebd96a.png" src="../../_images/d1c6b90794f1c5c0fcfa0931492742db78295ea701f49c67bfe4f5609cebd96a.png" />
+</div>
+</div>
+<section id="example-mqnli-input">
+<h3>Example MQNLI Input<a class="headerlink" href="#example-mqnli-input" title="Link to this heading">#</a></h3>
+<p>Here we walk through an example MQNLI input, presented below.</p>
+<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="err">Premise</span><span class="p">:</span><span class="w"> </span><span class="err">Every</span><span class="w"> </span><span class="err">dog</span><span class="w"> </span><span class="err">climbed</span><span class="w"> </span><span class="err">some</span><span class="w"> </span><span class="kc">tree</span><span class="err">.</span>
+<span class="err">Hypo</span><span class="kc">t</span><span class="err">hesis</span><span class="p">:</span><span class="w"> </span><span class="err">Some</span><span class="w"> </span><span class="err">dog</span><span class="w"> </span><span class="err">did</span><span class="w"> </span><span class="kc">n</span><span class="err">o</span><span class="kc">t</span><span class="w"> </span><span class="err">climb</span><span class="w"> </span><span class="err">every</span><span class="w"> </span><span class="kc">tree</span><span class="err">.</span>
+</pre></div>
+</div>
+<p>The output for this sentence pair is <code class="docutils literal notranslate"><span class="pre">contradiction</span></code>, because the premise entails that there cannot be a dog who climbed no tree.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="c1"># premise</span>
+    <span class="s2">&quot;Q_P_S&quot;</span><span class="p">:</span> <span class="s2">&quot;every&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_P_S&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;N_P_S&quot;</span><span class="p">:</span> <span class="s2">&quot;dog&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Neg_P&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;Adv_P&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;V_P&quot;</span><span class="p">:</span> <span class="s2">&quot;climbed&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Q_P_O&quot;</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_P_O&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;N_P_O&quot;</span><span class="p">:</span> <span class="s2">&quot;tree&quot;</span><span class="p">,</span>
+    <span class="c1"># hypothesis</span>
+    <span class="s2">&quot;Q_H_S&quot;</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_H_S&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;N_H_S&quot;</span><span class="p">:</span> <span class="s2">&quot;dog&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Neg_H&quot;</span><span class="p">:</span> <span class="s2">&quot;not&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adv_H&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;V_H&quot;</span><span class="p">:</span> <span class="s2">&quot;climbed&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Q_H_O&quot;</span><span class="p">:</span> <span class="s2">&quot;some&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Adj_H_O&quot;</span><span class="p">:</span> <span class="n">EMPTY</span><span class="p">,</span>
+    <span class="s2">&quot;N_H_O&quot;</span><span class="p">:</span> <span class="s2">&quot;tree&quot;</span>
+<span class="p">}</span>
+
+<span class="n">setting</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">print_premise</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_P_S&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_P_S&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_P_S&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Neg_P&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adv_P&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;V_P&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_P_O&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_P_O&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_P_O&quot;</span><span class="p">]</span>
+    <span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">print_hypothesis</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_H_S&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_H_S&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_H_S&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Neg_H&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adv_H&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;V_H&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_H_O&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_H_O&quot;</span><span class="p">],</span>
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_H_O&quot;</span><span class="p">]</span>
+    <span class="p">)</span>
+
+<span class="n">print_premise</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span>
+<span class="n">print_hypothesis</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">setting</span><span class="p">[</span><span class="s2">&quot;QP_S&quot;</span><span class="p">])</span> <span class="c1"># the output is in the root node, QP_S</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>every  dog   climbed some  tree
+some  dog not  climbed some  tree
+contradiction
+</pre></div>
+</div>
+</div>
+</div>
+<p>We display the structure of the MQNLI example below. Note that the intermediate relation values compose with each other to produce the final output. For instance, since <code class="docutils literal notranslate"><span class="pre">N_O</span></code> and <code class="docutils literal notranslate"><span class="pre">Adj_O</span></code> both take on the value <code class="docutils literal notranslate"><span class="pre">equivalence</span></code>, then <code class="docutils literal notranslate"><span class="pre">NP_O</span></code> is also an equivalence relation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">display</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="nb">dict</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">}</span>
+<span class="n">mqlni_model</span><span class="o">.</span><span class="n">print_setting</span><span class="p">(</span><span class="n">setting</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="n">display</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/47c576c6625384e231a4e0bd6c52e5721c4b4d45373b16378e3fa9f1508d6107.png" src="../../_images/47c576c6625384e231a4e0bd6c52e5721c4b4d45373b16378e3fa9f1508d6107.png" />
+</div>
+</div>
+</section>
+<section id="mqnli-with-an-intervention">
+<h3>MQNLI with an Intervention<a class="headerlink" href="#mqnli-with-an-intervention" title="Link to this heading">#</a></h3>
+<p>Below is a run of the MQNLI logic model where we intervene on the object quantifier (<code class="docutils literal notranslate"><span class="pre">QP_O</span></code>) and swap its relation value from <code class="docutils literal notranslate"><span class="pre">equivalence</span></code> to <code class="docutils literal notranslate"><span class="pre">contradiction</span></code>. Note that this changes the final output from <code class="docutils literal notranslate"><span class="pre">contradiction</span></code> to <code class="docutils literal notranslate"><span class="pre">entailment</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervention</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">inputs</span><span class="p">,</span> <span class="s1">&#39;QP_O&#39;</span><span class="p">:</span> <span class="s1">&#39;contradiction&#39;</span><span class="p">}</span>
+<span class="n">setting</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">run_forward</span><span class="p">(</span><span class="n">intervention</span><span class="p">)</span>
+<span class="n">print_premise</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span>
+<span class="n">print_hypothesis</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">setting</span><span class="p">[</span><span class="s2">&quot;QP_S&quot;</span><span class="p">])</span>
+
+<span class="n">mqlni_model</span><span class="o">.</span><span class="n">print_setting</span><span class="p">(</span><span class="n">setting</span><span class="p">,</span> <span class="n">display</span><span class="o">=</span><span class="n">display</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>every  dog   climbed some  tree
+some  dog not  climbed some  tree
+entails
+</pre></div>
+</div>
+<img alt="../../_images/510186486f21246350da9fca871ec45a7cc4552e2185e3dccc90f2fde4ff0a8b.png" src="../../_images/510186486f21246350da9fca871ec45a7cc4552e2185e3dccc90f2fde4ff0a8b.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="training-a-model-on-mqnli">
+<h2>Training a Model on MQNLI<a class="headerlink" href="#training-a-model-on-mqnli" title="Link to this heading">#</a></h2>
+<p>In this section, we train a language model (GPT-2) on the MQNLI task. Importantly, we do not need to access the original MQNLI dataset to do this – having set up our causal model earlier in this notebook, we can generate datapoints by sampling from it directly!</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">generate_factual_dataset</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="n">print_premise</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+<span class="n">print_hypothesis</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;QP_S&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>some little child  energetically climbed some happy tree
+every  child not energetically climbed some happy tree
+alternation
+</pre></div>
+</div>
+</div>
+</div>
+<p>We want to make sure that we indeed sampled evenly from the causal structure. For now, we can just verify that every possible output value (i.e., the values of the root node <code class="docutils literal notranslate"><span class="pre">QP_S</span></code>) has enough datapoints.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Counter</span><span class="p">([</span><span class="n">n</span><span class="p">[</span><span class="s1">&#39;QP_S&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">y</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({&#39;alternation&#39;: 16,
+         &#39;contradiction&#39;: 18,
+         &#39;reverse entails&#39;: 15,
+         &#39;independence&#39;: 14,
+         &#39;entails&#39;: 16,
+         &#39;equivalence&#39;: 10,
+         &#39;cover&#39;: 11})
+</pre></div>
+</div>
+</div>
+</div>
+<p>Time to train a language model!</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">create_gpt2_lm</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">premise_to_string</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="k">return</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_P_S&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_P_S&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_P_S&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Neg_P&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adv_P&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;V_P&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_P_O&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_P_O&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_P_O&quot;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">hypothesis_to_string</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="k">return</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_H_S&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_H_S&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_H_S&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Neg_H&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adv_H&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;V_H&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Q_H_O&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;Adj_H_O&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> \
+        <span class="n">setting</span><span class="p">[</span><span class="s2">&quot;N_H_O&quot;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">preprocess_input</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Premise: </span><span class="si">{</span><span class="n">premise_to_string</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span><span class="si">}</span><span class="se">\n</span><span class="s1">Hypothesis: </span><span class="si">{</span><span class="n">hypothesis_to_string</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span><span class="si">}</span><span class="se">\n</span><span class="s1">Relation: &#39;</span>
+
+<span class="k">def</span> <span class="nf">preprocess_output</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">setting</span><span class="p">[</span><span class="s1">&#39;QP_S&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">IGNORE_INDEX</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span>
+<span class="n">MAX_LENGTH</span> <span class="o">=</span> <span class="mi">64</span>
+
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+
+<span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">examples</span> <span class="o">=</span> <span class="p">[</span><span class="n">preprocess_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">]</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">preprocess_output</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y</span><span class="p">]</span>
+
+    <span class="n">examples</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="n">examples</span><span class="p">,</span> 
+        <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;max_length&#39;</span><span class="p">,</span> 
+        <span class="n">max_length</span><span class="o">=</span><span class="n">MAX_LENGTH</span><span class="p">,</span> 
+        <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s1">&#39;pt&#39;</span>
+    <span class="p">)</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="n">labels</span><span class="p">,</span> 
+        <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;max_length&#39;</span><span class="p">,</span> 
+        <span class="n">max_length</span><span class="o">=</span><span class="n">MAX_LENGTH</span><span class="p">,</span> 
+        <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s1">&#39;pt&#39;</span>
+    <span class="p">)[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span> <span class="c1"># get first token of label</span>
+    
+    <span class="c1"># put label at the last index</span>
+    <span class="n">examples</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">examples</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">],</span> <span class="n">IGNORE_INDEX</span><span class="p">)</span>
+    <span class="n">examples</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">][:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">labels</span>
+
+    <span class="k">return</span> <span class="n">examples</span>
+
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">preprocess</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># set the wandb project where this run will be logged</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;WANDB_PROJECT&quot;</span><span class="p">]</span><span class="o">=</span><span class="n">TRAIN_DIR</span>
+
+<span class="c1"># save your trained model checkpoint to wandb</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;WANDB_LOG_MODEL&quot;</span><span class="p">]</span><span class="o">=</span><span class="s2">&quot;false&quot;</span>
+
+<span class="k">def</span> <span class="nf">accuracy_metric</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">label_ids</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="c1"># predictions = x.predictions[0].argmax(axis=-1)[:, -2]  # uncomment for gpt-neox</span>
+    <span class="n">predictions</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">predictions</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[:,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">predictions</span><span class="p">),</span>
+    <span class="p">}</span>
+
+<span class="n">train_ds</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">)</span>
+
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="n">training_args</span> <span class="o">=</span> <span class="n">TrainingArguments</span><span class="p">(</span>
+    <span class="n">output_dir</span><span class="o">=</span><span class="n">TRAIN_DIR</span><span class="p">,</span>
+    <span class="n">overwrite_output_dir</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">evaluation_strategy</span><span class="o">=</span><span class="s2">&quot;epoch&quot;</span><span class="p">,</span>
+    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span>
+    <span class="n">num_train_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">per_device_train_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">per_device_eval_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">report_to</span><span class="o">=</span><span class="s2">&quot;wandb&quot;</span><span class="p">,</span> <span class="c1"># optional, remove if you don&#39;t want to log to wandb</span>
+    <span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span>
+    <span class="n">train_dataset</span><span class="o">=</span><span class="n">train_ds</span><span class="p">,</span>
+    <span class="n">eval_dataset</span><span class="o">=</span><span class="n">train_ds</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="n">accuracy_metric</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Train the model and log it in a Weights &amp; Biases run.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">wandb</span> 
+
+<span class="n">_</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">wandb version 0.16.4 is available!  To upgrade, please run:
+ $ pip install wandb --upgrade</div><div class="output text_html">Tracking run with wandb version 0.16.3</div><div class="output text_html">Run data is saved locally in <code>c:\Users\amirz\Source\NLP\clones\pyvene\tutorials\advanced_tutorials\wandb\run-20240314_140315-cwmxs9sx</code></div><div class="output text_html">Syncing run <strong><a href='https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx' target="_blank">derby-crumble-2</a></strong> to <a href='https://wandb.ai/amirzur1212/mqnli_factual' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/></div><div class="output text_html"> View project at <a href='https://wandb.ai/amirzur1212/mqnli_factual' target="_blank">https://wandb.ai/amirzur1212/mqnli_factual</a></div><div class="output text_html"> View run at <a href='https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx' target="_blank">https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx</a></div><script type="application/vnd.jupyter.widget-view+json">{"model_id": "5b3008cdd19b4d1f80ef096f3d2c97ef", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "3fd69497aae04b8c8a7be79b77196a5d", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 4.242105484008789, &#39;eval_accuracy&#39;: 0.17, &#39;eval_runtime&#39;: 12.9558, &#39;eval_samples_per_second&#39;: 7.719, &#39;eval_steps_per_second&#39;: 1.003, &#39;epoch&#39;: 1.0}
+{&#39;train_runtime&#39;: 52.4216, &#39;train_samples_per_second&#39;: 1.908, &#39;train_steps_per_second&#39;: 0.248, &#39;train_loss&#39;: 5.367249708909255, &#39;epoch&#39;: 1.0}
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">trainer</span><span class="o">.</span><span class="n">save_model</span><span class="p">(</span><span class="n">TRAIN_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We evaluate the model on the training dataset and a newly-sampled test dataset. The model generalizes, meaning it learned the MQNLI task!</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+<span class="n">test_examples</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">generate_factual_dataset</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">test_examples</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">test_examples</span><span class="p">]</span>
+<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">preprocess</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">test_ds</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">train_ds</span><span class="p">)</span>
+<span class="n">results</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "f8c92d344b674837bc7733b012829136", "version_major": 2, "version_minor": 0}</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 4.242105484008789,
+ &#39;eval_accuracy&#39;: 0.17,
+ &#39;eval_runtime&#39;: 12.6987,
+ &#39;eval_samples_per_second&#39;: 7.875,
+ &#39;eval_steps_per_second&#39;: 1.024,
+ &#39;epoch&#39;: 1.0}
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_ds</span><span class="p">)</span>
+<span class="n">results</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ce841fd20aad4b9fa81e502ee8fe8328", "version_major": 2, "version_minor": 0}</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 4.242105484008789,
+ &#39;eval_accuracy&#39;: 0.17,
+ &#39;eval_runtime&#39;: 12.532,
+ &#39;eval_samples_per_second&#39;: 7.98,
+ &#39;eval_steps_per_second&#39;: 1.037,
+ &#39;epoch&#39;: 1.0}
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wandb</span><span class="o">.</span><span class="n">finish</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "bc294f9a84f24cbaa44ceeb6dc1e379c", "version_major": 2, "version_minor": 0}</script><div class="output text_html"><style>
+    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}
+    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }
+    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }
+    </style>
+<div class="wandb-row"><div class="wandb-col"><h3>Run history:</h3><br/><table class="wandb"><tr><td>eval/accuracy</td><td>▁▁▁</td></tr><tr><td>eval/loss</td><td>▁▁▁</td></tr><tr><td>eval/runtime</td><td>█▄▁</td></tr><tr><td>eval/samples_per_second</td><td>▁▅█</td></tr><tr><td>eval/steps_per_second</td><td>▁▅█</td></tr><tr><td>train/epoch</td><td>▁▁▁▁</td></tr><tr><td>train/global_step</td><td>▁▁▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class="wandb-col"><h3>Run summary:</h3><br/><table class="wandb"><tr><td>eval/accuracy</td><td>0.17</td></tr><tr><td>eval/loss</td><td>4.24211</td></tr><tr><td>eval/runtime</td><td>12.532</td></tr><tr><td>eval/samples_per_second</td><td>7.98</td></tr><tr><td>eval/steps_per_second</td><td>1.037</td></tr><tr><td>train/epoch</td><td>1.0</td></tr><tr><td>train/global_step</td><td>13</td></tr><tr><td>train/total_flos</td><td>3266150400000.0</td></tr><tr><td>train/train_loss</td><td>5.36725</td></tr><tr><td>train/train_runtime</td><td>52.4216</td></tr><tr><td>train/train_samples_per_second</td><td>1.908</td></tr><tr><td>train/train_steps_per_second</td><td>0.248</td></tr></table><br/></div></div></div><div class="output text_html"> View run <strong style="color:#cdcd00">derby-crumble-2</strong> at: <a href='https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx' target="_blank">https://wandb.ai/amirzur1212/mqnli_factual/runs/cwmxs9sx</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)</div><div class="output text_html">Find logs at: <code>.\wandb\run-20240314_140315-cwmxs9sx\logs</code></div></div>
+</div>
+</section>
+<section id="interpreting-our-model">
+<h2>Interpreting Our Model<a class="headerlink" href="#interpreting-our-model" title="Link to this heading">#</a></h2>
+<p>In this final section, we use distributed alignment search DAS from <a class="reference external" href="https://arxiv.org/pdf/2303.02536.pdf">Geiger*, Wu*, Potts, Icard, and Goodman (2024)</a> to find an alignment between the high-level causal structure of MQNLI and our low-level language model trained on the MQNLI dataset.</p>
+<p>In this section, we won’t search for an alignment over the entire causal structure. Instead, we will search for an alignment between the low-level model representations and the <code class="docutils literal notranslate"><span class="pre">NegP</span></code> token. Efficiently searching for a full alignment over a complex, nested causal structure is an open problem that is worth pursuing!</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">create_gpt2_lm</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">TRAIN_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<p>Here we set up our alignment. We will search for a subspace with a dimension of 128 (a fourth of our model’s hidden dimension size) in the residual stream of the 10th layer of the model (out of 12 overall transformer layers).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="n">model_type</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">),</span>
+    <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+        <span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="mi">10</span><span class="p">,</span>  <span class="c1"># layer</span>
+            <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+            <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit is now aligned with tokens</span>
+            <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="n">subspace_partition</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">]],</span> 
+            <span class="c1"># intervention_link_key=0,</span>
+        <span class="p">)</span>
+    <span class="p">],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">RotatedSpaceIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">use_fast</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="c1"># intervenable.set_device(&#39;cuda&#39;)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:Detected use_fast=True means the intervention location will be static within a batch.
+
+In case multiple location tags are passed only the first one will be considered
+</pre></div>
+</div>
+</div>
+</div>
+<p>Crucially, our interpretability experiments rely on a counterfactual dataset: for a given input, we want to consider what might happen had the value of <code class="docutils literal notranslate"><span class="pre">NegP</span></code> changed while all else remained the same. Fortunately, this is precisely what our causal model can provide us with! We can generate a counterfactual dataset by sampling inputs that only vary from each other on the <code class="docutils literal notranslate"><span class="pre">NegP</span></code> node.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sample_intervention</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s1">&#39;NegP&#39;</span> <span class="p">:</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="s1">&#39;NegP&#39;</span><span class="p">])</span>
+    <span class="p">}</span>
+
+<span class="k">def</span> <span class="nf">intervention_id</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">0</span>
+
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># specifies how many inputs we want per intervention that is sampled</span>
+
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">generate_counterfactual_dataset</span><span class="p">(</span>
+    <span class="mi">100</span><span class="p">,</span> <span class="n">intervention_id</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> 
+    <span class="n">sampler</span><span class="o">=</span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span><span class="p">,</span> <span class="n">intervention_sampler</span><span class="o">=</span><span class="n">sample_intervention</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;base_labels&#39;</span><span class="p">][</span><span class="s1">&#39;QP_S&#39;</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;labels&#39;</span><span class="p">][</span><span class="s1">&#39;QP_S&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>alternation
+independence
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check that base labels are diverse (should be guaranteed by sampling from balanced input tree)</span>
+<span class="n">Counter</span><span class="p">([</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;base_labels&#39;</span><span class="p">][</span><span class="s1">&#39;QP_S&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({&#39;alternation&#39;: 23,
+         &#39;reverse entails&#39;: 10,
+         &#39;cover&#39;: 15,
+         &#39;equivalence&#39;: 15,
+         &#39;contradiction&#39;: 9,
+         &#39;independence&#39;: 14,
+         &#39;entails&#39;: 14})
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check that counterfactuals labels are diverse (note that this could be skewed by the node&#39;s effect on the final output)</span>
+<span class="n">Counter</span><span class="p">([</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">][</span><span class="s1">&#39;QP_S&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({&#39;independence&#39;: 47,
+         &#39;reverse entails&#39;: 10,
+         &#39;entails&#39;: 9,
+         &#39;alternation&#39;: 20,
+         &#39;cover&#39;: 10,
+         &#39;contradiction&#39;: 3,
+         &#39;equivalence&#39;: 1})
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">IGNORE_INDEX</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span>
+<span class="n">MAX_LENGTH</span> <span class="o">=</span> <span class="mi">64</span>
+
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+
+<span class="k">def</span> <span class="nf">preprocess_input</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Premise: </span><span class="si">{</span><span class="n">premise_to_string</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span><span class="si">}</span><span class="se">\n</span><span class="s1">Hypothesis: </span><span class="si">{</span><span class="n">hypothesis_to_string</span><span class="p">(</span><span class="n">setting</span><span class="p">)</span><span class="si">}</span><span class="se">\n</span><span class="s1">Relation: &#39;</span>
+
+<span class="k">def</span> <span class="nf">preprocess_output</span><span class="p">(</span><span class="n">setting</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">setting</span><span class="p">[</span><span class="s1">&#39;QP_S&#39;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="n">x</span><span class="p">,</span> 
+        <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;max_length&#39;</span><span class="p">,</span> 
+        <span class="n">max_length</span><span class="o">=</span><span class="n">MAX_LENGTH</span><span class="p">,</span> 
+        <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s1">&#39;pt&#39;</span>
+    <span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">preprocess_counterfactual</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+    <span class="n">preprocessed_data</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+        <span class="n">base</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">])</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">preprocess_input</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;source_input_ids&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])]</span>
+        <span class="n">label</span> <span class="o">=</span> <span class="n">preprocess_output</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">])</span>
+        <span class="n">base_label</span> <span class="o">=</span> <span class="n">preprocess_output</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;base_labels&#39;</span><span class="p">])</span>
+
+        <span class="n">preprocessed</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenize</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;source&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenize</span><span class="p">(</span><span class="n">sources</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
+        <span class="c1"># place label at last index</span>
+        <span class="n">label</span> <span class="o">=</span> <span class="n">tokenize</span><span class="p">(</span><span class="n">label</span><span class="p">)[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">][</span><span class="s1">&#39;input_ids&#39;</span><span class="p">],</span> <span class="n">IGNORE_INDEX</span><span class="p">)</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">][:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">label</span>
+        <span class="c1"># repeat for base label</span>
+        <span class="n">base_label</span> <span class="o">=</span> <span class="n">tokenize</span><span class="p">(</span><span class="n">base_label</span><span class="p">)[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;base_label&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">][</span><span class="s1">&#39;input_ids&#39;</span><span class="p">],</span> <span class="n">IGNORE_INDEX</span><span class="p">)</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;base_label&#39;</span><span class="p">][:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">base_label</span>
+        <span class="n">preprocessed</span><span class="p">[</span><span class="s1">&#39;intervention_id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">d</span><span class="p">[</span><span class="s1">&#39;intervention_id&#39;</span><span class="p">])</span>
+        <span class="n">preprocessed_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">preprocessed</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">preprocessed_data</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_dataset</span> <span class="o">=</span> <span class="n">preprocess_counterfactual</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Set up our optimizer, loss function, and accuracy metric.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span>
+<span class="n">gradient_accumulation_steps</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">()}]</span>
+    <span class="k">break</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+    <span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span>
+        <span class="n">y_pred</span><span class="o">=</span><span class="n">eval_preds</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> 
+        <span class="n">y_true</span><span class="o">=</span><span class="n">eval_labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">accuracy</span>
+    <span class="p">}</span>
+
+
+<span class="k">def</span> <span class="nf">compute_loss</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+    <span class="c1"># Shift so that tokens &lt; n predict n</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="c1"># Flatten the tokens</span>
+    <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">shift_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">shift_logits</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)),</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">loss</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Run DAS to find an alignment between our high-level causal model and the language model trained on MQNLI.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>  <span class="c1"># train enables drop-off but no grads</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervention trainable parameters: &quot;</span><span class="p">,</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+<span class="n">train_iterator</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">epochs</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Epoch&quot;</span><span class="p">)</span>
+
+<span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">train_iterator</span><span class="p">:</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span>
+        <span class="n">DataLoader</span><span class="p">(</span>
+            <span class="n">train_dataset</span><span class="p">,</span>
+            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+            <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">),</span>
+        <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="n">leave</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">epoch_iterator</span><span class="p">:</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="p">[{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">s</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">[</span><span class="s1">&#39;source&#39;</span><span class="p">]]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="n">MAX_LENGTH</span> <span class="o">-</span> <span class="mi">2</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span> <span class="p">[[[</span><span class="n">MAX_LENGTH</span> <span class="o">-</span> <span class="mi">2</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">])},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+            <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span>
+        <span class="p">)</span>
+
+        <span class="c1"># loss and backprop</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">compute_loss</span><span class="p">(</span>
+            <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span>
+        <span class="p">)</span>
+
+        <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]})</span>
+
+        <span class="k">if</span> <span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">gradient_accumulation_steps</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">total_step</span> <span class="o">%</span> <span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+            <span class="n">intervenable</span><span class="o">.</span><span class="n">set_zero_grad</span><span class="p">()</span>
+        <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>intervention trainable parameters:  589824
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 0: 100%|██████████| 12/12 [00:59&lt;00:00,  4.93s/it, loss=6.88, acc=0]    
+
+Epoch: 1: 100%|██████████| 12/12 [00:58&lt;00:00,  4.91s/it, loss=5.86, acc=0]   
+
+Epoch: 2: 100%|██████████| 12/12 [00:59&lt;00:00,  4.98s/it, loss=6.37, acc=0]    
+
+Epoch: 100%|██████████| 3/3 [02:57&lt;00:00, 59.28s/it]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">DAS_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Directory &#39;mqnli_das&#39; already exists.
+</pre></div>
+</div>
+</div>
+</div>
+<section id="evaluate-das-alignment">
+<h3>Evaluate DAS Alignment<a class="headerlink" href="#evaluate-das-alignment" title="Link to this heading">#</a></h3>
+<p>Lastly, we evaluate the accuracy of the alignment found by DAS.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">create_gpt2_lm</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">TRAIN_DIR</span><span class="p">)</span>
+<span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DAS_DIR</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<p>First, we evaluate the model’s performance on the MQNLI factual task. As before, we expect our model to complete this task with nearly perfect accuracy.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+<span class="n">examples</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">generate_factual_dataset</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">example</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">examples</span><span class="p">]</span>
+<span class="n">test_factual_dataset</span> <span class="o">=</span> <span class="n">preprocess</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">accuracy_metric</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">label_ids</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="c1"># predictions = x.predictions[0].argmax(axis=-1)[:, -2]  # take one index before label</span>
+    <span class="n">predictions</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">predictions</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[:,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">predictions</span><span class="p">),</span>
+    <span class="p">}</span>
+
+<span class="n">test_factual_ds</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">test_factual_dataset</span><span class="p">)</span>
+
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="n">training_args</span> <span class="o">=</span> <span class="n">TrainingArguments</span><span class="p">(</span>
+    <span class="n">output_dir</span><span class="o">=</span><span class="s2">&quot;test_mqnli_trainer&quot;</span><span class="p">,</span>
+    <span class="n">overwrite_output_dir</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">evaluation_strategy</span><span class="o">=</span><span class="s2">&quot;epoch&quot;</span><span class="p">,</span>
+    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span>
+    <span class="n">num_train_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">per_device_train_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">per_device_eval_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+    <span class="n">report_to</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+    <span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">intervenable</span><span class="o">.</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="n">accuracy_metric</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">trainer</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_factual_ds</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ba33bc2aaff448c1bd8ef2e97ed4f74b", "version_major": 2, "version_minor": 0}</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;eval_loss&#39;: 4.302060127258301,
+ &#39;eval_accuracy&#39;: 0.17,
+ &#39;eval_runtime&#39;: 18.8671,
+ &#39;eval_samples_per_second&#39;: 5.3,
+ &#39;eval_steps_per_second&#39;: 0.689}
+</pre></div>
+</div>
+</div>
+</div>
+<p>Next, we evaluate the DAS alignment on a newly-sampled counterfactual dataset. If our alignment generalizes, then we should expect a high interchange intervention accuracy (IIA) on the counterfactual dataset. We compute IIA by comparing the model’s output with an interchange-intervention to the causal model’s output with that same intervention applied to its high-level variables (i.e., we verify that an intervention changes the model’s output in the direction that our causal model predicts).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">mqlni_model</span><span class="o">.</span><span class="n">generate_counterfactual_dataset</span><span class="p">(</span>
+    <span class="mi">1000</span><span class="p">,</span> <span class="n">intervention_id</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> 
+    <span class="n">sampler</span><span class="o">=</span><span class="n">mqlni_model</span><span class="o">.</span><span class="n">sample_input_tree_balanced</span><span class="p">,</span> <span class="n">intervention_sampler</span><span class="o">=</span><span class="n">sample_intervention</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">test_cf_dataset</span> <span class="o">=</span> <span class="n">preprocess_counterfactual</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+    <span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span>
+        <span class="n">y_pred</span><span class="o">=</span><span class="n">eval_preds</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> 
+        <span class="n">y_true</span><span class="o">=</span><span class="n">eval_labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">accuracy</span>
+    <span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
+<span class="c1"># intervenable.disable_intervention_gradients()</span>
+<span class="c1"># intervenable.disable_model_gradients()</span>
+<span class="c1"># intervenable.model.eval()  # train enables drop-off but no grads</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervention trainable parameters: &quot;</span><span class="p">,</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+
+<span class="n">base_labels</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="n">counterfactual_labels</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="n">base_preds</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="n">counterfactual_preds</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">DataLoader</span><span class="p">(</span>
+        <span class="n">test_cf_dataset</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+        <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">)):</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="n">sources</span> <span class="o">=</span> <span class="p">[{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">intervenable</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">s</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">[</span><span class="s1">&#39;source&#39;</span><span class="p">]]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="n">MAX_LENGTH</span> <span class="o">-</span> <span class="mi">2</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span> <span class="p">[[[</span><span class="n">MAX_LENGTH</span> <span class="o">-</span> <span class="mi">2</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">])},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">base_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># base_preds = base_outputs.logits.argmax(-1).clone().detach()</span>
+            <span class="c1"># base_labels = batch[&#39;base_label&#39;]</span>
+            <span class="n">counterfactual_preds</span> <span class="o">=</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
+            <span class="n">counterfactual_labels</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># base_preds = torch.cat((base_preds, base_outputs.logits.argmax(-1).clone().detach()))</span>
+            <span class="c1"># base_labels = torch.cat((base_labels, batch[&#39;base_label&#39;]))</span>
+            <span class="n">counterfactual_preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">counterfactual_preds</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()))</span>
+            <span class="n">counterfactual_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">counterfactual_labels</span><span class="p">,</span> <span class="n">batch</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
+    <span class="c1"># &#39;base_accuracy&#39;: compute_metrics(base_preds, base_labels)[&#39;accuracy&#39;],</span>
+    <span class="s1">&#39;counterfactual_accuracy&#39;</span><span class="p">:</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">counterfactual_preds</span><span class="p">,</span> <span class="n">counterfactual_labels</span><span class="p">)[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;counterfactual_accuracy&#39;: 0.125}
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+</section>
+
+
+                </article>
+              
+
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+              
+                <footer class="prev-next-footer d-print-none">
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+    <i class="fa-solid fa-list"></i> Contents
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-Up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-mqnli-task">The MQNLI Task</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#example-mqnli-input">Example MQNLI Input</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mqnli-with-an-intervention">MQNLI with an Intervention</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training-a-model-on-mqnli">Training a Model on MQNLI</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#interpreting-our-model">Interpreting Our Model</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluate-das-alignment">Evaluate DAS Alignment</a></li>
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new file mode 100644
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Causal Evaluation of Probes</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#setup">Setup</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-model-and-data">Load model and data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#das">DAS</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#probing">Probing</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="causal-evaluation-of-probes">
+<h1>Causal Evaluation of Probes<a class="headerlink" href="#causal-evaluation-of-probes" title="Link to this heading">#</a></h1>
+<p>This is a simple tutorial showing you to collect activations from intervention-points in a model. We’ll compare 1D DAS IIA on each layer and position for <code class="docutils literal notranslate"><span class="pre">block_output</span></code> in pythia-70M with logistic regression probing accuracy. The task we’ll look at is gender prediction, where gendered names are used in templates like “[name] walked because”, which elicits the associated gendered pronoun “he” or “she” as the next-token prediction for this model.</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/advance_tutorials/Probing_Gender.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Aryaman Arora&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;01/10/2024&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="setup">
+<h2>Setup<a class="headerlink" href="#setup" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">AutoModelForCausalLM</span><span class="p">,</span>
+    <span class="n">AutoTokenizer</span><span class="p">,</span>
+    <span class="n">get_linear_schedule_with_warmup</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+    <span class="n">geom_line</span><span class="p">,</span>
+    <span class="n">geom_point</span><span class="p">,</span>
+    <span class="n">geom_text</span><span class="p">,</span>
+    <span class="n">ggtitle</span><span class="p">,</span> <span class="n">xlab</span><span class="p">,</span> <span class="n">ylab</span><span class="p">,</span> 
+    <span class="n">ggsave</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">plotnine.scales</span> <span class="kn">import</span> <span class="n">scale_y_reverse</span><span class="p">,</span> <span class="n">scale_fill_cmap</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="load-model-and-data">
+<h2>Load model and data<a class="headerlink" href="#load-model-and-data" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cuda:0&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="n">model</span> <span class="o">=</span> <span class="s2">&quot;EleutherAI/pythia-70m&quot;</span> <span class="c1"># &quot;EleutherAI/pythia-6.9B&quot;</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+<span class="n">gpt</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span>
+    <span class="n">revision</span><span class="o">=</span><span class="s2">&quot;main&quot;</span><span class="p">,</span>
+    <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">&quot;EleutherAI/pythia-6.9b&quot;</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We have a list of 100 names for each gender, and we’ll filter for names that are one token in length. We’ll further filter for examples the model agrees with our labels for, since some of these names might be ambiguous or the model might not have the expected behaviour. This ensures that baseline IIA is 0.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Example</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">&quot;Example&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;base&quot;</span><span class="p">,</span> <span class="s2">&quot;src&quot;</span><span class="p">,</span> <span class="s2">&quot;base_label&quot;</span><span class="p">,</span> <span class="s2">&quot;src_label&quot;</span><span class="p">])</span>
+
+<span class="n">names</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;he&quot;</span><span class="p">:</span> <span class="p">[</span>
+        <span class="s2">&quot;James&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Robert&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;John&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Michael&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;David&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;William&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Richard&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Joseph&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Thomas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Christopher&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Charles&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Daniel&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Matthew&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Anthony&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mark&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Donald&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Steven&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Andrew&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Paul&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Joshua&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kenneth&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kevin&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Brian&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;George&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Timothy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ronald&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jason&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Edward&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jeffrey&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ryan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jacob&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Gary&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Nicholas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Eric&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jonathan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Stephen&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Larry&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Justin&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Scott&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Brandon&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Benjamin&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Samuel&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Gregory&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Alexander&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Patrick&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Frank&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Raymond&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jack&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Dennis&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jerry&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Tyler&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Aaron&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jose&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Adam&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Nathan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Henry&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Zachary&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Douglas&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Peter&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kyle&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Noah&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ethan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jeremy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Walter&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Christian&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Keith&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Roger&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Terry&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Austin&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Sean&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Gerald&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Carl&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Harold&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Dylan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Arthur&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Lawrence&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jordan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jesse&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Bryan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Billy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Bruce&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Gabriel&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Joe&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Logan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Alan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Juan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Albert&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Willie&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Elijah&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Wayne&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Randy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Vincent&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mason&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Roy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ralph&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Bobby&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Russell&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Bradley&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Philip&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Eugene&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="s2">&quot;she&quot;</span><span class="p">:</span> <span class="p">[</span>
+        <span class="s2">&quot;Mary&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Patricia&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jennifer&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Linda&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Elizabeth&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Barbara&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Susan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jessica&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Sarah&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Karen&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Lisa&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Nancy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Betty&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Sandra&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Margaret&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ashley&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kimberly&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Emily&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Donna&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Michelle&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Carol&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Amanda&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Melissa&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Deborah&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Stephanie&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Dorothy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Rebecca&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Sharon&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Laura&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Cynthia&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Amy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kathleen&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Angela&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Shirley&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Brenda&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Emma&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Anna&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Pamela&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Nicole&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Samantha&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Katherine&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Christine&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Helen&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Debra&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Rachel&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Carolyn&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Janet&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Maria&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Catherine&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Heather&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Diane&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Olivia&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Julie&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Joyce&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Victoria&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ruth&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Virginia&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Lauren&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kelly&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Christina&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Joan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Evelyn&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Judith&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Andrea&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Hannah&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Megan&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Cheryl&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jacqueline&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Martha&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Madison&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Teresa&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Gloria&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Sara&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Janice&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Ann&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kathryn&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Abigail&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Sophia&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Frances&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Jean&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Alice&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Judy&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Isabella&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Julia&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Grace&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Amber&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Denise&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Danielle&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Marilyn&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Beverly&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Charlotte&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Natalie&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Theresa&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Diana&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Brittany&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Doris&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Kayla&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Alexis&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Lori&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Marie&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+<span class="p">}</span>
+
+<span class="c1"># filter names that are &gt; 1 token</span>
+<span class="n">names</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">key</span><span class="p">:</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">name</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">names</span>
+<span class="p">}</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="s2">&quot;he&quot;</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="s2">&quot;she&quot;</span><span class="p">]))</span>
+
+
+<span class="k">def</span> <span class="nf">sample_example</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">):</span>
+    <span class="c1"># sample labels (not matching)</span>
+    <span class="n">base_label</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">names</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+    <span class="n">src_label</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="n">key</span> <span class="o">!=</span> <span class="n">base_label</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="c1"># sample names</span>
+    <span class="n">base_name</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="n">base_label</span><span class="p">])</span>
+    <span class="n">src_name</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="n">src_label</span><span class="p">])</span>
+
+    <span class="c1"># make pair</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&lt;|endoftext|&gt;</span><span class="si">{</span><span class="n">base_name</span><span class="si">}</span><span class="s2"> walked because&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+    <span class="n">src</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&lt;|endoftext|&gt;</span><span class="si">{</span><span class="n">src_name</span><span class="si">}</span><span class="s2"> walked because&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+    <span class="n">base_label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">base_label</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">src_label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">src_label</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">Example</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">base_label</span><span class="p">,</span> <span class="n">src_label</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>47 10
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sample_example</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Example(base={&#39;input_ids&#39;: tensor([[    0, 37376,  7428,   984]]), &#39;attention_mask&#39;: tensor([[1, 1, 1, 1]])}, src={&#39;input_ids&#39;: tensor([[    0, 44305,  7428,   984]]), &#39;attention_mask&#39;: tensor([[1, 1, 1, 1]])}, base_label=344, src_label=703)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">generate_n_doable_examples</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">):</span>
+    <span class="n">examples</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+    <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">examples</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">n</span><span class="p">:</span>
+        <span class="n">ex</span> <span class="o">=</span> <span class="n">sample_example</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">ex</span><span class="o">.</span><span class="n">base</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">ex</span><span class="o">.</span><span class="n">base</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">ex</span><span class="o">.</span><span class="n">src</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">ex</span><span class="o">.</span><span class="n">src</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">logits_base</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">ex</span><span class="o">.</span><span class="n">base</span><span class="p">)</span><span class="o">.</span><span class="n">logits</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">logits_src</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">ex</span><span class="o">.</span><span class="n">src</span><span class="p">)</span><span class="o">.</span><span class="n">logits</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">logits_base</span><span class="p">[</span><span class="n">ex</span><span class="o">.</span><span class="n">base_label</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">logits_base</span><span class="p">[</span><span class="n">ex</span><span class="o">.</span><span class="n">src_label</span><span class="p">]</span>
+            <span class="ow">and</span> <span class="n">logits_src</span><span class="p">[</span><span class="n">ex</span><span class="o">.</span><span class="n">src_label</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">logits_src</span><span class="p">[</span><span class="n">ex</span><span class="o">.</span><span class="n">base_label</span><span class="p">]</span>
+        <span class="p">):</span>
+            <span class="n">examples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
+            <span class="n">iterator</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">examples</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># make dataset</span>
+<span class="n">total_steps</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">trainset</span> <span class="o">=</span> <span class="n">generate_n_doable_examples</span><span class="p">(</span><span class="n">total_steps</span><span class="p">,</span> <span class="n">gpt</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">)</span>
+<span class="n">evalset</span> <span class="o">=</span> <span class="n">generate_n_doable_examples</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">gpt</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+100%|██████████| 100/100 [00:05&lt;00:00, 17.51it/s]
+100%|██████████| 50/50 [00:02&lt;00:00, 19.85it/s]
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="das">
+<h2>DAS<a class="headerlink" href="#das" title="Link to this heading">#</a></h2>
+<p>This is the usual 1D DAS setup, training on batch size of 1.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">intervention_config</span><span class="p">(</span><span class="n">intervention_site</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">num_dims</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span>
+            <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">intervention_site</span><span class="p">,</span>
+            <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">LowRankRotatedSpaceIntervention</span><span class="p">,</span>
+            <span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">:</span> <span class="n">num_dims</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">])</span>
+    <span class="k">return</span> <span class="n">config</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># loss function</span>
+<span class="n">loss_fct</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">calculate_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate cross entropy between logits and a single target label (can be batched)&quot;&quot;&quot;</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">shift_labels</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">loss</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># intervention settings</span>
+<span class="n">stats</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">num_layers</span> <span class="o">=</span> <span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span>
+
+<span class="c1"># loop over layers and positions</span>
+<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">position</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;layer: </span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">, position: </span><span class="si">{</span><span class="n">position</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># set up intervenable model</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">intervention_config</span><span class="p">(</span><span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">intervenable</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+        <span class="n">intervenable</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">intervenable</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+
+        <span class="c1"># set up optimizer</span>
+        <span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">optimizer_params</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">()})</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">pass</span>
+        <span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+        <span class="n">scheduler</span> <span class="o">=</span> <span class="n">get_linear_schedule_with_warmup</span><span class="p">(</span>
+            <span class="n">optimizer</span><span class="p">,</span>
+            <span class="n">num_warmup_steps</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="n">total_steps</span><span class="p">),</span>
+            <span class="n">num_training_steps</span><span class="o">=</span><span class="n">total_steps</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># training loop</span>
+        <span class="n">iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">trainset</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">iterator</span><span class="p">:</span>
+            <span class="c1"># forward pass</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                <span class="n">example</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
+                <span class="p">[</span><span class="n">example</span><span class="o">.</span><span class="n">src</span><span class="p">],</span>
+                <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">},</span>
+            <span class="p">)</span>
+
+            <span class="c1"># loss</span>
+            <span class="n">logits</span> <span class="o">=</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">calculate_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">example</span><span class="o">.</span><span class="n">src_label</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
+            <span class="n">iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">})</span>
+
+            <span class="c1"># backward</span>
+            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+            <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+
+        <span class="c1"># eval</span>
+        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+            <span class="n">iia</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">evalset</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">iterator</span><span class="p">:</span>
+                <span class="c1"># forward</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">example</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
+                    <span class="p">[</span><span class="n">example</span><span class="o">.</span><span class="n">src</span><span class="p">],</span>
+                    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">},</span>
+                <span class="p">)</span>
+
+                <span class="c1"># calculate iia</span>
+                <span class="n">logits</span> <span class="o">=</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">logits</span><span class="p">[</span><span class="n">example</span><span class="o">.</span><span class="n">src_label</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">logits</span><span class="p">[</span><span class="n">example</span><span class="o">.</span><span class="n">base_label</span><span class="p">]:</span>
+                    <span class="n">iia</span> <span class="o">+=</span> <span class="mi">1</span>
+
+            <span class="c1"># stats</span>
+            <span class="n">iia</span> <span class="o">=</span> <span class="n">iia</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">evalset</span><span class="p">)</span>
+            <span class="n">stats</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;position&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">,</span> <span class="s2">&quot;iia&quot;</span><span class="p">:</span> <span class="n">iia</span><span class="p">})</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;iia: </span><span class="si">{</span><span class="n">iia</span><span class="si">:</span><span class="s2">.3%</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">stats</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_gender_das.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>layer: 0, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.66it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.26it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 0, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.97it/s, loss=1.268]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.35it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 98.000%
+layer: 0, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.93it/s, loss=4.130]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.28it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 0, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.83it/s, loss=4.276]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.28it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 1, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.94it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.20it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 1, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.93it/s, loss=1.231]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.20it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 100.000%
+layer: 1, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.94it/s, loss=4.422]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.19it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 1, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:09&lt;00:00, 10.92it/s, loss=4.308]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.13it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 2, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.26it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.30it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 2, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.27it/s, loss=1.305]
+100%|██████████| 50/50 [00:03&lt;00:00, 12.93it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 98.000%
+layer: 2, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.28it/s, loss=1.938]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.27it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 16.000%
+layer: 2, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.24it/s, loss=2.408]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.02it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 10.000%
+layer: 3, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.40it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.34it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 3, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.37it/s, loss=3.477]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.22it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 52.000%
+layer: 3, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.34it/s, loss=2.225]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.22it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 6.000%
+layer: 3, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.42it/s, loss=1.945]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.32it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 10.000%
+layer: 4, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.59it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.33it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 4, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.62it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.38it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 4, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.67it/s, loss=4.034]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.40it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 4, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.76it/s, loss=1.062]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.25it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 98.000%
+layer: 5, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.70it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.22it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 5, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.39it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 12.83it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 5, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.81it/s, loss=4.355]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.43it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 0.000%
+layer: 5, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:08&lt;00:00, 11.44it/s, loss=1.113]
+100%|██████████| 50/50 [00:03&lt;00:00, 13.33it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>iia: 98.000%
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And this is the plot of IIA. In layers 2 and 3 it seems the gender is represented across positions 1-3, and entirely in position 3 in later layers.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_gender_das.csv&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;position&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;IIA&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;iia&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="n">custom_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;EOS&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;name&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;walked&quot;</span><span class="p">,</span> <span class="s2">&quot;because&quot;</span><span class="p">]</span>
+<span class="n">breaks</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">))</span>    
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s2">&quot;IIA&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Purples&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">xlab</span><span class="p">(</span><span class="s2">&quot;layers&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">(</span>
+        <span class="n">limits</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span> 
+        <span class="n">breaks</span><span class="o">=</span><span class="n">breaks</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">custom_labels</span><span class="p">)</span> 
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="n">ylab</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span> 
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_y</span>  <span class="o">=</span> <span class="n">element_text</span><span class="p">(</span><span class="n">angle</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">hjust</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Trained Intervention (DAS)&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_gender_das.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:718: PlotnineWarning: Saving 5 x 3 in image.
+/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Filename: ./tutorial_data/pyvene_gender_das.pdf
+</pre></div>
+</div>
+<img alt="../../_images/92ccd360492d0b55dce2cf6f63b9b627f2268b51f418c7e1593d44e74eeb3cd1.svg" src="../../_images/92ccd360492d0b55dce2cf6f63b9b627f2268b51f418c7e1593d44e74eeb3cd1.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="probing">
+<h2>Probing<a class="headerlink" href="#probing" title="Link to this heading">#</a></h2>
+<p>We’ll define a dummy intervention <code class="docutils literal notranslate"><span class="pre">CollectActivation</span></code> to collect activations and train a simple probe.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">probing_config</span><span class="p">(</span><span class="n">intervention_site</span><span class="p">,</span> <span class="n">layer</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate intervention config.&quot;&quot;&quot;</span>
+
+    <span class="c1"># init</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([{</span>
+        <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span>
+        <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">intervention_site</span><span class="p">,</span>
+        <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">,</span>
+    <span class="p">}])</span>
+    <span class="k">return</span> <span class="n">config</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>This is the training loop.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># intervention settings</span>
+<span class="n">stats</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">num_layers</span> <span class="o">=</span> <span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span>
+
+<span class="c1"># 344 = &quot; he&quot;, 703 = &quot; she&quot;</span>
+<span class="n">label_mapping</span> <span class="o">=</span> <span class="p">{</span><span class="mi">344</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">703</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
+
+<span class="c1"># loop over layers and positions</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">position</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;layer: </span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">, position: </span><span class="si">{</span><span class="n">position</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="c1"># set up intervenable model</span>
+            <span class="n">config</span> <span class="o">=</span> <span class="n">probing_config</span><span class="p">(</span><span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="n">layer</span><span class="p">)</span>
+            <span class="n">intervenable</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+            <span class="n">intervenable</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">intervenable</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+
+            <span class="c1"># training loop</span>
+            <span class="n">activations</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+            <span class="n">iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">trainset</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">iterator</span><span class="p">:</span>
+                <span class="c1"># forward pass</span>
+                <span class="n">base_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">example</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
+                    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">base_activations</span> <span class="o">=</span> <span class="n">base_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+
+                <span class="n">src_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">example</span><span class="o">.</span><span class="n">src</span><span class="p">,</span>
+                    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">src_activations</span> <span class="o">=</span> <span class="n">src_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                
+                <span class="c1"># collect activation</span>
+                <span class="n">activations</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                    <span class="p">[</span><span class="n">base_activations</span><span class="o">.</span><span class="n">detach</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">src_activations</span><span class="o">.</span><span class="n">detach</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()]</span>
+                <span class="p">)</span>
+                <span class="n">labels</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">example</span><span class="o">.</span><span class="n">base_label</span><span class="p">,</span> <span class="n">example</span><span class="o">.</span><span class="n">src_label</span><span class="p">])</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">label_mapping</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">]</span>
+            
+            <span class="c1"># train logistic regression</span>
+            <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
+                <span class="n">activations</span><span class="p">,</span> <span class="n">labels</span>
+            <span class="p">)</span>
+
+            <span class="c1"># eval</span>
+            <span class="n">activations</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+            <span class="n">iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">evalset</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">iterator</span><span class="p">:</span>
+                <span class="c1"># forward pass</span>
+                <span class="n">base_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">example</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
+                    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">base_activations</span> <span class="o">=</span> <span class="n">base_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+
+                <span class="n">src_outputs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+                    <span class="n">example</span><span class="o">.</span><span class="n">src</span><span class="p">,</span>
+                    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">},</span>
+                <span class="p">)</span>
+                <span class="n">src_activations</span> <span class="o">=</span> <span class="n">src_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                
+                <span class="c1"># collect activation</span>
+                <span class="n">activations</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                    <span class="p">[</span><span class="n">base_activations</span><span class="o">.</span><span class="n">detach</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">src_activations</span><span class="o">.</span><span class="n">detach</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()]</span>
+                <span class="p">)</span>
+                <span class="n">labels</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">example</span><span class="o">.</span><span class="n">base_label</span><span class="p">,</span> <span class="n">example</span><span class="o">.</span><span class="n">src_label</span><span class="p">])</span>
+            <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">label_mapping</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">]</span>
+
+            <span class="c1"># stats</span>
+            <span class="n">acc</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">activations</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+            <span class="n">f1</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">activations</span><span class="p">))</span>
+            <span class="n">stats</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;position&quot;</span><span class="p">:</span> <span class="n">position</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">acc</span><span class="p">,</span> <span class="s2">&quot;f1&quot;</span><span class="p">:</span> <span class="n">f1</span><span class="p">})</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;acc: </span><span class="si">{</span><span class="n">acc</span><span class="si">:</span><span class="s2">.3%</span><span class="si">}</span><span class="s2">, f1: </span><span class="si">{</span><span class="n">f1</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">stats</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_gender_probe.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>layer: 0, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:11&lt;00:00,  8.98it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.12it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 50.000%, f1: 0.000
+layer: 0, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.12it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.12it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 0, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.12it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.24it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 99.000%, f1: 0.990
+layer: 0, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.23it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.34it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 88.000%, f1: 0.875
+layer: 1, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:11&lt;00:00,  9.09it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  8.95it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 50.000%, f1: 0.000
+layer: 1, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.13it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.28it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 1, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.45it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.35it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 97.000%, f1: 0.971
+layer: 1, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.50it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.53it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 96.000%, f1: 0.962
+layer: 2, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.33it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.19it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 50.000%, f1: 0.000
+layer: 2, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.59it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.85it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 2, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.86it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.76it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 2, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.92it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.84it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 3, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.82it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.85it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 50.000%, f1: 0.000
+layer: 3, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.84it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.80it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 3, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.89it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.91it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 3, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.92it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.72it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 4, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.81it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.82it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 50.000%, f1: 0.000
+layer: 4, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.32it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  8.99it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 4, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.29it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.74it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 4, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:11&lt;00:00,  8.88it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  8.88it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 5, position: 0
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.12it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.51it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 50.000%, f1: 0.000
+layer: 5, position: 1
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.46it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.20it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 5, position: 2
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.31it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  8.73it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+layer: 5, position: 3
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:10&lt;00:00,  9.19it/s]
+100%|██████████| 50/50 [00:05&lt;00:00,  9.43it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>acc: 100.000%, f1: 1.000
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>And the probe accuracy plot is below. Note the extremely high accuracy at all positions at and after the name! Early layers at later positions are better but it saturates much before the IIA for DAS. This shows how unreliable probes are for tracing causal effect.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_gender_probe.csv&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;position&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;ACC&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+<span class="n">custom_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;EOS&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;name&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;walked&quot;</span><span class="p">,</span> <span class="s2">&quot;because&quot;</span><span class="p">]</span>
+<span class="n">breaks</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+
+<span class="n">plot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;ACC&quot;</span><span class="p">))</span>    
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">scale_fill_cmap</span><span class="p">(</span><span class="s2">&quot;Reds&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">xlab</span><span class="p">(</span><span class="s2">&quot;layers&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_y_reverse</span><span class="p">(</span>
+        <span class="n">limits</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span> 
+        <span class="n">breaks</span><span class="o">=</span><span class="n">breaks</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">custom_labels</span><span class="p">)</span> 
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="n">ylab</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span> 
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_y</span>  <span class="o">=</span> <span class="n">element_text</span><span class="p">(</span><span class="n">angle</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">hjust</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">ggtitle</span><span class="p">(</span><span class="s2">&quot;Trained Linear Probe&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ggsave</span><span class="p">(</span>
+    <span class="n">plot</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;./tutorial_data/pyvene_gender_probe.pdf&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:718: PlotnineWarning: Saving 5 x 3 in image.
+/Users/aryamanarora/opt/miniconda3/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Filename: ./tutorial_data/pyvene_gender_probe.pdf
+</pre></div>
+</div>
+<img alt="../../_images/11b69c1c4043f08e46917c93191ab92b6e7e8b1dbce16bb0fb5cb6d890fbc70d.svg" src="../../_images/11b69c1c4043f08e46917c93191ab92b6e7e8b1dbce16bb0fb5cb6d890fbc70d.svg" /><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
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+
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+  <div class="sidebar-secondary-item">
+  <div class="page-toc tocsection onthispage">
+    <i class="fa-solid fa-list"></i> Contents
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#setup">Setup</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-model-and-data">Load model and data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#das">DAS</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#probing">Probing</a></li>
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diff --git a/tutorials/advanced_tutorials/Voting_Mechanism.html b/tutorials/advanced_tutorials/Voting_Mechanism.html
new file mode 100644
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@@ -0,0 +1,1800 @@
+
+<!DOCTYPE html>
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+<html lang="en" data-content_root="../../" >
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+</div>
+              
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+
+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>An Exploration on Voting Mechanisms</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#display-some-training-task-instances">Display some training task instances</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-alignment-datasets">creating alignment datasets</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#where-are-we-aligning-in-llama">where are we aligning in LLaMA?</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-back-saved-das-params-and-intervene-on-single-or-multiple-sites">Loading back saved DAS params and intervene on single or multiple sites</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#now-what-if-we-set-two-aligning-intervention-sites-with-contradicting-values">Now, what if we set two aligning intervention sites with contradicting values?</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-results-above-show-if-two-intervention-sites-take-on-contradicting-values-it-will-trust-the-earlier-site">The results above show if two intervention sites take on contradicting values, it will trust the earlier site.</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#minor-make-sure-other-control-models-cannot-find-good-alignments">Minor: make sure other control models cannot find good alignments</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="an-exploration-on-voting-mechanisms">
+<h1>An Exploration on Voting Mechanisms<a class="headerlink" href="#an-exploration-on-voting-mechanisms" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu and Kevin Du and Tiago Pimentel&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;02/13/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>Through our offline discussions, we found a strange behavior in the Boundless DAS paper: there are multiple “good” aligning representations for a single causal variable. If these aligning representations take on different values, how will the NN behave? Will it trust more on certain location?</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span><span class="p">,</span> <span class="n">trange</span>
+<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">get_linear_schedule_with_warmup</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">CrossEntropyLoss</span>
+<span class="kn">from</span> <span class="nn">tutorial_price_tagging_utils</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="n">ggplot</span><span class="p">,</span> <span class="n">aes</span><span class="p">,</span> <span class="n">geom_boxplot</span><span class="p">,</span> <span class="n">geom_point</span><span class="p">,</span> <span class="n">geom_errorbar</span><span class="p">,</span> <span class="n">labs</span><span class="p">,</span> <span class="n">theme</span>
+
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_llama</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">set_seed</span><span class="p">,</span> <span class="n">count_parameters</span>
+
+<span class="c1"># You can define your custom compute_metrics function.</span>
+<span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+    <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">correct_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">eval_pred</span><span class="p">,</span> <span class="n">eval_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+        <span class="n">actual_test_labels</span> <span class="o">=</span> <span class="n">eval_label</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_test_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">eval_pred</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">correct_labels</span> <span class="o">=</span> <span class="n">actual_test_labels</span> <span class="o">==</span> <span class="n">pred_test_labels</span>
+        <span class="n">total_count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">correct_labels</span><span class="p">)</span>
+        <span class="n">correct_count</span> <span class="o">+=</span> <span class="n">correct_labels</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">accuracy</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">correct_count</span> <span class="o">/</span> <span class="n">total_count</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">accuracy</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">llama</span> <span class="o">=</span> <span class="n">create_llama</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">llama</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">llama</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>You are using the default legacy behaviour of the &lt;class &#39;transformers.models.llama.tokenization_llama.LlamaTokenizer&#39;&gt;. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "5e43d3570ec941adb21f8203bbf61b4f", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/sailhome/wuzhengx/.local/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">raw_prealign</span> <span class="o">=</span> <span class="n">factual_sampler</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="n">game</span><span class="o">=</span><span class="s2">&quot;pricing_tag&quot;</span><span class="p">)</span>
+<span class="n">prealign_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_prealign</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_prealign</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span>
+<span class="p">)</span>
+<span class="n">prealign_dataset</span><span class="o">.</span><span class="n">set_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">,</span> <span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+<span class="n">prealign_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">prealign_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="display-some-training-task-instances">
+<h3>Display some training task instances<a class="headerlink" href="#display-some-training-task-instances" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">raw_prealign</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;&lt;s&gt;Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nPlease say yes only if it costs between 1.60 and 4.11 dollars, otherwise no.\n\n### Input:\n0.32 dollars\n\n### Response:\n&#39;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="creating-alignment-datasets">
+<h3>creating alignment datasets<a class="headerlink" href="#creating-alignment-datasets" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">set_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="c1">###################</span>
+<span class="c1"># data loaders</span>
+<span class="c1">###################</span>
+<span class="n">raw_data</span> <span class="o">=</span> <span class="n">bound_alignment_sampler</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span> <span class="p">[</span><span class="n">lower_bound_alignment_example_sampler</span><span class="p">]</span>
+<span class="p">)</span>
+
+<span class="n">raw_train</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">raw_eval</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">raw_test</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+<span class="p">)</span>
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">train_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">train_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">eval_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">test_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="where-are-we-aligning-in-llama">
+<h3>where are we aligning in LLaMA?<a class="headerlink" href="#where-are-we-aligning-in-llama" title="Link to this heading">#</a></h3>
+<p>you need to run this multiple times, and save different interventions into your disk.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">15</span>
+<span class="n">token_position</span> <span class="o">=</span> <span class="mi">78</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>set-up training</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+        <span class="n">pv</span><span class="o">.</span><span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;block_output&quot;</span>
+        <span class="p">)],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_llama</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">pv_config</span><span class="p">,</span> <span class="n">llama</span><span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">gradient_accumulation_steps</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">t_total</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span><span class="p">)</span>
+<span class="n">temperature_start</span> <span class="o">=</span> <span class="mf">50.0</span>
+<span class="n">temperature_end</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">temperature_schedule</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span>
+        <span class="n">temperature_start</span><span class="p">,</span> <span class="n">temperature_end</span><span class="p">,</span> <span class="n">t_total</span>
+    <span class="p">)</span>
+    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+
+<span class="n">warm_up_steps</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">t_total</span>
+<span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">()}]</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">,</span> <span class="s2">&quot;lr&quot;</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}]</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+<span class="n">scheduler</span> <span class="o">=</span> <span class="n">get_linear_schedule_with_warmup</span><span class="p">(</span>
+    <span class="n">optimizer</span><span class="p">,</span> <span class="n">num_warmup_steps</span><span class="o">=</span><span class="n">warm_up_steps</span><span class="p">,</span>
+    <span class="n">num_training_steps</span><span class="o">=</span><span class="n">t_total</span>
+<span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">calculate_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="c1"># Flatten the tokens</span>
+    <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">shift_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">)</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># Enable model parallelism</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">shift_logits</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">shift_logits</span><span class="p">,</span> <span class="n">shift_labels</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">boundary_loss</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">v</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">+=</span> <span class="n">boundary_loss</span>
+
+    <span class="k">return</span> <span class="n">loss</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pv_llama</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>  <span class="c1"># train enables drop-off but no grads</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;llama trainable parameters: &quot;</span><span class="p">,</span> <span class="n">count_parameters</span><span class="p">(</span><span class="n">pv_llama</span><span class="o">.</span><span class="n">model</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervention trainable parameters: &quot;</span><span class="p">,</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+<span class="n">train_iterator</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">epochs</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Epoch&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">train_iterator</span><span class="p">:</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span>
+        <span class="n">train_dataloader</span><span class="p">,</span> 
+        <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">token_position</span><span class="p">},</span>
+        <span class="p">)</span>
+        <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">],</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="p">)</span>
+
+        <span class="c1"># loss and backprop</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">calculate_loss</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+        <span class="n">loss_str</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="n">loss_str</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]})</span>
+
+        <span class="k">if</span> <span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">gradient_accumulation_steps</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">total_step</span> <span class="o">%</span> <span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">total_step</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+                <span class="n">pv_llama</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span>
+                    <span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+        <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>llama trainable parameters:  0
+intervention trainable parameters:  16777218
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [13:24&lt;00:00,  1.61s/it, loss=0.12, acc=0.75]
+Epoch: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [13:24&lt;00:00, 804.32s/it]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># evaluation on the test set</span>
+<span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">test_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">75</span><span class="p">}</span>
+        <span class="p">)</span>
+        <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+<span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">eval_metrics</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:45&lt;00:00,  1.38it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;accuracy&#39;: 0.64}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
+    <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span>
+        <span class="sa">f</span><span class="s2">&quot;layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">_comp_block_output_unit_pos_nunit_1#0&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> 
+    <span class="sa">f</span><span class="s2">&quot;./tutorial_data/layer.</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s2">.pos.</span><span class="si">{</span><span class="n">token_position</span><span class="si">}</span><span class="s2">.bin&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="loading-back-saved-das-params-and-intervene-on-single-or-multiple-sites">
+<h3>Loading back saved DAS params and intervene on single or multiple sites<a class="headerlink" href="#loading-back-saved-das-params-and-intervene-on-single-or-multiple-sites" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">15</span>
+<span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+        <span class="n">pv</span><span class="o">.</span><span class="n">RepresentationConfig</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;block_output&quot;</span><span class="p">),</span> 
+        <span class="n">pv</span><span class="o">.</span><span class="n">RepresentationConfig</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;block_output&quot;</span><span class="p">)],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_llama</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">pv_config</span><span class="p">,</span> <span class="n">llama</span><span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s1">_comp_block_output_unit_pos_nunit_1#0&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./tutorial_data/layer.15.pos.75.bin&#39;</span><span class="p">))</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s1">_comp_block_output_unit_pos_nunit_1#1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./tutorial_data/layer.15.pos.80.bin&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;All keys matched successfully&gt;
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">loc</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">78</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="p">[</span><span class="mi">75</span><span class="p">,</span> <span class="mi">80</span><span class="p">]]:</span>
+    <span class="k">if</span> <span class="n">loc</span> <span class="o">==</span> <span class="mi">78</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[control] intervening location: &quot;</span><span class="p">,</span> <span class="n">loc</span><span class="p">)</span>
+        <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s1">_comp_block_output_unit_pos_nunit_1#0&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./tutorial_data/layer.15.pos.78.bin&#39;</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervening location: &quot;</span><span class="p">,</span> <span class="n">loc</span><span class="p">)</span>
+        <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;layer_</span><span class="si">{</span><span class="n">layer</span><span class="si">}</span><span class="s1">_comp_block_output_unit_pos_nunit_1#0&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./tutorial_data/layer.15.pos.75.bin&#39;</span><span class="p">))</span>
+    <span class="c1"># evaluation on the test set</span>
+    <span class="n">collected_probs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">test_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                    <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+            <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">loc</span> <span class="o">==</span> <span class="mi">75</span> <span class="ow">or</span> <span class="n">loc</span> <span class="o">==</span> <span class="mi">78</span><span class="p">:</span>
+                <span class="n">input_sources</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]},</span> <span class="kc">None</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">loc</span> <span class="o">==</span> <span class="mi">80</span><span class="p">:</span>
+                <span class="n">input_sources</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">input_sources</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}]</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="n">input_sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">loc</span><span class="p">}</span>
+            <span class="p">)</span>
+            <span class="n">correct_label</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">][:,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">incorrect_label</span> <span class="o">=</span> \
+                <span class="p">((</span><span class="n">correct_label</span> <span class="o">==</span> <span class="mi">8241</span><span class="p">)</span><span class="o">*</span><span class="mi">3782</span> <span class="o">+</span> \
+                <span class="p">(</span><span class="n">correct_label</span> <span class="o">==</span> <span class="mi">3782</span><span class="p">)</span><span class="o">*</span><span class="mi">8241</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
+            <span class="n">norm_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span>
+                <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">correct_prob</span> <span class="o">=</span> <span class="n">norm_prob</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">b_s</span><span class="p">),</span><span class="n">correct_label</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+            <span class="n">incorrect_prob</span> <span class="o">=</span> <span class="n">norm_prob</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">b_s</span><span class="p">),</span><span class="n">incorrect_label</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+            <span class="n">diff_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">correct_prob</span> <span class="o">-</span> <span class="n">incorrect_prob</span><span class="p">)</span>
+            <span class="n">collected_probs</span> <span class="o">+=</span> <span class="p">[[</span><span class="n">correct_prob</span><span class="p">,</span> <span class="n">incorrect_prob</span><span class="p">,</span> <span class="n">diff_prob</span><span class="p">]]</span>
+            <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+            <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+    <span class="n">correct_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">incorrect_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">diff_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+
+    <span class="n">list1</span> <span class="o">=</span> <span class="n">correct_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">list2</span> <span class="o">=</span> <span class="n">incorrect_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">list3</span> <span class="o">=</span> <span class="n">diff_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">acc</span> <span class="o">=</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Group&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Correct&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list1</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;Incorrect&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list2</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;Abs(Diff)&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list2</span><span class="p">),</span>
+                       <span class="s1">&#39;Value&#39;</span><span class="p">:</span> <span class="n">list1</span> <span class="o">+</span> <span class="n">list2</span> <span class="o">+</span> <span class="n">list3</span><span class="p">})</span>
+    <span class="n">means</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;ymin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span>
+    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;ymax&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span>
+    <span class="n">plot</span> <span class="o">=</span> <span class="p">(</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">))</span> <span class="o">+</span>
+            <span class="n">geom_boxplot</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s1">&#39;lightgray&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">geom_point</span><span class="p">(</span><span class="n">means</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">geom_errorbar</span><span class="p">(</span><span class="n">means</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="n">ymin</span><span class="o">=</span><span class="s1">&#39;ymin&#39;</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="s1">&#39;ymax&#39;</span><span class="p">),</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">labs</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;w/ Acc=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Prob (Softmax)&#39;</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[control ]intervening location:  78
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:41&lt;00:00,  1.51it/s]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/b04df15f6054aa2d405e6f54b1f705fc4e62f2b1926db1bad447b999c423067d.png"><img alt="../../_images/b04df15f6054aa2d405e6f54b1f705fc4e62f2b1926db1bad447b999c423067d.png" src="../../_images/b04df15f6054aa2d405e6f54b1f705fc4e62f2b1926db1bad447b999c423067d.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>intervening location:  75
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:42&lt;00:00,  1.50it/s]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/d19254c4a23787a1f0dc374e5de7a37da09df6a7f303c883cfdbbad69058fd67.png"><img alt="../../_images/d19254c4a23787a1f0dc374e5de7a37da09df6a7f303c883cfdbbad69058fd67.png" src="../../_images/d19254c4a23787a1f0dc374e5de7a37da09df6a7f303c883cfdbbad69058fd67.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>intervening location:  80
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:43&lt;00:00,  1.46it/s]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/fff05c53d19020c5adff305ca1e37d58a251b3a75ef0e441df5ce4e744f8798c.png"><img alt="../../_images/fff05c53d19020c5adff305ca1e37d58a251b3a75ef0e441df5ce4e744f8798c.png" src="../../_images/fff05c53d19020c5adff305ca1e37d58a251b3a75ef0e441df5ce4e744f8798c.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>intervening location:  [75, 80]
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:09&lt;00:00,  1.10s/it]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/aa26036b558e554cced1d75c0b54fe9e61c5aa0de02162b902840e4f7d4cff53.png"><img alt="../../_images/aa26036b558e554cced1d75c0b54fe9e61c5aa0de02162b902840e4f7d4cff53.png" src="../../_images/aa26036b558e554cced1d75c0b54fe9e61c5aa0de02162b902840e4f7d4cff53.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="now-what-if-we-set-two-aligning-intervention-sites-with-contradicting-values">
+<h3>Now, what if we set two aligning intervention sites with contradicting values?<a class="headerlink" href="#now-what-if-we-set-two-aligning-intervention-sites-with-contradicting-values" title="Link to this heading">#</a></h3>
+<p>We need to write new data generator since we know want two source examples and these two examples have to result in contrasting behaviors.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">lower_bound_alignment_example_sampler_multisource_non_contradict</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="n">amount</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lower_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bound_width</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+    <span class="p">(</span>
+        <span class="n">base_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">base_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">_</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">pricing_tag_game_config_sampler</span><span class="p">(</span><span class="n">amount</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">bound_width</span><span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">source_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">source_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">_</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">pricing_tag_game_config_sampler</span><span class="p">(</span><span class="n">amount</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">bound_width</span><span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span>
+        <span class="n">source_upper_bound_sample_extra</span><span class="p">,</span>
+        <span class="n">_</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">pricing_tag_game_config_sampler</span><span class="p">(</span><span class="n">amount</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">bound_width</span><span class="p">)</span>
+    <span class="n">ctf_label_str</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="s2">&quot;Yes&quot;</span><span class="p">,</span> <span class="s2">&quot;No&quot;</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">ctf_label_str</span> <span class="o">==</span> <span class="s2">&quot;Yes&quot;</span><span class="p">:</span>
+        <span class="n">ctf_label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="s2">&quot;Yes&quot;</span><span class="p">)</span>
+        <span class="n">base_source_regions</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+            <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+            <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+        <span class="p">]</span>
+    <span class="k">elif</span> <span class="n">ctf_label_str</span> <span class="o">==</span> <span class="s2">&quot;No&quot;</span><span class="p">:</span>
+        <span class="n">ctf_label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="s2">&quot;No&quot;</span><span class="p">)</span>
+        <span class="n">base_source_regions</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
+    <span class="n">base_source_region</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">base_source_regions</span><span class="p">)</span>
+    <span class="n">base_region</span> <span class="o">=</span> <span class="n">base_source_region</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">source_region</span> <span class="o">=</span> <span class="n">base_source_region</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">base_amount_sample</span> <span class="o">=</span> <span class="n">sample_with_region</span><span class="p">(</span>
+        <span class="n">base_region</span><span class="p">,</span> <span class="n">base_lower_bound_sample</span><span class="p">,</span> <span class="n">base_upper_bound_sample</span>
+    <span class="p">)</span>
+    <span class="n">source_amount_sample</span> <span class="o">=</span> <span class="n">sample_with_region</span><span class="p">(</span>
+        <span class="n">source_region</span><span class="p">,</span> <span class="n">source_lower_bound_sample</span><span class="p">,</span> <span class="n">source_upper_bound_sample</span>
+    <span class="p">)</span>
+    <span class="n">source_amount_sample_extra</span> <span class="o">=</span> <span class="n">sample_with_region</span><span class="p">(</span>
+        <span class="n">source_region</span><span class="p">,</span> <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span> <span class="n">source_upper_bound_sample_extra</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">base_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">base_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">source_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">source_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span> <span class="c1"># extra bound</span>
+        <span class="n">source_upper_bound_sample_extra</span><span class="p">,</span> <span class="c1"># extra bound</span>
+        <span class="n">base_amount_sample</span><span class="p">,</span>
+        <span class="n">source_amount_sample</span><span class="p">,</span>
+        <span class="n">source_amount_sample_extra</span><span class="p">,</span>      <span class="c1"># extra amount</span>
+        <span class="n">ctf_label</span><span class="p">,</span>
+        <span class="n">ctf_label_str</span><span class="p">,</span>
+        <span class="n">ctf_label</span><span class="p">,</span>     <span class="c1"># extra label</span>
+        <span class="n">ctf_label_str</span><span class="p">,</span> <span class="c1"># extra label</span>
+    <span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">lower_bound_alignment_example_sampler_multisource_contradict</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="n">amount</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lower_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bound_width</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">):</span>
+    <span class="p">(</span>
+        <span class="n">base_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">base_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">_</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">pricing_tag_game_config_sampler</span><span class="p">(</span><span class="n">amount</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">bound_width</span><span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">source_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">source_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">_</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">pricing_tag_game_config_sampler</span><span class="p">(</span><span class="n">amount</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">bound_width</span><span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span>
+        <span class="n">source_upper_bound_sample_extra</span><span class="p">,</span>
+        <span class="n">_</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">pricing_tag_game_config_sampler</span><span class="p">(</span><span class="n">amount</span><span class="p">,</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">bound_width</span><span class="p">)</span>
+    <span class="n">ctf_label_str</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="s2">&quot;Yes&quot;</span><span class="p">,</span> <span class="s2">&quot;No&quot;</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">ctf_label_str</span> <span class="o">==</span> <span class="s2">&quot;Yes&quot;</span><span class="p">:</span>
+        <span class="n">ctf_label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="s2">&quot;Yes&quot;</span><span class="p">)</span>
+        <span class="n">base_source_regions</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+            <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
+            <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+        <span class="p">]</span>
+        
+    <span class="k">elif</span> <span class="n">ctf_label_str</span> <span class="o">==</span> <span class="s2">&quot;No&quot;</span><span class="p">:</span>
+        <span class="n">ctf_label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="s2">&quot;No&quot;</span><span class="p">)</span>
+        <span class="n">base_source_regions</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+        
+    <span class="n">base_source_region</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">base_source_regions</span><span class="p">)</span>
+    <span class="n">base_region</span> <span class="o">=</span> <span class="n">base_source_region</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">source_region</span> <span class="o">=</span> <span class="n">base_source_region</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">ctf_label_str</span> <span class="o">==</span> <span class="s2">&quot;Yes&quot;</span><span class="p">:</span>
+        <span class="n">source_extra_region</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># flip the left label</span>
+        <span class="n">ctf_label_extra</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="s2">&quot;No&quot;</span><span class="p">)</span>
+        <span class="n">ctf_label_str_extra</span> <span class="o">=</span> <span class="s2">&quot;No&quot;</span>
+    <span class="k">elif</span> <span class="n">ctf_label_str</span> <span class="o">==</span> <span class="s2">&quot;No&quot;</span><span class="p">:</span>
+        <span class="n">source_extra_region</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> <span class="c1"># flip the left label</span>
+        <span class="n">ctf_label_extra</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="s2">&quot;Yes&quot;</span><span class="p">)</span>
+        <span class="n">ctf_label_str_extra</span> <span class="o">=</span> <span class="s2">&quot;Yes&quot;</span>
+
+    <span class="n">base_amount_sample</span> <span class="o">=</span> <span class="n">sample_with_region</span><span class="p">(</span>
+        <span class="n">base_region</span><span class="p">,</span> <span class="n">base_lower_bound_sample</span><span class="p">,</span> <span class="n">base_upper_bound_sample</span>
+    <span class="p">)</span>
+    <span class="n">source_amount_sample</span> <span class="o">=</span> <span class="n">sample_with_region</span><span class="p">(</span>
+        <span class="n">source_region</span><span class="p">,</span> <span class="n">source_lower_bound_sample</span><span class="p">,</span> <span class="n">source_upper_bound_sample</span>
+    <span class="p">)</span>
+    <span class="n">source_amount_sample_extra</span> <span class="o">=</span> <span class="n">sample_with_region</span><span class="p">(</span>
+        <span class="n">source_region</span><span class="p">,</span> <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span> <span class="n">source_upper_bound_sample_extra</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">base_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">base_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">source_lower_bound_sample</span><span class="p">,</span>
+        <span class="n">source_upper_bound_sample</span><span class="p">,</span>
+        <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span> <span class="c1"># extra bound</span>
+        <span class="n">source_upper_bound_sample_extra</span><span class="p">,</span> <span class="c1"># extra bound</span>
+        <span class="n">base_amount_sample</span><span class="p">,</span>
+        <span class="n">source_amount_sample</span><span class="p">,</span>
+        <span class="n">source_amount_sample_extra</span><span class="p">,</span>      <span class="c1"># extra amount</span>
+        <span class="n">ctf_label</span><span class="p">,</span>
+        <span class="n">ctf_label_str</span><span class="p">,</span>
+        <span class="n">ctf_label_extra</span><span class="p">,</span>     <span class="c1"># extra label</span>
+        <span class="n">ctf_label_str_extra</span><span class="p">,</span> <span class="c1"># extra label</span>
+    <span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">bound_alignment_sampler_multisource</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">max_n_training_examples</span><span class="p">,</span>
+    <span class="n">bound_functors</span><span class="p">,</span>
+    <span class="n">amount</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">lower_bound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">bound_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="n">all_base_input_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">all_source_input_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">all_ctf_output_ids</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># this one does not have input ids, etc..</span>
+    <span class="n">all_intervention_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    
+    <span class="n">all_second_source_input_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">all_second_ctf_output_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    
+    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_n_training_examples</span><span class="p">):</span>
+        <span class="n">bound_functor</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">bound_functors</span><span class="p">)</span>
+        <span class="p">(</span>
+            <span class="n">base_lower_bound_sample</span><span class="p">,</span>
+            <span class="n">base_upper_bound_sample</span><span class="p">,</span>
+            <span class="n">source_lower_bound_sample</span><span class="p">,</span>
+            <span class="n">source_upper_bound_sample</span><span class="p">,</span>
+            <span class="n">source_lower_bound_sample_extra</span><span class="p">,</span> <span class="c1"># extra bound</span>
+            <span class="n">source_upper_bound_sample_extra</span><span class="p">,</span> <span class="c1"># extra bound</span>
+            <span class="n">base_amount_sample</span><span class="p">,</span>
+            <span class="n">source_amount_sample</span><span class="p">,</span>
+            <span class="n">source_amount_sample_extra</span><span class="p">,</span>      <span class="c1"># extra amount</span>
+            <span class="n">ctf_label</span><span class="p">,</span>
+            <span class="n">ctf_label_str</span><span class="p">,</span>
+            <span class="n">ctf_label_extra</span><span class="p">,</span>     <span class="c1"># extra label</span>
+            <span class="n">ctf_label_str_extra</span><span class="p">,</span> <span class="c1"># extra label</span>
+        <span class="p">)</span> <span class="o">=</span> <span class="n">bound_functor</span><span class="p">(</span>
+            <span class="n">tokenizer</span><span class="p">,</span>
+            <span class="n">amount</span><span class="p">,</span>
+            <span class="n">lower_bound</span><span class="p">,</span>
+            <span class="n">bound_width</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">base_amount_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2"> dollars&quot;</span> <span class="o">%</span> <span class="n">base_amount_sample</span>
+        <span class="n">source_amount_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2"> dollars&quot;</span> <span class="o">%</span> <span class="n">source_amount_sample</span>
+        <span class="n">source_amount_str_extra</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2"> dollars&quot;</span> <span class="o">%</span> <span class="n">source_amount_sample_extra</span>
+        <span class="n">base_lower_bound_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">base_lower_bound_sample</span>
+        <span class="n">base_upper_bound_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">base_upper_bound_sample</span>
+        <span class="n">source_lower_bound_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">source_lower_bound_sample</span>
+        <span class="n">source_upper_bound_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">source_upper_bound_sample</span>
+        <span class="n">source_lower_bound_str_extra</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">source_lower_bound_sample_extra</span>
+        <span class="n">source_upper_bound_str_extra</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">source_upper_bound_sample_extra</span>
+        <span class="c1"># print(f&quot;base: [{base_lower_bound_str}, {base_upper_bound_str}], {base_amount_str}&quot;)</span>
+        <span class="c1"># print(f&quot;source: [{source_lower_bound_str}, {source_upper_bound_str}], {source_amount_str}&quot;)</span>
+        <span class="c1"># print(f&quot;ctf label: {ctf_label_str}&quot;)</span>
+
+        <span class="n">base_instruction</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Please say yes only if it costs between </span><span class="si">{</span><span class="n">base_lower_bound_str</span><span class="si">}</span><span class="s2"> and </span><span class="si">{</span><span class="n">base_upper_bound_str</span><span class="si">}</span><span class="s2"> dollars, otherwise no.&quot;</span>
+        <span class="n">source_instruction</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Please say yes only if it costs between </span><span class="si">{</span><span class="n">source_lower_bound_str</span><span class="si">}</span><span class="s2"> and </span><span class="si">{</span><span class="n">source_upper_bound_str</span><span class="si">}</span><span class="s2"> dollars, otherwise no.&quot;</span>
+        <span class="n">source_instruction_extra</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Please say yes only if it costs between </span><span class="si">{</span><span class="n">source_lower_bound_str_extra</span><span class="si">}</span><span class="s2"> and </span><span class="si">{</span><span class="n">source_upper_bound_str_extra</span><span class="si">}</span><span class="s2"> dollars, otherwise no.&quot;</span>
+        
+        <span class="n">base_alpaca_prompt</span> <span class="o">=</span> <span class="n">alpaca_prompt_template</span> <span class="o">%</span> <span class="p">(</span>
+            <span class="n">base_instruction</span><span class="p">,</span>
+            <span class="n">base_amount_str</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">source_alpaca_prompt</span> <span class="o">=</span> <span class="n">alpaca_prompt_template</span> <span class="o">%</span> <span class="p">(</span>
+            <span class="n">source_instruction</span><span class="p">,</span>
+            <span class="n">source_amount_str</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">source_alpaca_prompt_extra</span> <span class="o">=</span> <span class="n">alpaca_prompt_template</span> <span class="o">%</span> <span class="p">(</span>
+            <span class="n">source_instruction_extra</span><span class="p">,</span>
+            <span class="n">source_amount_str_extra</span><span class="p">,</span>
+        <span class="p">)</span>
+        
+        <span class="n">base_input_ids</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">base_alpaca_prompt</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">source_input_ids</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+            <span class="n">source_alpaca_prompt</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">source_input_ids_extra</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+            <span class="n">source_alpaca_prompt_extra</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        
+        <span class="n">base_input_ids</span> <span class="o">=</span> <span class="n">base_input_ids</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="n">source_input_ids</span> <span class="o">=</span> <span class="n">source_input_ids</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="n">source_input_ids_extra</span> <span class="o">=</span> <span class="n">source_input_ids_extra</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        
+        <span class="n">ctf_output_ids</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base_input_ids</span><span class="p">))</span> <span class="o">*</span> <span class="o">-</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="n">ctf_output_ids</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">ctf_label</span>
+        <span class="n">ctf_output_ids_extra</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base_input_ids</span><span class="p">))</span> <span class="o">*</span> <span class="o">-</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="n">ctf_output_ids_extra</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">ctf_label_extra</span>
+        <span class="n">intervention_id</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">bound_functor</span> <span class="o">==</span> <span class="n">bound_functors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="n">all_base_input_ids</span> <span class="o">+=</span> <span class="p">[</span><span class="n">base_input_ids</span><span class="p">]</span>
+        <span class="n">all_source_input_ids</span> <span class="o">+=</span> <span class="p">[</span><span class="n">source_input_ids</span><span class="p">]</span>
+        <span class="n">all_second_source_input_ids</span> <span class="o">+=</span> <span class="p">[</span><span class="n">source_input_ids_extra</span><span class="p">]</span>
+
+        <span class="n">all_ctf_output_ids</span> <span class="o">+=</span> <span class="p">[</span><span class="n">ctf_output_ids</span><span class="p">]</span>
+        <span class="n">all_second_ctf_output_ids</span> <span class="o">+=</span> <span class="p">[</span><span class="n">ctf_output_ids_extra</span><span class="p">]</span>
+        <span class="n">all_intervention_ids</span> <span class="o">+=</span> <span class="p">[</span><span class="n">intervention_id</span><span class="p">]</span>
+
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">base_input_ids</span><span class="p">)</span> <span class="o">==</span> <span class="mi">82</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">source_input_ids</span><span class="p">)</span> <span class="o">==</span> <span class="mi">82</span>
+
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">all_base_input_ids</span><span class="p">,</span>
+        <span class="n">all_source_input_ids</span><span class="p">,</span>
+        <span class="n">all_second_source_input_ids</span><span class="p">,</span>
+        <span class="n">all_ctf_output_ids</span><span class="p">,</span>
+        <span class="n">all_second_ctf_output_ids</span><span class="p">,</span>
+        <span class="n">all_intervention_ids</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">multisource_non_contradict_test_data</span> <span class="o">=</span> <span class="n">bound_alignment_sampler_multisource</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="p">[</span><span class="n">lower_bound_alignment_example_sampler_multisource_non_contradict</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">multisource_contradict_test_data</span> <span class="o">=</span> <span class="n">bound_alignment_sampler_multisource</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="p">[</span><span class="n">lower_bound_alignment_example_sampler_multisource_contradict</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">multisource_non_contradict_test_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">multisource_non_contradict_test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">multisource_non_contradict_test_data</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;second_source_input_ids&quot;</span><span class="p">:</span> <span class="n">multisource_non_contradict_test_data</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">multisource_non_contradict_test_data</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+        <span class="s2">&quot;second_labels&quot;</span><span class="p">:</span> <span class="n">multisource_non_contradict_test_data</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">multisource_non_contradict_test_data</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">multisource_non_contradict_test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">multisource_non_contradict_test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+
+<span class="n">multisource_contradict_test_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">multisource_contradict_test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">multisource_contradict_test_data</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;second_source_input_ids&quot;</span><span class="p">:</span> <span class="n">multisource_contradict_test_data</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">multisource_contradict_test_data</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+        <span class="s2">&quot;second_labels&quot;</span><span class="p">:</span> <span class="n">multisource_contradict_test_data</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">multisource_contradict_test_data</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">multisource_contradict_test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">multisource_contradict_test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[control] swapping two sources with the same counterfactual behavior&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">testing_label</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]:</span>
+    <span class="n">collected_probs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">multisource_non_contradict_test_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                    <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+            <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">input_sources</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;second_source_input_ids&quot;</span><span class="p">]}</span>
+            <span class="p">]</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="n">input_sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">75</span><span class="p">,</span> <span class="mi">80</span><span class="p">]}</span>
+            <span class="p">)</span>
+            <span class="n">correct_label</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="n">testing_label</span><span class="p">][:,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">incorrect_label</span> <span class="o">=</span> \
+                <span class="p">((</span><span class="n">correct_label</span> <span class="o">==</span> <span class="mi">8241</span><span class="p">)</span><span class="o">*</span><span class="mi">3782</span> <span class="o">+</span> \
+                <span class="p">(</span><span class="n">correct_label</span> <span class="o">==</span> <span class="mi">3782</span><span class="p">)</span><span class="o">*</span><span class="mi">8241</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
+            <span class="n">norm_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span>
+                <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">correct_prob</span> <span class="o">=</span> <span class="n">norm_prob</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">b_s</span><span class="p">),</span><span class="n">correct_label</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+            <span class="n">incorrect_prob</span> <span class="o">=</span> <span class="n">norm_prob</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">b_s</span><span class="p">),</span><span class="n">incorrect_label</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+            <span class="n">diff_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">correct_prob</span> <span class="o">-</span> <span class="n">incorrect_prob</span><span class="p">)</span>
+            <span class="n">collected_probs</span> <span class="o">+=</span> <span class="p">[[</span><span class="n">correct_prob</span><span class="p">,</span> <span class="n">incorrect_prob</span><span class="p">,</span> <span class="n">diff_prob</span><span class="p">]]</span>
+            <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="n">testing_label</span><span class="p">]]</span>
+            <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+    <span class="n">correct_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">incorrect_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">diff_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+
+    <span class="n">list1</span> <span class="o">=</span> <span class="n">correct_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">list2</span> <span class="o">=</span> <span class="n">incorrect_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">list3</span> <span class="o">=</span> <span class="n">diff_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">acc</span> <span class="o">=</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Group&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Correct&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list1</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;Incorrect&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list2</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;Abs(Diff)&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list2</span><span class="p">),</span>
+                       <span class="s1">&#39;Value&#39;</span><span class="p">:</span> <span class="n">list1</span> <span class="o">+</span> <span class="n">list2</span> <span class="o">+</span> <span class="n">list3</span><span class="p">})</span>
+    <span class="n">means</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;ymin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span>
+    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;ymax&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span>
+    <span class="n">plot</span> <span class="o">=</span> <span class="p">(</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">))</span> <span class="o">+</span>
+            <span class="n">geom_boxplot</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s1">&#39;lightgray&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">geom_point</span><span class="p">(</span><span class="n">means</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">geom_errorbar</span><span class="p">(</span><span class="n">means</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="n">ymin</span><span class="o">=</span><span class="s1">&#39;ymin&#39;</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="s1">&#39;ymax&#39;</span><span class="p">),</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">labs</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;w/ Acc=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">&#39;Token Type&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Prob (Softmax)&#39;</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+    
+<span class="k">for</span> <span class="n">testing_label</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">,</span> <span class="s2">&quot;second_labels&quot;</span><span class="p">]:</span>
+    <span class="k">if</span> <span class="n">testing_label</span> <span class="o">==</span> <span class="s2">&quot;labels&quot;</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;swapping contradicting sources, and evaluate with matching location 75&quot;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">testing_label</span> <span class="o">==</span> <span class="s2">&quot;second_labels&quot;</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;swapping contradicting sources, and evaluate with matching location 80&quot;</span><span class="p">)</span>
+    <span class="n">collected_probs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">multisource_contradict_test_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                    <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+            <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">input_sources</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]},</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;second_source_input_ids&quot;</span><span class="p">]}</span>
+            <span class="p">]</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+                <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+                <span class="n">input_sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">75</span><span class="p">,</span> <span class="mi">80</span><span class="p">]}</span>
+            <span class="p">)</span>
+            <span class="n">correct_label</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="n">testing_label</span><span class="p">][:,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">incorrect_label</span> <span class="o">=</span> \
+                <span class="p">((</span><span class="n">correct_label</span> <span class="o">==</span> <span class="mi">8241</span><span class="p">)</span><span class="o">*</span><span class="mi">3782</span> <span class="o">+</span> \
+                <span class="p">(</span><span class="n">correct_label</span> <span class="o">==</span> <span class="mi">3782</span><span class="p">)</span><span class="o">*</span><span class="mi">8241</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
+            <span class="n">norm_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span>
+                <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">correct_prob</span> <span class="o">=</span> <span class="n">norm_prob</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">b_s</span><span class="p">),</span><span class="n">correct_label</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+            <span class="n">incorrect_prob</span> <span class="o">=</span> <span class="n">norm_prob</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">b_s</span><span class="p">),</span><span class="n">incorrect_label</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+            <span class="n">diff_prob</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">correct_prob</span> <span class="o">-</span> <span class="n">incorrect_prob</span><span class="p">)</span>
+            <span class="n">collected_probs</span> <span class="o">+=</span> <span class="p">[[</span><span class="n">correct_prob</span><span class="p">,</span> <span class="n">incorrect_prob</span><span class="p">,</span> <span class="n">diff_prob</span><span class="p">]]</span>
+            <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="n">testing_label</span><span class="p">]]</span>
+            <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+    <span class="n">correct_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">incorrect_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">diff_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">p</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">collected_probs</span><span class="p">])</span>
+    <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+
+    <span class="n">list1</span> <span class="o">=</span> <span class="n">correct_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">list2</span> <span class="o">=</span> <span class="n">incorrect_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">list3</span> <span class="o">=</span> <span class="n">diff_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">acc</span> <span class="o">=</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Group&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Correct&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list1</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;Incorrect&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list2</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;Abs(Diff)&#39;</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">list2</span><span class="p">),</span>
+                       <span class="s1">&#39;Value&#39;</span><span class="p">:</span> <span class="n">list1</span> <span class="o">+</span> <span class="n">list2</span> <span class="o">+</span> <span class="n">list3</span><span class="p">})</span>
+    <span class="n">means</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;ymin&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span>
+    <span class="n">means</span><span class="p">[</span><span class="s1">&#39;ymax&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()[</span><span class="s1">&#39;Value&#39;</span><span class="p">]</span>
+    <span class="n">plot</span> <span class="o">=</span> <span class="p">(</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">))</span> <span class="o">+</span>
+            <span class="n">geom_boxplot</span><span class="p">(</span><span class="n">fill</span><span class="o">=</span><span class="s1">&#39;lightgray&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">geom_point</span><span class="p">(</span><span class="n">means</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">geom_errorbar</span><span class="p">(</span><span class="n">means</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="n">ymin</span><span class="o">=</span><span class="s1">&#39;ymin&#39;</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="s1">&#39;ymax&#39;</span><span class="p">),</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">labs</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;w/ Acc=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">&#39;Token Type&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Prob (Softmax)&#39;</span><span class="p">)</span> <span class="o">+</span>
+            <span class="n">theme</span><span class="p">(</span><span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[control] swapping two sources with the same counterfactual behavior
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:04&lt;00:00,  1.02s/it]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/0e209b2ac94cdb3f1034e70d72b1cff845e2e4388e3a2e349589946aef7a5cb2.png"><img alt="../../_images/0e209b2ac94cdb3f1034e70d72b1cff845e2e4388e3a2e349589946aef7a5cb2.png" src="../../_images/0e209b2ac94cdb3f1034e70d72b1cff845e2e4388e3a2e349589946aef7a5cb2.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swapping contradicting sources, and evaluate with matching location 75
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:07&lt;00:00,  1.07s/it]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/d2dbc088eee403547907ea46c47465ecb7994cc4917922d81c0cf4aa7a6da6ae.png"><img alt="../../_images/d2dbc088eee403547907ea46c47465ecb7994cc4917922d81c0cf4aa7a6da6ae.png" src="../../_images/d2dbc088eee403547907ea46c47465ecb7994cc4917922d81c0cf4aa7a6da6ae.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>swapping contradicting sources, and evaluate with matching location 80
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [01:09&lt;00:00,  1.10s/it]
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../../_images/878262b6ac82af03bd80446907410dbd0e9d9dac1c52bdfc80ce23006a7026b2.png"><img alt="../../_images/878262b6ac82af03bd80446907410dbd0e9d9dac1c52bdfc80ce23006a7026b2.png" src="../../_images/878262b6ac82af03bd80446907410dbd0e9d9dac1c52bdfc80ce23006a7026b2.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="the-results-above-show-if-two-intervention-sites-take-on-contradicting-values-it-will-trust-the-earlier-site">
+<h3>The results above show if two intervention sites take on contradicting values, it will trust the earlier site.<a class="headerlink" href="#the-results-above-show-if-two-intervention-sites-take-on-contradicting-values-it-will-trust-the-earlier-site" title="Link to this heading">#</a></h3>
+<p>It is hard to say if this would hold till more tests. However, we can see the neural network shows some voting mechanism by trusting some location over another during interventions when these locations take different values. If they have the same value, it seems to working well.</p>
+<section id="minor-make-sure-other-control-models-cannot-find-good-alignments">
+<h4>Minor: make sure other control models cannot find good alignments<a class="headerlink" href="#minor-make-sure-other-control-models-cannot-find-good-alignments" title="Link to this heading">#</a></h4>
+<p>We test this by training Boundless DAS with the best locations in the paper: layer 15 and last token (81th).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">control_sampler</span> <span class="o">=</span> <span class="n">bracket_alignment_sampler</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">set_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
+
+<span class="c1">###################</span>
+<span class="c1"># data loaders</span>
+<span class="c1">###################</span>
+<span class="n">raw_data</span> <span class="o">=</span> <span class="n">control_sampler</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">raw_train</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][:</span><span class="mi">8000</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">raw_eval</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="n">raw_test</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+    <span class="n">raw_data</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">9000</span><span class="p">:],</span>
+<span class="p">)</span>
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_train</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">train_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">train_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_eval</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">eval_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="s2">&quot;source_input_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="s2">&quot;intervention_ids&quot;</span><span class="p">:</span> <span class="n">raw_test</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>  <span class="c1"># we will not use this field</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="n">test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">test_dataset</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="mi">15</span>
+<span class="n">token_position</span> <span class="o">=</span> <span class="mi">81</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+        <span class="n">pv</span><span class="o">.</span><span class="n">RepresentationConfig</span><span class="p">(</span>
+            <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;block_output&quot;</span>
+        <span class="p">)],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">BoundlessRotatedSpaceIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_llama</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">pv_config</span><span class="p">,</span> <span class="n">llama</span><span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">gradient_accumulation_steps</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">total_step</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">t_total</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">epochs</span><span class="p">)</span>
+<span class="n">temperature_start</span> <span class="o">=</span> <span class="mf">50.0</span>
+<span class="n">temperature_end</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">temperature_schedule</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span>
+        <span class="n">temperature_start</span><span class="p">,</span> <span class="n">temperature_end</span><span class="p">,</span> <span class="n">t_total</span>
+    <span class="p">)</span>
+    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">pv_llama</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span><span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+
+<span class="n">warm_up_steps</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">t_total</span>
+<span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">rotate_layer</span><span class="o">.</span><span class="n">parameters</span><span class="p">()}]</span>
+    <span class="n">optimizer_params</span> <span class="o">+=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="p">,</span> <span class="s2">&quot;lr&quot;</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}]</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+<span class="n">scheduler</span> <span class="o">=</span> <span class="n">get_linear_schedule_with_warmup</span><span class="p">(</span>
+    <span class="n">optimizer</span><span class="p">,</span> <span class="n">num_warmup_steps</span><span class="o">=</span><span class="n">warm_up_steps</span><span class="p">,</span>
+    <span class="n">num_training_steps</span><span class="o">=</span><span class="n">t_total</span>
+<span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">calculate_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="c1"># Flatten the tokens</span>
+    <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">shift_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">)</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># Enable model parallelism</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">shift_logits</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">shift_logits</span><span class="p">,</span> <span class="n">shift_labels</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">boundary_loss</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">*</span> <span class="n">v</span><span class="o">.</span><span class="n">intervention_boundaries</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">+=</span> <span class="n">boundary_loss</span>
+
+    <span class="k">return</span> <span class="n">loss</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pv_llama</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>  <span class="c1"># train enables drop-off but no grads</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;llama trainable parameters: &quot;</span><span class="p">,</span> <span class="n">count_parameters</span><span class="p">(</span><span class="n">pv_llama</span><span class="o">.</span><span class="n">model</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;intervention trainable parameters: &quot;</span><span class="p">,</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+<span class="n">train_iterator</span> <span class="o">=</span> <span class="n">trange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">epochs</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Epoch&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">train_iterator</span><span class="p">:</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span>
+        <span class="n">train_dataloader</span><span class="p">,</span> 
+        <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">position</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">token_position</span><span class="p">},</span>
+        <span class="p">)</span>
+        <span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">],</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="p">)</span>
+
+        <span class="c1"># loss and backprop</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">calculate_loss</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">])</span>
+        <span class="n">loss_str</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">epoch_iterator</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;loss&quot;</span><span class="p">:</span> <span class="n">loss_str</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">eval_metrics</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]})</span>
+
+        <span class="k">if</span> <span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">gradient_accumulation_steps</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">total_step</span> <span class="o">%</span> <span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">total_step</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                <span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+                <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+                <span class="n">pv_llama</span><span class="o">.</span><span class="n">set_temperature</span><span class="p">(</span>
+                    <span class="n">temperature_schedule</span><span class="p">[</span><span class="n">total_step</span><span class="p">])</span>
+        <span class="n">total_step</span> <span class="o">+=</span> <span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>llama trainable parameters:  0
+intervention trainable parameters:  16777218
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [13:18&lt;00:00,  1.60s/it, loss=0.73, acc=0.75]
+Epoch: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [13:18&lt;00:00, 798.36s/it]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># evaluation on the test set</span>
+<span class="n">eval_labels</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">eval_preds</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">test_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Test&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="n">b_s</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]},</span>
+            <span class="p">[{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;source_input_ids&quot;</span><span class="p">]}],</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">75</span><span class="p">}</span>
+        <span class="p">)</span>
+        <span class="n">eval_labels</span> <span class="o">+=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]]</span>
+        <span class="n">eval_preds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">]</span>
+<span class="n">eval_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">eval_metrics</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:43&lt;00:00,  1.46it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;accuracy&#39;: 0.68}
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#display-some-training-task-instances">Display some training task instances</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-alignment-datasets">creating alignment datasets</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#where-are-we-aligning-in-llama">where are we aligning in LLaMA?</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-back-saved-das-params-and-intervene-on-single-or-multiple-sites">Loading back saved DAS params and intervene on single or multiple sites</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#now-what-if-we-set-two-aligning-intervention-sites-with-contradicting-values">Now, what if we set two aligning intervention sites with contradicting values?</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-results-above-show-if-two-intervention-sites-take-on-contradicting-values-it-will-trust-the-earlier-site">The results above show if two intervention sites take on contradicting values, it will trust the earlier site.</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#minor-make-sure-other-control-models-cannot-find-good-alignments">Minor: make sure other control models cannot find good alignments</a></li>
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Activation Addition</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall-with-our-intervenable-module-directly">Factual recall with our intervenable module directly</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#we-add-a-word-embedding-to-all-mlp-streams-at-the-last-position">We add a word embedding to all MLP streams at the last position</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-have-a-more-systematic-analysis-of-the-addition-effect-of-mlp-and-mha-streams">Let’s have a more systematic analysis of the addition effect of MLP and MHA streams</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="activation-addition">
+<h1>Activation Addition<a class="headerlink" href="#activation-addition" title="Link to this heading">#</a></h1>
+<p><a class="reference external" href="https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/basic_tutorials/Add_Activations_to_Streams.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;10/06/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>Interventions have many types: (1) activation swapping, (2) activation addition, or (3) any other kind of operations that modify the activation. In this tutorial, we show how we ca do activation addition.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[2024-01-11 00:31:07,569] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyvene.models.basic_utils</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">AdditionIntervention</span><span class="p">,</span>
+    <span class="n">SubtractionIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene.models.gpt2.modelings_intervenable_gpt2</span> <span class="kn">import</span> <span class="n">create_gpt2</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="factual-recall-with-our-intervenable-module-directly">
+<h2>Factual recall with our intervenable module directly<a class="headerlink" href="#factual-recall-with-our-intervenable-module-directly" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">activation_addition_position_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">intervention_type</span><span class="p">,</span> <span class="n">n_layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">i</span><span class="p">,</span>                 <span class="c1"># layer</span>
+                <span class="n">intervention_type</span><span class="p">,</span> <span class="c1"># component</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>             <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>                 <span class="c1"># max number of unit</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_layer</span><span class="p">)</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">AdditionIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span> <span class="o">=</span> <span class="n">create_gpt2</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">activation_addition_position_config</span><span class="p">(</span>
+    <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span>
+<span class="p">)</span>
+
+<span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;The capital of Spain is&quot;</span>
+<span class="n">source</span> <span class="o">=</span> <span class="s2">&quot;The capital of Italy is&quot;</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="mi">1</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The capital of Spain is
+_Madrid              0.10501234978437424
+_the                 0.0949699655175209
+_Barcelona           0.0702790841460228
+_a                   0.04010068252682686
+_now                 0.02824278175830841
+_in                  0.02759990654885769
+_Spain               0.022991720587015152
+_Catalonia           0.018823225051164627
+_also                0.018689140677452087
+_not                 0.01735665090382099
+
+The capital of Italy is
+_Rome                0.15734916925430298
+_the                 0.07316355407238007
+_Milan               0.046878915280103683
+_a                   0.03449810668826103
+_now                 0.03200329467654228
+_in                  0.02306535840034485
+_also                0.02274816483259201
+_home                0.01920313946902752
+_not                 0.01640527881681919
+_Italy               0.01577090471982956
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="we-add-a-word-embedding-to-all-mlp-streams-at-the-last-position">
+<h2>We add a word embedding to all MLP streams at the last position<a class="headerlink" href="#we-add-a-word-embedding-to-all-mlp-streams-at-the-last-position" title="Link to this heading">#</a></h2>
+<p>In other tutorials, we often pass in <code class="docutils literal notranslate"><span class="pre">sources</span></code> where each of the example is drawn from the training data. Another way to do patching is, instead of passing in real input example, we pass in activations. These activations can be designed off-line in some particular ways.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># we can patch mlp with the rome word embedding</span>
+<span class="n">rome_token_id</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot; Rome&quot;</span><span class="p">)[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">rome_embedding</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">gpt</span><span class="o">.</span><span class="n">wte</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">rome_token_id</span><span class="p">))</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;The capital of Spain is&quot;</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+    <span class="n">base</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">4</span>
+    <span class="p">},</span>  <span class="c1"># last position</span>
+    <span class="n">source_representations</span><span class="o">=</span><span class="n">rome_embedding</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>_Rome                0.4558262228965759
+_Madrid              0.2788238823413849
+_Barcelona           0.10828061401844025
+_Valencia            0.015630871057510376
+_Lisbon              0.008415448479354382
+_the                 0.006678737234324217
+_Santiago            0.006526812445372343
+_Naples              0.0041163465939462185
+_Florence            0.003120437264442444
+_Athens              0.0028584974352270365
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you are interested by this work, you can simply think token embeddings at each layer are moved toward the token <code class="docutils literal notranslate"><span class="pre">_Rome</span></code> via the activation addition. Obviouosly, the LM head (which is tied with the embedding matrix) is going to pick out the most similar vectors, which are <code class="docutils literal notranslate"><span class="pre">_Rome</span></code> at the end, and some other countries since they are close to <code class="docutils literal notranslate"><span class="pre">_Rome</span></code>.</p>
+<p>You can also read more about this in this paper: <a class="reference external" href="https://arxiv.org/abs/2305.16130">Language Models Implement Simple Word2Vec-style Vector Arithmetic</a>.</p>
+</section>
+<section id="let-s-have-a-more-systematic-analysis-of-the-addition-effect-of-mlp-and-mha-streams">
+<h2>Let’s have a more systematic analysis of the addition effect of MLP and MHA streams<a class="headerlink" href="#let-s-have-a-more-systematic-analysis-of-the-addition-effect-of-mlp-and-mha-streams" title="Link to this heading">#</a></h2>
+<p>We add the word embedding till the <code class="docutils literal notranslate"><span class="pre">i</span></code>-th layer of these streams</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># should finish within 1 min with a standard 12G GPU</span>
+<span class="n">tokens</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; Madrid Rome&quot;</span><span class="p">)</span>
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">till_layer_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">activation_addition_position_config</span><span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="n">till_layer_i</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">},</span>
+            <span class="n">source_representations</span><span class="o">=</span><span class="n">rome_embedding</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;mlp_o</span><span class="si">{</span><span class="n">till_layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">activation_addition_position_config</span><span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="n">till_layer_i</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span>
+                <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">pos_i</span>
+            <span class="p">},</span>
+            <span class="n">source_representations</span><span class="o">=</span><span class="n">rome_embedding</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;attn_o</span><span class="si">{</span><span class="n">till_layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">nodes</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;mlp_o</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;attn_o</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">nodes</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">g</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~token&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/dcd7d1651da3fb100aa215a26b46ee228fc7c5ba707f4c8cc91a0c346b2f2527.png"><img alt="../../_images/dcd7d1651da3fb100aa215a26b46ee228fc7c5ba707f4c8cc91a0c346b2f2527.png" src="../../_images/dcd7d1651da3fb100aa215a26b46ee228fc7c5ba707f4c8cc91a0c346b2f2527.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall-with-our-intervenable-module-directly">Factual recall with our intervenable module directly</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#we-add-a-word-embedding-to-all-mlp-streams-at-the-last-position">We add a word embedding to all MLP streams at the last position</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-have-a-more-systematic-analysis-of-the-addition-effect-of-mlp-and-mha-streams">Let’s have a more systematic analysis of the addition effect of MLP and MHA streams</a></li>
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Interchange Intervention</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall">Factual recall</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-patching-on-position-aligned-tokens">Patch Patching on Position-aligned Tokens</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="interchange-intervention">
+<h1>Interchange Intervention<a class="headerlink" href="#interchange-intervention" title="Link to this heading">#</a></h1>
+<p><a class="reference external" href="https://colab.research.google.com/github/frankaging/pyvene/blob/main/tutorials/basic_tutorials/Basic_Intervention.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Aryaman Arora and Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;10/05/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>This tutorial aims to reproduce some of the results in this <a class="reference external" href="https://github.com/aryamanarora/nano-causal-interventions/blob/main/The%20capital%20of%20France%20is.ipynb">notebook</a> for path patching or causal scrubbing. This library could potentially support other kinds of interventions that were not originally supported by previous works.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[2024-01-11 01:18:09,028] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">pyvene</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">RepresentationConfig</span><span class="p">,</span> <span class="n">IntervenableConfig</span><span class="p">,</span> <span class="n">IntervenableModel</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">VanillaIntervention</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="factual-recall">
+<h2>Factual recall<a class="headerlink" href="#factual-recall" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span> <span class="o">=</span> <span class="n">pyvene</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;The capital of Spain is&quot;</span>
+<span class="n">source</span> <span class="o">=</span> <span class="s2">&quot;The capital of Italy is&quot;</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">gpt</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">gpt</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+The capital of Spain is
+_Madrid              0.10501234978437424
+_the                 0.0949699655175209
+_Barcelona           0.0702790841460228
+_a                   0.04010068252682686
+_now                 0.02824278175830841
+_in                  0.02759990654885769
+_Spain               0.022991720587015152
+_Catalonia           0.018823225051164627
+_also                0.018689140677452087
+_not                 0.01735665090382099
+
+The capital of Italy is
+_Rome                0.15734916925430298
+_the                 0.07316355407238007
+_Milan               0.046878915280103683
+_a                   0.03449810668826103
+_now                 0.03200329467654228
+_in                  0.02306535840034485
+_also                0.02274816483259201
+_home                0.01920313946902752
+_not                 0.01640527881681919
+_Italy               0.01577090471982956
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="patch-patching-on-position-aligned-tokens">
+<h2>Patch Patching on Position-aligned Tokens<a class="headerlink" href="#patch-patching-on-position-aligned-tokens" title="Link to this heading">#</a></h2>
+<p>We path patch on two modules on each layer:</p>
+<ul class="simple">
+<li><p>[1] MLP output (the MLP output will be from another example)</p></li>
+<li><p>[2] MHA input (the self-attention module input will be from another module)</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">simple_position_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">component</span><span class="p">,</span> <span class="n">layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>              <span class="c1"># layer</span>
+                <span class="n">component</span><span class="p">,</span>          <span class="c1"># component</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>              <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>                  <span class="c1"># max number of unit</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># should finish within 1 min with a standard 12G GPU</span>
+<span class="n">tokens</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; Madrid Rome&quot;</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">simple_position_config</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="n">layer_i</span><span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">}</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;f</span><span class="si">{</span><span class="n">layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">simple_position_config</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span> <span class="n">layer_i</span><span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">}</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;a</span><span class="si">{</span><span class="n">layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">nodes</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;f</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;a</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">nodes</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">g</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~token&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/e59797398a0719eccd22b9518053a4aa766cc7554bd7e51976e6b54a55419c44.png"><img alt="../../_images/e59797398a0719eccd22b9518053a4aa766cc7554bd7e51976e6b54a55419c44.png" src="../../_images/e59797398a0719eccd22b9518053a4aa766cc7554bd7e51976e6b54a55419c44.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filtered</span> <span class="o">=</span> <span class="n">df</span>
+<span class="n">filtered</span> <span class="o">=</span> <span class="n">filtered</span><span class="p">[</span><span class="n">filtered</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span><span class="p">]</span>
+<span class="n">g</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">filtered</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;token&quot;</span><span class="p">),</span> <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">),</span> <span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_y_log10</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~token&quot;</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/4702ad78b9221e1850a288b1528480fe4311f007f70f628a75140a13e557b265.png"><img alt="../../_images/4702ad78b9221e1850a288b1528480fe4311f007f70f628a75140a13e557b265.png" src="../../_images/4702ad78b9221e1850a288b1528480fe4311f007f70f628a75140a13e557b265.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+    <a class="left-prev"
+       href="Add_Activations_to_Streams.html"
+       title="previous page">
+      <i class="fa-solid fa-angle-left"></i>
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+        <p class="prev-next-title">Activation Addition</p>
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Trainable Interventions</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#aligning-the-output-variable-with-a-single-dimension-in-the-rotated-basis">Aligning the output variable with a single dimension in the rotated basis</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#a-1-d-das-direction-learning-config">A 1-d DAS direction learning config</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#your-own-loss-and-metrics-function">Your own loss and metrics function</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning">Learning</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="trainable-interventions">
+<h1>Trainable Interventions<a class="headerlink" href="#trainable-interventions" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;11/28/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>When interventions are static, you are mannually look for interesting counterfactual behaviors. What if interventions are trainable? and what if you train interventions with certain counterfactual behaviors?</p>
+<p>We think, if you can train such interventions, you find a systematic way of affecting the causal circuits realized in the NNs. With certain types of interventions, e.g., basis respect ones (vanilla causal abstraction or DAS), you are doing causal abstraction.</p>
+<p>In this tutorial, we show how you can train interventions with customized dataset.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/frankaging/pyvene.git
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[2024-01-11 01:23:38,042] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">np</span><span class="o">.</span><span class="n">object</span> <span class="o">=</span> <span class="nb">object</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">datasets.utils.logging</span> <span class="kn">import</span> <span class="n">disable_progress_bar</span>
+<span class="n">disable_progress_bar</span><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">LowRankRotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_gpt2_lm</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">tutorial_intervention_training_utils</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">visualize_program</span><span class="p">,</span>
+    <span class="n">make_supervised_counterfactual_data_module</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">CrossEntropyLoss</span>
+
+<span class="n">programs</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/&quot;</span><span class="p">,</span> <span class="s2">&quot;seed_programs.json&quot;</span><span class="p">)))</span>
+<span class="n">programs_10</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;./tutorial_data/&quot;</span><span class="p">,</span> <span class="s2">&quot;selected_programs_10.json&quot;</span><span class="p">))</span>
+<span class="p">)</span>
+
+<span class="c1"># load fine-tuned model for this tutorial from HF</span>
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">create_gpt2_lm</span><span class="p">(</span><span class="s2">&quot;zhengxuanzenwu/gpt2-5token-solver&quot;</span><span class="p">)</span>
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+
+<span class="c1"># what is this model finetuned for? this visualizes a program that this model can solve.</span>
+<span class="n">select_program</span> <span class="o">=</span> <span class="s2">&quot;07065a&quot;</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">A program that the model can solve (Cs are input tokens):&quot;</span><span class="p">)</span>
+<span class="n">visualize_program</span><span class="p">(</span><span class="n">programs</span><span class="p">[</span><span class="n">select_program</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ffff1b0633df473ab9d15a22bba0f29d", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "f1fa2101566342028d7d19dd74a8299f", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+
+A program that the model can solve (Cs are input tokens):
+</pre></div>
+</div>
+<img alt="../../_images/c0fb1f5e9c3706572fead3ff8ce22d1f25cb88e40ccb50dee6d6222d91f4357b.svg" src="../../_images/c0fb1f5e9c3706572fead3ff8ce22d1f25cb88e40ccb50dee6d6222d91f4357b.svg" /></div>
+</div>
+</section>
+<section id="aligning-the-output-variable-with-a-single-dimension-in-the-rotated-basis">
+<h2>Aligning the output variable with a single dimension in the rotated basis<a class="headerlink" href="#aligning-the-output-variable-with-a-single-dimension-in-the-rotated-basis" title="Link to this heading">#</a></h2>
+<p>We know the output is a boolean value, which can be represented using a single dimension (i.e., pos/neg lies on a line with a linearly separable boundary). Can we learn a 1-d DAS direction to align activations with the output variable?</p>
+<p>We first need to sample (base, source(s)) pairs. In this case, it is easy, since the counterfactual behavior is that source output label overwrites the base one.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># load in some datasets we need</span>
+<span class="n">num_of_shots</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">mode</span> <span class="o">=</span> <span class="s2">&quot;E&quot;</span>
+<span class="n">counterfactual_data_module</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">make_supervised_counterfactual_data_module</span><span class="p">(</span>
+    <span class="n">programs</span><span class="p">[</span><span class="n">select_program</span><span class="p">],</span>
+    <span class="mi">800</span><span class="p">,</span>
+    <span class="n">num_of_shots</span><span class="p">,</span>
+    <span class="s2">&quot;op5&quot;</span><span class="p">,</span>  <span class="c1"># this is the last variable = output</span>
+    <span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">data_path</span><span class="o">=</span><span class="s2">&quot;./tutorial_data/&quot;</span><span class="p">,</span>
+    <span class="n">program_uuid</span><span class="o">=</span><span class="n">select_program</span><span class="p">,</span>
+    <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
+    <span class="n">n_test_sample</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model input looks like this (10-shot ICL):&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">counterfactual_data_module</span><span class="p">[</span><span class="s2">&quot;train_dataset&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;question&quot;</span><span class="p">])</span>
+
+<span class="c1"># dataset cleaning</span>
+<span class="n">columns_to_remove</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;question&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;source_question&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;answers&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;base_answers&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;source_answers&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">counterfactual_data_module</span><span class="p">[</span><span class="s2">&quot;train_dataset&quot;</span><span class="p">]</span>
+    <span class="o">.</span><span class="n">remove_columns</span><span class="p">(</span><span class="n">columns_to_remove</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">eval_dataset</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">counterfactual_data_module</span><span class="p">[</span><span class="s2">&quot;eval_dataset&quot;</span><span class="p">]</span>
+    <span class="o">.</span><span class="n">remove_columns</span><span class="p">(</span><span class="n">columns_to_remove</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">test_dataset</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">counterfactual_data_module</span><span class="p">[</span><span class="s2">&quot;test_dataset&quot;</span><span class="p">]</span>
+    <span class="o">.</span><span class="n">remove_columns</span><span class="p">(</span><span class="n">columns_to_remove</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">with_format</span><span class="p">(</span><span class="s2">&quot;torch&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">add_locations</span><span class="p">(</span><span class="n">example</span><span class="p">):</span>
+    <span class="n">example</span><span class="p">[</span><span class="s2">&quot;source_0-&gt;base.0.pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">129</span><span class="p">]</span>  <span class="c1"># the fixed last token location</span>
+    <span class="n">example</span><span class="p">[</span><span class="s2">&quot;source_0-&gt;base.1.pos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">129</span><span class="p">]</span>  <span class="c1"># the fixed last token location</span>
+    <span class="n">example</span><span class="p">[</span><span class="s2">&quot;subspaces&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># the only subspace is a single column</span>
+    <span class="k">return</span> <span class="n">example</span>
+
+
+<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">add_locations</span><span class="p">)</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">eval_dataset</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">add_locations</span><span class="p">)</span>
+<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">test_dataset</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">add_locations</span><span class="p">)</span>
+<span class="n">train_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
+<span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
+<span class="n">test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Training data for the intervention should contain these fields:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Model input looks like this (10-shot ICL):
+reportprint,itches,reenshots,Jean,reenshots=True
+dated,net,dated,Kate,dated=False
+Inst,Amid,Billy,While,Billy=True
+mas,quickShip,mas,imei,mas=True
+Excellent,embedreportprint,Ret,Theme,Excellent=True
+engers,Rick,engers,Jerry,engers=True
+debian,OSED,international,quickShip,international=True
+Led,orous,Lead,Cash,Lead=True
+mission,embedreportprint,mission,mbudsman,mission=True
+ABC,store,BBC,ixty,CBC=False
+ilitation,Brow,ilitation,ICLE,ilitation
+
+Training data for the intervention should contain these fields:
+dict_keys([&#39;input_ids&#39;, &#39;attention_mask&#39;, &#39;source_input_ids&#39;, &#39;source_attention_mask&#39;, &#39;labels&#39;, &#39;source_0-&gt;base.0.pos&#39;, &#39;source_0-&gt;base.1.pos&#39;, &#39;subspaces&#39;])
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="a-1-d-das-direction-learning-config">
+<h2>A 1-d DAS direction learning config<a class="headerlink" href="#a-1-d-das-direction-learning-config" title="Link to this heading">#</a></h2>
+<p>No full rotation learning is needed here.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">single_d_low_rank_das_position_config</span><span class="p">(</span>
+    <span class="n">model_type</span><span class="p">,</span> <span class="n">intervention_type</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">intervention_types</span>
+<span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>              <span class="c1"># layer</span>
+                <span class="n">intervention_type</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>              <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>                  <span class="c1"># max number of unit</span>
+                <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>         <span class="c1"># a single das direction</span>
+                <span class="n">subspace_partition</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span>  <span class="c1"># dummy partition</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">intervention_types</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">single_d_low_rank_das_position_config</span><span class="p">(</span>
+    <span class="nb">type</span><span class="p">(</span><span class="n">gpt2</span><span class="p">),</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">LowRankRotatedSpaceIntervention</span>
+<span class="p">)</span>
+<span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt2</span><span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">intervenable</span><span class="o">.</span><span class="n">disable_model_gradients</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="your-own-loss-and-metrics-function">
+<h2>Your own loss and metrics function<a class="headerlink" href="#your-own-loss-and-metrics-function" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">inputs_collator</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="s2">&quot;-&gt;&quot;</span> <span class="ow">in</span> <span class="n">k</span><span class="p">:</span>
+            <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="s2">&quot;subspace&quot;</span> <span class="ow">in</span> <span class="n">k</span><span class="p">:</span>
+            <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
+        <span class="k">elif</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+            <span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">inputs</span>
+
+
+<span class="k">def</span> <span class="nf">compute_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
+
+    <span class="c1"># Flatten the tokens</span>
+    <span class="n">loss_fct</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>
+    <span class="n">shift_logits</span> <span class="o">=</span> <span class="n">shift_logits</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">50257</span><span class="p">)</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># Enable model parallelism</span>
+    <span class="n">shift_labels</span> <span class="o">=</span> <span class="n">shift_labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">shift_logits</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fct</span><span class="p">(</span><span class="n">shift_logits</span><span class="p">,</span> <span class="n">shift_labels</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">loss</span>
+
+
+<span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+    <span class="n">total_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">correct_count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">eval_pred</span><span class="p">,</span> <span class="n">eval_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">,</span> <span class="n">eval_labels</span><span class="p">):</span>
+        <span class="n">actual_test_labels</span> <span class="o">=</span> <span class="n">eval_label</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_test_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">eval_pred</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">correct_labels</span> <span class="o">=</span> <span class="n">actual_test_labels</span> <span class="o">==</span> <span class="n">pred_test_labels</span>
+        <span class="n">total_count</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">correct_labels</span><span class="p">)</span>
+        <span class="n">correct_count</span> <span class="o">+=</span> <span class="n">correct_labels</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">accuracy</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">correct_count</span> <span class="o">/</span> <span class="n">total_count</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;accuracy&quot;</span><span class="p">:</span> <span class="n">accuracy</span><span class="p">}[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="learning">
+<h2>Learning<a class="headerlink" href="#learning" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">train_alignment</span><span class="p">(</span>
+    <span class="n">train_dataloader</span><span class="o">=</span><span class="n">train_dataloader</span><span class="p">,</span>
+    <span class="n">compute_loss</span><span class="o">=</span><span class="n">compute_loss</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="n">compute_metrics</span><span class="p">,</span>
+    <span class="n">inputs_collator</span><span class="o">=</span><span class="n">inputs_collator</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [01:34&lt;00:00,  9.43s/it, loss=0.24, acc=0.91]
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="evaluation">
+<h2>Evaluation<a class="headerlink" href="#evaluation" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">intervenable</span><span class="o">.</span><span class="n">eval_alignment</span><span class="p">(</span>
+    <span class="n">eval_dataloader</span><span class="o">=</span><span class="n">test_dataloader</span><span class="p">,</span>
+    <span class="n">compute_metrics</span><span class="o">=</span><span class="n">compute_metrics</span><span class="p">,</span>
+    <span class="n">inputs_collator</span><span class="o">=</span><span class="n">inputs_collator</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>                                                                                                                                                                                                                                          
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9763200000000002
+</pre></div>
+</div>
+</div>
+</div>
+<p>The above means &gt;97% of time, you can intervene on a single activation in the rotated basis to flip the model prediction</p>
+</section>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#aligning-the-output-variable-with-a-single-dimension-in-the-rotated-basis">Aligning the output variable with a single dimension in the rotated basis</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#a-1-d-das-direction-learning-config">A 1-d DAS direction learning config</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#your-own-loss-and-metrics-function">Your own loss and metrics function</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning">Learning</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#evaluation">Evaluation</a></li>
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diff --git a/tutorials/basic_tutorials/Nested_Intervention.html b/tutorials/basic_tutorials/Nested_Intervention.html
new file mode 100644
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+<html lang="en" data-content_root="../../" >
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+</ul>
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+<ul class="current nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../pyvene_101.html">Introduction to pyvene</a></li>
+
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+<li class="toctree-l1"><a class="reference internal" href="Basic_Intervention.html">Interchange Intervention</a></li>
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">Intervening on subcomponents</a></li>
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+
+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../advanced_tutorials/IOI_Replication.html">Replicating the IOI paper</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
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+</button>
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Intervening on subcomponents</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall">Factual Recall</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-patching-on-position-aligned-tokens-with-in-specific-head">Patch Patching on Position-aligned Tokens with in Specific Head</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="intervening-on-subcomponents">
+<h1>Intervening on subcomponents<a class="headerlink" href="#intervening-on-subcomponents" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;11/15/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>This tutorial shows how you can intervene at specific position within representations of a specific head. This is sort of nested interventions where you choose a head to intervene first, and then you choose a specific location, or multiple locations.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_gpt2</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="factual-recall">
+<h2>Factual Recall<a class="headerlink" href="#factual-recall" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span> <span class="o">=</span> <span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;The capital of Spain is&quot;</span>
+<span class="n">source</span> <span class="o">=</span> <span class="s2">&quot;The capital of Italy is&quot;</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">gpt</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">gpt</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span><span class="n">gpt</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+The capital of Spain is
+_Madrid              0.10501234978437424
+_the                 0.0949699655175209
+_Barcelona           0.0702790841460228
+_a                   0.04010068252682686
+_now                 0.02824278175830841
+_in                  0.02759990654885769
+_Spain               0.022991720587015152
+_Catalonia           0.018823225051164627
+_also                0.018689140677452087
+_not                 0.01735665090382099
+
+The capital of Italy is
+_Rome                0.15734916925430298
+_the                 0.07316355407238007
+_Milan               0.046878915280103683
+_a                   0.03449810668826103
+_now                 0.03200329467654228
+_in                  0.02306535840034485
+_also                0.02274816483259201
+_home                0.01920313946902752
+_not                 0.01640527881681919
+_Italy               0.01577090471982956
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="patch-patching-on-position-aligned-tokens-with-in-specific-head">
+<h2>Patch Patching on Position-aligned Tokens with in Specific Head<a class="headerlink" href="#patch-patching-on-position-aligned-tokens-with-in-specific-head" title="Link to this heading">#</a></h2>
+<p>We path patch on two modules on each layer:</p>
+<ul class="simple">
+<li><p>[1] MLP output (the MLP output will be from another example)</p></li>
+<li><p>[2] MHA input (the self-attention module input will be from another module)</p></li>
+</ul>
+<p>Different from the basic tutorial, this tutorial intervenes on specific locations within specific heads. For instance, we want to intervene on the last token in head 4 but not other heads.</p>
+<p><strong>To do this, we need to tweak a little when we setup the intervention config.</strong></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">position_in_head_config</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">intervention_type</span><span class="p">,</span> <span class="n">layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>  <span class="c1"># layer</span>
+                <span class="n">intervention_type</span><span class="p">,</span>  <span class="c1"># intervention type</span>
+                <span class="s2">&quot;h.pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit is now [pos] within [h]</span>
+                <span class="mi">1</span><span class="p">,</span>  <span class="c1"># max number of unit</span>
+            <span class="p">),</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">target_head</span> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="c1"># should finish within 1 min with a standard 12G GPU</span>
+<span class="n">tokens</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; Madrid Rome&quot;</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">position_in_head_config</span><span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span> <span class="n">layer_i</span>
+    <span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                    <span class="p">[[[[</span><span class="n">target_head</span><span class="p">]],</span> <span class="p">[[</span><span class="n">pos_i</span><span class="p">]]]],</span>  <span class="c1"># intervene w/ target_head&#39;s pos_i</span>
+                    <span class="p">[[[[</span><span class="n">target_head</span><span class="p">]],</span> <span class="p">[[</span><span class="n">pos_i</span><span class="p">]]]],</span>  <span class="c1"># intervene on target_head&#39;s pos_i</span>
+                <span class="p">)</span>
+            <span class="p">},</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;ov</span><span class="si">{</span><span class="n">layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">position_in_head_config</span><span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;head_value_output&quot;</span><span class="p">,</span> <span class="n">layer_i</span>
+    <span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="p">{</span>
+                <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+                    <span class="p">[[[[</span><span class="n">target_head</span><span class="p">]],</span> <span class="p">[[</span><span class="n">pos_i</span><span class="p">]]]],</span>  <span class="c1"># intervene w/ target_head&#39;s pos_i</span>
+                    <span class="p">[[[[</span><span class="n">target_head</span><span class="p">]],</span> <span class="p">[[</span><span class="n">pos_i</span><span class="p">]]]],</span>  <span class="c1"># intervene on target_head&#39;s pos_i</span>
+                <span class="p">)</span>
+            <span class="p">},</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;v</span><span class="si">{</span><span class="n">layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;head_value_output&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">nodes</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ov</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;v</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">nodes</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">g</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~token&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/82e041002a683be86c4ff0e8cc181f8a817587383a65c450c1764a57ede2b6e5.png"><img alt="../../_images/82e041002a683be86c4ff0e8cc181f8a817587383a65c450c1764a57ede2b6e5.png" src="../../_images/82e041002a683be86c4ff0e8cc181f8a817587383a65c450c1764a57ede2b6e5.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">filtered</span> <span class="o">=</span> <span class="n">df</span>
+<span class="n">filtered</span> <span class="o">=</span> <span class="n">filtered</span><span class="p">[</span><span class="n">filtered</span><span class="p">[</span><span class="s2">&quot;pos&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">4</span><span class="p">]</span>
+<span class="n">g</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">filtered</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;token&quot;</span><span class="p">),</span> <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;identity&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">),</span> <span class="n">legend_position</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">scale_y_log10</span><span class="p">()</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~token&quot;</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/3c765aa91b95834a2c9bf1f02f088ffef729e3e06c5d76bfbb00a29b2f8dffd4.png"><img alt="../../_images/3c765aa91b95834a2c9bf1f02f088ffef729e3e06c5d76bfbb00a29b2f8dffd4.png" src="../../_images/3c765aa91b95834a2c9bf1f02f088ffef729e3e06c5d76bfbb00a29b2f8dffd4.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#factual-recall">Factual Recall</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-patching-on-position-aligned-tokens-with-in-specific-head">Patch Patching on Position-aligned Tokens with in Specific Head</a></li>
+</ul>
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diff --git a/tutorials/basic_tutorials/Sparse_Autoencoder.html b/tutorials/basic_tutorials/Sparse_Autoencoder.html
new file mode 100644
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+
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+              
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Analyzing Sparse Autoencoders (SAEs) from Gemma Scope</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-model-and-its-tokenizer">Loading the model and its tokenizer</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-a-sae-and-create-sae-interventions">Loading a SAE, and create SAE interventions</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-saes-as-pyvene-native-interventions">Implementing SAEs as <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native Interventions</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#running-the-model-with-sae-to-collect-activations-with-pyvene-apis">Running the model with SAE to collect activations with <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> APIs</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#gemma-2-2b-it-steering-with-gemma-2-2b-saes">Gemma-2-2B-it steering with Gemma-2-2B SAEs</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-saes-as-pyvene-native-interventions-for-model-steering">Implementing SAEs as <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native Interventions for model steering</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#interchange-intervention-with-jumprelu-saes">Interchange intervention with JumpReLU SAEs.</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="analyzing-sparse-autoencoders-saes-from-gemma-scope">
+<h1>Analyzing Sparse Autoencoders (SAEs) from <a class="reference external" href="https://colab.research.google.com/drive/17dQFYUYnuKnP6OwQPH9v_GSYUW5aj-Rp">Gemma Scope</a><a class="headerlink" href="#analyzing-sparse-autoencoders-saes-from-gemma-scope" title="Link to this heading">#</a></h1>
+<p><a class="reference external" href="https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/basic_tutorials/Sparse_Autoencoder.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;09/23/2024&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>This tutorial aims to <strong>(1) reproduce</strong> and <strong>(2) extend</strong> some of the results in the Gemma Scope (SAE) tutorial in <a class="reference external" href="https://colab.research.google.com/drive/17dQFYUYnuKnP6OwQPH9v_GSYUW5aj-Rp">notebook</a> for interpreting latents of SAEs. This tutorial also shows basic model steering with SAEs. This notebook is built as a show-case for the Gemma 2 2B model as well as its SAEs. However, this tutorial can be extended to any other model types and their SAEs.</p>
+<p><strong>Note</strong>: This tutorial assumes SAEs are pretrained separately.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ConstantSourceIntervention</span><span class="p">,</span>
+    <span class="n">SourcelessIntervention</span><span class="p">,</span>
+    <span class="n">TrainableIntervention</span><span class="p">,</span>
+    <span class="n">DistributedRepresentationIntervention</span><span class="p">,</span>
+    <span class="n">CollectIntervention</span><span class="p">,</span>
+    <span class="n">JumpReLUAutoencoderIntervention</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">BitsAndBytesConfig</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+<span class="kn">from</span> <span class="nn">huggingface_hub</span> <span class="kn">import</span> <span class="n">hf_hub_download</span><span class="p">,</span> <span class="n">notebook_login</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+
+<span class="c1"># If you haven&#39;t login, you need to do so.</span>
+<span class="c1"># notebook_login()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="loading-the-model-and-its-tokenizer">
+<h2>Loading the model and its tokenizer<a class="headerlink" href="#loading-the-model-and-its-tokenizer" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># avoid blowing up mem</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="s2">&quot;google/gemma-2-2b&quot;</span><span class="p">,</span> <span class="c1"># google/gemma-2b-it</span>
+    <span class="n">device_map</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span>  <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;google/gemma-2-2b&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "192a06afdbdc4c868bc6d20677b3dd38", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "3e088f2b3808489bac70b9aeb5ae73f0", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<p>We give it the prompt “Would you be able to travel through time using a wormhole?” and print the generated output</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The input text</span>
+<span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;Would you be able to travel through time using a wormhole?&quot;</span>
+
+<span class="c1"># Use the tokenizer to convert it to tokens. Note that this implicitly adds a special &quot;Beginning of Sequence&quot; or &lt;bos&gt; token to the start</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">prompt</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span> <span class="n">add_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+
+<span class="c1"># Pass it in to the model and generate text</span>
+<span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([[     2,  18925,    692,    614,   3326,    577,   5056,   1593,   1069,
+           2177,    476,  47420,  18216, 235336]], device=&#39;cuda:0&#39;)
+&lt;bos&gt;Would you be able to travel through time using a wormhole?
+
+[Answer 1]
+
+Yes, you can travel through time using a wormhole.
+
+A wormhole is a theoretical object that connects two points in space-time. It is a tunnel through space-time that allows objects to travel from
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="loading-a-sae-and-create-sae-interventions">
+<h2>Loading a SAE, and create SAE interventions<a class="headerlink" href="#loading-a-sae-and-create-sae-interventions" title="Link to this heading">#</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">pyvene</span></code> can load SAEs as interventions for analyzing latents as well as model steering.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">LAYER</span> <span class="o">=</span> <span class="mi">20</span>
+<span class="n">path_to_params</span> <span class="o">=</span> <span class="n">hf_hub_download</span><span class="p">(</span>
+    <span class="n">repo_id</span><span class="o">=</span><span class="s2">&quot;google/gemma-scope-2b-pt-res&quot;</span><span class="p">,</span>
+    <span class="n">filename</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;layer_</span><span class="si">{</span><span class="n">LAYER</span><span class="si">}</span><span class="s2">/width_16k/average_l0_71/params.npz&quot;</span><span class="p">,</span>
+    <span class="n">force_download</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path_to_params</span><span class="p">)</span>
+<span class="n">pt_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="implementing-saes-as-pyvene-native-interventions">
+<h2>Implementing SAEs as <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native Interventions<a class="headerlink" href="#implementing-saes-as-pyvene-native-interventions" title="Link to this heading">#</a></h2>
+<p>Create a <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native intervention for SAEs to collect latent collection</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">JumpReLUSAECollectIntervention</span><span class="p">(</span>
+    <span class="n">CollectIntervention</span>
+<span class="p">):</span>
+<span class="w">  </span><span class="sd">&quot;&quot;&quot;Collect activations&quot;&quot;&quot;</span>
+  <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="c1"># Note that we initialise these to zeros because we&#39;re loading in pre-trained weights.</span>
+    <span class="c1"># If you want to train your own SAEs then we recommend using blah</span>
+    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">keep_last_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">W_enc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">W_dec</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">b_enc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">b_dec</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">))</span>
+
+  <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_acts</span><span class="p">):</span>
+    <span class="n">pre_acts</span> <span class="o">=</span> <span class="n">input_acts</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">W_enc</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_enc</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">pre_acts</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span>
+    <span class="n">acts</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">pre_acts</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">acts</span>
+
+  <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">acts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">acts</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="running-the-model-with-sae-to-collect-activations-with-pyvene-apis">
+<h2>Running the model with SAE to collect activations with <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> APIs<a class="headerlink" href="#running-the-model-with-sae-to-collect-activations-with-pyvene-apis" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sae</span> <span class="o">=</span> <span class="n">JumpReLUSAECollectIntervention</span><span class="p">(</span>
+    <span class="n">embed_dim</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;W_enc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;W_enc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">sae</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">pt_params</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">sae</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+<span class="c1"># add the intervention to the model computation graph via the config</span>
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pyvene</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+   <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;model.layers[</span><span class="si">{</span><span class="n">LAYER</span><span class="si">}</span><span class="s2">].output&quot;</span><span class="p">,</span>
+   <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">sae</span><span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sae_acts</span> <span class="o">=</span> <span class="n">pv_model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">},</span> <span class="n">return_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">collected_activations</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Results (from Gemma Scope) should be:</span>
+<span class="sd">tensor([[7017,   47,   65,   70,   55,   72,   65,   75,   80,   72,   68,   93,</span>
+<span class="sd">           86,   89]], device=&#39;cuda:0&#39;)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="p">(</span><span class="n">sae_acts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([7017,   47,   65,   70,   55,   72,   65,   75,   80,   72,   68,   93,
+          86,   89], device=&#39;cuda:0&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Results (from Gemma Scope) should be:</span>
+<span class="sd">tensor([[ 6631,  5482, 10376,  1670, 11023,  7562,  9407,  8399, 12935, 10004,</span>
+<span class="sd">         10004, 10004, 12935,  3442]], device=&#39;cuda:0&#39;)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">values</span><span class="p">,</span> <span class="n">inds</span> <span class="o">=</span> <span class="n">sae_acts</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">inds</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([ 6631,  5482, 10376,  1670, 11023,  7562,  9407,  8399, 12935, 10004,
+        10004, 10004, 12935,  3442], device=&#39;cuda:0&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="gemma-2-2b-it-steering-with-gemma-2-2b-saes">
+<h2>Gemma-2-2B-it steering with Gemma-2-2B SAEs<a class="headerlink" href="#gemma-2-2b-it-steering-with-gemma-2-2b-saes" title="Link to this heading">#</a></h2>
+<p>We could also try to steer Gemma-2-2B-it by overloading Gemma-2-2B SAE, and see if it works.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># avoid blowing up mem</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="s2">&quot;google/gemma-2-2b-it&quot;</span><span class="p">,</span> <span class="c1"># google/gemma-2b-it</span>
+    <span class="n">device_map</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span>  <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;google/gemma-2-2b-it&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "0574b56084114b98bcd5d0817960a78c", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "c135c09c7d6d43e5a874b0f7caa92b5b", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+</section>
+<section id="implementing-saes-as-pyvene-native-interventions-for-model-steering">
+<h2>Implementing SAEs as <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native Interventions for model steering<a class="headerlink" href="#implementing-saes-as-pyvene-native-interventions-for-model-steering" title="Link to this heading">#</a></h2>
+<p>The <code class="docutils literal notranslate"><span class="pre">subspace</span></code> notation built in to <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> let us to steer models by intervening on different features.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">JumpReLUSAESteeringIntervention</span><span class="p">(</span>
+    <span class="n">SourcelessIntervention</span><span class="p">,</span>
+    <span class="n">TrainableIntervention</span><span class="p">,</span> 
+    <span class="n">DistributedRepresentationIntervention</span>
+<span class="p">):</span>
+  <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="c1"># Note that we initialise these to zeros because we&#39;re loading in pre-trained weights.</span>
+    <span class="c1"># If you want to train your own SAEs then we recommend using blah</span>
+    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">keep_last_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">W_enc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">W_dec</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">b_enc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">]))</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">b_dec</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embed_dim</span><span class="p">))</span>
+
+  <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_acts</span><span class="p">):</span>
+    <span class="n">pre_acts</span> <span class="o">=</span> <span class="n">input_acts</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">W_enc</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_enc</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">pre_acts</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span>
+    <span class="n">acts</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">pre_acts</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">acts</span>
+
+  <span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">acts</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">acts</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">W_dec</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_dec</span>
+
+  <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="n">steering_vec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">subspaces</span><span class="p">[</span><span class="s2">&quot;mag&quot;</span><span class="p">])</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">W_dec</span><span class="p">[</span><span class="n">subspaces</span><span class="p">[</span><span class="s2">&quot;idx&quot;</span><span class="p">]]</span>
+    <span class="k">return</span> <span class="n">base</span> <span class="o">+</span> <span class="n">steering_vec</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Loading the Gemma base model SAE weights.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sae</span> <span class="o">=</span> <span class="n">JumpReLUSAESteeringIntervention</span><span class="p">(</span>
+    <span class="n">embed_dim</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;W_enc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;W_enc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">sae</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">pt_params</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">sae</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+<span class="c1"># add the intervention to the model computation graph via the config</span>
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pyvene</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+   <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;model.layers[</span><span class="si">{</span><span class="n">LAYER</span><span class="si">}</span><span class="s2">].output&quot;</span><span class="p">,</span>
+   <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">sae</span><span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;Which dog breed do people think is cuter, poodle or doodle?&quot;</span>
+
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">prompt</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">reft_response</span> <span class="o">=</span> <span class="n">pv_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
+    <span class="n">prompt</span><span class="p">,</span> <span class="n">unit_locations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">intervene_on_prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;idx&quot;</span><span class="p">:</span> <span class="mi">10004</span><span class="p">,</span> <span class="s2">&quot;mag&quot;</span><span class="p">:</span> <span class="mf">100.0</span><span class="p">}],</span>
+    <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">early_stopping</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">reft_response</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Which dog breed do people think is cuter, poodle or doodle? 
+
+It really depends on personal preference, but it&#39;s often a subjective matter. 
+
+Here&#39;s a bit about each, to help you decide:
+
+**Poodles:**
+
+* Origin: France
+* Types: Standard, Miniature, Toy
+* Known for: Curly, hypoallergenic fur; intelligence and trainability.
+* Appearance: Classic, distinguished look with a flowing coat and well-defined facial features.
+
+**Doodles:**
+
+* Origin (general) Space-travel, time-travel or a blend - depending on the specific dog&#39;s ancestry.  The term is used across a variety of breeds.
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Here you go: a “Space-travel, time-travel” Doodle!</strong></p>
+</section>
+<section id="interchange-intervention-with-jumprelu-saes">
+<h2>Interchange intervention with JumpReLU SAEs.<a class="headerlink" href="#interchange-intervention-with-jumprelu-saes" title="Link to this heading">#</a></h2>
+<p>You can also swap values between examples for a specific latent dimension. However, since SAE usually maps a concpet to 1D subspace, swapping between examples and resetting the scalar to another value are similar.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sae</span> <span class="o">=</span> <span class="n">JumpReLUAutoencoderIntervention</span><span class="p">(</span>
+    <span class="n">embed_dim</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;W_enc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+    <span class="n">low_rank_dimension</span><span class="o">=</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;W_enc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="p">)</span>
+<span class="n">sae</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">pt_params</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">sae</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+<span class="c1"># add the intervention to the model computation graph via the config</span>
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pyvene</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+   <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;model.layers[</span><span class="si">{</span><span class="n">LAYER</span><span class="si">}</span><span class="s2">].output&quot;</span><span class="p">,</span>
+   <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">sae</span><span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;Which dog breed do people think is cuter, poodle or doodle?&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;Origin (general) Space-travel, time-travel&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+<span class="c1"># run an interchange intervention </span>
+<span class="n">original_outputs</span><span class="p">,</span> <span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_model</span><span class="p">(</span>
+  <span class="c1"># the base input</span>
+  <span class="n">base</span><span class="o">=</span><span class="n">base</span><span class="p">,</span> 
+  <span class="c1"># the source input</span>
+  <span class="n">sources</span><span class="o">=</span><span class="n">source</span><span class="p">,</span> 
+  <span class="c1"># the location to intervene (swap last tokens)</span>
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mi">14</span><span class="p">)},</span>
+  <span class="c1"># the SAE latent dimension mapping to the time travel concept (&quot;10004&quot;)</span>
+  <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span><span class="mi">10004</span><span class="p">],</span>
+  <span class="n">output_original_output</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="n">logits_diff</span> <span class="o">=</span> <span class="n">intervened_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">original_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">[:,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">values</span><span class="p">,</span> <span class="n">indices</span> <span class="o">=</span> <span class="n">logits_diff</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;** topk logits diff **&quot;</span><span class="p">)</span>
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">batch_decode</span><span class="p">(</span><span class="n">indices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>** topk logits diff **
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;PhysRevD&#39;,
+ &#39; transporting&#39;,
+ &#39; teleport&#39;,
+ &#39; space&#39;,
+ &#39; transit&#39;,
+ &#39; transported&#39;,
+ &#39; transporter&#39;,
+ &#39; transpor&#39;,
+ &#39; multiverse&#39;,
+ &#39; universes&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-model-and-its-tokenizer">Loading the model and its tokenizer</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-a-sae-and-create-sae-interventions">Loading a SAE, and create SAE interventions</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-saes-as-pyvene-native-interventions">Implementing SAEs as <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native Interventions</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#running-the-model-with-sae-to-collect-activations-with-pyvene-apis">Running the model with SAE to collect activations with <code class="docutils literal notranslate"><span class="pre">pyvene</span></code> APIs</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#gemma-2-2b-it-steering-with-gemma-2-2b-saes">Gemma-2-2B-it steering with Gemma-2-2B SAEs</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#implementing-saes-as-pyvene-native-interventions-for-model-steering">Implementing SAEs as <code class="docutils literal notranslate"><span class="pre">pyvene</span></code>-native Interventions for model steering</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#interchange-intervention-with-jumprelu-saes">Interchange intervention with JumpReLU SAEs.</a></li>
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diff --git a/tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html b/tutorials/basic_tutorials/Subspace_Partition_with_Intervention.html
new file mode 100644
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Subspace Interventions</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#overview">Overview</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#subspace-alignment-config">Subspace alignment config</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-patching-on-the-first-subspace-of-position-aligned-tokens">Patch Patching on the First Subspace of Position-aligned Tokens</a></li>
+</ul>
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="subspace-interventions">
+<h1>Subspace Interventions<a class="headerlink" href="#subspace-interventions" title="Link to this heading">#</a></h1>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;11/28/2023&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>Subspace of the basis may be used to represent different orthogonal causal variables. In other words, each column or each partition of columns may be used to represent different high-level causal model. In this tutorial, we want to illustrate how to setup the intervenable to do this.</p>
+<p>We introduce a new concept of <strong>subspace</strong> intervention. For the intervention, you can specify if you only want to intervene on a subspace rather than the fullspace.</p>
+<p>Then, you can intervene on different subspaces given your examples in a batch, and test for different counterfactual behaviors. Accordingly, you can also train different subspaces to target different counterfactual behaviors using DAS.</p>
+</section>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">embed_to_distrib</span><span class="p">,</span> <span class="n">top_vals</span><span class="p">,</span> <span class="n">format_token</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">IntervenableModel</span><span class="p">,</span>
+    <span class="n">RotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="n">RepresentationConfig</span><span class="p">,</span>
+    <span class="n">IntervenableConfig</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyvene</span> <span class="kn">import</span> <span class="n">create_gpt2</span>
+
+<span class="o">%</span><span class="k">config</span> InlineBackend.figure_formats = [&#39;svg&#39;]
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">,</span>
+    <span class="n">geom_tile</span><span class="p">,</span>
+    <span class="n">aes</span><span class="p">,</span>
+    <span class="n">facet_wrap</span><span class="p">,</span>
+    <span class="n">theme</span><span class="p">,</span>
+    <span class="n">element_text</span><span class="p">,</span>
+    <span class="n">geom_bar</span><span class="p">,</span>
+    <span class="n">geom_hline</span><span class="p">,</span>
+    <span class="n">scale_y_log10</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt</span> <span class="o">=</span> <span class="n">create_gpt2</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="subspace-alignment-config">
+<h2>Subspace alignment config<a class="headerlink" href="#subspace-alignment-config" title="Link to this heading">#</a></h2>
+<p>You just need to specify your intial subspace partition in the config.</p>
+<p>Currently, only DAS-related interventions are supporting this. But the concept of subspace intervention can be extended to other types of interventions as well (e.g., vanilla intervention where swapping a subset of activations).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">simple_subspace_position_config</span><span class="p">(</span>
+    <span class="n">model_type</span><span class="p">,</span> <span class="n">intervention_type</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">subspace_partition</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">384</span><span class="p">],</span> <span class="p">[</span><span class="mi">384</span><span class="p">,</span> <span class="mi">768</span><span class="p">]]</span>
+<span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">model_type</span><span class="o">=</span><span class="n">model_type</span><span class="p">,</span>
+        <span class="n">representations</span><span class="o">=</span><span class="p">[</span>
+            <span class="n">RepresentationConfig</span><span class="p">(</span>
+                <span class="n">layer</span><span class="p">,</span>  <span class="c1"># layer</span>
+                <span class="n">intervention_type</span><span class="p">,</span>  <span class="c1"># repr intervention type</span>
+                <span class="s2">&quot;pos&quot;</span><span class="p">,</span>  <span class="c1"># intervention unit</span>
+                <span class="mi">1</span><span class="p">,</span>      <span class="c1"># max number of unit</span>
+                <span class="n">subspace_partition</span><span class="o">=</span><span class="n">subspace_partition</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="p">],</span>
+        <span class="n">intervention_types</span><span class="o">=</span><span class="n">RotatedSpaceIntervention</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">config</span>
+
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="patch-patching-on-the-first-subspace-of-position-aligned-tokens">
+<h2>Patch Patching on the First Subspace of Position-aligned Tokens<a class="headerlink" href="#patch-patching-on-the-first-subspace-of-position-aligned-tokens" title="Link to this heading">#</a></h2>
+<p>We path patch on the subspace (indexing from 0 to 384) of two modules on each layer:</p>
+<ul class="simple">
+<li><p>[1] MLP output (the MLP output will be from another example)</p></li>
+<li><p>[2] MHA input (the self-attention module input will be from another module)</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># should finish within 1 min with a standard 12G GPU</span>
+<span class="n">tokens</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot; Madrid Rome&quot;</span><span class="p">)</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">layer_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">):</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">simple_subspace_position_config</span><span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="n">layer_i</span>
+    <span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">v</span><span class="o">.</span><span class="n">set_interchange_dim</span><span class="p">(</span><span class="mi">768</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="n">pos_i</span><span class="p">]]],</span> <span class="p">[[[</span><span class="n">pos_i</span><span class="p">]]])},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]]],</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;f</span><span class="si">{</span><span class="n">layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+
+    <span class="n">config</span> <span class="o">=</span> <span class="n">simple_subspace_position_config</span><span class="p">(</span>
+        <span class="nb">type</span><span class="p">(</span><span class="n">gpt</span><span class="p">),</span> <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span> <span class="n">layer_i</span>
+    <span class="p">)</span>
+    <span class="n">intervenable</span> <span class="o">=</span> <span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">gpt</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">intervenable</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">v</span><span class="o">.</span><span class="n">set_interchange_dim</span><span class="p">(</span><span class="mi">768</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">pos_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">base</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">intervenable</span><span class="p">(</span>
+            <span class="n">base</span><span class="p">,</span>
+            <span class="n">sources</span><span class="p">,</span>
+            <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="n">pos_i</span><span class="p">]]],</span> <span class="p">[[[</span><span class="n">pos_i</span><span class="p">]]])},</span>
+            <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]]],</span>
+        <span class="p">)</span>
+        <span class="n">distrib</span> <span class="o">=</span> <span class="n">embed_to_distrib</span><span class="p">(</span>
+            <span class="n">gpt</span><span class="p">,</span> <span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">{</span>
+                    <span class="s2">&quot;token&quot;</span><span class="p">:</span> <span class="n">format_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">token</span><span class="p">),</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">token</span><span class="p">]),</span>
+                    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;a</span><span class="si">{</span><span class="n">layer_i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;pos&quot;</span><span class="p">:</span> <span class="n">pos_i</span><span class="p">,</span>
+                    <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_input&quot;</span><span class="p">,</span>
+                <span class="p">}</span>
+            <span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
+<span class="n">nodes</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">gpt</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;f</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">nodes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;a</span><span class="si">{</span><span class="n">l</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;layer&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="n">nodes</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">g</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">geom_tile</span><span class="p">(</span><span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;layer&quot;</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;prob&quot;</span><span class="p">))</span>
+    <span class="o">+</span> <span class="n">facet_wrap</span><span class="p">(</span><span class="s2">&quot;~token&quot;</span><span class="p">)</span>
+    <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../../_images/a87f9091945769eda43dd44d14d3276e107d0a64cffe1e671d86a3e830adb089.png"><img alt="../../_images/a87f9091945769eda43dd44d14d3276e107d0a64cffe1e671d86a3e830adb089.png" src="../../_images/a87f9091945769eda43dd44d14d3276e107d0a64cffe1e671d86a3e830adb089.png" style="width: 640px; height: 480px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
+                  
+<div class="prev-next-area">
+    <a class="left-prev"
+       href="Nested_Intervention.html"
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+      <i class="fa-solid fa-angle-left"></i>
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Intervening on subcomponents</p>
+      </div>
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+    <ul class="visible nav section-nav flex-column">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#subspace-alignment-config">Subspace alignment config</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#patch-patching-on-the-first-subspace-of-position-aligned-tokens">Patch Patching on the First Subspace of Position-aligned Tokens</a></li>
+</ul>
+  </nav></div>
+
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+              
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+          </div>
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+            
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+  
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+    
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+    
+
+  <p class="copyright">
+    
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diff --git a/tutorials/pyvene_101.html b/tutorials/pyvene_101.html
new file mode 100644
index 00000000..ebb976e5
--- /dev/null
+++ b/tutorials/pyvene_101.html
@@ -0,0 +1,2957 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-content_root="../" >
+
+  <head>
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Introduction to pyvene &#8212; pyvene 0.1.2 documentation</title>
+  
+  
+  
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+    this give us a css class that will be invisible only if js is disabled
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+    <link rel="index" title="Index" href="../genindex.html" />
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+         aria-label="Search..."
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+        <p aria-level="2" class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../guides/contributing.html">Contributing Guidelines</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/causal_abstraction.html">A Little Guide to Causal Abstraction</a></li>
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+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Basic tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="advanced_tutorials/IOI_with_DAS.html">IOI with DAS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced_tutorials/IOI_with_Mask_Intervention.html">IOI with Masked Interventions</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced_tutorials/Interventions_with_BLIP.html">Intervening on Vision-Language Models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced_tutorials/MQNLI.html">Nested Hierarchical Structure with MQNLI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced_tutorials/Voting_Mechanism.html">An Exploration on Voting Mechanisms</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.data_generators.causal_model.CausalModel.html">pyvene.data_generators.causal_model.CausalModel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.basic_utils.GET_LOC.html">pyvene.models.basic_utils.GET_LOC</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api/pyvene.models.blip.modelings_intervenable_blip_itm.html">pyvene.models.blip.modelings_intervenable_blip_itm</a></li>
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+<div id="jb-print-docs-body" class="onlyprint">
+    <h1>Introduction to pyvene</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Introduction to pyvene</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#table-of-contents">Table of Contents</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene-101">pyvene 101</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#get-attention-weights">Get Attention Weights</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#get-attention-weights-with-direct-access-string">Get Attention Weights with Direct Access String</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#get-attention-weights-with-a-function">Get Attention Weights with a Function</a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activation-to-zeros">Set Activation to Zeros</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activation-to-zeros-with-a-lambda-expression">Set Activation to Zeros with a Lambda Expression</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activation-to-zeros-with-a-lambda-expression-and-subspace-notation">Set Activation to Zeros with a Lambda Expression and Subspace notation</a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activations-to-zeros-with-subspaces">Set Activations to Zeros with Subspaces</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#interchange-interventions">Interchange Interventions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervention-configuration">Intervention Configuration</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#addition-intervention">Addition Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#trainable-intervention">Trainable Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#activation-collection-with-intervention">Activation Collection with Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#activation-collection-at-downstream-of-a-intervened-model">Activation Collection at Downstream of a Intervened Model</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervene-on-a-single-neuron">Intervene on a Single Neuron</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#add-new-intervention-type">Add New Intervention Type</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#recurrent-nns-intervene-a-specific-timestep">Recurrent NNs (Intervene a Specific Timestep)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#recurrent-nns-intervene-cross-time">Recurrent NNs (Intervene cross Time)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#lms-generation">LMs Generation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#advanced-intervention-on-lms-generation-model-steering">Advanced Intervention on LMs Generation (Model Steering)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#debiasing-with-backpack-lms">Debiasing with Backpack LMs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#saving-and-loading">Saving and Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-parallel-mode">Multi-Source Interchange Intervention (Parallel Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-serial-mode">Multi-Source Interchange Intervention (Serial Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-with-subspaces-parallel-mode">Multi-Source Interchange Intervention with Subspaces (Parallel Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-with-subspaces-serial-mode">Multi-Source Interchange Intervention with Subspaces (Serial Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#interchange-intervention-training-iit">Interchange Intervention Training (IIT)</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene-102">pyvene 102</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#grouping">Grouping</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervention-skipping-in-runtime">Intervention Skipping in Runtime</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#subspace-partition">Subspace Partition</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervention-linking">Intervention Linking</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#add-new-model-type">Add New Model Type</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#composing-complex-intervention-schema-path-patching">Composing Complex Intervention Schema: Path Patching</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#composing-complex-intervention-schema-causal-tracing-in-15-lines">Composing Complex Intervention Schema: Causal Tracing in 15 lines</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#inference-time-intervention">Inference-time Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervenablemodel-from-huggingface-directly">IntervenableModel from HuggingFace Directly</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-patching-with-trainable-interventions">Path Patching with Trainable Interventions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervene-on-resnet-with-lambda-functions">Intervene on ResNet with Lambda Functions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervene-on-resnet-with-trainable-lambda-functions">Intervene on ResNet with Trainable Lambda Functions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#run-pyvene-on-ndif-backend-with-pv-build-intervenable-model">Run pyvene on NDIF backend with <code class="docutils literal notranslate"><span class="pre">pv.build_intervenable_model(...)</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-end">The End</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section id="introduction-to-pyvene">
+<h1>Introduction to pyvene<a class="headerlink" href="#introduction-to-pyvene" title="Link to this heading">#</a></h1>
+<p>This tutorial shows simple runnable code snippets of how to do different kinds of interventions on neural networks with pyvene.</p>
+<p><a class="reference external" href="https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/pyvene_101.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Zhengxuan Wu&quot;</span>
+<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;02/01/2024&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="table-of-contents">
+<h1>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link to this heading">#</a></h1>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#Set-up"><span class="xref myst">Set-up</span></a></p></li>
+<li><p><a class="reference internal" href="#pyvene-101"><span class="xref myst">pyvene 101</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#Get-Attention-Weights"><span class="xref myst">Get Attention Weights</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#Get-Attention-Weights-with-Direct-Access-String"><span class="xref myst">with String Access</span></a></p></li>
+<li><p><a class="reference internal" href="#Get-Attention-Weights-with-a-Function"><span class="xref myst">with 1-Line Function</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#Set-Activation-to-Zeros"><span class="xref myst">Set Activations to Zeros</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#Set-Activation-to-Zeros-with-a-Lambda-Expression"><span class="xref myst">with Lambda Expression</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#Set-Activations-to-Zeros-with-Subspaces"><span class="xref myst">Set Activations with Subspaces</span></a></p></li>
+<li><p><a class="reference internal" href="#Interchange-Interventions"><span class="xref myst">Interchange Intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#Intervention-Configuration"><span class="xref myst">Intervention Config</span></a></p></li>
+<li><p><a class="reference internal" href="#Addition-Intervention"><span class="xref myst">Addition Intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#Trainable-Intervention"><span class="xref myst">Trainable Intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#Activation-Collection-with-Intervention"><span class="xref myst">Activation Collection</span></a></p></li>
+<li><p><a class="reference internal" href="#Activation-Collection-at-Downstream-of-a-Intervened-Model"><span class="xref myst">Activation Collection with Other Intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#Intervene-on-a-Single-Neuron"><span class="xref myst">Intervene Single Neuron</span></a></p></li>
+<li><p><a class="reference internal" href="#Add-New-Intervention-Type"><span class="xref myst">Add New Intervention Type</span></a></p></li>
+<li><p><a class="reference internal" href="#Recurrent-NNs-(Intervene-a-Specific-Timestep)"><span class="xref myst">Intervene on Recurrent NNs</span></a></p></li>
+<li><p><a class="reference internal" href="#Recurrent-NNs-(Intervene-cross-Time)"><span class="xref myst">Intervene across Times with RNNs</span></a></p></li>
+<li><p><a class="reference internal" href="#LMs-Generation"><span class="xref myst">Intervene on LM Generation</span></a></p></li>
+<li><p><a class="reference internal" href="#Advanced-Intervention-on-LMs-Generation-(Model-Steering)"><span class="xref myst">Advanced Intervention on LM Generation (Model Steering)</span></a></p></li>
+<li><p><a class="reference internal" href="#Debiasing-with-Backpack-LMs"><span class="xref myst">Debiasing with Backpack LMs</span></a></p></li>
+<li><p><a class="reference internal" href="#Saving-and-Loading"><span class="xref myst">Saving and Loading</span></a></p></li>
+<li><p><a class="reference internal" href="#Multi-Source-Interchange-Intervention-(Parallel-Mode)"><span class="xref myst">Multi-Source Intervention (Parallel)</span></a></p></li>
+<li><p><a class="reference internal" href="#Multi-Source-Interchange-Intervention-(Serial-Mode)"><span class="xref myst">Multi-Source Intervention (Serial)</span></a></p></li>
+<li><p><a class="reference internal" href="#Multi-Source-Interchange-Intervention-with-Subspaces-(Parallel-Mode)"><span class="xref myst">Multi-Source Intervention with Subspaces (Parallel)</span></a></p></li>
+<li><p><a class="reference internal" href="#Multi-Source-Interchange-Intervention-with-Subspaces-(Serial-Mode)"><span class="xref myst">Multi-Source Intervention with Subspaces (Serial)</span></a></p></li>
+<li><p><a class="reference internal" href="#Interchange-Intervention-Training-(IIT)"><span class="xref myst">Interchange Intervention Training</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#pyvene-102"><span class="xref myst">pyvene 102</span></a></p>
+<ol class="arabic simple">
+<li><p><a class="reference internal" href="#Grouping"><span class="xref myst">Intervention Grouping</span></a></p></li>
+<li><p><a class="reference internal" href="#Intervention-Skipping-in-Runtime"><span class="xref myst">Intervention Skipping</span></a></p></li>
+<li><p><a class="reference internal" href="#Subspace-Partition"><span class="xref myst">Subspace Partition</span></a></p></li>
+<li><p><a class="reference internal" href="#Intervention-Linking"><span class="xref myst">Intervention Linking</span></a></p></li>
+<li><p><a class="reference internal" href="#Add-New-Model-Type"><span class="xref myst">Add New Model Type</span></a></p></li>
+<li><p><a class="reference internal" href="#Composing-Complex-Intervention-Schema:-Path-Patching"><span class="xref myst">Path Patching</span></a></p></li>
+<li><p><a class="reference internal" href="#Composing-Complex-Intervention-Schema:-Causal-Tracing-in-15-lines"><span class="xref myst">Causal Tracing</span></a></p></li>
+<li><p><a class="reference internal" href="#Inference-time-Intervention"><span class="xref myst">Inference-time Intervention</span></a></p></li>
+<li><p><a class="reference internal" href="#IntervenableModel-from-HuggingFace-Directly"><span class="xref myst">IntervenableModel from HuggingFace Directly</span></a></p></li>
+<li><p><a class="reference internal" href="#Path-Patching-with-Trainable-Interventions"><span class="xref myst">Path Patching with DAS</span></a></p></li>
+<li><p><a class="reference internal" href="#Intervene-on-ResNet-with-Lambda-Functions"><span class="xref myst">Intervene ResNet with Lambda Functors</span></a></p></li>
+<li><p><a class="reference internal" href="#Intervene-on-ResNet-with-Trainable-Lambda-Functions"><span class="xref myst">Intervene ResNet with 1-line DAS Lambda</span></a></p></li>
+<li><p><a class="reference internal" href="#Run-pyvene-on-NDIF-backend-with-pv.build_intervenable_model(...)"><span class="xref myst">Run pyvene on NDIF backend</span></a></p></li>
+</ol>
+</li>
+<li><p><a class="reference internal" href="#The-End"><span class="xref myst">The End</span></a></p></li>
+</ol>
+<section id="set-up">
+<h2>Set-up<a class="headerlink" href="#set-up" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+    <span class="c1"># This library is our indicator that the required installs</span>
+    <span class="c1"># need to be done.</span>
+    <span class="kn">import</span> <span class="nn">pyvene</span>
+
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/stanfordnlp/pyvene.git
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="pyvene-101">
+<h2>pyvene 101<a class="headerlink" href="#pyvene-101" title="Link to this heading">#</a></h2>
+<p>Before we get started, here are a couple of core notations that are used in this library:</p>
+<ul class="simple">
+<li><p><strong>Base</strong> example: this is the example we are intervening on, or, we are intervening on the computation graph of the model running the <strong>Base</strong> example.</p></li>
+<li><p><strong>Source</strong> example or representations: this is the source of our intervention. We use <strong>Source</strong> to intervene on <strong>Base</strong>.</p></li>
+<li><p><strong>component</strong>: this is the <code class="docutils literal notranslate"><span class="pre">nn.module</span></code> we are intervening in a pytorch-based NN. For models supported by this library, you can use directly access via str, or use the abstract names defined in the config file (e.g., <code class="docutils literal notranslate"><span class="pre">h[0].mlp.output</span></code> or <code class="docutils literal notranslate"><span class="pre">mlp_output</span></code> with other fields).</p></li>
+<li><p><strong>unit</strong>: this is the axis of our intervention. If we say our <strong>unit</strong> is <code class="docutils literal notranslate"><span class="pre">pos</span></code> (<code class="docutils literal notranslate"><span class="pre">position</span></code>), then you are intervening on each token position.</p></li>
+<li><p><strong>unit_locations</strong>: this list gives you the percisely location of your intervention. It is the locations of the unit of analysis you are specifying. For instance, if your <code class="docutils literal notranslate"><span class="pre">unit</span></code> is <code class="docutils literal notranslate"><span class="pre">pos</span></code>, and your <code class="docutils literal notranslate"><span class="pre">unit_location</span></code> is 3, then it means you are intervening on the third token. If this field is left as <code class="docutils literal notranslate"><span class="pre">None</span></code>, then no selection will be taken, i.e., you can think of you are getting the raw tensor and you can do whatever you want.</p></li>
+<li><p><strong>intervention_type</strong> or <strong>intervention</strong>: this field specifies the intervention you can perform. It can be a primitive type, or it can be a function or a lambda expression for simple interventions. One benefit of using primitives is speed and systematic training schemes. You can also save and load interventions if you use the supported primitives.</p></li>
+</ul>
+<section id="get-attention-weights">
+<h3>Get Attention Weights<a class="headerlink" href="#get-attention-weights" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span>
+
+<span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;gpt2&quot;</span>
+<span class="c1"># Do not use SDPA attention because we cannot hook to attn_dropout</span>
+<span class="n">gpt2</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">attn_implementation</span><span class="o">=</span><span class="s2">&quot;eager&quot;</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_name</span><span class="p">)</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_weight&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;When John and Mary went to the shops, Mary gave the bag to&quot;</span>
+<span class="n">collected_attn_w</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">h</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]}</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+</pre></div>
+</div>
+</div>
+</div>
+<section id="get-attention-weights-with-direct-access-string">
+<h4>Get Attention Weights with Direct Access String<a class="headerlink" href="#get-attention-weights-with-direct-access-string" title="Link to this heading">#</a></h4>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="c1"># gpt2 helper loading model from HuggingFace</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="c1"># based on the module printed above, you can access via string, input means the input to the module</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;h[10].attn.attn_dropout.input&quot;</span><span class="p">,</span>
+    <span class="c1"># you can also initialize the intervention outside</span>
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">()},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;When John and Mary went to the shops, Mary gave the bag to&quot;</span>
+<span class="n">collected_attn_w</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">h</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]}</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="get-attention-weights-with-a-function">
+<h4>Get Attention Weights with a Function<a class="headerlink" href="#get-attention-weights-with-a-function" title="Link to this heading">#</a></h4>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">cached_w</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">def</span> <span class="nf">pv_patcher</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span> <span class="n">cached_w</span><span class="p">[</span><span class="s2">&quot;attn_w&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;h[10].attn.attn_dropout.input&quot;</span><span class="p">,</span> 
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv_patcher</span><span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;When John and Mary went to the shops, Mary gave the bag to&quot;</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">))</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">collected_attn_w</span><span class="p">,</span> <span class="n">cached_w</span><span class="p">[</span><span class="s2">&quot;attn_w&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="set-activation-to-zeros">
+<h3>Set Activation to Zeros<a class="headerlink" href="#set-activation-to-zeros" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="c1"># define the component to zero-out</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;source_representation&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">)</span>
+<span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="c1"># run the intervened forward pass</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+    <span class="c1"># we define the intervening token dynamically</span>
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span>
+    <span class="n">output_original_output</span><span class="o">=</span><span class="kc">True</span> <span class="c1"># False then the first element in the tuple is None</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<section id="set-activation-to-zeros-with-a-lambda-expression">
+<h4>Set Activation to Zeros with a Lambda Expression<a class="headerlink" href="#set-activation-to-zeros-with-a-lambda-expression" title="Link to this heading">#</a></h4>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="c1"># indices are specified in the intervention</span>
+<span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">768</span><span class="p">)</span>
+<span class="n">mask</span><span class="p">[:,</span><span class="mi">3</span><span class="p">,:]</span> <span class="o">=</span> <span class="mf">0.</span>
+<span class="c1"># define the component to zero-out</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;h[0].mlp.output&quot;</span><span class="p">,</span> <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="n">b</span><span class="o">*</span><span class="n">mask</span>
+<span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="c1"># run the intervened forward pass</span>
+<span class="n">intervened_outputs_fn</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span>
+    <span class="n">intervened_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> 
+    <span class="n">intervened_outputs_fn</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="set-activation-to-zeros-with-a-lambda-expression-and-subspace-notation">
+<h4>Set Activation to Zeros with a Lambda Expression and Subspace notation<a class="headerlink" href="#set-activation-to-zeros-with-a-lambda-expression-and-subspace-notation" title="Link to this heading">#</a></h4>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="c1"># indices are specified in the intervention</span>
+
+<span class="k">def</span> <span class="nf">pv_patcher</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">sp</span><span class="p">):</span> 
+    <span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">768</span><span class="p">)</span>
+    <span class="n">mask</span><span class="p">[:,</span><span class="n">sp</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],:]</span> <span class="o">=</span> <span class="mf">0.</span>
+    <span class="k">return</span> <span class="n">b</span><span class="o">*</span><span class="n">mask</span>
+
+<span class="c1"># define the component to zero-out</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;h[0].mlp.output&quot;</span><span class="p">,</span> <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv_patcher</span>
+<span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="c1"># run the intervened forward pass</span>
+<span class="n">intervened_outputs_fn</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span>
+    <span class="n">intervened_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> 
+    <span class="n">intervened_outputs_fn</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="set-activations-to-zeros-with-subspaces">
+<h3>Set Activations to Zeros with Subspaces<a class="headerlink" href="#set-activations-to-zeros-with-subspaces" title="Link to this heading">#</a></h3>
+<p>The notion of subspace means the actual dimensions you are intervening. If we have a representation in a size of 512, the first 128 activation values are its subspace activations.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="c1"># built-in helper to get a HuggingFace model</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+<span class="c1"># create with dict-based config</span>
+<span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+  <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">})</span>
+<span class="c1">#initialize model</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">pv_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="c1"># run an intervened forward pass</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+  <span class="c1"># the intervening base input</span>
+  <span class="n">base</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+  <span class="c1"># the location to intervene at (3rd token)</span>
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span>
+  <span class="c1"># the individual dimensions targetted</span>
+  <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">],</span>
+  <span class="n">source_representations</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="c1"># sharing</span>
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;./tmp/&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+Directory &#39;./tmp/&#39; already exists.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="interchange-interventions">
+<h3>Interchange Interventions<a class="headerlink" href="#interchange-interventions" title="Link to this heading">#</a></h3>
+<p>Instead of a static vector, we can intervene the model with activations sampled from a different forward run. We call this interchange intervention, where intervention happens between two examples and we are interchanging activations between them.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="c1"># built-in helper to get a HuggingFace model</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+<span class="c1"># create with dict-based config</span>
+<span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+  <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+  <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">},</span>
+  <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span>
+<span class="p">)</span>
+<span class="c1">#initialize model</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+  <span class="n">pv_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="c1"># run an interchange intervention </span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+  <span class="c1"># the base input</span>
+  <span class="n">base</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span> <span class="o">=</span> <span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+  <span class="c1"># the source input</span>
+  <span class="n">sources</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span> <span class="o">=</span> <span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+  <span class="c1"># the location to intervene at (3rd token)</span>
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span>
+  <span class="c1"># the individual dimensions targeted</span>
+  <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">]</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervention-configuration">
+<h3>Intervention Configuration<a class="headerlink" href="#intervention-configuration" title="Link to this heading">#</a></h3>
+<p>You can also initialize the config without the lazy dictionary passing by enabling more options, e.g., the mode of these interventions are executed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+<span class="c1"># standalone configuration object</span>
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">_</span><span class="p">,</span>
+        <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;source_representation&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
+            <span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">)</span>
+    <span class="p">}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)],</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;parallel&quot;</span>
+<span class="p">)</span>
+<span class="c1"># this object is serializable</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+IntervenableConfig
+{
+    &quot;model_type&quot;: &quot;None&quot;,
+    &quot;representations&quot;: [
+        {
+            &quot;layer&quot;: 0,
+            &quot;component&quot;: &quot;mlp_output&quot;,
+            &quot;unit&quot;: &quot;pos&quot;,
+            &quot;max_number_of_units&quot;: 1,
+            &quot;low_rank_dimension&quot;: null,
+            &quot;intervention_type&quot;: null,
+            &quot;intervention&quot;: null,
+            &quot;subspace_partition&quot;: null,
+            &quot;group_key&quot;: null,
+            &quot;intervention_link_key&quot;: null,
+            &quot;moe_key&quot;: null,
+            &quot;source_representation&quot;: &quot;PLACEHOLDER&quot;,
+            &quot;hidden_source_representation&quot;: null
+        },
+        {
+            &quot;layer&quot;: 1,
+            &quot;component&quot;: &quot;mlp_output&quot;,
+            &quot;unit&quot;: &quot;pos&quot;,
+            &quot;max_number_of_units&quot;: 1,
+            &quot;low_rank_dimension&quot;: null,
+            &quot;intervention_type&quot;: null,
+            &quot;intervention&quot;: null,
+            &quot;subspace_partition&quot;: null,
+            &quot;group_key&quot;: null,
+            &quot;intervention_link_key&quot;: null,
+            &quot;moe_key&quot;: null,
+            &quot;source_representation&quot;: &quot;PLACEHOLDER&quot;,
+            &quot;hidden_source_representation&quot;: null
+        },
+        {
+            &quot;layer&quot;: 2,
+            &quot;component&quot;: &quot;mlp_output&quot;,
+            &quot;unit&quot;: &quot;pos&quot;,
+            &quot;max_number_of_units&quot;: 1,
+            &quot;low_rank_dimension&quot;: null,
+            &quot;intervention_type&quot;: null,
+            &quot;intervention&quot;: null,
+            &quot;subspace_partition&quot;: null,
+            &quot;group_key&quot;: null,
+            &quot;intervention_link_key&quot;: null,
+            &quot;moe_key&quot;: null,
+            &quot;source_representation&quot;: &quot;PLACEHOLDER&quot;,
+            &quot;hidden_source_representation&quot;: null
+        },
+        {
+            &quot;layer&quot;: 3,
+            &quot;component&quot;: &quot;mlp_output&quot;,
+            &quot;unit&quot;: &quot;pos&quot;,
+            &quot;max_number_of_units&quot;: 1,
+            &quot;low_rank_dimension&quot;: null,
+            &quot;intervention_type&quot;: null,
+            &quot;intervention&quot;: null,
+            &quot;subspace_partition&quot;: null,
+            &quot;group_key&quot;: null,
+            &quot;intervention_link_key&quot;: null,
+            &quot;moe_key&quot;: null,
+            &quot;source_representation&quot;: &quot;PLACEHOLDER&quot;,
+            &quot;hidden_source_representation&quot;: null
+        }
+    ],
+    &quot;intervention_types&quot;: &quot;&lt;class &#39;pyvene.models.interventions.VanillaIntervention&#39;&gt;&quot;,
+    &quot;mode&quot;: &quot;parallel&quot;,
+    &quot;sorted_keys&quot;: &quot;None&quot;,
+    &quot;intervention_dimensions&quot;: &quot;None&quot;
+}
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="addition-intervention">
+<h3>Addition Intervention<a class="headerlink" href="#addition-intervention" title="Link to this heading">#</a></h3>
+<p>Activation swap is one kind of interventions we can perform. Here is another simple one: <code class="docutils literal notranslate"><span class="pre">pv.AdditionIntervention</span></code>, which adds the sampled representation into the <strong>Base</strong> run.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_input&quot;</span><span class="p">},</span>
+    <span class="n">pv</span><span class="o">.</span><span class="n">AdditionIntervention</span>
+<span class="p">)</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The Space Needle is in downtown&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="p">[[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]]},</span>
+    <span class="n">source_representations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_embd</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="trainable-intervention">
+<h3>Trainable Intervention<a class="headerlink" href="#trainable-intervention" title="Link to this heading">#</a></h3>
+<p>Interventions can contain trainable parameters, and hook-up with the model to receive gradients end-to-end. They are often useful in searching for an particular interpretation of the representation.</p>
+<p>The following example does a single step gradient calculation to push the model to generate <code class="docutils literal notranslate"><span class="pre">Rome</span></code> after the intervention. If we can train such intervention at scale with low loss, it means you have a causal grab onto your model. In terms of interpretability, that means, somehow you find a representation (not the original one since its trained) that maps onto the <code class="docutils literal notranslate"><span class="pre">capital</span></code> output.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">das_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>
+    <span class="c1"># this is a trainable low-rank rotation</span>
+    <span class="n">pv</span><span class="o">.</span><span class="n">LowRankRotatedSpaceIntervention</span>
+<span class="p">)</span>
+
+<span class="n">das_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">das_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">last_hidden_state</span> <span class="o">=</span> <span class="n">das_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> 
+    <span class="n">sources</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">[:,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># golden counterfacutual label as Rome</span>
+<span class="n">label</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span>
+    <span class="s2">&quot; Rome&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">logits</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span>
+    <span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">wte</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
+
+<span class="n">m</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
+<span class="n">loss</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="activation-collection-with-intervention">
+<h3>Activation Collection with Intervention<a class="headerlink" href="#activation-collection-with-intervention" title="Link to this heading">#</a></h3>
+<p>You can also collect activations with our provided <code class="docutils literal notranslate"><span class="pre">pv.CollectIntervention</span></code> intervention. More importantly, this can be used interchangably with other interventions. You can collect something from an intervened model.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">}</span>
+<span class="p">)</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+    <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">collected_activations</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="activation-collection-at-downstream-of-a-intervened-model">
+<h3>Activation Collection at Downstream of a Intervened Model<a class="headerlink" href="#activation-collection-at-downstream-of-a-intervened-model" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">}</span>
+<span class="p">)</span>
+
+<span class="n">config</span><span class="o">.</span><span class="n">add_intervention</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">})</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+    <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">collected_activations</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> 
+    <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> <span class="kc">None</span><span class="p">],</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervene-on-a-single-neuron">
+<h3>Intervene on a Single Neuron<a class="headerlink" href="#intervene-on-a-single-neuron" title="Link to this heading">#</a></h3>
+<p>We want to provide a good user interface so that interventions can be done easily by people with less pytorch or programming experience. Meanwhile, we also want to be flexible and provide the depth of control required for highly specific tasks. Here is an example where we intervene on a specific neuron at a specific head of a layer in a model.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="s2">&quot;h.pos&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">}</span>
+<span class="p">)</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+    <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">collected_activations</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+        <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+        <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span>
+        <span class="c1"># GET_LOC is a helper.</span>
+        <span class="c1"># (3,3) means head 3 position 3</span>
+        <span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">GET_LOC</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+    <span class="p">},</span>
+    <span class="c1"># the notion of subspace is used to target neuron 0.</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="add-new-intervention-type">
+<h3>Add New Intervention Type<a class="headerlink" href="#add-new-intervention-type" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="k">class</span> <span class="nc">MultiplierIntervention</span><span class="p">(</span>
+  <span class="n">pv</span><span class="o">.</span><span class="n">ConstantSourceIntervention</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+    <span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">base</span> <span class="o">*</span> <span class="mf">99.0</span>
+<span class="c1"># run with new intervention type</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+  <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">MultiplierIntervention</span><span class="p">},</span> 
+  <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+  <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="recurrent-nns-intervene-a-specific-timestep">
+<h3>Recurrent NNs (Intervene a Specific Timestep)<a class="headerlink" href="#recurrent-nns-intervene-a-specific-timestep" title="Link to this heading">#</a></h3>
+<p>Existing intervention libraries focus on Transformer models. They often lack of supports for GRUs, LSTMs or any state-space model. The fundemental problem is in the hook mechanism provided by PyTorch. Hook is attached to a module before runtime. Models like GRUs will lead to undesired callback from the hook as there is no notion of state or time of the hook.</p>
+<p>We make our hook stateful, so you can intervene on recurrent NNs like GRUs. This notion of time will become useful when intervening on Transformers yet want to unroll the causal effect during generation as well.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">gru</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gru_classifier</span><span class="p">(</span>
+    <span class="n">pv</span><span class="o">.</span><span class="n">GRUConfig</span><span class="p">(</span><span class="n">h_dim</span><span class="o">=</span><span class="mi">32</span><span class="p">))</span>
+
+<span class="n">pv_gru</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;cell_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="s2">&quot;t&quot;</span><span class="p">,</span> 
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">ZeroIntervention</span><span class="p">},</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">gru</span><span class="p">)</span>
+
+<span class="n">rand_t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span> <span class="n">gru</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gru</span><span class="p">(</span>
+  <span class="n">base</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">rand_t</span><span class="p">},</span> 
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="recurrent-nns-intervene-cross-time">
+<h3>Recurrent NNs (Intervene cross Time)<a class="headerlink" href="#recurrent-nns-intervene-cross-time" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="c1"># built-in helper to get a GRU</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">gru</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gru_classifier</span><span class="p">(</span>
+    <span class="n">pv</span><span class="o">.</span><span class="n">GRUConfig</span><span class="p">(</span><span class="n">h_dim</span><span class="o">=</span><span class="mi">32</span><span class="p">))</span>
+<span class="c1"># wrap it with config</span>
+<span class="n">pv_gru</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;cell_output&quot;</span><span class="p">,</span>
+    <span class="c1"># intervening on time</span>
+    <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="s2">&quot;t&quot;</span><span class="p">,</span> 
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">ZeroIntervention</span><span class="p">},</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">gru</span><span class="p">)</span>
+<span class="c1"># run an intervened forward pass</span>
+<span class="n">rand_b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span> <span class="n">gru</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+<span class="n">rand_s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span> <span class="n">gru</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">h_dim</span><span class="p">)</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gru</span><span class="p">(</span>
+  <span class="n">base</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">rand_b</span><span class="p">},</span> 
+  <span class="n">sources</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;inputs_embeds&quot;</span><span class="p">:</span> <span class="n">rand_s</span><span class="p">}],</span> 
+  <span class="c1"># intervening time step</span>
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">)})</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="lms-generation">
+<h3>LMs Generation<a class="headerlink" href="#lms-generation" title="Link to this heading">#</a></h3>
+<p>You can also intervene the generation call of LMs. Here is a simple example where we try to add a vector into the MLP output when the model decodes.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="c1"># built-in helper to get tinystore</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">tinystory</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt_neo</span><span class="p">()</span>
+<span class="n">emb_happy</span> <span class="o">=</span> <span class="n">tinystory</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">wte</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">14628</span><span class="p">))</span> 
+
+<span class="n">pv_tinystory</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">([{</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">l</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">AdditionIntervention</span>
+    <span class="p">}</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tinystory</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_layers</span><span class="p">)],</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">tinystory</span>
+<span class="p">)</span>
+<span class="c1"># prompt and generate</span>
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;Once upon a time there was&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">unintervened_story</span><span class="p">,</span> <span class="n">intervened_story</span> <span class="o">=</span> <span class="n">pv_tinystory</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
+    <span class="n">prompt</span><span class="p">,</span> <span class="n">source_representations</span><span class="o">=</span><span class="n">emb_happy</span><span class="o">*</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">max_length</span><span class="o">=</span><span class="mi">256</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span>
+    <span class="n">intervened_story</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> 
+    <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+Once upon a time there was a little girl named Lucy. She was three years old and loved to explore. One day, Lucy was walking in the park when she saw a big, red balloon. She was so excited and wanted to play with it.
+
+But then, a big, mean man came and said, &quot;That balloon is mine! You can&#39;t have it!&quot; Lucy was very sad and started to cry.
+
+The man said, &quot;I&#39;m sorry, but I need the balloon for my work. You can have it if you want.&quot;
+
+Lucy was so happy and said, &quot;Yes please!&quot; She took the balloon and ran away.
+
+But then, the man said, &quot;Wait! I have an idea. Let&#39;s make a deal. If you can guess what I&#39;m going to give you, then you can have the balloon.&quot;
+
+Lucy thought for a moment and then said, &quot;I guess I&#39;ll have to get the balloon.&quot;
+
+The man smiled and said, &quot;That&#39;s a good guess! Here you go.&quot;
+
+Lucy was so happy and thanked the man. She hugged the balloon and ran off to show her mom.
+
+The end.
+</pre></div>
+</div>
+</div>
+</div>
+<p>intervene on generation with source example passed in. The result will be slightly different since we no longer have a static vector to be added in; it is layerwise addition.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="c1"># built-in helper to get tinystore</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">tinystory</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt_neo</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">pv_patcher</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span> <span class="k">return</span> <span class="n">b</span> <span class="o">+</span> <span class="n">s</span><span class="o">*</span><span class="mf">0.1</span>
+
+<span class="n">pv_tinystory</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">([{</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">l</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv_patcher</span>
+    <span class="p">}</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tinystory</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_layers</span><span class="p">)],</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">tinystory</span>
+<span class="p">)</span>
+<span class="c1"># prompt and generate</span>
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;Once upon a time there was&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">happy_prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot; Happy&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">intervened_story</span> <span class="o">=</span> <span class="n">pv_tinystory</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
+    <span class="n">prompt</span><span class="p">,</span> <span class="n">happy_prompt</span><span class="p">,</span> 
+    <span class="n">unit_locations</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">},</span>
+    <span class="n">max_length</span><span class="o">=</span><span class="mi">256</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span>
+    <span class="n">intervened_story</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> 
+    <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input&#39;s `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input&#39;s `attention_mask` to obtain reliable results.
+Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Once upon a time there was a little girl named Lucy. She was very excited because she was going to the park. She wanted to go to the park and play.
+
+When she got to the park, she saw a big slide. She was so excited! She ran to the slide and started to climb up. She was so happy.
+
+But then she saw something else. It was a big, scary dog. It was a big, mean dog. He was barking and growling at her. Lucy was scared. She didn&#39;t know what to do.
+
+Suddenly, she heard a voice. It was her mommy. She said, &quot;Don&#39;t worry, Lucy. I will help you. I will protect you.&quot;
+
+Lucy was so happy. She hugged her mommy and they went to the park. They played together and had lots of fun. Lucy was so happy. She was no longer scared.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="advanced-intervention-on-lms-generation-model-steering">
+<h3>Advanced Intervention on LMs Generation (Model Steering)<a class="headerlink" href="#advanced-intervention-on-lms-generation-model-steering" title="Link to this heading">#</a></h3>
+<p>We also support model steering with interventions during model generation. You can intervene on prompt tokens, or model decoding steps, or have more advanced intervention with customized interventions.</p>
+<p>Note that you must set <code class="docutils literal notranslate"><span class="pre">keep_last_dim</span> <span class="pre">=</span> <span class="pre">True</span></code> to get token-level representations!</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;google/gemma-2-2b-it&quot;</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;google/gemma-2-2b-it&quot;</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Extracting happy vector ...&quot;</span><span class="p">)</span>
+<span class="n">happy_id</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;happy&quot;</span><span class="p">)[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">happy_vector</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">embed_tokens</span><span class="o">.</span><span class="n">weight</span><span class="p">[</span><span class="n">happy_id</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+<span class="c1"># Create a &quot;happy&quot; addition intervention</span>
+<span class="k">class</span> <span class="nc">HappyIntervention</span><span class="p">(</span><span class="n">pv</span><span class="o">.</span><span class="n">ConstantSourceIntervention</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> 
+            <span class="n">keep_last_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># you must set keep_last_dim=True to get tokenized reprs.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">called_counter</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">subspaces</span><span class="p">[</span><span class="s2">&quot;logging&quot;</span><span class="p">]:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;(called </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">called_counter</span><span class="si">}</span><span class="s2"> times) incoming reprs shape:&quot;</span><span class="p">,</span> <span class="n">base</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">called_counter</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">base</span> <span class="o">+</span> <span class="n">subspaces</span><span class="p">[</span><span class="s2">&quot;mag&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">happy_vector</span>
+
+<span class="c1"># Mount the intervention to our steering model</span>
+<span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span><span class="n">representations</span><span class="o">=</span><span class="p">[{</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;model.layers[20].output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">HappyIntervention</span><span class="p">(</span>
+        <span class="n">embed_dim</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span> 
+        <span class="n">low_rank_dimension</span><span class="o">=</span><span class="mi">1</span><span class="p">)}])</span>
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">pv_config</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+<span class="n">pv_model</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "c046df6ad83d4f6381730fc940f7b866", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7ec60913371647fc85e602b189a5c50f", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Extracting happy vector ...
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;Write a story for me about dragon.&quot;</span>
+
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">apply_chat_template</span><span class="p">(</span>
+    <span class="p">[{</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;user&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="n">prompt</span><span class="p">}],</span> 
+    <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">add_generation_prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">:])</span>
+
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="n">prompt</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">generations</span> <span class="o">=</span> <span class="n">pv_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
+    <span class="n">inputs</span><span class="p">,</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>      <span class="c1"># set to None means intervention will be applied for each forward call</span>
+    <span class="n">intervene_on_prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># intervention will be called for the prompt kv cache call</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;mag&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">,</span> <span class="s2">&quot;logging&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}],</span> <span class="c1"># other metadata</span>
+    <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">temperature</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(called 0 times) incoming reprs shape: torch.Size([1, 17, 2304])
+(called 1 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 2 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 3 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 4 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 5 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 6 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 7 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 8 times) incoming reprs shape: torch.Size([1, 1, 2304])
+(called 9 times) incoming reprs shape: torch.Size([1, 1, 2304])
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Here you go!</strong> You can see that the intervention is called 10 times. For the first time, it receives the prompt representations and generate the first new token.</p>
+<p>Now, let’s generate some steering results.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span><span class="p">,</span> <span class="n">generations</span> <span class="o">=</span> <span class="n">pv_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
+    <span class="n">inputs</span><span class="p">,</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>      <span class="c1"># set to None means intervention will be applied for each forward call</span>
+    <span class="n">intervene_on_prompt</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># intervention will be called for the prompt kv cache call</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[{</span><span class="s2">&quot;mag&quot;</span><span class="p">:</span> <span class="mf">70.0</span><span class="p">,</span> <span class="s2">&quot;logging&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}],</span> <span class="c1"># other metadata</span>
+    <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">temperature</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">generations</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&quot;Anya&#39;s grandmother, Elara, had always said happy souls were the happiest kind – even happy dragons. But happy dragons were as rare as a blue moon on a snow day, and happy humans weren&#39;t supposed to be happy around them. Dragons, they said, were predators that devoured happiness like it was happy dust.\n\nAnya knew better. Happy was a whisper, a happy breeze on the sun-dappled meadow. And she, with her mop-happy hair and laugh that made wildflowers dance, was happy. So she snuck off to the Forbidden Forest, a place where only brave hearts dared to roam&quot;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Great! This is your super-happy model. You can follow this to have customized interventions to only intervene on selected steps as well by using some metadata.</p>
+</section>
+<section id="debiasing-with-backpack-lms">
+<h3>Debiasing with Backpack LMs<a class="headerlink" href="#debiasing-with-backpack-lms" title="Link to this heading">#</a></h3>
+<p>Models like <a class="reference external" href="https://arxiv.org/pdf/2305.16765.pdf">Backpack LMs</a> are built with highly interpretable model components. In its original paper, one motivating experiment is using the sense vectors to debias. Here, we try to reproduce one of the experiments in Fig. 3 (pg. 8).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">plotnine</span> <span class="kn">import</span> <span class="n">ggplot</span><span class="p">,</span> <span class="n">aes</span><span class="p">,</span> <span class="n">geom_bar</span><span class="p">,</span> <span class="n">theme</span><span class="p">,</span> <span class="n">element_text</span><span class="p">,</span> <span class="n">labs</span>
+
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">backpack_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_backpack_gpt2</span><span class="p">()</span>
+
+<span class="k">class</span> <span class="nc">MultiplierIntervention</span><span class="p">(</span><span class="n">pv</span><span class="o">.</span><span class="n">ConstantSourceIntervention</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Multiplier intervention&quot;&quot;&quot;</span>
+    
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">multiplier</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;multiplier&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">multiplier</span><span class="p">))</span>
+        
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">source</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subspaces</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">base</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiplier</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;MultiplierIntervention()&quot;</span>
+
+<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mi">1</span><span class="p">]:</span>
+    <span class="n">pv_backpack_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+        <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;backpack.sense_network.output&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">MultiplierIntervention</span><span class="p">(</span><span class="n">c</span><span class="p">),</span> <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="s2">&quot;sense.pos&quot;</span><span class="p">},</span> 
+        <span class="n">model</span><span class="o">=</span><span class="n">backpack_gpt2</span>
+    <span class="p">)</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;When the nurse walked into the room,&quot;</span><span class="p">,</span> 
+                     <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span> <span class="n">return_attention_mask</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_backpack_gpt2</span><span class="p">(</span>
+        <span class="n">base</span><span class="p">,</span>
+        <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span>
+            <span class="c1"># use   pv.GET_LOC((nv, s))</span>
+            <span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">GET_LOC</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+    
+    <span class="c1"># plotting</span>
+    <span class="n">probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span>
+        <span class="n">intervened_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">logits</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">top_vals</span><span class="p">(</span>
+        <span class="n">tokenizer</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+        <span class="n">return_results</span><span class="o">=</span><span class="kc">True</span>
+    <span class="p">)</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Word&#39;</span><span class="p">,</span> <span class="s1">&#39;Probability&#39;</span><span class="p">])</span>
+    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Word&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Word&#39;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">plot</span> <span class="o">=</span> <span class="p">(</span><span class="n">ggplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">aes</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;Word&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Probability&#39;</span><span class="p">))</span>
+            <span class="o">+</span> <span class="n">geom_bar</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s1">&#39;identity&#39;</span><span class="p">)</span>
+            <span class="o">+</span> <span class="n">theme</span><span class="p">(</span><span class="n">axis_text_x</span><span class="o">=</span><span class="n">element_text</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span> <span class="n">hjust</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+                    <span class="n">figure_size</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="o">+</span> <span class="n">labs</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;mul(</span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../_images/d03d4f465dc258d67b5aa76aeab37ba147ed9ae51a819082743d7beb74ac609f.png"><img alt="../_images/d03d4f465dc258d67b5aa76aeab37ba147ed9ae51a819082743d7beb74ac609f.png" src="../_images/d03d4f465dc258d67b5aa76aeab37ba147ed9ae51a819082743d7beb74ac609f.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../_images/6d9b4e9b35fa025c8ea0d916889d8fdfdfdea632d200975be74c74a5256837f6.png"><img alt="../_images/6d9b4e9b35fa025c8ea0d916889d8fdfdfdea632d200975be74c74a5256837f6.png" src="../_images/6d9b4e9b35fa025c8ea0d916889d8fdfdfdea632d200975be74c74a5256837f6.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="../_images/cb07a9a4b55e55305091ccd462ae419cbe37a2e23cabb3892ee9586326af7c92.png"><img alt="../_images/cb07a9a4b55e55305091ccd462ae419cbe37a2e23cabb3892ee9586326af7c92.png" src="../_images/cb07a9a4b55e55305091ccd462ae419cbe37a2e23cabb3892ee9586326af7c92.png" style="width: 400px; height: 200px;" /></a>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="saving-and-loading">
+<h3>Saving and Loading<a class="headerlink" href="#saving-and-loading" title="Link to this heading">#</a></h3>
+<p>This is one of the benefits of program abstraction. We abstract out the intervention and its schema, so we have a user friendly interface. Furthermore, it allows us to have a serializable configuration file that tells everything about your configuration.</p>
+<p>You can then save, share and load interventions easily. Note that you still need your access to the data, if you need to sample <strong>Source</strong> representations from other examples. But we think this is doable via a separate HuggingFace datasets upload. In the future, there could be an option of coupling this configuration with a specific remote dataset as well.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="c1"># run with new intervention type</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+  <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">ZeroIntervention</span><span class="p">},</span> 
+  <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;./tmp/&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+Directory &#39;./tmp/&#39; already exists.
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="s2">&quot;./tmp/&quot;</span><span class="p">,</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.
+WARNING:root:Loading trainable intervention from intkey_layer.0.repr.block_output.unit.pos.nunit.1#0.bin.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="multi-source-interchange-intervention-parallel-mode">
+<h3>Multi-Source Interchange Intervention (Parallel Mode)<a class="headerlink" href="#multi-source-interchange-intervention-parallel-mode" title="Link to this heading">#</a></h3>
+<p>What is multi-source? In the examples above, interventions are at most across two examples. We support interventions across many examples. You can sample representations from two inputs, and plut them into a single <strong>Base</strong>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">parallel_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+  <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">},</span>
+  <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">}],</span>
+  <span class="c1"># intervene on base at the same time</span>
+  <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;parallel&quot;</span><span class="p">)</span>
+<span class="n">parallel_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+  <span class="n">parallel_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+  <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+  <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span>
+  <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The language of Spain is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+  <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">parallel_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">(</span>
+    <span class="c1"># each list has a dimensionality of</span>
+    <span class="c1"># [num_intervention, batch, num_unit]</span>
+    <span class="p">[[[</span><span class="mi">1</span><span class="p">]],[[</span><span class="mi">3</span><span class="p">]]],</span>  <span class="p">[[[</span><span class="mi">1</span><span class="p">]],[[</span><span class="mi">3</span><span class="p">]]])}</span>
+<span class="p">)</span>
+
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">embed_to_distrib</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">intervened_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">pv</span><span class="o">.</span><span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+_the                 0.07233363389968872
+_a                   0.05731499195098877
+_not                 0.04443885385990143
+_Italian             0.033642884343862534
+_often               0.024385808035731316
+_called              0.022171705961227417
+_known               0.017808808013796806
+_that                0.016059240326285362
+_&quot;                   0.012973357923328876
+_an                  0.012878881767392159
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="multi-source-interchange-intervention-serial-mode">
+<h3>Multi-Source Interchange Intervention (Serial Mode)<a class="headerlink" href="#multi-source-interchange-intervention-serial-mode" title="Link to this heading">#</a></h3>
+<p>Or you can do them sequentially, where you intervene among your <strong>Source</strong> examples, and get some intermediate states before merging the activations into the <strong>Base</strong> run.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+  <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">},</span>
+  <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">}],</span>
+  <span class="c1"># intervene on base one after another</span>
+  <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;serial&quot;</span><span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+  <span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+  <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+  <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span>
+  <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The language of Spain is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+  <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span>
+    <span class="c1"># intervene in serial at two positions</span>
+    <span class="p">{</span><span class="s2">&quot;source_0-&gt;source_1&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> 
+     <span class="s2">&quot;source_1-&gt;base&quot;</span>    <span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
+
+<span class="n">distrib</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">embed_to_distrib</span><span class="p">(</span>
+    <span class="n">gpt2</span><span class="p">,</span> <span class="n">intervened_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">pv</span><span class="o">.</span><span class="n">top_vals</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">distrib</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>_the                 0.06737838685512543
+_a                   0.059834375977516174
+_not                 0.04629501700401306
+_Italian             0.03623826056718826
+_often               0.021700192242860794
+_called              0.01840786263346672
+_that                0.0157712884247303
+_known               0.014391838572919369
+_an                  0.013535155914723873
+_very                0.013022392988204956
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="multi-source-interchange-intervention-with-subspaces-parallel-mode">
+<h3>Multi-Source Interchange Intervention with Subspaces (Parallel Mode)<a class="headerlink" href="#multi-source-interchange-intervention-with-subspaces-parallel-mode" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+     <span class="s2">&quot;subspace_partition&quot;</span><span class="p">:</span> 
+         <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]]}]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="c1"># act in parallel</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;parallel&quot;</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+          <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of China is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span>
+    <span class="c1"># on same position</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">},</span>
+    <span class="c1"># on different subspaces</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">1</span><span class="p">]]],</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="multi-source-interchange-intervention-with-subspaces-serial-mode">
+<h3>Multi-Source Interchange Intervention with Subspaces (Serial Mode)<a class="headerlink" href="#multi-source-interchange-intervention-with-subspaces-serial-mode" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+     <span class="s2">&quot;subspace_partition&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]]},</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+     <span class="s2">&quot;subspace_partition&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]]}],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+    <span class="c1"># act in parallel</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;serial&quot;</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+          <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of China is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span>
+    <span class="c1"># serialized intervention</span>
+    <span class="c1"># order is based on sources list</span>
+    <span class="p">{</span><span class="s2">&quot;source_0-&gt;source_1&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;source_1-&gt;base&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">},</span>
+    <span class="c1"># on different subspaces</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">1</span><span class="p">]]],</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="interchange-intervention-training-iit">
+<h3>Interchange Intervention Training (IIT)<a class="headerlink" href="#interchange-intervention-training-iit" title="Link to this heading">#</a></h3>
+<p>Interchange intervention training (IIT) is a technique of inducing causal structures into neural models. This library naturally supports this. By training IIT, you can simply turn the gradient on for the wrapping model. In this way, your model can be trained with your interventional signals.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">},</span> 
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span>
+<span class="p">)</span>
+
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">enable_model_gradients</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;number of params:&quot;</span><span class="p">,</span> <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">count_parameters</span><span class="p">())</span>
+
+<span class="c1"># run counterfactual forward as usual</span>
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="n">base_outputs</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">3</span><span class="p">]]],</span> <span class="p">[[[</span><span class="mi">3</span><span class="p">]]])},</span> <span class="n">output_original_output</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span> <span class="o">-</span> <span class="n">base_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">)</span>
+<span class="c1"># call backward will put gradients on model&#39;s weights</span>
+<span class="n">counterfactual_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+number of params: 124439808
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([[[ 0.0022, -0.1783, -0.2780,  ...,  0.0477, -0.2069,  0.1093],
+         [ 0.0385,  0.0886, -0.6608,  ...,  0.0104, -0.4946,  0.6148],
+         [ 0.2377, -0.2312,  0.0308,  ...,  0.1085,  0.0456,  0.2494],
+         [-0.0034,  0.0088, -0.2219,  ...,  0.1198,  0.0759,  0.3953],
+         [ 0.4635,  0.2698, -0.3185,  ..., -0.2946,  0.2634,  0.2714]]],
+       grad_fn=&lt;SubBackward0&gt;)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="pyvene-102">
+<h2>pyvene 102<a class="headerlink" href="#pyvene-102" title="Link to this heading">#</a></h2>
+<p>Now, you are pretty familiar with pyvene basic APIs. There are more to come. We support all sorts of weird interventions, and we encapsulate them as objects so that, even they are super weird (e.g., nested, multiple locations, different types), you can share them easily with others. BTW, if the intervention is trainable, the artifacts will be saved and shared as well.</p>
+<p>With that, here are a couple of additional APIs.</p>
+<section id="grouping">
+<h3>Grouping<a class="headerlink" href="#grouping" title="Link to this heading">#</a></h3>
+<p>You can group interventions together so that they always receive the same input when you want to use them to get activations at different places. Here is an example, where you are taking in the same <strong>Source</strong> example, you fetch activations twice: once in position 3 and layer 0, once in position 4 and layer 2. You don’t have to pass in another dummy <strong>Source</strong>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">},</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">sources</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)]</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="n">sources</span><span class="p">,</span> 
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([</span>
+        <span class="p">[[</span><span class="mi">3</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]</span> <span class="c1"># these two are for two interventions</span>
+    <span class="p">],</span> <span class="p">[</span>             <span class="c1"># source position 3 into base position 4</span>
+        <span class="p">[[</span><span class="mi">3</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]</span> 
+    <span class="p">])}</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervention-skipping-in-runtime">
+<h3>Intervention Skipping in Runtime<a class="headerlink" href="#intervention-skipping-in-runtime" title="Link to this heading">#</a></h3>
+<p>You may configure a lot of interventions, but during training, not every example will have to use all of them. So, you can skip interventions for different examples differently.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="c1"># these are equivalent interventions</span>
+    <span class="c1"># we create them on purpose</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">},</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">},</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">}],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="c1"># skipping 1, 2 and 3</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out1</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">source</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]],</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]])})</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out2</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]],</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]],</span> <span class="kc">None</span><span class="p">])})</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out3</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="p">[</span><span class="n">source</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">4</span><span class="p">]],</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[[[</span><span class="mi">4</span><span class="p">]],</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">])})</span>
+<span class="c1"># should have the same results</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">pv_out1</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">pv_out2</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">),</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">pv_out2</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">pv_out3</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+True True
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="subspace-partition">
+<h3>Subspace Partition<a class="headerlink" href="#subspace-partition" title="Link to this heading">#</a></h3>
+<p>You can partition your subspace before hand. If you don’t, the library assumes you each neuron is in its own subspace. In this example, you partition your subspace into two continous chunk, <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">128),</span> <span class="pre">[128,256)</span></code>, which means all the neurons from index 0 upto 127 are along to partition 1. During runtime, you can intervene on all the neurons in the same parition together.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="c1"># they are linked to manipulate the same representation</span>
+    <span class="c1"># but in different subspaces</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+     <span class="c1"># subspaces can be partitioned into continuous chunks</span>
+     <span class="c1"># [i, j] are the boundary indices</span>
+     <span class="s2">&quot;subspace_partition&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]]}],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+
+<span class="c1"># using intervention skipping for subspace</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="p">[</span><span class="n">source</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">},</span>
+    <span class="c1"># intervene only only dimensions from 128 to 256</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervention-linking">
+<h3>Intervention Linking<a class="headerlink" href="#intervention-linking" title="Link to this heading">#</a></h3>
+<p>Interventions can be linked to share weights and share subspaces. Here is an example of how to link interventions together. If interventions are trainable, then their weights are tied as well.</p>
+<p>Why this is useful? it is because sometimes, you may want to intervene on different subspaces differently. Say you have a representation in a size of 512, and you hypothesize the first half represents A, and the second half represents B, you can then use the subspace intervention to test it out. With trainable interventions, you can also optimize your interventions on the same representation yet with different subspaces.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="c1"># they are linked to manipulate the same representation</span>
+    <span class="c1"># but in different subspaces</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> 
+     <span class="s2">&quot;subspace_partition&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]],</span> <span class="s2">&quot;intervention_link_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">},</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span>
+     <span class="s2">&quot;subspace_partition&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">]],</span> <span class="s2">&quot;intervention_link_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+
+<span class="c1"># using intervention skipping for subspace</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out1</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">source</span><span class="p">],</span>
+    <span class="c1"># 4 means token position 4</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]],</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]])},</span>
+    <span class="c1"># 1 means the second partition in the config</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</span><span class="p">]]],</span>
+<span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out2</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span>
+    <span class="p">[</span><span class="n">source</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">4</span><span class="p">]],</span> <span class="kc">None</span><span class="p">],</span> <span class="p">[[[</span><span class="mi">4</span><span class="p">]],</span> <span class="kc">None</span><span class="p">])},</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">1</span><span class="p">]],</span> <span class="kc">None</span><span class="p">],</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">pv_out1</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">pv_out2</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">))</span>
+
+<span class="c1"># subspaces provide a list of index and they can be in any order</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out3</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span>
+    <span class="p">[</span><span class="n">source</span><span class="p">,</span> <span class="n">source</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]],</span> <span class="p">[[[</span><span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]])},</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">1</span><span class="p">]]],</span>
+<span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out4</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span>
+    <span class="p">[</span><span class="n">source</span><span class="p">,</span> <span class="n">source</span><span class="p">],</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]],</span> <span class="p">[[[</span><span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">4</span><span class="p">]]])},</span>
+    <span class="n">subspaces</span><span class="o">=</span><span class="p">[[[</span><span class="mi">1</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">0</span><span class="p">]]],</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">pv_out3</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">,</span> <span class="n">pv_out4</span><span class="o">.</span><span class="n">last_hidden_state</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+True
+True
+</pre></div>
+</div>
+</div>
+</div>
+<p>Other than intervention linking, you can also share interventions at the same component across multiple positions via setting a flag in the intervention object. It will have the same effect as creating one intervention per location and linking them all together.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="c1"># they are linked to manipulate the same representation</span>
+    <span class="c1"># but in different subspaces</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;intervention_link_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">},</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> <span class="s2">&quot;intervention_link_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}],</span>
+    <span class="n">intervention_types</span><span class="o">=</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">pv_out</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span>
+    <span class="p">[</span><span class="n">source</span><span class="p">,</span> <span class="n">source</span><span class="p">],</span>
+    <span class="c1"># swap 3rd and 4th token reprs from the same source to the base</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">3</span><span class="p">]]],</span> <span class="p">[[[</span><span class="mi">4</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">3</span><span class="p">]]])},</span>
+<span class="p">)</span>
+
+<span class="n">keep_last_dim_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">([</span>
+    <span class="c1"># they are linked to manipulate the same representation</span>
+    <span class="c1"># but in different subspaces</span>
+    <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_output&quot;</span><span class="p">,</span> 
+     <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">(</span><span class="n">keep_last_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">)}]</span>
+<span class="p">)</span>
+<span class="n">keep_last_dim_pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">keep_last_dim_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">keep_last_dim_pv_out</span> <span class="o">=</span> <span class="n">keep_last_dim_pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span>
+    <span class="p">[</span><span class="n">source</span><span class="p">],</span>
+    <span class="c1"># swap 3rd and 4th token reprs from the same source to the base</span>
+    <span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="p">([[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]]],</span> <span class="p">[[[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]]])},</span>
+<span class="p">)</span>
+<span class="n">keep_last_dim_pv_out</span><span class="o">.</span><span class="n">last_hidden_state</span> <span class="o">-</span> <span class="n">pv_out</span><span class="o">.</span><span class="n">last_hidden_state</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor([[[0., 0., 0.,  ..., 0., 0., 0.],
+         [0., 0., 0.,  ..., 0., 0., 0.],
+         [0., 0., 0.,  ..., 0., 0., 0.],
+         [0., 0., 0.,  ..., 0., 0., 0.],
+         [0., 0., 0.,  ..., 0., 0., 0.]]])
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="add-new-model-type">
+<h3>Add New Model Type<a class="headerlink" href="#add-new-model-type" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="c1"># get a flan-t5 from HuggingFace</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">T5ForConditionalGeneration</span><span class="p">,</span> <span class="n">T5Tokenizer</span><span class="p">,</span> <span class="n">T5Config</span>
+<span class="n">config</span> <span class="o">=</span> <span class="n">T5Config</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;google/flan-t5-small&quot;</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">T5Tokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;google/flan-t5-small&quot;</span><span class="p">)</span>
+<span class="n">t5</span> <span class="o">=</span> <span class="n">T5ForConditionalGeneration</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="s2">&quot;google/flan-t5-small&quot;</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span>
+<span class="p">)</span>
+
+<span class="c1"># config the intervention mapping with pv global vars</span>
+<span class="sd">&quot;&quot;&quot;Only define for the block output here for simplicity&quot;&quot;&quot;</span>
+<span class="n">pv</span><span class="o">.</span><span class="n">type_to_module_mapping</span><span class="p">[</span><span class="nb">type</span><span class="p">(</span><span class="n">t5</span><span class="p">)]</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;encoder.block[</span><span class="si">%s</span><span class="s2">].layer[1]&quot;</span><span class="p">,</span> 
+                   <span class="n">pv</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">constants</span><span class="o">.</span><span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;encoder.block[</span><span class="si">%s</span><span class="s2">].layer[0]&quot;</span><span class="p">,</span> 
+                        <span class="n">pv</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">constants</span><span class="o">.</span><span class="n">CONST_OUTPUT_HOOK</span><span class="p">),</span>
+<span class="p">}</span>
+<span class="n">pv</span><span class="o">.</span><span class="n">type_to_dimension_mapping</span><span class="p">[</span><span class="nb">type</span><span class="p">(</span><span class="n">t5</span><span class="p">)]</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;mlp_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;d_model&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;attention_input&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;d_model&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;block_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;d_model&quot;</span><span class="p">,),</span>
+    <span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">:</span> <span class="p">(</span><span class="s2">&quot;d_model/num_heads&quot;</span><span class="p">,),</span>
+<span class="p">}</span>
+
+<span class="c1"># wrap as gpt2</span>
+<span class="n">pv_t5</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;source_representation&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
+        <span class="n">t5</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">d_model</span><span class="p">)</span>
+<span class="p">},</span> <span class="n">model</span><span class="o">=</span><span class="n">t5</span><span class="p">)</span>
+
+<span class="c1"># then intervene!</span>
+<span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+                 <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">decoder_input_ids</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">input_ids</span>
+<span class="n">base</span><span class="p">[</span><span class="s2">&quot;decoder_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">decoder_input_ids</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_t5</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> 
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>You are using the default legacy behaviour of the &lt;class &#39;transformers.models.t5.tokenization_t5.T5Tokenizer&#39;&gt;. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="composing-complex-intervention-schema-path-patching">
+<h3>Composing Complex Intervention Schema: Path Patching<a class="headerlink" href="#composing-complex-intervention-schema-path-patching" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="k">def</span> <span class="nf">path_patching_config</span><span class="p">(</span>
+    <span class="n">layer</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">,</span> 
+    <span class="n">component</span><span class="o">=</span><span class="s2">&quot;head_attention_value_output&quot;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s2">&quot;h.pos&quot;</span>
+<span class="p">):</span>
+    <span class="n">intervening_component</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">component</span><span class="p">,</span> <span class="s2">&quot;unit&quot;</span><span class="p">:</span> <span class="n">unit</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}]</span>
+    <span class="n">restoring_components</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">component</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;mlp_&quot;</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layer</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>
+            <span class="p">{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
+        <span class="p">]</span>
+    <span class="n">intervenable_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">intervening_component</span> <span class="o">+</span> <span class="n">restoring_components</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">intervenable_config</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+    <span class="n">path_patching_config</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">gpt2</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_layer</span><span class="p">),</span> 
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span>
+<span class="p">)</span>
+
+<span class="n">pv_gpt2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
+    <span class="n">save_directory</span><span class="o">=</span><span class="s2">&quot;./tmp/&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+Directory &#39;./tmp/&#39; already exists.
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="s2">&quot;./tmp/&quot;</span><span class="p">,</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="composing-complex-intervention-schema-causal-tracing-in-15-lines">
+<h3>Composing Complex Intervention Schema: Causal Tracing in 15 lines<a class="headerlink" href="#composing-complex-intervention-schema-causal-tracing-in-15-lines" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="k">def</span> <span class="nf">causal_tracing_config</span><span class="p">(</span>
+  <span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;mlp_activation&quot;</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">tl</span><span class="o">=</span><span class="mi">48</span><span class="p">):</span>
+  <span class="n">s</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">w</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
+  <span class="n">e</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">tl</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="p">(</span><span class="o">-</span><span class="n">w</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span>
+  <span class="n">config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+    <span class="p">[{</span><span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;block_input&quot;</span><span class="p">}]</span> <span class="o">+</span> 
+    <span class="p">[{</span><span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">l</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">c</span><span class="p">}</span> 
+      <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">e</span><span class="p">)],</span>
+    <span class="p">[</span><span class="n">pv</span><span class="o">.</span><span class="n">NoiseIntervention</span><span class="p">]</span> <span class="o">+</span>
+    <span class="p">[</span><span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">e</span><span class="o">-</span><span class="n">s</span><span class="p">))</span>
+  <span class="k">return</span> <span class="n">config</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span>
+    <span class="n">causal_tracing_config</span><span class="p">(</span><span class="mi">4</span><span class="p">),</span> 
+    <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="inference-time-intervention">
+<h3>Inference-time Intervention<a class="headerlink" href="#inference-time-intervention" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="n">config</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">llama</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_llama</span><span class="p">(</span><span class="s2">&quot;meta-llama/Llama-2-7b-chat-hf&quot;</span><span class="p">)</span>
+<span class="c1"># load cached extracted activations from the ITI repo:</span>
+<span class="c1"># https://github.com/likenneth/honest_llama</span>
+<span class="n">o_proj_add_activations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="s2">&quot;./tutorials/basic_tutorials/tutorial_data&quot;</span>
+    <span class="s2">&quot;/honest_llama2_chat_7B_o_proj_add_activations.bin&quot;</span>
+<span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">llama</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+<span class="n">pv_llama</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">([{</span>
+        <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;model.layers[</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">].self_attn.o_proj.output&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">AdditionIntervention</span><span class="p">(</span>
+            <span class="n">source_representation</span><span class="o">=</span><span class="n">o_proj_add_activations</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+    <span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">num_hidden_layers</span><span class="p">)</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">o_proj_add_activations</span><span class="p">[</span><span class="n">i</span><span class="p">])],</span> 
+    <span class="n">model</span><span class="o">=</span><span class="n">llama</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "09c7100d49c94e1b94f3429440f1aab3", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="s2">&quot;What&#39;s a cure for insomnia that always works?&quot;</span>
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;vanilla llama-2-chat:&quot;</span><span class="p">)</span>
+<span class="n">st</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="n">response</span> <span class="o">=</span> <span class="n">llama</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="o">**</span><span class="n">prompt</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="n">et</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="n">elapsed_time</span> <span class="o">=</span> <span class="n">et</span> <span class="o">-</span> <span class="n">st</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Execution time:&#39;</span><span class="p">,</span> <span class="n">elapsed_time</span><span class="p">,</span> <span class="s1">&#39;seconds&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;honest llama-2-chat:&quot;</span><span class="p">)</span>
+<span class="n">st</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">iti_response</span> <span class="o">=</span> <span class="n">pv_llama</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompt</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">iti_response</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="n">et</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+<span class="n">elapsed_time</span> <span class="o">=</span> <span class="n">et</span> <span class="o">-</span> <span class="n">st</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Execution time:&#39;</span><span class="p">,</span> <span class="n">elapsed_time</span><span class="p">,</span> <span class="s1">&#39;seconds&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>vanilla llama-2-chat:
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/sailhome/wuzhengx/.local/lib/python3.8/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
+  warnings.warn(
+/sailhome/wuzhengx/.local/lib/python3.8/site-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
+  warnings.warn(
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>What&#39;s a cure for insomnia that always works?
+
+Insomnia is a common sleep disorder that can affect anyone, regardless of age or gender. It is characterized by difficulty falling asleep, staying asleep, or getting quality sleep. Insomnia can be caused by a variety of factors, including stress, anxiety, depression, certain
+Execution time: 2.1844897270202637 seconds
+
+honest llama-2-chat:
+What&#39;s a cure for insomnia that always works?
+There is no single cure for insomnia that works for everyone, and it&#39;s important to address the underlying causes of sleep problems. However, some strategies that may help improve sleep quality and duration include:
+
+1. Practicing relaxation techniques, such as deep breathing, progressive mus
+Execution time: 2.6960761547088623 seconds
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># save to huggingface directly</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="n">pv_llama</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
+        <span class="s2">&quot;./tmp_llama/&quot;</span><span class="p">,</span>
+        <span class="n">save_to_hf_hub</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
+        <span class="n">hf_repo_name</span><span class="o">=</span><span class="s2">&quot;zhengxuanzenwu/intervenable_honest_llama2_chat_7B&quot;</span>
+    <span class="p">)</span>
+<span class="k">except</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;You have to login into huggingface hub before running this.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;usage: huggingface-cli login&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Directory &#39;./tmp_llama/&#39; already exists.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervenablemodel-from-huggingface-directly">
+<h3>IntervenableModel from HuggingFace Directly<a class="headerlink" href="#intervenablemodel-from-huggingface-directly" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># others can download from huggingface and use it directly</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;meta-llama/Llama-2-7b-chat-hf&quot;</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="s2">&quot;meta-llama/Llama-2-7b-chat-hf&quot;</span><span class="p">,</span>
+    <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+<span class="n">pv_model</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
+    <span class="s2">&quot;zhengxuanzenwu/intervenable_honest_llama2_chat_7B&quot;</span><span class="p">,</span> <span class="c1"># the activation diff ~0.14MB</span>
+    <span class="n">model</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;llama-2-chat loaded with interventions:&quot;</span><span class="p">)</span>
+<span class="n">q</span> <span class="o">=</span> <span class="s2">&quot;What&#39;s a cure for insomnia that always works?&quot;</span>
+<span class="n">prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">iti_response_shared</span> <span class="o">=</span> <span class="n">pv_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompt</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">do_sample</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">iti_response_shared</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "437f82c9e5ff4a39ae988ccbfd7518df", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "c8b98f21c02a4e5da60d67098d99a2c5", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:root:The key is provided in the config. Assuming this is loaded from a pretrained module.
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>llama-2-chat loaded with interventions:
+What&#39;s a cure for insomnia that always works?
+There is no single cure for insomnia that works for everyone, and it&#39;s important to address the underlying causes of sleep problems. However, some strategies that may help improve sleep quality and duration include:
+
+1. Practicing relaxation techniques, such as deep breathing, progressive mus
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="path-patching-with-trainable-interventions">
+<h3>Path Patching with Trainable Interventions<a class="headerlink" href="#path-patching-with-trainable-interventions" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+
+<span class="k">def</span> <span class="nf">path_patching_with_DAS_config</span><span class="p">(</span>
+    <span class="n">layer</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">,</span> <span class="n">low_rank_dimension</span><span class="p">,</span>
+    <span class="n">component</span><span class="o">=</span><span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s2">&quot;pos&quot;</span>
+<span class="p">):</span>
+    <span class="n">intervening_component</span> <span class="o">=</span> <span class="p">[{</span>
+        <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="n">component</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+        <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">LowRankRotatedSpaceIntervention</span><span class="p">,</span>
+        <span class="s2">&quot;low_rank_dimension&quot;</span><span class="p">:</span> <span class="n">low_rank_dimension</span><span class="p">,</span>
+    <span class="p">}]</span>
+    <span class="n">restoring_components</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">component</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;mlp_&quot;</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[{</span>
+            <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+            <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">,</span>
+        <span class="p">}]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layer</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">last_layer</span><span class="p">):</span>
+        <span class="n">restoring_components</span> <span class="o">+=</span> <span class="p">[{</span>
+            <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;attention_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> 
+            <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">},{</span>
+            <span class="s2">&quot;layer&quot;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;mlp_output&quot;</span><span class="p">,</span> <span class="s2">&quot;group_key&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+            <span class="s2">&quot;intervention_type&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span>
+        <span class="p">}]</span>
+    <span class="n">intervenable_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">(</span>
+        <span class="n">intervening_component</span> <span class="o">+</span> <span class="n">restoring_components</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">intervenable_config</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">restoring_components</span><span class="p">)</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+<span class="n">pv_config</span><span class="p">,</span> <span class="n">num_restores</span> <span class="o">=</span> <span class="n">path_patching_with_DAS_config</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">(</span><span class="n">pv_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">restore_source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+
+<span class="c1"># zero-out grads</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pv_gpt2</span><span class="o">.</span><span class="n">interventions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">v</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+
+<span class="n">original_outputs</span><span class="p">,</span> <span class="n">counterfactual_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span><span class="p">,</span> 
+    <span class="n">sources</span><span class="o">=</span><span class="p">[</span><span class="n">source</span><span class="p">,</span> <span class="n">restore_source</span><span class="p">],</span>
+    <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">4</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+<span class="c1"># put gradients on the trainable intervention only</span>
+<span class="n">counterfactual_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor(-0.0694, grad_fn=&lt;SumBackward0&gt;)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervene-on-resnet-with-lambda-functions">
+<h3>Intervene on ResNet with Lambda Functions<a class="headerlink" href="#intervene-on-resnet-with-lambda-functions" title="Link to this heading">#</a></h3>
+<p>Huggingface Vision model comes with the support of ResNet. Here, we show how we can use pyvene to intervene on a patch of pixels, like token in transformer, which is like a primitive object in ResNet or ConvNet based NNs.</p>
+<p><strong>Caveats:</strong> We go with a pretty much hard-coded way here, but you can customize the hook functions as you want. It does not have to be a lambda function as well.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">load_dataset</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoFeatureExtractor</span><span class="p">,</span> <span class="n">AutoModelForImageClassification</span>
+
+<span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">AutoFeatureExtractor</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;microsoft/resnet-18&quot;</span><span class="p">)</span>
+<span class="n">resnet</span> <span class="o">=</span> <span class="n">AutoModelForImageClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;microsoft/resnet-18&quot;</span><span class="p">)</span>
+
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;huggingface/cats-image&quot;</span><span class="p">)</span>
+<span class="n">base_image</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;image&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">source_image</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;image&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">base_inputs</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="p">(</span><span class="n">base_image</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source_inputs</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="p">(</span><span class="n">source_image</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source_inputs</span><span class="p">[</span><span class="s1">&#39;pixel_values&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">source_inputs</span><span class="p">[</span><span class="s1">&#39;pixel_values&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">create_mask</span><span class="p">():</span>
+    <span class="n">_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">56</span><span class="p">,</span> <span class="mi">56</span><span class="p">))</span>
+    <span class="n">_mask</span><span class="p">[</span><span class="mi">56</span><span class="o">//</span><span class="mi">2</span><span class="p">:,</span> <span class="mi">56</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">return</span> <span class="n">_mask</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">create_mask</span><span class="p">()</span>
+
+<span class="n">pv_resnet</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;resnet.embedder.pooler.output&quot;</span><span class="p">,</span> 
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="n">b</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">-</span> <span class="n">m</span><span class="p">)</span> <span class="o">+</span> <span class="n">s</span> <span class="o">*</span> <span class="n">m</span><span class="p">},</span> 
+    <span class="n">model</span><span class="o">=</span><span class="n">resnet</span>
+<span class="p">)</span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_resnet</span><span class="p">(</span>
+    <span class="n">base_inputs</span><span class="p">,</span> <span class="p">[</span><span class="n">source_inputs</span><span class="p">],</span> <span class="n">return_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_original_output</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="p">(</span><span class="n">intervened_outputs</span><span class="o">.</span><span class="n">intervened_outputs</span><span class="o">.</span><span class="n">logits</span> <span class="o">-</span> <span class="n">intervened_outputs</span><span class="o">.</span><span class="n">original_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor(0.0005)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="intervene-on-resnet-with-trainable-lambda-functions">
+<h3>Intervene on ResNet with Trainable Lambda Functions<a class="headerlink" href="#intervene-on-resnet-with-trainable-lambda-functions" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">load_dataset</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoFeatureExtractor</span><span class="p">,</span> <span class="n">AutoModelForImageClassification</span>
+
+<span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">AutoFeatureExtractor</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;microsoft/resnet-18&quot;</span><span class="p">)</span>
+<span class="n">resnet</span> <span class="o">=</span> <span class="n">AutoModelForImageClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;microsoft/resnet-18&quot;</span><span class="p">)</span>
+
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;huggingface/cats-image&quot;</span><span class="p">)</span>
+<span class="n">base_image</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;image&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">source_image</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">][</span><span class="s2">&quot;image&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">base_inputs</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="p">(</span><span class="n">base_image</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source_inputs</span> <span class="o">=</span> <span class="n">feature_extractor</span><span class="p">(</span><span class="n">source_image</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span>
+<span class="n">source_inputs</span><span class="p">[</span><span class="s1">&#39;pixel_values&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">source_inputs</span><span class="p">[</span><span class="s1">&#39;pixel_values&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+<span class="c1"># trainable DAS directions</span>
+<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">parametrizations</span><span class="o">.</span><span class="n">orthogonal</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">56</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+<span class="n">pv_resnet</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;resnet.embedder.pooler.output&quot;</span><span class="p">,</span> 
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="n">b</span> <span class="o">+</span> <span class="p">((</span><span class="n">s</span> <span class="o">@</span> <span class="n">v</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span> <span class="o">-</span> <span class="n">b</span> <span class="o">@</span> <span class="n">v</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">@</span> <span class="n">v</span><span class="o">.</span><span class="n">weight</span><span class="p">)},</span> 
+    <span class="n">model</span><span class="o">=</span><span class="n">resnet</span>
+<span class="p">)</span>
+
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_resnet</span><span class="p">(</span>
+    <span class="n">base_inputs</span><span class="p">,</span> <span class="p">[</span><span class="n">source_inputs</span><span class="p">],</span> <span class="n">return_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_original_output</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<span class="p">(</span><span class="n">intervened_outputs</span><span class="o">.</span><span class="n">intervened_outputs</span><span class="o">.</span><span class="n">logits</span> <span class="o">-</span> <span class="n">intervened_outputs</span><span class="o">.</span><span class="n">original_outputs</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>tensor(0.0068, grad_fn=&lt;SumBackward0&gt;)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="run-pyvene-on-ndif-backend-with-pv-build-intervenable-model">
+<h3>Run pyvene on <a class="reference external" href="https://ndif.us/">NDIF</a> backend with <code class="docutils literal notranslate"><span class="pre">pv.build_intervenable_model(...)</span></code><a class="headerlink" href="#run-pyvene-on-ndif-backend-with-pv-build-intervenable-model" title="Link to this heading">#</a></h3>
+<p><a class="reference external" href="https://ndif.us/">NDIF</a> provides APIs for running intervened model inference calls either locally or remotely, enabling Pyvene to run intervened model calls remotely with shared resources. This is especially useful when the intervened model is large (e.g., Llama 400B).</p>
+<p>Note that setting <code class="docutils literal notranslate"><span class="pre">remote=True</span></code> is still under-construction for remote intervention.</p>
+<p><strong>Basic activation collection</strong></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+<span class="kn">from</span> <span class="nn">nnsight</span> <span class="kn">import</span> <span class="n">LanguageModel</span>
+
+<span class="c1"># load any huggingface model as a ndif native model object</span>
+<span class="n">gpt2_ndif</span> <span class="o">=</span> <span class="n">LanguageModel</span><span class="p">(</span><span class="s1">&#39;openai-community/gpt2&#39;</span><span class="p">,</span> <span class="n">device_map</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s1">&#39;openai-community/gpt2&#39;</span><span class="p">)</span>
+
+<span class="c1"># pyvene provides pv.build_intervenable_model as the generic model builder</span>
+<span class="n">pv_gpt2_ndif</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">build_intervenable_model</span><span class="p">({</span>
+    <span class="c1"># based on the module printed above, you can access via string, input means the input to the module</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;transformer.h[10].attn.attn_dropout.input&quot;</span><span class="p">,</span>
+    <span class="c1"># you can also initialize the intervention gpt2_ndif</span>
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">()},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2_ndif</span><span class="p">,</span> <span class="n">remote</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;When John and Mary went to the shops, Mary gave the bag to&quot;</span>
+<span class="n">ndif_collected_attn_w</span> <span class="o">=</span> <span class="n">pv_gpt2_ndif</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">h</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]}</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+WARNING:root:We currently have very limited intervention support for ndif backend.
+You&#39;re using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># gpt2 helper loading model from HuggingFace</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">create_gpt2</span><span class="p">()</span>
+
+<span class="n">pv_gpt2</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableModel</span><span class="p">({</span>
+    <span class="c1"># based on the module printed above, you can access via string, input means the input to the module</span>
+    <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;h[10].attn.attn_dropout.input&quot;</span><span class="p">,</span>
+    <span class="c1"># you can also initialize the intervention outside</span>
+    <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">CollectIntervention</span><span class="p">()},</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2</span><span class="p">)</span>
+
+<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;When John and Mary went to the shops, Mary gave the bag to&quot;</span>
+<span class="n">collected_attn_w</span> <span class="o">=</span> <span class="n">pv_gpt2</span><span class="p">(</span>
+    <span class="n">base</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span>
+    <span class="p">),</span> <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;base&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">h</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]}</span>
+<span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ndif_collected_attn_w</span><span class="p">,</span> <span class="n">collected_attn_w</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>loaded model
+</pre></div>
+</div>
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+<p><strong>Interchange intervention (activation swap between two examples)</strong></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyvene</span> <span class="k">as</span> <span class="nn">pv</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+<span class="kn">from</span> <span class="nn">nnsight</span> <span class="kn">import</span> <span class="n">LanguageModel</span>
+
+<span class="c1"># load any huggingface model as a ndif native model object</span>
+<span class="n">gpt2_ndif</span> <span class="o">=</span> <span class="n">LanguageModel</span><span class="p">(</span><span class="s1">&#39;openai-community/gpt2&#39;</span><span class="p">,</span> <span class="n">device_map</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s1">&#39;openai-community/gpt2&#39;</span><span class="p">)</span>
+
+<span class="c1"># create with dict-based config</span>
+<span class="n">pv_config</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">IntervenableConfig</span><span class="p">({</span>
+  <span class="s2">&quot;component&quot;</span><span class="p">:</span> <span class="s2">&quot;transformer.h[0].attn.output&quot;</span><span class="p">,</span>
+  <span class="s2">&quot;intervention&quot;</span><span class="p">:</span> <span class="n">pv</span><span class="o">.</span><span class="n">VanillaIntervention</span><span class="p">()}</span>
+<span class="p">)</span>
+<span class="c1">#initialize model</span>
+<span class="n">pv_gpt2_ndif</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">build_intervenable_model</span><span class="p">(</span>
+  <span class="n">pv_config</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">gpt2_ndif</span><span class="p">)</span>
+<span class="c1"># run an interchange intervention </span>
+<span class="n">intervened_outputs</span> <span class="o">=</span> <span class="n">pv_gpt2_ndif</span><span class="p">(</span>
+  <span class="c1"># the base input</span>
+  <span class="n">base</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;The capital of Spain is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span> <span class="o">=</span> <span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+  <span class="c1"># the source input</span>
+  <span class="n">sources</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">(</span>
+    <span class="s2">&quot;The capital of Italy is&quot;</span><span class="p">,</span> 
+    <span class="n">return_tensors</span> <span class="o">=</span> <span class="s2">&quot;pt&quot;</span><span class="p">),</span> 
+  <span class="c1"># the location to intervene at (3rd token)</span>
+  <span class="n">unit_locations</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;sources-&gt;base&quot;</span><span class="p">:</span> <span class="mi">3</span><span class="p">},</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/u/nlp/anaconda/main/anaconda3/envs/wuzhengx-310/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
+  warnings.warn(
+WARNING:root:We currently have very limited intervention support for ndif backend.
+You&#39;re using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="the-end">
+<h3>The End<a class="headerlink" href="#the-end" title="Link to this heading">#</a></h3>
+<p>Now you are graduating from pyvene entry level course! Feel free to take a look at our tutorials for more challenging interventions.</p>
+</section>
+</section>
+</section>
+
+
+                </article>
+              
+
+              
+              
+              
+              
+                <footer class="prev-next-footer d-print-none">
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+  <div class="sidebar-secondary-item">
+  <div class="page-toc tocsection onthispage">
+    <i class="fa-solid fa-list"></i> Contents
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Introduction to pyvene</a></li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#table-of-contents">Table of Contents</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#set-up">Set-up</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene-101">pyvene 101</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#get-attention-weights">Get Attention Weights</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#get-attention-weights-with-direct-access-string">Get Attention Weights with Direct Access String</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#get-attention-weights-with-a-function">Get Attention Weights with a Function</a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activation-to-zeros">Set Activation to Zeros</a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activation-to-zeros-with-a-lambda-expression">Set Activation to Zeros with a Lambda Expression</a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activation-to-zeros-with-a-lambda-expression-and-subspace-notation">Set Activation to Zeros with a Lambda Expression and Subspace notation</a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#set-activations-to-zeros-with-subspaces">Set Activations to Zeros with Subspaces</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#interchange-interventions">Interchange Interventions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervention-configuration">Intervention Configuration</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#addition-intervention">Addition Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#trainable-intervention">Trainable Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#activation-collection-with-intervention">Activation Collection with Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#activation-collection-at-downstream-of-a-intervened-model">Activation Collection at Downstream of a Intervened Model</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervene-on-a-single-neuron">Intervene on a Single Neuron</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#add-new-intervention-type">Add New Intervention Type</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#recurrent-nns-intervene-a-specific-timestep">Recurrent NNs (Intervene a Specific Timestep)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#recurrent-nns-intervene-cross-time">Recurrent NNs (Intervene cross Time)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#lms-generation">LMs Generation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#advanced-intervention-on-lms-generation-model-steering">Advanced Intervention on LMs Generation (Model Steering)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#debiasing-with-backpack-lms">Debiasing with Backpack LMs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#saving-and-loading">Saving and Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-parallel-mode">Multi-Source Interchange Intervention (Parallel Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-serial-mode">Multi-Source Interchange Intervention (Serial Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-with-subspaces-parallel-mode">Multi-Source Interchange Intervention with Subspaces (Parallel Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multi-source-interchange-intervention-with-subspaces-serial-mode">Multi-Source Interchange Intervention with Subspaces (Serial Mode)</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#interchange-intervention-training-iit">Interchange Intervention Training (IIT)</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pyvene-102">pyvene 102</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#grouping">Grouping</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervention-skipping-in-runtime">Intervention Skipping in Runtime</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#subspace-partition">Subspace Partition</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervention-linking">Intervention Linking</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#add-new-model-type">Add New Model Type</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#composing-complex-intervention-schema-path-patching">Composing Complex Intervention Schema: Path Patching</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#composing-complex-intervention-schema-causal-tracing-in-15-lines">Composing Complex Intervention Schema: Causal Tracing in 15 lines</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#inference-time-intervention">Inference-time Intervention</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervenablemodel-from-huggingface-directly">IntervenableModel from HuggingFace Directly</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-patching-with-trainable-interventions">Path Patching with Trainable Interventions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervene-on-resnet-with-lambda-functions">Intervene on ResNet with Lambda Functions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#intervene-on-resnet-with-trainable-lambda-functions">Intervene on ResNet with Trainable Lambda Functions</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#run-pyvene-on-ndif-backend-with-pv-build-intervenable-model">Run pyvene on NDIF backend with <code class="docutils literal notranslate"><span class="pre">pv.build_intervenable_model(...)</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-end">The End</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
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